# Patent

## Title
Data storage device incorporating a two-dimensional code

## Abstract

A data storage device includes a data carrier having at least one planar surface. An array of detectable items is positioned on the planar surface and is detectable with a sensing device. The array is configured to represent a two-dimensional code that defines at least executable instructions and redundancy encoding to impart fault tolerant characteristics to the code. The executable instructions include image processing algorithms.

## Background
CROSS REFERENCES TO RELATED APPLICATIONS
This is a Continuation Application of U.S. application Ser. No. 10/963,542 filed Oct. 14, 2004, which is continuation of U.S. application Ser. No. 10/269,998, filed on Oct. 15, 2002, now issued U.S. Pat. No. 6,827,282, which is a Continuation Application of U.S. application Ser. No. 09/112,781, filed on Jul. 10, 1998, now issued U.S. Pat. No. 6,786,420, all of which are herein incorporated by reference.
The following Australian provisional patent applications are hereby incorporated by cross-reference. For the purposes of location and identification, U.S. patent applications identified by their U.S. patent application Ser. Nos. (USSN) are listed alongside the Australian applications front which the U.S. patent applications claim the right of priority.












Cross-Referenced
US Patent/Patent



Australian
Application (Claiming


Provisional
Right of Priority


Patent
from Australian


Application No.
Provisional Application)
Docket No.







PO7991
6750901
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6476863
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6322181
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6597817
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6227648
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6727948
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6690419
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6195150
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6362868
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6831681
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6431669
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6362869
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6356715
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6356715
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6894694
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6636216
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6329990
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6459495
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6381361
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6317192
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6850274
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09/113054
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6646757
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6624848
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6357135
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6271931
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STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
Not applicable.
FIELD OF THE INVENTION
The present invention relates to a data distribution system and in particular discloses a data distribution mechanism in the form of Dotcards.
BACKGROUND OF THE INVENTION
Methods for distribution of data for automatic reading by computer systems are well known. For example, barcodes are often utilised in conjunction with an optical scanner for the distribution of corresponding barcode data. Further, magnetic ink scanning systems have particular application on bank cheques which are automatically scanned and the original data determined from the cheque.
There is a general need for a print media scanning system that allows for high volumes of computer data to be stored on simple print media, such as a card, and to simultaneously be able to tolerate a high degree of corruption of the data. For example, the form of distribution can suffer a number of data corruption errors when the surface is scanned by a scanning device. The errors can include:
1. Dead pixel errors which are a result of reading the surface of the card with a linear CCD having a faulty pixel reader for a line thereby producing the same value for all points on the line.
2. The system adopted should tolerate writing errors wherein text is written by the owner of the card on the surface. Such text writing errors are ideally tolerated by any scanning system scanning the card.
3. Various data errors on the surface of the card may rise and any scuffs or blotches should be tolerated by any system determining the information stored on the surface of the card.
4. A certain degree of “play” exists in the insertion of the card into a card reader. This play can comprise a degree of rotation of the card when read by a card reader.
5. Further, the card reader is assumed to be driven past a CCD type scanner device by means of an electric motor. The electric motor may experience a degree of fluctuation which will result in fluctuations in the rate of transmission of the data across the surface of the CCD. These motor fluctuation errors should also be tolerated by the data encoding method on the surface of the card.
6. The scanner of the surface of the card may experience various device fluctuations such that the intensity of individual pixels may vary. Reader intensity variations should also be accounted for in any system or method implemented in the data contained on the surface of the card.
Many forms of condensed information storage are well known. For example, in the field of computer devices, it is common to utilize magnetic disc drives which can be of a fixed or portable nature. In respect of portable discs, “Floppy Discs”, “Zip Discs”, and other forms of portable magnetic storage media have achieved a large degree of acceptance on the market place.
Another form of portable storage is the compact disc “CD” which utilizes a series of elongated pits along a spiral track which is read by a laser beam device. The utilization of Compact Disks provides for an extremely low cost form of storage. However, the technologies involved are quite complex and the use of rewritable CD type devices is extremely limited.
Other forms of storage include magnetic cards, often utilized for credit cards or the like. These cards normally have a magnetic strip on the back for recording information which is of relevance to the card user. Recently, the convenience of magnetic cards has been extended in the form of SmartCard technology which includes incorporation of integrated circuit type devices on to the card. Unfortunately, the cost of such devices is often high and the complexity of the technology utilized can also be significant.
SUMMARY OF THE INVENTION
According to a first aspect of the invention, there is provided an image processing and printing apparatus which includes
a processor that is configured to process image data to apply effects to the image data;
image data input circuitry for receiving image data for processing;
a card reader for reading a card having a two-dimensional code printed on one side of the card, configured to generate data representing executable code upon reading the card, and connected to the processor to write the data to the processor, which is configured to execute the code and to perform an image processing operation on the image data according to the code;
a printhead interface connected to the processor to receive processed image data from the processor and to generate print data; and
a printhead connected to the printhead interface to print processed images.
The image data input circuitry may include a memory which is connected to an image capture device to receive image data from the image capture device, the memory being accessible by the processor to write the image data to the processor. The memory may be a random access memory.
The card reader may include a linear sensor having a scanning resolution which is at least twice a resolution of the two-dimensional code on the card.
The apparatus may include a reader motor for propelling the card past the linear sensor at a substantially constant rate.
The processor may be configured to detect, via the card reader, a data area on the card and subsequently to detect a bit pattern represented by the two-dimensional code in the data area. The processor may further be configured to perform a decoding operation on the bit pattern to generate redundancy encoded data and then to decode the redundancy encoded data to generate the executable code.
The printhead may be a pagewidth inkjet printhead.
In accordance with a second aspect of the present invention, there is provided an identifying card comprising: a first surface carrying human readable information relevant to an owner of the identifying card; and a second, opposed surface carrying encoded information encoded in a highly fault tolerant manner, said encoded information being adapted for sensing by a sensing device and decoded by a computational processor, so as to provide information relevant to the owner in a human readable form, the encoded information comprising an array of dots applied to said second surface; wherein the encoded information comprises spatially distributed redundancy encoded data such that the information is encoded in a highly fault tolerant manner and can be decoded by said processor despite a localized obliteration of the encoded information on the card.



BRIEF DESCRIPTION OF THE DRAWINGS
Notwithstanding any other forms which may fall within the scope of the present invention, preferred forms of the invention will now be described, by way of example only, with reference to the accompanying drawings in which:
FIG. 1 illustrates an Artcam device constructed in accordance with the preferred embodiment;
FIG. 2 is a schematic block diagram of the main Artcam electronic components;
FIG. 3 illustrates a time line of the process of sampling an Artcard;
FIG. 4 illustrates the super sampling process;
FIG. 5 illustrates the process of reading a rotated Artcard;
FIG. 6 illustrates a flow chart of the steps necessary to decode an Artcard;
FIG. 7 illustrates an enlargement of the left hand corner of a single Artcard;
FIG. 8 illustrates a single target for detection;
FIG. 9 illustrates the method utilised to detect targets;
FIG. 10 illustrates the method of calculating the distance between two targets;
FIG. 11 illustrates the process of centroid drift;
FIG. 12 shows one form of centroid lookup table;
FIG. 13 illustrates the centroid updating process;
FIG. 14 illustrates a delta processing lookup table utilised in the preferred embodiment;
FIG. 15 illustrates the process of unscrambling Artcard data;
FIG. 16 illustrates a magnified view of a series of dots;
FIG. 17 illustrates the data surface of a dot card;
FIG. 18 illustrates schematically the layout of a single datablock;
FIG. 19 illustrates a single datablock;
FIG. 20 and FIG. 21 illustrate magnified views of portions of the datablock of FIG. 19;
FIG. 22 illustrates a single target structure;
FIG. 23 illustrates the target structure of a datablock;
FIG. 24 illustrates the positional relationship of targets relative to border clocking regions of a data region;
FIG. 25 illustrates the orientation columns of a datablock;
FIG. 26 illustrates the array of dots of a datablock;
FIG. 27 illustrates schematically the structure of data for Reed-Solomon encoding;
FIG. 28 illustrates an example Reed-Solomon encoding;
FIG. 29 illustrates the Reed-Solomon encoding process;
FIG. 30 illustrates the layout of encoded data within a datablock;
FIG. 31 illustrates the sampling process in sampling an alternative Artcard;
FIG. 32 illustrates, in exaggerated form, an example of sampling a rotated alternative Artcard;
FIG. 33 illustrates the scanning process;
FIG. 34 illustrates the likely scanning distribution of the scanning process;
FIG. 35 illustrates the relationship between probability of symbol errors and Reed-Solomon block errors;
FIG. 36 illustrates a flow chart of the decoding process;
FIG. 37 illustrates a process utilization diagram of the decoding process;
FIG. 38 illustrates the dataflow steps in decoding;
FIG. 39 illustrates the reading process in more detail;
FIG. 40 illustrates the process of detection of the start of an alternative Artcard in more detail;
FIG. 41 illustrates the extraction of bit data process in more detail;
FIG. 42 illustrates the segmentation process utilized in the decoding process;
FIG. 43 illustrates the decoding process of finding targets in more detail;
FIG. 44 illustrates the data structures utilized in locating targets;
FIG. 45 illustrates the Lancos 3 function structure;
FIG. 46 illustrates an enlarged portion of a datablock illustrating the clockmark and border region;
FIG. 47 illustrates the processing steps in decoding a bit image;
FIG. 48 illustrates the dataflow steps in decoding a bit image;
FIG. 49 illustrates the descrambling process of the preferred embodiment;
FIG. 50 illustrates the process of generating an 8 bit dot output;
FIG. 51 illustrates a perspective view of the card reader;
FIG. 52 illustrates an exploded perspective of a card reader;
FIG. 53 illustrates a close up view of the Artcard reader;
FIG. 54 illustrates a perspective view of the print roll and print head;
FIG. 55 illustrates a first exploded perspective view of the print roll;
FIG. 56 illustrates a second exploded perspective view of the print roll;
FIG. 57 illustrates the print roll authentication chip
FIG. 58 illustrates an enlarged view of the print roll authentication chip;



DESCRIPTION OF PREFERRED AND OTHER EMBODIMENTS
The digital image processing camera system constructed in accordance with the preferred embodiment is as illustrated in FIG. 1. The camera unit 1 includes means for the insertion of an integral print roll (not shown). The camera unit 1 can include an area image sensor 2 which sensors an image 3 for captured by the camera. Optionally, the second area image sensor can be provided to also image the scene 3 and to optionally provide for the production of stereographic output effects.
The camera 1 can include an optional color display 5 for the display of the image being sensed by the sensor 2. When a simple image is being displayed on the display 5, the button 6 can be depressed resulting in the printed image 8 being output by the camera unit 1. A series of cards, herein after known as “Artcards” 9 contain, on one surface encoded information and on the other surface, contain an image distorted by the particular effect produced by the Artcard 9. The Artcard 9 is inserted in an Artcard reader 10 in the side of camera 1 and, upon insertion, results in output image 8 being distorted in the same manner as the distortion appearing on the surface of Artcard 9. Hence, by means of this simple user interface a user wishing to produce a particular effect can insert one of many Artcards 9 into the Artcard reader 10 and utilize button 19 to take a picture of the image 3 resulting in a corresponding distorted output image 8.
The camera unit 1 can also include a number of other control button 13, 14 in addition to a simple LCD output display 15 for the display of informative information including the number of printouts left on the internal print roll on the camera unit. Additionally, different output formats can be controlled by CHP switch 17.
Turning now to FIG. 2, there is illustrated a schematic view of the internal hardware of the camera unit 1. The internal hardware is based around an Artcam central processor unit (ACP) 31.
Artcam Central Processor 31
The Artcam central processor 31 provides many functions which form the ‘heart’ of the system. The ACP 31 is preferably implemented as a complex, high speed, CMOS system on-a-chip. Utilising standard cell design with some full custom regions is recommended. Fabrication on a 0.25μ CMOS process will provide the density and speed required, along with a reasonably small die area.
The functions provided by the ACP 31 include:
1. Control and digitization of the area image sensor 2. A 3D stereoscopic version of the ACP requires two area image sensor interfaces with a second optional image sensor 4 being provided for stereoscopic effects.
2. Area image sensor compensation, reformatting, and image enhancement.
3. Memory interface and management to a memory store 33.
4. Interface, control, and analog to digital conversion of an Artcard reader linear image sensor 34 which is provided for the reading of data from the Artcards 9.
5. Extraction of the raw Artcard data from the digitized and encoded Artcard image.
6. Reed-Solomon error detection and correction of the Artcard encoded data. The encoded surface of the Artcard 9 includes information on how to process an image to produce the effects displayed on the image distorted surface of the Artcard 9. This information is in the form of a script, hereinafter known as a “Vark script”. The Vark script is utilised by an interpreter running within the ACP 31 to produce the desired effect.
7. Interpretation of the Vark script on the Artcard 9.
8. Performing image processing operations as specified by the Vark script.
9. Controlling various motors for the paper transport 36, zoom lens 38, autofocus 39 and Artcard driver 37.
10. Controlling a guillotine actuator 40 for the operation of a guillotine 41 for the cutting of photographs 8 from print roll 42.
11. Half-toning of the image data for printing.
12. Providing the print data to a print-head 44 at the appropriate times.
13. Controlling the print head 44.
14. Controlling the ink pressure feed to print-head 44.
15. Controlling optional flash unit 56.
16. Reading and acting on various sensors in the camera, including camera orientation sensor 46, autofocus 47 and Artcard insertion sensor 49.
17. Reading and acting on the user interface buttons 6, 13, 14.
18. Controlling the status display 15.
19. Providing viewfinder and preview images to the color display 5.
20. Control of the system power consumption, including the ACP power consumption via power management circuit 51.
21. Providing external communications 52 to general purpose computers (using part USB).
22. Reading and storing information in a printing roll authentication chip 53.
23. Reading and storing information in a camera authentication chip 54.
24. Communicating with an optional mini-keyboard 57 for text modification.
Quartz Crystal 58
A quartz crystal 58 is used as a frequency reference for the system clock. As the system clock is very high, the ACP 31 includes a phase locked loop clock circuit to increase the frequency derived from the crystal 58.
Artcard 9
The Artcard 9 is a program storage medium for the Artcam unit. As noted previously, the programs are in the form of Vark scripts. Vark is a powerful image processing language especially developed for the Artcam unit. Each Artcard 9 contains one Vark script, and thereby defines one image processing style.
Preferably, the VARK language is highly image processing specific. By being highly image processing specific, the amount of storage required to store the details on the card are substantially reduced. Further, the ease with which new programs can be created, including enhanced effects, is also substantially increased. Preferably, the language includes facilities for handling many image processing functions including image warping via a warp map, convolution, color lookup tables, posterizing an image, adding noise to an image, image enhancement filters, painting algorithms, brush jittering and manipulation edge detection filters, tiling, illumination via light sources, bump maps, text, face detection and object detection attributes, fonts, including three dimensional fonts, and arbitrary complexity pre-rendered icons. Further details of the operation of the Vark language interpreter are contained hereinafter.
Hence, by utilizing the language constructs as defined by the created language, new affects on arbitrary images can be created and constructed for inexpensive storage on Artcard and subsequent distribution to camera owners. Further, on one surface of the card can be provided an example illustrating the effect that a particular VARK script, stored on the other surface of the card, will have on an arbitrary captured image.
By utilizing such a system, camera technology can be distributed without a great fear of obsolescence in that, provided a VARK interpreter is incorporated in the camera device, a device independent scenario is provided whereby the underlying technology can be completely varied over time. Further, the VARK scripts can be updated as new filters are created and distributed in an inexpensive manner, such as via simple cards for card reading.
The Artcard 9 is a piece of thin white plastic with the same format as a credit card (86 mm long by 54 mm wide). The Artcard is printed on both sides using a high resolution ink jet printer. The inkjet printer technology is assumed to be the same as that used in the Artcam, with 1600 dpi (63 dpmm) resolution. A major feature of the Artcard 9 is low manufacturing cost. Artcards can be manufactured at high speeds as a wide web of plastic film. The plastic web is coated on both sides with a hydrophilic dye fixing layer. The web is printed simultaneously on both sides using a ‘pagewidth’ color ink jet printer. The web is then cut and punched into individual cards. On one face of the card is printed a human readable representation of the effect the Artcard 9 will have on the sensed image. This can be simply a standard image which has been processed using the Vark script stored on the back face of the card.
On the back face of the card is printed an array of dots which can be decoded into the Vark script that defines the image processing sequence. The print area is 80 mm×50 mm, giving a total of 15,876,000 dots. This array of dots could represent at least 1.89 Mbytes of data. To achieve high reliability, extensive error detection and correction is incorporated in the array of dots. This allows a substantial portion of the card to be defaced, worn, creased, or dirty with no effect on data integrity. The data coding used is Reed-Solomon coding, with half of the data devoted to error correction. This allows the storage of 967 Kbytes of error corrected data on each Artcard 9.
Linear Image Sensor 34
The Artcard linear sensor 34 converts the aforementioned Artcard data image to electrical signals. As with the area image sensor 2, 4, the linear image sensor can be fabricated using either CCD or APS CMOS technology. The active length of the image sensor 34 is 50 mm, equal to the width of the data array on the Artcard 9. To satisfy Nyquist's sampling theorem, the resolution of the linear image sensor 34 must be at least twice the highest spatial frequency of the Artcard optical image reaching the image sensor. In practice, data detection is easier if the image sensor resolution is substantially above this. A resolution of 4800 dpi (189 dpmm) is chosen, giving a total of 9,450 pixels. This resolution requires a pixel sensor pitch of 5.3 μm. This can readily be achieved by using four staggered rows of 20 μm pixel sensors.
The linear image sensor is mounted in a special package which includes a LED 65 to illuminate the Artcard 9 via a light-pipe (not shown).
The Artcard reader light-pipe can be a molded light-pipe which has several function:
1. It diffuses the light from the LED over the width of the card using total internal reflection facets.
2. It focuses the light onto a 16 μm wide strip of the Artcard 9 using an integrated cylindrical lens.
3. It focuses light reflected from the Artcard onto the linear image sensor pixels using a molded array of microlenses.
The operation of the Artcard reader is explained further hereinafter.
Artcard Reader Motor 37
The Artcard reader motor propels the Artcard past the linear image sensor 34 at a relatively constant rate. As it may not be cost effective to include extreme precision mechanical components in the Artcard reader, the motor 37 is a standard miniature motor geared down to an appropriate speed to drive a pair of rollers which move the Artcard 9. The speed variations, rumble, and other vibrations will affect the raw image data as circuitry within the APC 31 includes extensive compensation for these effects to reliably read the Artcard data.
The motor 37 is driven in reverse when the Artcard is to be ejected.
Artcard Motor Driver 61
The Artcard motor driver 61 is a small circuit which amplifies the digital motor control signals from the APC 31 to levels suitable for driving the motor 37.
Card Insertion Sensor 49
The card insertion sensor 49 is an optical sensor which detects the presence of a card as it is being inserted in the card reader 34. Upon a signal from this sensor 49, the APC 31 initiates the card reading process, including the activation of the Artcard reader motor 37.
Card Eject Button 16
A card eject button 16 (FIG. 1) is used by the user to eject the current Artcard, so that another Artcard can be inserted. The APC 31 detects the pressing of the button, and reverses the Artcard reader motor 37 to eject the card.
Card Status Indicator 66
A card status indicator 66 is provided to signal the user as to the status of the Artcard reading process. This can be a standard bi-color (red/green) LED. When the card is successfully read, and data integrity has been verified, the LED lights up green continually. If the card is faulty, then the LED lights up red.
If the camera is powered from a 1.5 V instead of 3V battery, then the power supply voltage is less than the forward voltage drop of the greed LED, and the LED will not light. In this case, red LEDs can be used, or the LED can be powered from a voltage pump which also powers other circuits in the Artcam which require higher voltage.
64 Mbit DRAM 33
To perform the wide variety of image processing effects, the camera utilizes 8 Mbytes of memory 33. This can be provided by a single 64 Mbit memory chip. Of course, with changing memory technology increased Dram storage sizes may be substituted.
High speed access to the memory chip is required. This can be achieved by using a Rambus DRAM (burst access rate of 500 Mbytes per second) or chips using the new open standards such as double data rate (DDR) SDRAM or Synclink DRAM.
Inserting an Artcard
When a user inserts an Artcard 9, the Artcard Sensor 49 detects it notifying the ACP72. This results in the software inserting an ‘Artcard Inserted’ event into the event queue. When the event is processed several things occur:
The current Artcard is marked as invalid (as opposed to ‘none’).
The Print Image is marked as invalid.
The Artcard motor 37 is started up to load the Artcard.
The Artcard Interface 87 is instructed to read the Artcard.
The Artcard Interface 87 accepts signals from the Artcard scanner linear CCD 34, detects the bit pattern printed on the card, and corrects errors in the detected bit pattern, producing a valid Artcard data block in DRAM.
Reading Data from the Artcard CCD—General Considerations
As illustrated in FIG. 3, the Data Card reading process has 4 phases operated while the pixel data is read from the card. The phases are as follows:
Phase 1. Detect data area on Artcard
Phase 2. Detect bit pattern from Artcard based on CCD pixels, and write as bytes.
Phase 3. Descramble and XOR the byte-pattern
Phase 4. Decode data (Reed-Solomon decode)
As illustrated in FIG. 4, the Artcard 9 must be sampled at least at double the printed resolution to satisfy Nyquist's Theorem. In practice it is better to sample at a higher rate than this. Preferably, the pixels are sampled 230 at 3 times the resolution of a printed dot in each dimension, requiring 9 pixels to define a single dot. Thus if the resolution of the Artcard 9 is 1600 dpi, and the resolution of the sensor 34 is 4800 dpi, then using a 50 mm CCD image sensor results in 9450 pixel column. Therefore if we require 2 MB of dot data (at 9 pixels per dot) then this requires 2 MB*8*9/9450=15,978 colum approximately 16,000 columns. Of course if a dot is not exactly aligned with the sampling CCD the worst and most likely case is that a dot will be sensed over a 16 pixel area (4×4) 231.
An Artcard 9 may be slightly warped due to heat damage, slightly rotated (up to, say 1 degree) due to differences in insertion into an Artcard reader, and can have slight differences in true data rate due to fluctuations in the speed of the reader motor 37. These changes will cause columns of data from the card not to be read as corresponding columns of pixel data. As illustrated in FIG. 5, a 1 degree rotation in the Artcard 9 can cause the pixels from a column on the card to be read as pixels across 166 columns:
Finally, the Artcard 9 should be read in a reasonable amount of time with respect to the human operator. The data on the Artcard covers most of the Artcard surface, so timing concerns can be limited to the Artcard data itself. A reading time of 1.5 seconds is adequate for Artcard reading.
The Artcard should be loaded in 1.5 seconds. Therefore all 16,000 columns of pixel data must be read from the CCD 34 in 1.5 second, i.e. 10,667 columns per second. Therefore the time available to read one column is 1/10667 or 93,747 ns. Pixel data can be written to the DRAM one column at a time, completely independently from any processes that are reading the pixel data.
The time to write one column of data (9450/2 bytes since the reading can be 4 bits per pixel giving 2×4 bit pixels per byte) to DRAM is reduced by using 8 cache lines. If 4 lines were written out at one time, the 4 banks can be written to independently, and thus overlap latency reduced. Thus the 4725 bytes can be written in 11,840 ns (4725/128*320 ns). Thus the time taken to write a given column's data to DRAM uses just under 13% of the available bandwidth.
Decoding an Artcard
A simple look at the data sizes shows the impossibility of fitting the process into the 8 MB of memory 33 if the entire Artcard pixel data (140 MB if each bit is read as a 3×3 array) as read by the linear CCD 34 is kept. For this reason, the reading of the linear CCD, decoding of the bitmap, and the un-bitmap process should take place in real-time (while the Artcard 9 is traveling past the linear CCD 34), and these processes must effectively work without having entire data stores available.
When an Artcard 9 is inserted, the old stored Print Image and any expanded Photo Image becomes invalid. The new Artcard 9 can contain directions for creating a new image based on the currently captured Photo Image. The old Print Image is invalid, and the area holding expanded Photo Image data and image pyramid is invalid, leaving more than 5 MB that can be used as scratch memory during the read process. Strictly speaking, the 1 MB area where the Artcard raw data is to be written can also be used as scratch data during the Artcard read process as long as by the time the final Reed-Solomon decode is to occur, that 1 MB area is free again. The reading process described here does not make use of the extra 1 MB area (except as a final destination for the data).
It should also be noted that the unscrambling process requires two sets of 2 MB areas of memory since unscrambling cannot occur in place. Fortunately the 5 MB scratch area contains enough space for this process.
Turning now to FIG. 6, there is shown a flowchart 220 of the steps necessary to decode the Artcard data. These steps include reading in the Artcard 221, decoding the read data to produce corresponding encoded XORed scrambled bitmap data 223. Next a checkerboard XOR is applied to the data to produces encoded scrambled data 224. This data is then unscrambled 227 to produce data 225 before this data is subjected to Reed-Solomon decoding to produce the original raw data 226. Alternatively, unscrambling and XOR process can take place together, not requiring a separate pass of the data. Each of the above steps is discussed in further detail hereinafter. As noted previously with reference to FIG. 6, the Artcard Interface, therefore, has 4 phases, the first 2 of which are time-critical, and must take place while pixel data is being read from the CCD:
Phase 1. Detect data area on Artcard
Phase 2. Detect bit pattern from Artcard based on CCD pixels, and write as bytes.
Phase 3. Descramble and XOR the byte-pattern
Phase 4. Decode data (Reed-Solomon decode)
The four phases are described in more detail as follows:
Phase 1. As the Artcard 9 moves past the CCD 34 the AI must detect the start of the data area by robustly detecting special targets on the Artcard to the left of the data area. If these cannot be detected, the card is marked as invalid. The detection must occur in real-time, while the Artcard 9 is moving past the CCD 34.
If necessary, rotation invariance can be provided. In this case, the targets are repeated on the right side of the Artcard, but relative to the bottom right corner instead of the top corner. In this way the targets end up in the correct orientation if the card is inserted the “wrong” way. Phase 3 below can be altered to detect the orientation of the data, and account for the potential rotation.
Phase 2. Once the data area has been determined, the main read process begins, placing pixel data from the CCD into an ‘Artcard data window’, detecting bits from this window, assembling the detected bits into bytes, and constructing a byte-image in DRAM. This must all be done while the Artcard is moving past the CCD.
Phase 3. Once all the pixels have been read from the Artcard data area, the Artcard motor 37 can be stopped, and the byte image descrambled and XORed. Although not requiring real-time performance, the process should be fast enough not to annoy the human operator. The process must take 2 MB of scrambled bit-image and write the unscrambled/XORed bit-image to a separate 2 MB image.
Phase 4. The final phase in the Artcard read process is the Reed-Solomon decoding process, where the 2 MB bit-image is decoded into a 1 MB valid Artcard data area. Again, while not requiring real-time performance it is still necessary to decode quickly with regard to the human operator. If the decode process is valid, the card is marked as valid. If the decode failed, any duplicates of data in the bit-image are attempted to be decoded, a process that is repeated until success or until there are no more duplicate images of the data in the bit image.
The four phase process described requires 4.5 MB of DRAM. 2 MB is reserved for Phase 2 output, and 0.0.5 MB is reserved for scratch data during phases 1 and 2. The remaining 2 MB of space can hold over 440 columns at 4725 by column. In practice, the pixel data being read is a few columns ahead of the phase I algorithm, and in the worst case, about 180 columns behind phase 2, comfortably inside the 440 column limit.
A description of the actual operation of each phase will now be provided in greater detail.
Phase 1—Detect Data Area on Artcard
This phase is concerned with robustly detecting the left-hand side of the data area on the Artcard 9. Accurate detection of the data area is achieved by accurate detection of special targets printed on the left side of the card. These targets are especially designed to be easy to detect even if rotated up to 1 degree.
Turning to FIG. 7, there is shown an enlargement of the left hand side of an Artcard 9. The side of the card is divided into 16 bands, 239 with a target eg. 241 located at the center of each band. The bands are logical in that there is no line drawn to separate bands. Turning to FIG. 8, there is shown a single target 241. The target 241, is a printed black square containing a single white dot. The idea is to detect firstly as many targets 241 as possible, and then to join at least 8 of the detected white-dot locations into a single logical straight line. If this can be done, the start of the data area 243 is a fixed distance from this logical line. If it cannot be done, then the card is rejected as invalid.
As shown in FIG. 7, the height of the card 9 is 3150 dots. A target (Target0) 241 is placed a fixed distance of 24 dots away from the top left corner 244 of the data area so that it falls well within the first of 16 equal sized regions 239 of 192 dots (576 pixels) with no target in the final pixel region of the card. The target 241 must be big enough to be easy to detect, yet be small enough not to go outside the height of the region if the card is rotated 1 degree. A suitable size for the target is a 31×31 dot (93×93 sensed pixels) black square 241 with the white dot 242.
At the worst rotation of 1 degree, a 1 column shift occurs every 57 pixels. Therefore in a 590 pixel sized band, we cannot place any part of our symbol in the top or bottom 12 pixels or so of the band or they could be detected in the wrong band at CCD read time if the card is worst case rotated.
Therefore, if the black part of the rectangle is 57 pixels high (19 dots) we can be sure that at least 9.5 black pixels will be read in the same column by the CCD (worst case is half the pixels are in one column and half in the next). To be sure of reading at least 10 black dots in the same column, we must have a height of 20 dots. To give room for erroneous detection on the edge of the start of the black dots, we increase the number of dots to 31, giving us 15 on either side of the white dot at the target's local coordinate (15, 15). 31 dots is 91 pixels, which at most suffers a 3 pixel shift in column, easily 576 pixel band.
Thus each target is a block of 31×31 dots (93×93 pixels) each with the composition:
15 columns of 31 black dots each (45 pixel width columns of 93 pixels).
1 column of 15 black dots (45 pixels) followed by 1 white dot (3 pixels) and then a further 15 black do
15 columns of 31 black dots each (45 pixel width columns of 93 pixels).
Detect Targets
Targets are detected by reading columns of pixels, one column at a time rather than by detecting dots. It is necessary to look within a given band for a number of columns consisting of large numbers of contiguous black pixels to build up the left side of a target. Next, it is expected to see a white region in the center of further black columns, and finally the black columns to the left of the target center.
Eight cache lines are required for good cache performance on the reading of the pixels. Each logical read fills 4 cache lines via 4 sub-reads while the other 4 cache-lines are being used. This effectively uses up 13% of the available DRAM bandwidth.
As illustrated in FIG. 9, the detection mechanism FIFO for detecting the targets uses a filter 245, run-length encoder 246, and a FIFO 247 that requires special wiring of the top 3 elements (S1, S2, and S3) for random access.
The columns of input pixels are processed one at a time until either all the targets are found, or until a specified number of columns have been processed. To process a column, the pixels are read from DRAM, passed through a filter 245 to detect a 0 or 1, and then run length encoded 246. The bit value and the number of contiguous bits of the same value are placed in FIFO 247. Each entry of the FIFO 249 is in 8 bits, 7 bits 250 to hold the run-length, and 1 bit 249 to hold the val of the bit detected.
The run-length encoder 246 only encodes contiguous pixels within a 576 pixel (192 dot) region.
The top 3 elements in the FIFO 247 can be accessed 252 in any random order. The run lengths (in pixels) of these entries are filtered into 3 values: short, medium, and long in accordance with the following table:














Short
Used to detect white dot.
RunLength < 16


Medium
Used to detect runs of black above
16 <= RunLength < 48



or below the white dot in the



center of the target.


Long
Used to detect run lengths of black to
RunLength >= 48



the left and right of the center



dot in the target.









Looking at the top three entries in the FIFO 247 there are 3 specific cases of interest:















Case 1
S1 = white long
We have detected a black column of the



S2 = black long
target to the left of or to the right of the



S3 = white medium/
white center dot.



long


Case 2
S1 = white long
If we've been processing a series of



S2 = black medium
columns of Case 1s, then we have probab-



S3 = white short
ly detected the white dot in this column.



Previous 8 columns
We know that the next entry will be black



were Case 1
(or it would have been included in the




white S3 entry), but the number of black




pixels is in question. Need to verify by




checking after the next FIFO advance (see




Case 3).


Case 3
Prev = Case 2
We have detected part of the white dot.



S3 = black med
We expect around 3 of these, and then




some more columns of Case 1.









Preferably, the following information per region band is kept:














TargetDetected
 1 bit


BlackDetectCount
 4 bits


WhiteDetectCount
 3 bits


PrevColumnStartPixel
15 bits


TargetColumn ordinate
16 bits (15:1)


TargetRow ordinate
16 bits (15:1)


TOTAL
 7 bytes (rounded to 8 bytes for easy addressing)









Given a total of 7 bytes. It makes address generation easier if the total is assumed to be 8 bytes. Thus 16 entries requires 16*8=128 bytes, which fits in 4 cache lines. The address range should be inside the scratch 0.5 MB DRAM area since other phases make use of the remaining 4 MB data area.
When beginning to process a given pixel column, the register value S2StartPixel 254 is reset to 0. As entries in the FIFO advance from S2 to S1, they are also added 255 to the existing S2StartPixel value, giving the exact pixel position the run currently defined in S2. Looking at each of the 3 cases of interest in the FIFO, S2StartPixel can be used to determin the start of the black area of a target (Cases 1 and 2), and also the start of the white dot in the center of the target (Case 3 An algorithm for processing columns can be as follows:













1
TargetDetected[0–15] := 0



BlackDetectCount[0–15] := 0



WhiteDetectCount[0–15] := 0



TargetRow[0–15] := 0



TargetColumn[0–15] := 0



PrevColStartPixel[0–15] := 0



CurrentColumn  := 0


2
Do ProcessColumn


3
CurrentColumn++


4
If (CurrentColumn <= LastValidColumn)



Goto 2









The steps involved in the processing a column (Process Column) are as follows:














1
S2StartPixel := 0



FIFO := 0



BlackDetectCount := 0



WhiteDetectCount := 0



ThisColumnDetected := FALSE



PrevCaseWasCase2 := FALSE


2
If (! TargetDetected[Target]) & (! ColumnDetected[Target])



  ProcessCases



EndIf


3
PrevCaseWasCase2 := Case=2


4
Advance FIFO









The processing for each of the 3 (Process Cases) cases is as follows:
Case 1:













BlackDetectCount[target] < 8
√ := ABS(S2StartPixel −


OR
PrevColStartPixel[Target])


WhiteDetectCount[Target] = 0
If (0<=√ < 2)



  BlackDetectCount[Target]++



  (max value =8)



Else



  BlackDetectCount[Target] := 1



  WhiteDetectCount[Target] := 0



EndIf



PrevColStartPixel[Target] :=



S2StartPixel



ColumnDetected[Target] := TRUE



BitDetected = 1


BlackDetectCount[target] >= 8
PrevColStartPixel[Target] :=


WhiteDetectCount[Target] != 0
S2StartPixel



ColumnDetected[Target] := TRUE



BitDetected = 1



TargetDetected[Target] := TRUE



TargetColumn[Target] :=



CurrentColumn − 8 −



    (WhiteDetectCount[Target]/2)









Case 2:

No special processing is recorded except for setting the ‘PrevCaseWasCase2’ flag for identifying Case 3 (see Step 3 of processing a column described above).
Case 3:













PrevCaseWasCase2 = TRUE
If (WhiteDetectCount[Target] < 2)


BlackDetectCount[Target] >= 8
  TargetRow[Target] =


WhiteDetectCount=1
S2StartPixel + (S2RunLength/2)



EndIf



√ := ABS(S2StartPixel −



PrevColStartPixel[Target])



If (0<=√ < 2)



  WhiteDetectCount[Target]++



Else



  WhiteDetectCount[Target] := 1



EndIf



PrevColStartPixel[Target] :=



S2StartPixel



ThisColumnDetected := TRUE



BitDetected = 0









At the end of processing a given column, a comparison is made of the current column to the maximum number of columns for target detection. If the number of columns allowed has been exceeded, then it is necessary to check how many targets have been found. If fewer than 8 have been found, the card is considered invalid.
Process Targets
After the targets have been detected, they should be processed. All the targets may be available or merely some of them. Some targets may also have been erroneously detected.
This phase of processing is to determine a mathematical line that passes through the center of as many targets as possible. The more targets that the line passes through, the more confident the target position has been found. The limit is set to be 8 targets. If a line passes through at least 8 targets, then it is taken to be the right one.
It is all right to take a brute-force but straightforward approach since there is the time to do so (see below), and lowering complexity makes testing easier. It is necessary to determine the line between targets 0 and 1 (if both targets are considered valid) and then determine how many targets fall on this line. Then we determine the line between targets 0 and 2, and repeat the process. Eventually we do the same for the line between targets 1 and 2, 1 and 3 etc. and finally for the line between targets 14 and 15. Assuming all the targets have been found, we need to perform 15+14+13+ . . . =90 sets of calculations (with each set of calculations requiring 16 tests=1440 actual calculations), and choose the line which has the maximum number of targets found along the line. The algorithm for target location can be as follows:















TargetA := 0



MaxFound := 0



BestLine := 0



While (TargetA < 15)



  If (TargetA is Valid)



  TargetB:= TargetA + 1



  While (TargetB<= 15)



  If (TargetB is valid)



  CurrentLine := line between TargetA and TargetB



  TargetC := 0;



  While (TargetC <= 15)



    If (TargetC valid AND TargetC on line AB)



      TargetsHit++



    EndIf



    If (TargetsHit > MaxFound)



        MaxFound := TargetsHit



        BestLine := CurrentLine



      EndIf



      TargetC++



    EndWhile



      EndIf



  TargetB ++



    EndWhile



  EndIf



  TargetA++



EndWhile



If (MaxFound < 8)



  Card is Invalid



Else



  Store expected centroids for rows based on BestLine



EndIf










As illustrated in FIG. 3, in the algorithm above, to determine a CurrentLine 260 from Target A 261 and target B, it is necessary to calculate Δrow (264) & Δcolumn (263) between targets 261, 262, and the location of Target A. It is then possible to move from Target 0 to Target 1 etc. by adding Δrow and Δcolumn. The found (if actually found) location of target N can be compared to the calculated expected position of Target N on the line, and if it falls within the tolerance, then Target N is determined to be on the line.
To calculate Δrow & Δcolumn:

Δrow=(rowTargetA−rowTargetB)/(B−A)

Δcolumn=(columnTargetA−columnTargetB)/(B−A)

Then we calculate the position of Target0:

row=rowTargetA−(A*Δrow)

column=columnTargetA−(A*Δcolumn)

And compare (row, column) against the actual rowTarget0 and columnTarget0. To move from one expected target to the next (e.g. from Target0 to Target1), we simply add Δrow and Δcolumn to row and column respectively. To check if each target is on the line, we must calculate the expected position of Target0, and then perform one add and one comparison for each target ordinate.
At the end of comparing all 16 targets against a maximum of 90 lines, the result is the best line through the valid targets. If that line passes through at least 8 targets (i.e. MaxFound>=8), it can be said that enough targets have been found to form a line, and thus the card can be processed. If the best line passes through fewer than 8, then the card is considered invalid.
The resulting algorithm takes 180 divides to calculate Δrow and Δcolumn, 180 multiply/adds to calculate target0 position, and then 2880 adds/comparisons. The time we have to perform this processing is the time taken to read 36 columns of pixel data=3,374,892 ns. Not even accounting for the fact that an add takes less time than a divide, it is necessary to perform 3240 mathematical operations in 3,374,892 ns. That gives approximately 1040 ns per operation, or 104 cycles. The CPU can therefore safely perform the entire processing of targets, reducing complexity of design.
Update Centroids Based on Data Edge Border and Clockmarks
Step 0: Locate the Data Area
From Target 0 (241 of FIG. 7) it is a predetermined fixed distance in rows and columns to the top left border 244 of the data area, and then a further 1 dot column to the vertical clock marks 276. So we use TargetA, Δrow and Δcolumn found in the previous stage (Δrow and Δcolumn refer to distances between targets) to calculate the centroid or expected location for Target0 as described previously.
Since the fixed pixel offset from Target0 to the data area is related to the distance between targets (192 dots between targets, and 24 dots between Target0 and the data area 243), simply add Δrow/8 to Target0's centroid column coordinate (aspect ratio of dots is 1:1). Thus the top co-ordinate can be defined as:

(columnDotcColumnTop=columnTarget0+(Δrow/8)

(rowDotColumnTop=rowTarget0+(Δcolumn/8)

Next Δrow and Δcolumn are updated to give the number of pixels between dots in a single column (instead of between targets) by dividing them by the number of dots between targets:

Δrow=Δrow/192

Δcolumn=Δcolumn/192

We also set the currentColumn register (see Phase 2) to be −1 so that after step 2, when phase 2 begins, the currentColumn register will increment from −1 to 0.
Step 1: Write Out the Initial Centroid Deltas (Δ) and Bit History
This simply involves writing setup information required for Phase 2.
This can be achieved by writing 0s to all the Δrow and Δcolumn entries for each row, and a bit history. The bit history is actually an expected bit history since it is known that to the left of the clock mark column 276 is a border column 277, and before that, a white area. The bit history therefore is 011, 010, 011, 010 etc.
Step 2: Update the Centroids Based on Actual Pixels Read.
The bit history is set up in Step 1 according to the expected clock marks and data border. The actual centroids for each dot row can now be more accurately set (they were initially 0) by comparing the expected data against the actual pixel values. The centroid updating mechanism is achieved by simply performing step 3 of Phase 2.
Phase 2—Detect Bit Pattern from Artcard Based on Pixels Read, and Write as Bytes.
Since a dot from the Artcard 9 requires a minimum of 9 sensed pixels over 3 columns to be represented, there is little point in performing dot detection calculations every sensed pixel column. It is better to average the time required for processing over the average dot occurrence, and thus make the most of the available processing time. This allows processing of a column of dots from an Artcard 9 in the time it takes to read 3 columns of data from the Artcard. Although the most likely case is that it takes 4 columns to represent a dot, the 4th column will be the last column of one dot and the first column of a next dot. Processing should therefore be limited to only 3 columns.
As the pixels from the CCD are written to the DRAM in 13% of the time available, 83% of the time is available for processing of 1 column of dots i.e. 83% of (93,747*3)=83% of 281,241 ns=233,430 ns.
In the available time, it is necessary to detect 3150 dots, and write their bit values into the raw data area of memory. The processing therefore requires the following steps:
For each column of dots on the Artcard:
Step 0: Advance to the next dot column
Step 1: Detect the top and bottom of an Artcard dot column (check clock marks)
Step 2: Process the dot column, detecting bits and storing them appropriately
Step 3: Update the centroids
Since we are processing the Artcard's logical dot columns, and these may shift over 165 pixels, the worst case is that we cannot process the first column until at least 165 columns have been read into DRAM. Phase 2 would therefore finish the same amount of time after the read process had terminated. The worst case time is: 165*93,747 ns=15,468,255 ns or 0.015 seconds.
Step 0: Advance to the Next Dot Column
In order to advance to the next column of dots we add Δrow and Δcolumn to the dotColumnTop to give us the centroid of the dot at the top of the column. The first time we do this, we are currently at the clock marks column 276 to the left of the bit image data area, and so we advance to the first column of data. Since Δrow and Δcolumn refer to distance between dots within a column, to move between dot columns it is necessary to add Δrow to columndotColumnTop and Δcolumn to rowdotColumnTop.
To keep track of what column number is being processed, the column number is recorded in a register called CurrentColumn. Every time the sensor advances to the next dot column it is necessary to increment the CurrentColumn register. The first time it is incremented, it is incremented from −1 to 0 (see Step 0 Phase 1). The CurrentColumn row determines when to terminate the read process (when reaching maxColumns), and also is used to advance the DataOut Pointer to the next column of byte information once all 8 bits have been written to the byte (once every 8 dot columns). The lower 3 bits determine what bit we're up to within the current byte. It will be the same bit being written for the whole column.
Step 1: Detect the Top and Bottom of an Artcard Dot Column.
In order to process a dot column from an Artcard, it is necessary to detect the top and bottom of a column. The column should form a straight line between the top and bottom of the column (except for local warping etc.). Initially dotColumnTop points to the clock mark column 276. We simply toggle the expected value, write it out into the bit history, and move on to step 2, whose first task will be to add the Δrow and Δcolumn values to dotColumnTop to arrive at the first data dot of the column.
Step 2: Process an Artcard's Dot Column
Given the centroids of the top and bottom of a column in pixel coordinates the column should form a straight line between them, with possible minor variances due to warping etc.
Assuming the processing is to start at the top of a column (at the top centroid coordinate) and move down to the bottom of the column, subsequent expected dot centroids are given as:

rownext=row+Δrow

columnnext=column+Δcolumn

This gives us the address of the expected centroid for the next dot of the column. However to account for local warping and error we add another Δrow and Δcolumn based on the last time we found the dot in a given row. In this way we can account for small drifts that accumulate into a maximum drift of some percentage from the straight linejoining the top of the column to the bottom.
We therefore keep 2 values for each row, but store them in separate tables since the row history is used in step 3 of this phase.
*Δrow and Δcolumn (2@4 bits each=1 byte)
*row history (3 bits per row, 2 rows are stored per byte).
For each row we need to read a Δrow and Δcolumn to determine the change to the centroid. The read process takes 5% of the bandwidth and 2 cache lines:

76*(3150/32)+2*3150=13,824 ns=5% of bandwidth

Once the centroid has been determined, the pixels around the centroid need to be examined to detect the status of the dot and hence the value of the bit. In the worst case a dot covers a 4×4 pixel area. However, thanks to the fact that we are sampling at 3 times the resolution of the dot, the number of pixels required to detect the status of the dot and hence the bit value is much less than this. We only require access to 3 columns of pixel columns at any one time.
In the worst case of pixel drift due to a 1% rotation, centroids will shift I column every 57 pixel rows, but since a dot is 3 pixels in diameter, a given column will be valid for 171 pixel rows (3*57). As a byte contains 2 pixels, the number of bytes valid in each buffered read (4 cache lines) will be a worst case of 86 (out of 128 read).
Once the bit has been detected it must be written out to DRAM. We store the bits from 8 columns as a set of contiguous bytes to minimize DRAM delay. Since all the bits from a given dot column will correspond to the next bit position in a data byte, we can read the old value for the byte, shift and OR in the new bit, and write the byte back.
The read/shift&OR/write process requires 2 cache lines.
We need to read and write the bit history for the given row as we update it. We only require 3 bits of history per row, allowing the storage of 2 rows of history in a single byte. The read/shift&OR/write process requires 2 cache lines.
The total bandwidth required for the bit detection and storage is summarised in the following table:
















Read centroid Δ
5%



Read 3 columns of pixel data
19%



Read/Write detected bits into byte buffer
10%



Read/Write bit history
5%



TOTAL
39%










Detecting a Dot

The process of detecting the value of a dot (and hence the value of a bit) given a centroid is accomplished by examining 3 pixel values and getting the result from a lookup table. The process is fairly simple and is illustrated in FIG. 11. A dot 290 has a radius of about 1.5 pixels. Therefore the pixel 291 that holds the centroid, regardless of the actual position of the centroid within that pixel, should be 100% of the dot's value. If the centroid is exactly in the center of the pixel 291, then the pixels above 292 & below 293 the centroid's pixel, as well as the pixels to the left 294 & right 295 of the centroid's pixel will contain a majority of the dot's value. The further a centroid is away from the exact center of the pixel 295, the more likely that more than the center pixel will have 100% coverage by the dot.
Although FIG. 11 only shows centroids differing to the left and below the center, the same relationship obviously holds for centroids above and to the right of center center. In Case 1, the centroid is exactly in the center of the middle pixel 295. The center pixel 295 is completely covered by the dot, and the pixels above, below, left, and right are also well covered by the dot. In Case 2, the centroid is to the left of the center of the middle pixel 291. The center pixel is still completely covered by the dot, and the pixel 294 to the left of the center is now completely covered by the dot. The pixels above 292 and below 293 are still well covered. In Case 3, the centroid is below the center of the middle pixel 291. The center pixel 291 is still completely covered by the dot 291, and the pixel below center is now completely covered by the dot. The pixels left 294 and right 295 of center are still well covered. In Case 4, the centroid is left and below the center of the middle pixel. The center pixel 291 is still completely covered by the dot, and both the pixel to the left of center 294 and the pixel below center 293 are completely covered by the dot.
The algorithm for updating the centroid uses the distance of the centroid from the center of the middle pixel 291 in order to select 3 representative pixels and thus decide the value of the dot:
Pixel 1: the pixel containing the centroid
Pixel 2: the pixel to the left of Pixel 1 if the centroid's X coordinate (column value) is <1⁄2, otherwis the right of Pixel 1.
Pixel 3: the pixel above pixel 1 if the centroid's Y coordinate (row value) is <1⁄2, otherwise the pixel
As shown in FIG. 12, the value of each pixel is output to a pre-calculated lookup table 301. The 3 pixels are fed into a 12-bit lookup table, which outputs a single bit indicating the value of the dot—on or off. The lookup table 301 is constructed at chip definition time, and can be compiled into about 500 gates. The lookup table can be a simple threshold table, with the exception that the center pixel (Pixel 1) is weighted more heavily.
Step 3: Update the Centroid as for Each Row in the Column
The idea of the Δs processing is to use the previous bit history to generate a ‘perfect’ dot at the expected centroid location for each row in a current column. The actual pixels (from the CCD) are compared with the expected ‘perfect’ pixels. If the two match, then the actual centroid location must be exactly in the expected position, so the centroid Δs must be valid and not need updating. Otherwise a process of changing the centroid Δs needs to occur in order to best fit the expected centroid location to the actual data. The new centroid Δs will be used for processing the dot in the next column.
Updating the centroid Δs is done as a subsequent process from Step 2 for the following reasons:
to reduce complexity in design, so that it can be performed as Step 2 of Phase 1 there is enough bandwidth remaining to allow it to allow reuse of DRAM buffers, and to ensure that all the data required for centroid updating is available at the start of the process without special pipelining.
The centroid Δ are processed as Δcolumn Δrow respectively to reduce complexity.
Although a given dot is 3 pixels in diameter, it is likely to occur in a 4×4 pixel area. However the edge of one dot will as a result be in the same pixel as the edge of the next dot. For this reason, centroid updating requires more than simply the information about a given single dot.
FIG. 13 shows a single dot 310 from the previous column with a given centroid 311. In this example, the dot 310 extend Δ over 4 pixel column's 312–315 and in fact, part of the previous dot column's dot (coordinate=(Prevcolumn, Current Row)) has entered the current column for the dot on the current row. If the dot in the current row and column was white, we would expect the rightmost pixel column 314 from the previous dot column to be a low value, since there is only the dot information from the previous column's dot (the current column's dot is white). From this we can see that the higher the pixel value is in this pixel column 315, the more the centroid should be to the right Of course, if the dot to the right was also black, we cannot adjust the centroid as we cannot get information sub-pixel. The same can be said for the dots to the left, above and below the dot at dot coordinates (PrevColumn, CurrentRow).
From this we can say that a maximum of 5 pixel columns and rows are required. It is possible to simplify the situation by taking the cases of row and column centroid Δs separately, treating them as the same problem, only rotated 90 degrees.
Taking the horizontal case first, it is necessary to change the column centroid Δs if the expected pixels don't match the detected pixels. From the bit history, the value of the bits found for the Current Row in the current dot column, the previous dot column, and the (previous-1)th dot column are known. The expected centroid location is also known. Using these two pieces of information, it is possible to generate a 20 bit expected bit pattern should the read be ‘perfect’. The 20 bit-pattern represents the expected Δ values for each of the 5 pixels across the horizontal dimension. The first nibble would represent the rightmost pixel of the leftmost dot. The next 3 nibbles represent the 3 pixels across the center of the dot 310 from the previous column, and the last nibble would be the leftmost pixel 317 of the rightmost dot (from the current column).
If the expected centroid is in the center of the pixel, we would expect a 20 bit pattern based on the following table:














Bit history
Expected pixels









000
00000



001
0000D



010
0DFD0



011
0DFDD



100
D0000



101
D000D



110
DDFD0



111
DDFDD










The pixels to the left and right of the center dot are either 0 or D depending on whether the bit was a 0 or 1 respectively. The center three pixels are either 000 or DFD depending on whether the bit was a 0 or 1 respectively. These values are based on the physical area taken by a dot for a given pixel. Depending on the distance of the centroid from the exact center of the pixel, we would expect data shifted slightly, which really only affects the pixels either side of the center pixel. Since there are 16 possibilities, it is possible to divide the distance from the center by 16 and use that amount to shift the expected pixels.
Once the 20 bit 5 pixel expected value has been determined it can be compared against the actual pixels read. This can proceed by subtracting the expected pixels from the actual pixels read on a pixel by pixel basis, and finally adding the differences together to obtain a distance from the expected Δ values.
FIG. 14 illustrates one form of implementation of the above algorithm which includes a look up table 320 which receives the bit history 322 and central fractional component 323 and outputs 324 the corresponding 20 bit number which is subtracted 321 from the central pixel input 326 to produce a pixel difference 327.
This process is carried out for the expected centroid and once for a shift of the centroid left and right by 1 amount in Δcolumn. The centroid with the smallest difference from the actual pixels is considered to be the ‘winner’ and the Δcolumn updated accordingly (which hopefully is ‘no change’). As a result, a Δcolumn cannot change by more than 1 each dot column.
The process is repeated for the vertical pixels, and Δrow is consequentially updated.
There is a large amount of scope here for parallelism. Depending on the rate of the clock chosen for the ACP unit 31 these units can be placed in series (and thus the testing of 3 different Δ could occur in consecutive clock cycles), or in parallel where all 3 can be tested simultaneously. If the clock rate is fast enough, there is less need for parallelism.
Bandwidth Utilization
It is necessary to read the old Δ of the Δs, and to write them out again. This takes 10% of the bandwidth:

2*(76(3150/32)+2*3150)=27,648 ns=10% of bandwidth

It is necessary to read the bit history for the given row as we update its Δs. Each byte contains 2 row's bit histories, thus taking 2.5% of the bandwidth:

76((3150/2)/32)+2*(3150/2)=4,085 ns=2.5% of bandwidth

In the worst case of pixel drift due to a 1% rotation, centroids will shift 1 column every 57 pixel rows, but since a dot is 3 pixels in diameter, a given pixel column will be valid for 171 pixel rows (3*57). As a byte contains 2 pixels, number of bytes valid in cached reads will be a worst case of 86 (out of 128 read). The worst case timing for 5 columns is therefore 31% bandwidth.

5*(((9450/(128*2))*320)*128/86)=88,112 ns=31% of bandwidth.

The total bandwidth required for the updating the centroid Δ is summarised in the following table:
















Read/Write centroid Δ
  10%



Read bit history
 2.5%



Read 5 columns of pixel data
  31%



TOTAL
43.5%










Memory Usage for Phase 2:

The 2 MB bit-image DRAM area is read from and written to during Phase 2 processing. The 2 MB pixel-data DRAM area is read.
The 0.5 MB scratch DRAM area is used for storing row data, namely:
















Centroid array
24bits (16:8) * 2 * 3150 = 18,900 byes



Bit History array
3 bits * 3150 entries (2 per byte) = 1575 bytes










Phase 3—Unscramble and XOR the Raw Data

Returning to FIG. 6, the next step in decoding is to unscramble and XOR the raw data. The 2 MB byte image, as taken from the Artcard, is in a scrambled XORed form. It must be unscrambled and re-XORed to retrieve the bit image necessary for the Reed Solomon decoder in phase 4.
Turning to FIG. 15, the unscrambling process 330 takes a 2 MB scrambled byte image 331 and writes an unscrambled 2 MB image 332. The process cannot reasonably be performed in-place, so 2 sets of 2 MB areas are utilised. The scrambled data 331 is in symbol block order arranged in a 16×16 array, with symbol block 0 (334) having all the symbol 0's from all the code words in random order. Symbol block 1 has all the symbol 1's from all the code words in random order etc. Since there are only 255 symbols, the 256th symbol block is currently unused.
A linear feedback shift register is used to determine the relationship between the position within a symbol block eg. 334 and what code word eg. 355 it came from. This works as long as the same seed is used when generating the original Artcard images. The XOR of bytes from alternative source lines with 0×AA and 0×55 respectively is effectively free (in time) since the bottleneck of time is waiting for the DRAM to be ready to read/write to non-sequential addresses.
The timing of the unscrambling XOR process is effectively 2 MB of random byte-reads, and 2 MB of random byte-writes i.e. 2*(2 MB*76 ns+2 MB*2 ns)=327,155,712 ns or approximately 0.33 seconds. This timing assumes
Phase 4—Reed Solomon Decode
This phase is a loop, iterating through copies of the data in the bit image, passing them to the Reed-Solomon decode module until either a successful decode is made or until there are no more copies to attempt decode from.
The Reed-Solomon decoder used can be the VLIW processor, suitably programmed or, alternatively, a separate hardwired core such as LSI Logic's L64712. The L64712 has a throughput of 50 Mbits per second (around 6.25 MB per second), so the time may be bound by the speed of the Reed-Solomon decoder rather than the 2 MB read and 1 MB write memory access time (500 MB/sec for sequential accesses). The time taken in the worst case is thus 2/6.25s=approximately 0.32 seconds.
Phase 5 Running the Vark Script
The overall time taken to read the Artcard 9 and decode it is therefore approximately 2.15 seconds. The apparent delay to the user is actually only 0.65 seconds (the total of Phases 3 and 4), since the Artcard stops moving after 1.5
Once the Artcard is loaded, the Artvark script must be interpreted, Rather than run the script immediately, the script is only run upon the pressing of the ‘Print’ button 13 (FIG. 1). The time taken to run the script will vary depending on the complexity of the script, and must be taken into account for the perceived delay between pressing the print button and the actual print button and the actual printing.
Alternative Artcard Format
Of course, other artcard formats are possible. There will now be described one such alternative artcard format with a number of preferable feature. Described hereinafter will be the alternative Artcard data format, a mechanism for mapping user data onto dots on an alternative Artcard, and a fast alternative Artcard reading algorithm for use in embedded systems where resources are scarce.
Alternative Artcard Overview
The Alternative Artcards can be used in both embedded and PC type applications, providing a user-friendly interface to large amounts of data or configuration information.
While the back side of an alternative Artcard has the same visual appearance regardless of the application (since it stores the data), the front of an alternative Artcard can be application dependent. It must make sense to the user in the context of the application.
Alternative Artcard technology can also be independent of the printing resolution. The notion of storing data as dots on a card simply means that if it is possible put more dots in the same space (by increasing resolution), then those dots can represent more data. The preferred embodiment assumes utilisation of 1600 dpi printing on a 86 mm×55 mm card as the sample Artcard, but it is simple to determine alternative equivalent layouts and data sizes for other card sizes and/or other print resolutions. Regardless of the print resolution, the reading technique remain the same. After all decoding and other overhead has been taken into account, alternative Artcards are capable of storing up to 1 Megabyte of data at print resolutions up to 1600 dpi. Alternative Artcards can store megabytes of data at print resolutions greater than 1600 dpi. The following two tables summarize the effective alternative Artcard data storage capacity for certain print resolutions:
Format of an Alternative Artcard
The structure of data on the alternative Artcard is therefore specifically designed to aid the recovery of data. This section describes the format of the data (back) side of an alternative Artcard.
Dots
The dots on the data side of an alternative Artcard can be monochrome. For example, black dots printed on a white background at a predetermined desired print resolution. Consequently a “black dot” is physically different from a “white dot”. FIG. 16 illustrates various examples of magnified views of black and white dots. The monochromatic scheme of black dots on a white background is preferably chosen to maximize dynamic range in blurry reading environments. Although the black dots are printed at a particular pitch (eg. 1600 dpi), the dots themselves are slightly larger in order to create continuous lines when dots are printed contiguously. In the example images of FIG. 16, the dots are not as merged as they may be in reality as a result of bleeding. There would be more smoothing out of the black indentations. Although the alternative Artcard system described in the preferred embodiment allows for flexibly different dot sizes, exact dot sizes and ink/printing behaviour for a particular printing technology should be studied in more detail in order to obtain best results.
In describing this artcard embodiment, the term dot refers to a physical printed dot (ink, thermal, electro-photographic, silver-halide etc) on an alternative Artcard. When an alternative Artcard reader scans an alternative Artcard, the dots must be sampled at least double the printed resolution to satisfy Nyquist's Theorem. The term pixel refers to a sample value from an alternative Artcard reader device. For example, when 1600 dpi dots are scanned at 4800 dpi there are 3 pixels in each dimension of a dot, or 9 pixels per dot. The sampling process will be further explained hereinafter.
Turning to FIG. 17, there is shown the data surface 1101 a sample of alternative Artcard. Each alternative Artcard consists of an “active” region 1102 surrounded by a white border region 1103. The white border 1103 contains no data information, but can be used by an alternative Artcard reader to calibrate white levels. The active region is an array of data blocks eg. 1104, with each data block separated from the next by a gap of 8 white dots eg. 1106. Depending on the print resolution, the number of data blocks on an alternative Artcard will vary. On a 1600 dpi alternative Artcard, the array can be 8×8. Each data block 1104 has dimensions of 627×394 dots. With an inter-block gap 1106 of 8 white dots, the active area of an alternative Artcard is therefore 5072×3208 dots (8.1 mm×5.1 mm at 1600 dpi).
Data Blocks
Turning now to FIG. 18, there is shown a single data block 1107. The active region of an alternative Artcard consists of an array of identically structured data blocks 1107. Each of the data blocks has the following structure: a data region 1108 surrounded by clock-marks 1109, borders 1110, and targets 1111. The data region holds the encoded data proper, while the clock-marks, borders and targets are present specifically to help locate the data region and ensure accurate recovery of data from within the region.
Each data block 1107 has dimensions of 627×394 dots. Of this, the central area of 595×384 dots is the data region 1108. The surrounding dots are used to hold the clock-marks, borders, and targets.
Borders and Clockmarks
FIG. 19 illustrates a data block with FIG. 20 and FIG. 21 illustrating magnified edge portions thereof. As illustrated in FIG. 20 and FIG. 21, there are two 5 dot high border and clockmark regions 1170, 1177 in each data block: one above and one below the data region. For example, The top 5 dot high region consists of an outer black dot border line 1112 (which stretches the length of the data block), a white dot separator line 1113 (to ensure the border line is independent), and a 3 dot high set of clock marks 1114. The clock marks alternate between a white and black row, starting with a black clock mark at the 8th column from either end of the data block. There is no separation between clockmark dots and dots in the data region.
The clock marks are symmetric in that if the alternative Artcard is inserted rotated 180 degrees, the same relative border/clockmark regions will be encountered. The border 1112, 1113 is intended for use by an alternative Artcard reader to keep vertical tracking as data is read from the data region. The clockmarks 1114 are intended to keep horizontal tracking as data is read from the data region. The separation between the border and clockmarks by a white line of dots is desirable as a result of blurring occurring during reading. The border thus becomes a black line with white on either side, making for a good frequency response on reading. The clockmarks alternating between white and black have a similar result, except in the horizontal rather than the vertical dimension. Any alternative Artcard reader must locate the clockmarks and border if it intends to use them for tracking. The next section deals with targets, which are designed to point the way to the clockmarks, border and data.
Targets in the Target Region
As shown in FIG. 23, there are two 15-dot wide target regions 1116, 1117 in each data block: one to the left and one to the right of the data region. The target regions are separated from the data region by a single column of dots used for orientation. The purpose of the Target Regions 1116, 1117 is to point the way to the clockmarks, border and data regions. Each Target Region contains 6 targets eg. 1118 that are designed to be easy to find by an alternative Artcard reader. Turning now to FIG. 22 there is shown the structure of a single target 1120. Each target 1120 is a 15×15 dot black square with a center structure 1121 and a run-length encoded target number 1122. The center structure 1121 is a simple white cross, and the target number component 1122 is simply two columns of white dots, each being 2 dots long for each part of the target number. Thus target number 1's target id 1122 is 2 dots long, target number 2's target id 1122 is 4 dots wide etc.
As shown in FIG. 23, the targets are arranged so that they are rotation invariant with regards to card insertion. This means that the left targets and right targets are the same, except rotated 180 degrees. In the left Target Region 1116, the targets are arranged such that targets 1 to 6 are located top to bottom respectively. In the right Target Region, the targets are arranged so that target numbers 1 to 6 are located bottom to top. The target number id is always in the half closest to the data region. The magnified view portions of FIG. 23 reveals clearly the how the right targets are simply the same as the left targets, except rotated 180 degrees.
As shown in FIG. 24, the targets 1124, 1125 are specifically placed within the Target Region with centers 55 dots apart. In addition, there is a distance of 55 dots from the center of target 1 (1124) to the first clockmark dot 1126 in the upper clockmark region, and a distance of 55 dots from the center of the target to the first clockmark dot in the lower clockmark region (not shown). The first black clockmark in both regions begins directly in line with the target center (the 8th dot position is the center of the 15 dot-wide target).
The simplified schematic illustrations of FIG. 24 illustrates the distances between target centers as well as the distance from Target 1 (1124) to the first dot of the first black clockmark (1126) in the upper border/clockmark region. Since there is a distance of 55 dots to the clockmarks from both the upper and lower targets, and both sides of the alternative Artcard are symmetrical (rotated through 180 degrees), the card can be read left-to-right or right-to-left. Regardless of reading direction, the orientation does need to be determined in order to extract the data from the data region.
Orientation Columns
As illustrated in FIG. 25, there are two 1 dot wide Orientation Columns 1127, 1128 in each data block: one directly to the left and one directly to the right of the data region. The Orientation Columns are present to give orientation information to an alternative Artcard reader: On the left side of the data region (to the right of the Left Targets) is a single column of white dots 1127. On the right side of the data region (to the left of the Right Targets) is a single column of black dots 1128. Since the targets are rotation invariant, these two columns of dots allow an alternative Artcard reader to determine the orientation of the alternative Artcard—has the card been inserted the right way, or back to front. From the alternative Artcard reader's point of view, assuming no degradation to the dots, there are two possibilities:

    
    
        If the column of dots to the left of the data region is white, and the column to the right of the data region is black, then the reader will know that the card has been inserted the same way as it was written.
        If the column of dots to the left of the data region is black, and the column to the right of the data region is white, then the reader will know that the card has been inserted backwards, and the data region is appropriately rotated. The reader must take appropriate action to correctly recover the information from the alternative Artcard.

Data Region

    
    


As shown in FIG. 26, the data region of a data block consists of 595 columns of 384 dots each, for a total of 228,480 dots. These dots must be interpreted and decoded to yield the original data. Each dot represents a single bit, so the 228,480 dots represent 228,480 bits, or 28,560 bytes. The interpretation of each dot can be as follows:
















Black
1



White
0










The actual interpretation of the bits derived from the dots, however, requires understanding of the mapping from the original data to the dots in the data regions of the alternative Artcard.
Mapping Original Data to Data Region Dots
There will now be described the process of taking an original data file of maximum size 910,082 bytes and mapping it to the dots in the data regions of the 64 data blocks on a 1600 dpi alternative Artcard. An alternative Artcard reader would reverse the process in order to extract the original data from the dots on an alternative Artcard. At first glance it seems trivial to map data onto dots: binary data is comprised of 1s and 0s, so it would be possible to simply write black and white dots onto the card. This scheme however, does not allow for the fact that ink can fade, parts of a card may be damaged with dirt, grime, or even scratches. Without error-detection encoding, there is no way to detect if the data retrieved from the card is correct. And without redundancy encoding, there is no way to correct the detected errors. The aim of the mapping process then, is to make the data recovery highly robust, and also give the alternative Artcard reader the ability to know it read the data correctly.
There are three basic steps involved in mapping an original data file to data region dots:

    
    
        Redundancy encode the original data
        Shuffle the encoded data in a deterministic way to reduce the effect of localized alternative Artcard damage
        Write out the shuffled, encoded data as dots to the data blocks on the alternative Artcard
    
    


Each of these steps is examined in detail in the following sections.
Redundancy Encode Using Reed-Solomon Encoding
The mapping of data to alternative Artcard dots relies heavily on the method of redundancy encoding employed. Reed-Solomon encoding is preferably chosen for its ability to deal with burst errors and effectively detect and correct errors using a minimum of redundancy. Reed Solomon encoding is adequately discussed in the standard texts such as Wicker, S., and Bhargava, V., 1994, Reed-Solomon Codes and their Applications, IEEE Press. Rorabaugh, C, 1996, Error Coding Cookbook, McGraw-Hill. Lyppens, H., 1997, Reed-Solomon Error Correction, Dr. Dobb's Journal, Jan. 1997 (Volume 22, Issue 1).
A variety of different parameters for Reed-Solomon encoding can be used, including different symbol sizes and different levels of redundancy. Preferably, the following encoding parameters are used:

    
    
        m=8
        t=64
    
    


Having m=8 means that the symbol size is 8 bits (1 byte). It also means that each Reed-Solomon encoded block size n is 255 bytes (28−1 symbols). In order to allow correction of up to t symbols, 2t symbols in the final block be taken up with redundancy symbols. Having t=64 means that 64 bytes (symbols) can be corrected per block if they are in error. Each 255 byte block therefore has 128 (2×64) redundancy bytes, and the remaining 127 bytes (k=127) are used to hold original data. Thus:

    
    
        n=255
        k=127
    
    


The practical result is that 127 bytes of original data are encoded to become a 255-byte block of Reed-Solomon encoded data. The encoded 255-byte blocks are stored on the alternative Artcard and later decoded back to the original 127 bytes again by the alternative Artcard reader. The 384 dots in a single column of a data block's data region can hold 48 bytes (384/8). 595 of these columns can hold 28,560 bytes. This amounts to 112 Reed-Solomon blocks (each block having 255 bytes). The 64 data blocks of a complete alternative Artcard can hold a total of 7168 Reed-Solomon blocks (1,827,840 bytes, at 255 bytes per Reed-Solomon block). Two of the 7,168 Reed-Solomon blocks are reserved for control information, but the remaining 7166 are used to store data. Since each Reed-Solomon block holds 127 bytes of actual data, the total amount of data that can be stored on an alternative Artcard is 910,082 bytes (7166×127). If the original data is less than this amount, the data can be encoded to fit an exact number of Reed-Solomon blocks, and then the encoded blocks can be replicated until all 7,166 are used. FIG. 27 illustrates the overall form of encoding utilised.
Each of the 2 Control blocks 1132, 1133 contain the same encoded information required for decoding the remaining 7,166 Reed-Solomon blocks:
The number of Reed-Solomon blocks in a full message (16 bits stored lo/hi), and
The number of data bytes in the last Reed-Solomon block of the message (8 bits)
These two numbers are repeated 32 times (consuming. 96 bytes) with the remaining 31 bytes reserved and set to 0. Each control block is then Reed-Solomon encoded, turning the 127 bytes of control information into 255 bytes of Reed-Solomon encoded data.
The Control Block is stored twice to give greater chance of it surviving. In addition, the repetition of the data within the Control Block has particular significance when using Reed-Solomon encoding. In an uncorrupted Reed-Solomon encoded block, the first 127 bytes of data are exactly the original data, and can be looked at in an attempt to recover the original message if the Control Block fails decoding (more than 64 symbols are corrupted). Thus, if a Control Block fails decoding, it is possible to examine sets of 3 bytes in an effort to determine the most likely values for the 2 decoding parameters. It is not guaranteed to be recoverable, but it has a better chance through redundancy. Say the last 159 bytes of the Control Block are destroyed, and the first 96 bytes are perfectly ok. Looking at the first 96 bytes will show a repeating set of numbers. These numbers can be sensibly used to decode the remainder of the message in the remaining 7,166 Reed-Solomon blocks.
By way of example, assume a data file containing exactly 9,967 bytes of data. The number of Reed-Solomon blocks required is 79. The first 78 Reed-Solomon blocks are completely utilized, consuming 9,906 bytes (78×127). The 79th block has only 61 bytes of data (with the remaining 66 bytes all 0s).
The alternative Artcard would consist of 7,168 Reed-Solomon blocks. The first 2 blocks would be Control Blocks, the next 79 would be the encoded data, the next 79 would be a duplicate of the encoded data, the next 79 would be another duplicate of the encoded data, and so on. After storing the 79 Reed-Solomon blocks 90 times, the remaining 56 Reed-Solomon blocks would be another duplicate of the first 56 blocks from the 79 blocks of encoded data (the final 23 blocks of encoded data would not be stored again as there is not enough room on the alternative Artcard). A hex representation of the 127 bytes in each Control Block data before being Reed-Solomon encoded would be as illustrated in FIG. 28.
Scramble the Encoded Data
Assuming all the encoded blocks have been stored contiguously in memory, a maximum 1,827,840 bytes of data can be stored on the alternative Artcard (2 Control Blocks and 7,166 information blocks, totalling 7,168 Reed-Solomon encoded blocks). Preferably, the data is not directly stored onto the alternative Artcard at this stage however, or all 255 bytes of one Reed-Solomon block will be physically together on the card. Any dirt, grime, or stain that causes physical damage to the card has the potential of damaging more than 64 bytes in a single Reed-Solomon block, which would make that block unrecoverable. If there are no duplicates of that Reed-Solomon block, then the entire alternative Artcard cannot be decoded.
The solution is to take advantage of the fact that there are a large number of bytes on the alternative Artcard, and that the alternative Artcard has a reasonable physical size. The data can therefore be scrambled to ensure that symbols from a single Reed-Solomon block are not in close proximity to one another. Of course pathological cases of card degradation can cause Reed-Solomon blocks to be unrecoverable, but on average, the scrambling of data makes the card much more robust. The scrambling scheme chosen is simple and is illustrated schematically in FIG. 29. All the Byte 0s from each Reed-Solomon block are placed together 1136, then all the Byte 1s etc. There will therefore be 7,168 byte 0's, then 7,168 Byte 1's etc. Each data block on the alternative Artcard can store 28,560 bytes. Consequently there are approximately 4 bytes from each Reed-Solomon block in each of the 64 data blocks on the alternative Artcard.
Under this scrambling scheme, complete damage to 16 entire data blocks on the alternative Artcard will result in 64 symbol errors per Reed-Solomon block. This means that if there is no other damage to the alternative Artcard, the entire data is completely recoverable, even if there is no data duplication.
Write the Scrambled Encoded Data to the Alternative Artcard
Once the original data has been Reed-Solomon encoded, duplicated, and scrambled, there are 1,827,840 bytes of data to be stored on the alternative Artcard. Each of the 64 data blocks on the alternative Artcard stores 28,560 bytes.
The data is simply written out to the alternative Artcard data blocks so that the first data block contains the first 28,560 bytes of the scrambled data, the second data block contains the next 28,560 bytes etc.
As illustrated in FIG. 30, within a data block, the data is written out column-wise left to right. Thus the left-most column within a data block contains the first 48 bytes of the 28,560 bytes of scrambled data, and the last column contains the last 48 bytes of the 28,560 bytes of scrambled data. Within a column, bytes are written out top to bottom, one bit at a time, starting from bit 7 and finishing with bit 0. If the bit is set (1), a black dot is placed on the alternative Artcard, if the bit is clear (0), no dot is placed, leaving it the white background color of the card.
For example, a set of 1,827,840 bytes of data can be created by scrambling 7,168 Reed-Solomon encoded blocks to be stored onto an alternative Artcard. The first 28,560 bytes of data are written to the first data block. The first 48 bytes of the first 28,560 bytes are written to the first column of the data block, the next 48 bytes to the next column and so on. Suppose the first two bytes of the 28,560 bytes are hex D3 5F. Those first two bytes will be stored in column 0 of the data block. Bit 7 of byte 0 will be stored first, then bit 6 and so on. Then Bit 7 of byte 1 will be stored through to bit 0 of byte 1. Since each “1” is stored as a black dot, and each “0” as a white dot, these two bytes will be represented on the alternative Artcard as the following set of dots:

    
    
        D3 (1101 0011) becomes: black, black, white, black, white, white, black, black
        5F (0101 1111) becomes: white, black, white, black, black, black, black, black

Decoding an Alternative Artcard

    
    


This section deals with extracting the original data from an alternative Artcard in an accurate and robust manner. Specifically, it assumes the alternative Artcard format as described in the previous chapter, and describes a method of extracting the original pre-encoded data from the alternative Artcard.
There are a number of general considerations that are part of the assumptions for decoding an alternative Artcard.
User
The purpose of an alternative Artcard is to store data for use in different applications. A user inserts an alternative Artcard into an alternative Artcard reader, and expects the data to be loaded in a “reasonable time”. From the user's perspective, a motor transport moves the alternative Artcard into an alternative Artcard reader. This is not perceived as a problematic delay, since the alternative Artcard is in motion. Any time after the alternative Artcard has stopped is perceived as a delay, and should be minimized in any alternative Artcard reading scheme. Ideally, the entire alternative Artcard would be read while in motion, and thus there would be no perceived delay after the card had stopped moving.
For the purpose of the preferred embodiment, a reasonable time for an alternative Artcard to be physically loaded is defined to be 1.5 seconds. There should be a minimization of time for additional decoding after the alternative Artcard has stopped moving. Since the Active region of an alternative Artcard covers most of the alternative Artcard surface we can limit our timing concerns to that region.
Sampling Dots
The dots on an alternative Artcard must be sampled by a CCD reader or the like at least at double the printed resolution to satisfy Nyquist's Theorem. In practice it is better to sample at a higher rate than this. In the alternative Artcard reader environment, dots are preferably sampled at 3 times their printed resolution in each dimension, requiring 9 pixels to define a single dot. If the resolution of the alternative Artcard dots is 1600 dpi, the alternative Artcard reader's image sensor must scan pixels at 4800 dpi. Of course if a dot is not exactly aligned with the sampling sensor, the worst and most likely case as illustrated in FIG. 31, is that a dot will be sensed over a 4×4 pixel area.
Each sampled pixel is 1 byte (8 bits). The lowest 2 bits of each pixel can contain significant noise. Decoding algorithms must therefore be noise tolerant.
Alignment/Rotation
It is extremely unlikely that a user will insert an alternative Artcard into an alternative Artcard reader perfectly aligned with no rotation. Certain physical constraints at a reader entrance and motor transport grips will help ensure that once inserted, an alternative Artcard will stay at the original angle of insertion relative to the CCD. Preferably this angle of rotation, as illustrated in FIG. 32 is a maximum of 1 degree. There can be some slight aberrations in angle due to jitter and motor rumble during the reading process, but these are assumed to essentially stay within the 1-degree limit.
The physical dimensions of an alternative Artcard are 86 mm×55 mm. A I degree rotation adds 1.5 mm to the effective height of the card as 86 mm passes under the CCD (86 sin 1°), which will affect the required CCD length.
The effect of a 1 degree rotation on alternative Artcard reading is that a single scanline from the CCD will include a number of different columns of dots from the alternative Artcard. This is illustrated in an exaggerated form in FIG. 32 which shows the drift of dots across the columns of pixels. Although exaggerated in this diagram, the actual drift will be a maximum 1 pixel column shift every 57 pixels.
When an alternative Artcard is not rotated, a single column of dots can be read over 3 pixel scanlines. The more an alternative Artcard is rotated, the greater the local effect. The more dots being read, the longer the rotation effect is applied. As either of these factors increase, the larger the number of pixel scanlines that are needed to be read to yield a given set of dots from a single column on an alternative Artcard. The following table shows how many pixel scanlines are required for a single column of dots in a particular alternative Artcard structure.













Region
Height
0° rotation
1° rotation







Active region
3208 dots
3 pixel columns
168 pixel columns


Data block
 394 dots
3 pixel columns
 21 pixel columns









To read an entire alternative Artcard, we need to read 87 mm (86 mm+1 mm due to 1° rotation). At 4800 implies 16,252 pixel columns.
CCD (or Other Linear Image Sensor) Length
The length of the CCD itself must accommodate:

    
    
        the physical height of the alternative Artcard (55 mm),
        vertical slop on physical alternative Artcard insertion (1 mm)
        insertion rotation of up to I degree (86 sin 1°=1.5 mm)
    
    


These factors combine to form a total length of 57.5 mm.
When the alternative Artcard Image sensor CCD in an alternative Artcard reader scans at 4800 dpi, a single scanline is 10,866 pixels. For simplicity, this figure has been rounded up to 11,000 pixels. The Active Region of an alternative Artcard has a height of 3208 dots, which implies 9,624 pixels. A Data Region has a height of 384 dots, which implies 1,152 pixels.
DRAM Size
The amount of memory required for alternative Artcard reading and decoding is ideally minimized. The typical placement of an alternative Artcard reader is an embedded system where memory resources are precious. This is made more problematic by the effects of rotation. As described above, the more an alternative Artcard is rotated, the more scanlines are required to effectively recover original dots.
There is a trade-off between algorithmic complexity, user perceived delays, robustness, and memory usage. One of the simplest reader algorithms would be to simply scan the whole alternative Artcard, and then to process the whole data without real-time constraints. Not only would this require huge reserves of memory, it would take longer than a reader algorithm that occurred concurrently with the alternative Artcard reading process.
The actual amount of memory required for reading and decoding an alternative Artcard is twice the amount of space required to hold the encoded data, together with a small amount of scratch space (1–2 KB). For the 1600 dpi alternative Artcard, this implies a 4 MB memory requirement. The actual usage of the memory is detailed in the following algorithm description.
Transfer Rate
DRAM bandwidth assumptions need to be made for timing considerations and to a certain extent affect algorithmic design, especially since alternative Artcard readers are typically part of an embedded system.
A standard Rambus Direct RDRAM architecture is assumed, as defined in Rambus Inc, Oct. 1997, Direct Rambus Technology Disclosure, with a peak data transfer rate of 1.6 GB/sec. Assuming 75% efficiency (easily achieved), we have an average of 1.2 GB/sec data transfer rate. The average time to access a block of 16 bytes is therefore 12 ns.
Dirty Data
Physically damaged alternative Artcards can be inserted into a reader. Alternative Artcards may be scratched, or be stained with grime or dirt. A alternative Artcard reader can't assume to read everything perfectly. The effect of dirty data is made worse by blurring, as the dirty data affects the surrounding clean dots.
Blurry Environment
There are two ways that blurring is introduced into the alternative Artcard reading environment:

    
    
        Natural blurring due to nature of the CCD's distance from the alternative Artcard.
        Warping of alternative Artcard
    
    


Natural blurring of an alternative Artcard image occurs when there is overlap of sensed data from the CCD. Blurring can be useful, as the overlap ensures there are no high frequencies in the sensed data, and that there is no data missed by the CCD. However if the area covered by a CCD pixel is too large, there will be too much blurring and the sampling required to recover the data will not be met. FIG. 33 is a schematic illustration of the overlapping of sensed data.
Another form of blurring occurs when an alternative Artcard is slightly warped due to heat damage. When the warping is in the vertical dimension, the distance between the alternative Artcard and the CCD will not be constant, and the level of blurring will vary across those areas.
Black and white dots were chosen for alternative Artcards to give the best dynamic range in blurry reading environments. Blurring can cause problems in attempting to determine whether a given dot is black or white.
As the blurring increases, the more a given dot is influenced by the surrounding dots. Consequently the dynamic range for a particular dot decreases. Consider a white dot and a black dot, each surrounded by all possible sets of dots. The 9 dots are blurred, and the center dot sampled. FIG. 34 shows the distribution of resultant center dot values for black and white dots.
The diagram is intended to be a representative blurring. The curve 1140 from 0 to around 180 shows the range of black dots. The curve 1141 from 75 to 250 shows the range of white dots. However the greater the blurring, the more the two curves shift towards the center of the range and therefore the greater the intersection area, which means the more difficult it is to determine whether a given dot is black or white. A pixel value at the center point of intersection is ambiguous —the dot is equally likely to be a black or a white.
As the blurring increases, the likelihood of a read bit error increases. Fortunately, the Reed-Solomon decoding algorithm can cope with these gracefully up to t symbol errors. FIG. 34 is a graph of number predicted number of alternative Artcard Reed-Solomon blocks that cannot be recovered given a particular symbol error rate. Notice how the Reed-Solomon decoding scheme performs well and then substantially degrades. If there is no Reed-Solomon block duplication, then only 1 block needs to be in error for the data to be unrecoverable. Of course, with block duplication the chance of an alternative Artcard decoding increases.
FIG. 35 only illustrates the symbol (byte) errors corresponding to the number of Reed-Solomon blocks in error. There is a trade-off between the amount of blurring that can be coped with, compared to the amount of damage that has been done to a card. Since all error detection and correction is performed by a Reed-Solomon decoder, there is a finite number of errors per Reed-Solomon data block that can be coped with. The more errors introduced through blurring, the fewer the number of errors that can be coped with due to alternative Artcard damage.
Overview of Alternative Artcard Decoding
As noted previously, when the user inserts an alternative Artcard into an alternative Artcard reading unit, a motor transport ideally carries the alternative Artcard past a monochrome linear CCD image sensor. The card is sampled in each dimension at three times the printed resolution. Alternative Artcard reading hardware and software compensate for rotation up to 1 degree, jitter and vibration due to the motor transport, and blurring due to variations in alternative Artcard to CCD distance. A digital bit image of the data is extracted from the sampled image by a complex method described here. Reed-Solomon decoding corrects arbitrarily distributed data corruption of up to 25% of the raw data on the alternative Artcard. Approximately 1 MB of corrected data is extracted from a 1600 dpi card.
The steps involved in decoding are so as indicated in FIG. 36.
The decoding process requires the following steps:

    
    
        Scan 1144 the alternative Artcard at three times printed resolution (eg scan 1600 dpi alternative Artcard at 4800 dpi)
        Extract 1145 the data bitmap from the scanned dots on the card.
        Reverse 1146 the bitmap if the alternative Artcard was inserted backwards.
        Unscramble 1147 the encoded data
        Reed-Solomon 1148 decode the data from the bitmap

Algorithmic Overview

Phase 1—Real Time Bit Image Extraction

    
    


A simple comparison between the available memory (4 MB) and the memory required to hold all the scanned pixels for a 1600 dpi alternative Artcard (172.5 MB) shows that unless the card is read multiple times (not a realistic option), the extraction of the bitmap from the pixel data must be done on the fly, in real time, while the alternative Artcard is moving past the CCD. Two tasks must be accomplished in this phase:

    
    
        Scan the alternative Artcard at 4800 dpi
        Extract the data bitmap from the scanned dots on the card
    
    


The rotation and unscrambling of the bit image cannot occur until the whole bit image has been extracted. It is therefore necessary to assign a memory region to hold the extracted bit image. The bit image fits easily within 2 MB, leaving 2 MB for use in the extraction process.
Rather than extracting the bit image while looking only at the current scanline of pixels from the CCD, it is possible to allocate a buffer to act as a window onto the alternative Artcard, storing the last N scanlines read. Memory requirements do not allow the entire alternative Artcard to be stored this way (172.5 MB would be required), but allocating 2 MB to store 190 pixel columns (each scanline takes less than 11,000 bytes) makes the bit image extraction process simpler.
The 4 MB memory is therefore used as follows:

    
    
        2 MB for the extracted bit image
         ̃2 MB for the scanned pixels
        1.5 KB for Phase 1 scratch data (as required by algorithm)
    
    


The time taken for Phase 1 is 1.5 seconds, since this is the time taken for the alternative Artcard to travel past the CCD and physically load.
Phase 2—Data Extraction from Bit Image
Once the bit image has been extracted, it must be unscrambled and potentially rotated 180°. It must then be decoded. Phase 2 has no real-time requirements, in that the alternative Artcard has stopped moving, and we are only concerned with the user's perception of elapsed time. Phase 2 therefore involves the remaining tasks of decoding an alternative Artcard:

    
    
        Re-organize the bit image, reversing it if the alternative Artcard was inserted backwards
        Unscramble the encoded data
        Reed-Solomon decode the data from the bit image
    
    


The input to Phase 2 is the 2 MB bit image buffer. Unscrambling and rotating cannot be performed in situ, so a second 2 MB buffer is required. The 2 MB buffer used to hold scanned pixels in Phase I is no longer required and can be used to store the rotated unscrambled data.
The Reed-Solomon decoding task takes the unscrambled bit image and decodes it to 910,082 bytes. The decoding can be performed in situ, or to a specified location elsewhere. The decoding process does not require any additional memory buffers.
The 4 MB memory is therefore used as follows:

    
    
        2 MB for the extracted bit image (from Phase 1)
         ̃2 MB for the unscrambled, potentially rotated bit image
        <1 KB for Phase 2 scratch data (as required by algorithm)
    
    


The time taken for Phase 2 is hardware dependent and is bound by the time taken for Reed-Solomon decoding. Using a dedicated core such as LSI Logic's L64712, or an equivalent CPU/DSP combination, it is estimated that Phase 2 would take 0.32 seconds.
Phase 1—Extract Bit Image
This is the real-time phase of the algorithm, and is concerned with extracting the bit image from the alternative Artcard as scanned by the CCD.
As shown in FIG. 37 Phase 1 can be divided into 2 asynchronous process streams. The first of these streams is simply the real-time reader of alternative Artcard pixels from the CCD, writing the pixels to DRAM. The second stream involves looking at the pixels, and extracting the bits. The second process stream is itself divided into 2 processes. The first process is a global process, concerned with locating the start of the alternative Artcard. The second process is the bit image extraction proper.
FIG. 38 illustrates the data flow from a data/process perspective.
Timing
For an entire 1600 dpi alternative Artcard, it is necessary to read a maximum of 16,252 pixel-columns. Given a total time of 1.5 seconds for the whole alternative Artcard, this implies a maximum time of 92,296 ns per pixel column during the course of the various processes.
Process 1—Read Pixels from CCD
The CCD scans the alternative Artcard at 4800 dpi, and generates 11,000 1-byte pixel samples per column. This process simply takes the data from the CCD and writes it to DRAM, completely independently of any other process that is reading the pixel data from DRAM. FIG. 39 illustrates the steps involved.
The pixels are written contiguously to a 2 MB buffer that can hold 190 full columns of pixels. The buffer always holds the 190 columns most recently read. Consequently, any process that wants to read the pixel data (such as Processes 2 and 3) must firstly know where to look for a given column, and secondly, be fast enough to ensure that the data required is actually in the buffer.
Process 1 makes the current scanline number (CurrentScanLine) available to other processes so they can ensure they are not attempting to access pixels from scanlines that have not been read yet.
The time taken to write out a single column of data (11,000 bytes) to DRAM is:

11,000/16*12=8,256 ns

Process 1 therefore uses just under 9% of the available DRAM bandwidth (8256/92296).
Process 2—Detect Start of Alternative Artcard
This process is concerned with locating the Active Area on a scanned alternative Artcard. The input to this stage is the pixel data from DRAM (placed there by Process 1). The output is a set of bounds for the first 8 data blocks on the alternative Artcard, required as input to Process 3. A high level overview of the process can be seen in FIG. 40.
An alternative Artcard can have vertical slop of 1 mm upon insertion. With a rotation of 1 degree there is further vertical slop of 1.5 mm (86 sin 1°). Consequently there is a total vertical slop of 2.5 mm. At 1600 dpi, this approximately 160 dots. Since a single data block is only 394 dots high, the slop is just under half a data block. To get a better estimate of where the data blocks are located the alternative Artcard itself needs to be detected.
Process 2 therefore consists of two parts:

    
    
        Locate the start of the alternative Artcard, and if found,
        Calculate the bounds of the first 8 data blocks based on the start of the alternative Artcard.

Locate the Start of the Alternative Artcard

    
    


The scanned pixels outside the alternative Artcard area are black (the surface can be black plastic or some other non-reflective surface). The border of the alternative Artcard area is white. If we process the pixel columns one by one, and filter the pixels to either black or white, the transition point from black to white will mark the start of the alternative Artcard. The highest level process is as follows:












for (Column=0; Column < MAX_COLUMN; Column++)


{


  Pixel = ProcessColumn(Column)


  if (Pixel)


      return (Pixel, Column)     // success!


}


return failure           // no alternative Artcard found









The ProcessColumn function is simple. Pixels from two areas of the scanned column are passed through a threshold filter to determine if they are black or white. It is possible to then wait for a certain number of white pixels and announce the start of the alternative Artcard once the given number has been detected. The logic of processing a pixel column is shown in the following pseudocode. 0 is returned if the alternative Artcard has not been detected during the column. Otherwise the pixel number of the detected location is returned.












// Try upper region first


count = 0


for (i=0; i<UPPER_REGION_BOUND; i++)


{


if (GetPixel(column, i) < THRESHOLD)


{









count = 0       // pixel is black


  }


  else


  {


count++
  // pixel is white







if (count > WHITE_ALTERNATIVE ARTCARD)









  return i







  }


}


// Try lower region next. Process pixels in reverse


count = 0


for (i=MAX_PIXEL_BOUND; i>LOWER_REGION_BOUND; i−)


{


if (GetPixel(column, i) < THRESHOLD)


{








count = 0
  // pixel is black


}


else


{


count++
  // pixel is white







if (count > WHITE_ALTERNATIVE ARTCARD)









  return i







  }


}


//Not in upper bound or in lower bound. Return failure


return 0









Calculate Data Block Bounds

At this stage, the alternative Artcard has been detected. Depending on the rotation of the alternative Artcard, either the top of the alternative Artcard has been detected or the lower part of the alternative Artcard has been detected. The second step of Process 2 determines which was detected and sets the data block bounds for Phase 3 appropriately.
A look at Phase 3 reveals that it works on data block segment bounds: each data block has a StartPixel and an EndPixel to determine where to look for targets in order to locate the data block's data region.
If the pixel value is in the upper half of the card, it is possible to simply use that as the first StartPixel bounds. If the pixel value is in the lower half of the card, it is possible to move back so that the pixel value is the last segment's EndPixel bounds. We step forwards or backwards by the alternative Artcard data size, and thus set up each segment with appropriate bounds. We are now ready to begin extracting data from the alternative Artcard.















// Adjust to become first pixel if is lower pixel



if (pixel > LOWER_REGION_BOUND)



{



pixel −= 6 * 1152



if (pixel < 0)



      pixel = 0



}



for (i=0; i<6; i++)



{



endPixel = pixel + 1152



segment[i].MaxPixel = MAX_PIXEL_BOUND



segment[i].SetBounds(pixel, endPixel)



pixel = endPixel



}










The MaxPixel value is defined in Process 3, and the SetBounds function simply sets StartPixel and EndPixel clipping with respect to 0 and MaxPixel.
Process 3—Extract Bit Data from Pixels
This is the heart of the alternative Artcard Reader algorithm. This process is concerned with extracting the bit data from the CCD pixel data. The process essentially creates a bit-image from the pixel data, based on scratch information created by Process 2, and maintained by Process 3. A high level overview of the process can be seen in FIG. 41.
Rather than simply read an alternative Artcard's pixel column and determine what pixels belong to what data block, Process 3 works the other way around. It knows where to look for the pixels of a given data block. It does this by dividing a logical alternative Artcard into 8 segments, each containing 8 data blocks as shown in FIG. 42.
The segments as shown match the logical alternative Artcard. Physically, the alternative Artcard is likely to be rotated by some amount. The segments remain locked to the logical alternative Artcard structure, and hence are rotation-independent. A given segment can have one of two states:

    
    
        LookingForTargets: where the exact data block position for this segment has not yet been determined. Targets are being located by scanning pixel column data in the bounds indicated by the segment bounds. Once the data block has been located via the targets, and bounds set for black & white, the state changes to ExtractingBitImage.
        ExtractingBitImage: where the data block has been accurately located, and bit data is being extracted one dot column at a time and written to the alternative Artcard bit image. The following of data block clockmarks gives accurate dot recovery regardless of rotation, and thus the segment bounds are ignored. Once the entire data block has been extracted, new segment bounds are calculated for the next data block based on the current position. The state changes to LookingForTargets.
    
    


The process is complete when all 64 data blocks have been extracted, 8 from each region.
Each data block consists of 595 columns of data, each with 48 bytes. Preferably, the 2 orientation columns for the data block are each extracted at 48 bytes each, giving a total of 28,656 bytes extracted per data block. For simplicity, it is possible to divide the 2 MB of memory into 64×32k chunks. The nth data block for a given segment is stored at the location:

StartBuffer+(256 k*n)

Data Structure for Segments

Each of the 8 segments has an associated data structure. The data structure defining each segment is stored in the scratch data area. The structure can be as set out in the following table:











DataName
Comment







CurrentState
Defines the current state of the segment. Can be



one of:



LookingForTargets



ExtractingBitImage



Initial value is LookingForTargets



Used during LookingForTargets:


StartPixel
Upper pixel bound of segment. Initially set by



Process 2.


EndPixel
Lower pixel bound of segment. Initially set by



Process 2


MaxPixel
The maximum pixel number for any scanline.



It is set to the same value for each



segment: 10,866.


CurrentColumn
Pixel column we're up to while looking for



targets.


FinalColumn
Defines the last pixel column to look in for



targets.


LocatedTargets
Points to a list of located Targets.


PossibleTargets
Points to a set of pointers to Target structures that



represent currently investigated pixel shapes that



may be targets


AvailableTargets
Points to a set of pointers to Target structures that



are currently unused.


TargetsFound
The number of Targets found so far in this data



block.


PossibleTargetCount
The number of elements in the PossibleTargets



list


AvailabletargetCount
The number of elements in the AvailableTargets



list



Used during Extracting BitImage:


BitImage
The start of the Bit Image data area in DRAM



where to store the next data block:



Segment 1 = X, Segment 2 = X+32k etc



Advances by 256k each time the state changes



from ExtractingBitImageData to Looking



ForTargets


CurrentByte
Offset within BitImage where to store next ex-



tracted byte


CurrentDotColumn
Holds current clockmark/dot column number.



Set to −8 when transitioning from state



LookingForTarget to ExtractingBitImage.


UpperClock
Coordinate (column/pixel) of current upper



clockmark/border


LowerClock
Coordinate (column/pixel) of current lower



clockmark/border


CurrentDot
The center of the current data dot for the current



dot column. Initially set to the center of the



first (topmost) dot of the data column.


DataDelta
What to add (column/pixel) to CurrentDot to ad-



vance to the center of the next dot.


BlackMax
Pixel value above which a dot is definitely white


WhiteMin
Pixel value below which a dot is definitely black


MidRange
The pixel value that has equal likelihood of



coming from black or white. When all smarts



have not determined the dot, this value is used to



determine it. Pixels below this value are black,



and above it are white.









High Level of Process 3

Process 3 simply iterates through each of the segments, performing a single line of processing depending on the segment's current state. The pseudocode is straightforward:















blockCount = 0



while (blockCount < 64)



  for (i=0; i<8; i++)



  {



  finishedBlock = segment[i].ProcessState( )



  if (finishedBlock)



        blockCount++



}










Process 3 must be halted by an external controlling process if it has not terminated after a specified amount of time. This will only be the case if the data cannot be extracted. A simple mechanism is to start a countdown after Process 1 has finished reading the alternative Artcard. If Process 3 has not finished by that time, the data from the alternative Artcard cannot be recovered.
CurrentState=LookingForTargets
Targets are detected by reading columns of pixels, one pixel-column at a time rather than by detecting dots within a given band of pixels (between StartPixel and EndPixel) certain patterns of pixels are detected. The pixel columns are processed one at a time until either all the targets are found, or until a specified number of columns have been processed. At that time the targets can be processed and the data area located via clockmarks. The state is changed to ExtractingBitImage to signify that the data is now to be extracted. If enough valid targets are not located, then the data block is ignored, skipping to a column definitely within the missed data block, and then beginning again the process of looking for the targets in the next data block. This can be seen in the following pseudocode:















finishedBlock = FALSE



if(CurrentColumn < Process1.CurrentScanLine)



{



ProcessPixelColumn( )



CurrentColumn++



}



if ((TargetsFound ==6) || (CurrentColumn > LastColumn))



{



if (TargetsFound >= 2)



      ProcessTargets( )



  if (TargetsFound >= 2)



  {



BuildClockmarkEstimates( )



SetBlackAndWhiteBounds( )



CurrentState = ExtractingBitImage



CurrentDotColumn = −8



  }



  else



  {



// data block cannot be recovered. Look for



// next instead. Must adjust pixel bounds to



// take account of possible 1 degree rotation.



finishedBlock = TRUE



SetBounds(StartPixel−12, EndPixel+12)



BitImage += 256KB



CurrentByte = 0



LastColumn += 1024



TargetsFound = 0



  }



}



return finishedBlock










ProcessPixelColumn

Each pixel column is processed within the specified bounds (between StartPixel and EndPixel) to search for certain patterns of pixels which will identify the targets. The structure of a single target (target number 2) is as previously shown in FIG. 23:
From a pixel point of view, a target can be identified by:

    
    
        Left black region, which is a number of pixel columns consisting of large numbers of contiguous black pixels to build up the first part of the target.
        Target center, which is a white region in the center of further black columns
        Second black region, which is the 2 black dot columns after the target center
        Target number, which is a black-surrounded white region that defines the target number by its length
        Third black region, which is the 2 black columns after the target number
    
    


An overview of the required process is as shown in FIG. 43.
Since identification only relies on black or white pixels, the pixels 1150 from each column are passed through a filter 1151 to detect black or white, and then run length encoded 1152. The run-lengths are then passed to a state machine 1153 that has access to the last 3 run lengths and the 4th last color. Based on these values, possible targets pass through each of the identification stages.
The GatherMin&Max process 1155 simply keeps the minimum & maximum pixel values encountered during the processing of the segment. These are used once the targets have been located to set BlackMax, WhiteMin, and MidRange values.
Each segment keeps a set of target structures in its search for targets. While the target structures themselves don't move around in memory, several segment variables point to lists of pointers to these target structures. The three pointer lists are repeated here:













LocatedTargets
Points to a set of Target structures that represent



located targets.


PossibleTargets
Points to a set of pointers to Target structures that



represent currently investigated pixel shapes that may



be targets.


AvailableTargets
Points to a set of pointers to Target structures that are



currently unused.









There are counters associated with each of these list pointers: TargetsFound, PossibleTargetCount, and AvailableTargetCount respectively.
Before the alternative Artcard is loaded, TargetsFound and PossibleTargetCount are set to 0, and AvailableTargetCount is set to 28 (the maximum number of target structures possible to have under investigation since the minimum size of a target border is 40 pixels, and the data area is approximately 1152 pixels). An example of the target pointer layout is as illustrated in FIG. 44.
As potential new targets are found, they are taken from the AvailableTargets list 1157, the target data structure is updated, and the pointer to the structure is added to the PossibleTargets list 1158. When a target is completely verified, it is added to the LocatedTargets list 1159. If a possible target is found not to be a target after all, it is placed back onto the AvailableTargets list 1157. Consequently there are always 28 target pointers in circulation at any time, moving between the lists.
The Target data structure 1160 can have the following form:











DataName
Comment







CurrentState
The current state of the target search


DetectCount
Counts how long a target has been in a given state


StartPixel
Where does the target start? All the lines of pixels in this



target should start within a tolerance of this pixel value.


TargetNumber
Which target number is this (according to what was read)


Column
Best estimate of the target's center column ordinate


Pixel
Best estimate of the target's center pixel ordinate









The ProcessPixelColumn function within the find targets module 1162 (FIG. 43) then, goes through all the run lengths one by one, comparing the runs against existing possible targets (via StartPixel), or creating new possible targets if a potential target is found where none was previously known. In all cases, the comparison is only made if S0.color is white and S1.color is black.
The pseudocode for the ProcessPixelColumn set out hereinafter. When the first target is positively identified, the last column to be checked for targets can be determined as being within a maximum distance from it. For 1° rotation, the maximum distance is 18 pixel columns.












pixel = StartPixel


t = 0


target=PossibleTarget[t]


while ((pixel < EndPixel) && (TargetsFound < 6))


{


 if((S0.Color == white) && (S1.Color == black))


 {


   do


   {


    keepTrying = FALSE


    if


   (


    (target != NULL)


    &&


    (target->AddToTarget(Column, pixel, S1, S2, S3))


   )


   {


    if(target->CurrentState == IsATarget)


    {


     Remove target from PossibleTargets List


     Add target to LocatedTargets List


     TargetsFound++


     if (TargetsFound == 1)


      FinalColumn = Column + MAX_TARGET_DELTA}


    }


    else if (target->CurrentState == NotATarget)


    {


     Remove target from PossibleTargets List


     Add target to AvailableTargets List


     keepTrying = TRUE


    }


    else


    {


     t++  // advance to next target


    }


    target = PossibleTarget[t]


   }


   else


   {


    tmp = AvailableTargets[0]


    if (tmp->AddToTarget(Column,pixel,S1,S2,S3)


    {


     Remove tmp from AvailableTargets list


     Add tmp to PossibleTargets list


     t++  // target t has been shifted right


    }


   }


  } while (keepTrying)


 }


 pixel += S1.RunLength


 Advance S0/S1/S2/S3


}









AddToTarget is a function within the find targets module that determines whether it is possible or not to add the specific run to the given target:

    
    
        If the run is within the tolerance of target's starting position, the run is directly related to the current target, and can therefore be applied to it.
        If the run starts before the target, we assume that the existing target is still ok, but not relevant to the run. The target is therefore left unchanged, and a return value of FALSE tells the caller that the run was not applied. The caller can subsequently check the run to see if it starts a whole new target of its own.
        If the run starts after the target, we assume the target is no longer a possible target. The state is changed to be NotATarget, and a return value of TRUE is returned.
    
    


If the run is to be applied to the target, a specific action is performed based on the current state and set of runs in S1, S2, and S3. The AddToTarget pseudocode is as follows:















MAX_TARGET_DELTA = 1



if (CurrentState != NothingKnown)



{



  if (pixel > StartPixel)    // run starts after target



  {



      diff = pixel − StartPixel



      if (diff > MAX_TARGET_DELTA)



      {



        CurrentState = NotATarget



        return TRUE



      }



  }



  else



  {



      diff = StartPixel − pixel



      if (diff > MAX_TARGET_DELTA)



        return FALSE



    }



}



runType = DetermineRunType(S1, S2, S3)



EvaluateState(runType)



StartPixel = currentPixel



return TRUE










Types of pixel runs are identified in DetermineRunType is as follows:










Types of Pixel Runs










Type
How identified (S1 is always black)







TargetBorder
S1 = 40 < RunLength < 50




S2 = white run



TargetCenter
S1 = 15 < RunLength < 26




S2 = white run with [RunLength < 12]




S3 = black run with [15 < RunLength < 26]



TargetNumber
S2 = white run with [RunLength <= 40]










The EvaluateState procedure takes action depending on the current state and the run type.
The actions are shown as follows in tabular form:













Type of



CurrentState
Pixel Run
Action







NothingKnown
TargetBorder
DetectCount = 1




CurrentState = LeftOfCenter


LeftOfCenter
TargetBorder
DetectCount++




if (DetectCount > 24)




 CurrentState = NotATarget



TargetCenter
DetectCount = 1




CurrentState = InCenter




Column = currentColumn




Pixel = currentPixel + S1.RunLength




CurrentState = NotATarget


InCenter
TargetCenter
DetectCount++




tmp = currentPixel + S1.RunLength




if (tmp < Pixel)




 Pixel = tmp




if (DetectCount > 13)




 CurrentState = NotATarget



TargetBorder
DetectCount = 1




CurrentState = RightOfCenter




CurrentState = NotATarget


RightOfCenter
TargetBorder
DetectCount++




if (DetectCount >= 12)




 CurrentState = NotATarget



TargetNumber
DetectCount = 1




CurrentState = InTargetNumber




TargetNumber =




(S2.RunLength+ 2)/6




CurrentState = NotATarget


InTargetNumber
TargetNumber
tmp = (S2.RunLength+ 2)/6




if (tmp > TargetNumber)




 TargetNumber = tmp




DetectCount++




if (DetectCount >= 12)




 CurrentState = NotATarget



TargetBorder
if (DetectCount >= 3)




 CurrentState = IsATarget




else




 CurrentState = NotATarget




CurrentState = NotATarget


IsATarget or
—
—


NotATarget









Processing Targets

The located targets (in the LocatedTargets list) are stored in the order they were located. Depending on alternative Artcard rotation these targets will be in ascending pixel order or descending pixel order. In addition, the target numbers recovered from the targets may be in error. We may have also have recovered a false target. Before the clockmark estimates can be obtained, the targets need to be processed to ensure that invalid targets are discarded, and valid targets have target numbers fixed if in error (e.g. a damaged target number due to dirt). Two main steps are involved:

    
    
        Sort targets into ascending pixel order
        Locate and fix erroneous target numbers
    
    


The first step is simple. The nature of the target retrieval means that the data should already be sorted in either ascending pixel or descending pixel. A simple swap sort ensures that if the 6 targets are already sorted correctly a maximum of 14 comparisons is made with no swaps. If the data is not sorted, 14 comparisons are made, with 3 swaps. The following pseudocode shows the sorting process:















for (i = 0; i < TargetsFound−1; i++)



{



  oldTarget = LocatedTargets[i]



  bestPixel = oldTarget->Pixel



  best = i



  j = i+1



  while (j<TargetsFound)



  {



      if (LocatedTargets[j]-> Pixel < bestPixel)



        best = j



      j++



  }



  if (best != i) // move only if necessary



      LocatedTargets[i] = LocatedTargets[best]



      LocatedTargets[best] = oldTarget



  }



}










Locating and fixing erroneous target numbers is only slightly more complex. One by one, each of the N targets found is assumed to be correct. The other targets are compared to this “correct” target and the number of targets that require change should target N be correct is counted. If the number of changes is 0, then all the targets must already be correct. Otherwise the target that requires the fewest changes to the others is used as the base for change. A change is registered if a given target's target number and pixel position do not correlate when compared to the “correct” target's pixel position and target number. The change may mean updating a target's target number, or it may mean elimination of the target. It is possible to assume that ascending targets have pixels in ascending order (since they have already been sorted).












kPixelFactor = 1/(55 * 3)


bestTarget = 0


bestChanges = TargetsFound + 1


for (i=0; i< TotalTargetsFound; i++)


{


 numberOfChanges = 0;


fromPixel = (LocatedTargets[i])->Pixel


fromTargetNumber = LocatedTargets[i].TargetNumber


for (j=1;j< TotalTargetsFound;j++)


{


toPixel = LocatedTargets[j]->Pixel


deltaPixel = toPixel − fromPixel


if (deltaPixel >= 0)


   deltaPixel += PIXELS_BETWEEN_TARGET_CENTRES/2


 else


   deltaPixel −= PIXELS_BETWEEN_TARGET_CENTRES/2


targetNumber =deltaPixel * kPixelFactor


targetNumber += fromTargetNumber


if


 (


   (targetNumber < 1)||(targetNumber > 6)


   ||


   (targetNumber != LocatedTargets[j]-> TargetNumber)


  )


   numberOfChanges++


}


 if (numberOfChanges < bestChanges)


 {


bestTarget = i


bestChanges = numberOfChanges


 }


 if (bestChanges < 2)


   break;


}









In most cases this function will terminate with bestChanges=0, which means no changes are required. Otherwise the changes need to be applied. The functionality of applying the changes is identical to counting the changes (in the pseudocode above) until the comparison with targetNumber. The change application is:












if ((targetNumber < 1)||(targetNumber > TARGETS_PER_BLOCK))


{


  LocatedTargets[j] = NULL


  TargetsFound−−


}


else


{


  LocatedTargets[j]-> TargetNumber = targetNumber


}









At the end of the change loop, the LocatedTargets list needs to be compacted and all NULL targets removed.
At the end of this procedure, there may be fewer targets. Whatever targets remain may now be used (at least 2 targets are required) to locate the clockmarks and the data region.
Building Clockmark Estimates from Targets
As shown previously in FIG. 24, the upper region's first clockmark dot 1126 is 55 dots away from the center of the first target 1124 (which is the same as the distance between target centers). The center of the clockmark dots is a further 1 dot away, and the black border line 1123 is a further 4 dots away from the first clockmark dot. The lower region's first clockmark dot is exactly 7 targets-distance away (7×55 dots) from the upper region's first clockmark dot 1126.
It cannot be assumed that Targets 1 and 6 have been located, so it is necessary to use the upper-most and lower-most targets, and use the target numbers to determine which targets are being used. It is necessary at least 2 targets at this point. In addition, the target centers are only estimates of the actual target centers. It is to locate the target center more accurately. The center of a target is white, surrounded by black. We therefore want to find the local maximum in both pixel & column dimensions. This involves reconstructing the continuous image since the maximum is unlikely to be aligned exactly on an integer boundary (our estimate).
Before the continuous image can be constructed around the target's center, it is necessary to create a better estimate of the 2 target centers. The existing target centers actually are the top left coordinate of the bounding box of the target center. It is a simple process to go through each of the pixels for the area defining the center of the target, and find the pixel with the highest value. There may be more than one pixel with the same maximum pixel value, but the estimate of the center value only requires one pixel.
The pseudocode is straightforward, and is performed for each of the 2 targets:















CENTER_WIDTH = CENTER_HEIGHT = 12



maxPixel = 0x00



for (i=0; i<CENTER_WIDTH; i++)



  for (j=0; j<CENTER_HEIGHT; j++)



  {



p = GetPixel(column+i, pixel+j)



if (p > maxPixel)



{



maxPixel = p



centerColumn = column + i



centerPixel = pixel + j



      }



  }



Target.Column = centerColumn



Target.Pixel = centerPixel










At the end of this process the target center coordinates point to the whitest pixel of the target, which should be within one pixel of the actual center. The process of building a more accurate position for the target center involves reconstructing the continuous signal for 7 scanline slices of the target, 3 to either side of the estimated target center. The 7 maximum values found (one for each of these pixel dimension slices) are then used to reconstruct a continuous signal in the column dimension and thus to locate the maximum value in that dimension.















// Given estimates column and pixel, determine a



// betterColumn and betterPixel as the center of



// the target



for (y=0; y<7; y++)



{



  for (x=0; x<7; x++)



      samples[x] = GetPixel(column−3+y, pixel−3+x)



  FindMax(samples, pos, maxVal)



  reSamples[y] = maxVal



  if (y == 3)



      betterPixel = pos + pixel



}



FindMax(reSamples, pos, maxVal)



betterColumn = pos + column










FindMax is a function that reconstructs the original 1 dimensional signal based sample points and returns the position of the maximum as well as the maximum value found. The method of signal reconstruction/resampling used is the Lanczos3 windowed sinc function as shown in FIG. 45.
The Lanczos3 windowed sinc function takes 7 (pixel) samples from the dimension being reconstructed, centered around the estimated position X, i.e. at X−3, X−2, X−1, X, X+1, X+2, X+3. We reconstruct an interval of 0.1, and determine which point is the maximum. The position that is the maximum value becomes the new center. Due to the nature of the kernel, only 6 entries are required in the convolution kernel for points between X and X+1. We use 6 points for X−1 to X, and 6 points for X to X+1, requiring 7 points overall in order to get pixel values from X−1 to X+1 since some of the pixels required are the same:
Given accurate estimates for the upper-most target from and lower-most target to, it is possible to calculate the position of the first clockmark dot for the upper and lower regions as follows:












TARGETS_PER_BLOCK = 6


numTargetsDiff = to.TargetNum − from.TargetNum


deltaPixel = (to.Pixel − from.Pixel) / numTargetsDiff


deltaColumn = (to.Column − from.Column) / numTargetsDiff


UpperClock.pixel = from.Pixel − (from.TargetNum*deltaPixel)


UpperClock.column = from.Column−(from.TargetNum*deltaColumn)


// Given the first dot of the upper clockmark, the


// first dot of the lower clockmark is straightforward.










LowerClock.pixel
=
 UpperClock.pixel
+







  ((TARGETS_PER_BLOCK+1) * deltaPixel)










LowerClock.column
 =
UpperClock.column
+







  ((TARGETS_PER_BLOCK+1) * deltaColumn)









This gets us to the first clockmark dot. It is necessary move the column position a further 1 dot away from the data area to reach the center of the clockmark. It is necessary to also move the pixel position a further 4 dots away to reach the center of the border line. The pseudocode values for deltaColumn and deltaPixel are based on a 55 dot distance (the distance between targets), so these deltas must be scaled by 1/55 and 4/55 respectively before being applied to the coordinates. This is represented as:

    kDeltaDotFactor=1/DOTS_BETWEEN_TARGET_CENTRES
    deltaColumn*=kDeltaDotFactor
    deltaPixel*=4*kDeltaDotFactor
    UpperClock.pixel−=deltaPixel
    UpperClock.column−=deltaColumn
    LowerClock.pixel +=deltaPixel
    LowerClock.column +=deltaColumn


UpperClock and LowerClock are now valid clockmark estimates for the first clockmarks directly in line with the centers of the targets.
Setting Black and White Pixel/Dot Ranges
Before the data can be extracted from the data area, the pixel ranges for black and white dots needs to be ascertained. The minimum and maximum pixels encountered during the search for targets were stored in WhiteMin and BlackMax respectively, but these do not represent valid values for these variables with respect to data extraction. They are merely used for storage convenience. The following pseudocode shows the method of obtaining good values for WhiteMin and BlackMax based on the min & max pixels encountered:

    MinPixel=WhiteMin
    MaxPixel BlackMax
    MidRange=(MinPixel+MaxPixel)/2
    WhiteMin=MaxPixel−105
    BlackMax=MinPixel+84

CurrentState=ExtractingBitImage



The ExtractingBitImage state is one where the data block has already been accurately located via the targets, and bit data is currently being extracted one dot column at a time and written to the alternative Artcard bit image. The following of data block clockmarks/borders gives accurate dot recovery regardless of rotation, and thus the segment bounds are ignored. Once the entire data block has been extracted (597 columns of 48 bytes each; 595 columns of data+2 orientation columns new segment bounds are calculated for the next data block based on the current position. The state is changed to LookingForTargets.
Processing a given dot column involves two tasks:

    
    
        The first task is to locate the specific dot column of data via the clockmarks.
        The second task is to run down the dot column gathering the bit values, one bit per dot.
    
    


These two tasks can only be undertaken if the data for the column has been read off the alternative Artcard and transferred to DRAM. This can be determined by checking what scanline Process 1 is up to, and comparing it to the clockmark columns. If the dot data is in DRAM we can update the clockmarks and then extract the data from the column before advancing the clockmarks to the estimated value for the next dot column. The process overview is given in the following pseudocode, with specific functions explained hereinafter:















finishedBlock = FALSE



if((UpperClock.column < Process1.CurrentScanLine)



  &&



  (LowerClock.column < Process1.CurrentScanLine))



{



  DetermineAccurateClockMarks( )



  DetermineDataInfo( )



  if(CurrentDotColumn >= 0)



      ExtractDataFromColumn( )



  AdvanceClockMarks( )



  if(CurrentDotColumn == FINAL_COLUMN)



  {



finishedBlock = TRUE



currentState = LookingForTargets



SetBounds(UpperClock.pixel, LowerClock.pixel)



BitImage += 256KB



CurrentByte = 0



TargetsFound = 0



  }



}



return finishedBlock










Locating the Dot Column

A given dot column needs to be located before the dots can be read and the data extracted. This is accomplished by following the clockmarks/borderline along the upper and lower boundaries of the data block. A software equivalent of a phase-locked-loop is used to ensure that even if the clockmarks have been damaged, good estimations of clockmark positions will be made. FIG. 46 illustrates an example data block's top left which corner reveals that there are clockmarks 3 dots high 1166 extending out to the target area, a white row, and then a black border line.
Initially, an estimation of the center of the first black clockmark position is provided (based on the target positions). We use the black border 1168 to achieve an accurate vertical position (pixel), and the clockmark eg. 1166 to get an accurate horizontal position (column). These are reflected in the UpperClock and LowerClock positions.
The clockmark estimate is taken and by looking at the pixel data in its vicinity, the continuous signal is reconstructed and the exact center is determined. Since we have broken out the two dimensions into a clockmark and border, this is a simple one-dimensional process that needs to be performed twice. However, this is only done every second dot column, when there is a black clockmark to register against. For the white clockmarks we simply use the estimate and leave it at that. Alternatively, we could update the pixel coordinate based on the border each dot column (since it is always present). In practice it is sufficient to update both ordinates every other column (with the black clockmarks) since the resolution being worked at is so fine. The process therefore becomes:















// Turn the estimates of the clockmarks into accurate



// positions only when there is a black clockmark



// (ie every 2nd dot column, starting from −8)



if (Bit0(CurrentDotColumn) == 0)   // even column



{



  DetermineAccurateUpperDotCenter( )



  DetermineAccurateLowerDotCenter( )



}










If there is a deviation by more than a given tolerance (MAX_CLOCKMARK_DEVIATION), the found signal is ignored and only deviation from the estimate by the maximum tolerance is allowed. In this respect the functionality is similar to that of a phase-locked loop. Thus DetermineAccurateUpperDotCenter is implemented via the following pseudocode:












// Use the estimated pixel position of


// the border to determine where to look for


// a more accurate clockmark center. The clockmark


// is 3 dots high so even if the estimated position


// of the border is wrong, it won't affect the


// fixing of the clockmark position.


MAX_CLOCKMARK_DEVIATION = 0.5


diff      =      GetAccurateColumn(UpperClock.column,


          UpperClock.pixel+(3*PIXELS_PER_DOT))


diff −= UpperClock.column


if (diff > MAX_CLOCKMARK_DEVIATION)


  diff = MAX_CLOCKMARK_DEVIATION


else


if (diff < −MAX_CLOCKMARK_DEVIATION)


  diff = −MAX_CLOCKMARK_DEVIATION


UpperClock.column += diff


// Use the newly obtained clockmark center to


// determine a more accurate border position.


diff = GetAccuratePixel(UpperClock.column, UpperClock.pixel)


diff −= UpperClock.pixel


if (diff > MAX_CLOCKMARK_DEVIATION)


  diff = MAX_CLOCKMARK_DEVIATION


else


if (diff < −MAX_CLOCKMARK_DEVIATION)


  diff = −MAX_CLOCKMARK_DEVIATION


UpperClock.pixel += diff









DetermineAccurateLowerDotCenter is the same, except that the direction from the border to the clockmark is in the negative direction (−3 dots rather than +3 dots).
GetAccuratePixel and GetAccurateColumn are functions that determine an accurate dot center given a coordinate, but only from the perspective of a single dimension. Determining accurate dot centers is a process of signal reconstruction and then finding the location where the minimum signal value is found (this is different to locating a target center, which is locating the maximum value of the signal since the target center is white, not black). The method chosen for signal reconstruction/resampling for this application is the Lanczos3 windowed sinc function as previously discussed with reference to FIG. 45.
It may be that the clockmark or border has been damaged in some way—perhaps it has been scratched. If the new center value retrieved by the resampling differs from the estimate by more than a tolerance amount, the center value is only moved by the maximum tolerance. If it is an invalid position, it should be close enough to use for data retrieval, and future clockmarks will resynchronize the position.
Determining the Center of the First Data Dot and the Deltas to Subsequent Dots
Once an accurate UpperClock and LowerClock position has been determined, it is possible to calculate the center of the first data dot (CurrentDot), and the delta amounts to be added to that center position in order to advance to subsequent dots in the column (DataDelta).
The first thing to do is calculate the deltas for the dot column. This is achieved simply by subtracting the UpperClock from the LowerClock, and then dividing by the number of dots between the two points. It is possible to actually multiply by the inverse of the number of dots since it is constant for an alternative Artcard, and multiplying is faster. It is possible to use different constants for obtaining the deltas in pixel and column dimensions. The delta in pixels is the distance between the two borders, while the delta in columns is between the centers of the two clockmarks. Thus the function DetermineDataInfo is two parts. The first is given by the pseudocode:

kDeltaColumnFactor=1/(DOTS_PER_DATA_COLUMN+2+2−1)

kDeltaPixelFactor=1/(DOTS_PER_DATA_COLUMN+5+5−1)

    delta=LowerClock.column−UpperClock.column
    DataDelta.column=delta*kDeltaColumnFactor
    delta=LowerClock.pixel−UpperClock.pixel
    DataDelta.pixel=delta*kDeltaPixelFactor


It is now possible to determine the center of the first data dot of the column. There is a distance of 2 dots from the center of the clockmark to the center of the first data dot, and 5 dots from the center of the border to the center of the first data dot. Thus the second part of the function is given by the pseudocode:

CurrentDot.column=UpperClock.column+(2*DataDelta.column)

CurrentDot.pixel=UpperClock.pixel+(5*DataDelta.pixel)

Running Down a Dot Column

Since the dot column has been located from the phase-locked loop tracking the clockmarks, all that remains is to sample the dot column at the center of each dot down that column. The variable CurrentDot points is determined to the center of the first dot of the current column. We can get to the next dot of the column by simply adding DataDelta (2 additions: 1 for the column ordinate, the other for the pixel ordinate). A sample of the dot at the given coordinate (bi-linear interpolation) is taken, and a pixel value representing the center of the dot is determined. The pixel value is then used to determine the bit value for that dot. However it is possible to use the pixel value in context with the center value for the two surrounding dots on the same dot line to make a better bit judgement.
We can be assured that all the pixels for the dots in the dot column being extracted are currently loaded in DRAM, for if the two ends of the line (clockmarks) are in DRAM, then the dots between those two clockmarks must also be in DRAM. Additionally, the data block height is short enough (only 384 dots high) to ensure that simple deltas are enough to traverse the length of the line. One of the reasons the card is divided into 8 data blocks high is that we cannot make the same rigid guarantee across the entire height of the card that we can about a single data block.
The high level process of extracting a single line of data (48 bytes) can be seen in the following pseudocode. The dataBuffer pointer increments as each byte is stored, ensuring that consecutive bytes and columns of data are stored consecutively.















bitCount = 8



curr = 0x00       // definitely black



next = GetPixel(CurrentDot)



for (i=0; i < DOTS_PER_DATA_COLUMN; i++)



{



  CurrentDot += DataDelta



  prev = curr



  curr = next



  next = GetPixel(CurrentDot)



  bit = DetermineCenterDot(prev, curr, next)



  byte = (byte << 1) | bit



  bitCount−−



  if (bitCount == 0)



  {



    *(BitImage | CurrentByte) = byte



    CurrentByte++



    bitCount = 8



  }



}










The GetPixel function takes a dot coordinate (fixed point) and samples 4 CCD pixels to arrive at a center pixel value via bilinear interpolation.
The DetermineCenterDot function takes the pixel values representing the dot centers to either side of the dot whose bit value is being determined, and attempts to intelligently guess the value of that center dot's bit value. From the generalized blurring curve of FIG. 33 there are three common cases to consider:

    
    
        The dot's center pixel value is lower than WhiteMin, and is therefore definitely a black dot. The bit value is therefore definitely 1.
        The dot's center pixel value is higher than BlackMax, and is therefore definitely a white dot. The bit value is therefore definitely 0.
        The dot's center pixel value is somewhere between BlackMax and WhiteMin. The dot may be black, and it may be white. The value for the bit is therefore in question. A number of schemes can be devised to make a reasonable guess as to the value of the bit. These schemes must balance complexity against accuracy, and also take into account the fact that in some cases, there is no guaranteed solution. In those cases where we make a wrong bit decision, the bit's Reed-Solomon symbol will be in error, and must be corrected by the Reed-Solomon decoding stage in Phase 2.
    
    


The scheme used to determine a dot's value if the pixel value is between BlackMax and WhiteMin is not too complex, but gives good results. It uses the pixel values of the dot centers to the left and right of the dot in question, using their values to help determine a more likely value for the center dot:

    
    
        If the two dots to either side are on the white side of MidRange (an average dot value), then we can guess that if the center dot were white, it would likely be a “definite” white. The fact that it is in the not-sure region would indicate that the dot was black, and had been affected by the surrounding white dots to make the value less sure. The dot value is therefore assumed to be black, and hence the bit value is 1.
        If the two dots to either side are on the black side of MidRange, then we can guess that if the center dot were black, it would likely be a “definite” black. The fact that it is in the not-sure region would indicate that the dot was white, and had been affected by the surrounding black dots to make the value less sure. The dot value is therefore assumed to be white, and hence the bit value is 0.
        If one dot is on the black side of MidRange, and the other dot is on the white side of MidRange, we simply use the center dot value to decide. If the center dot is on the black side of MidRange, we choose black (bit value 1). Otherwise we choose white (bit value 0).
    
    


The logic is represented by the following:
















if (pixel < WhiteMin)
// definitely black



     bit = 0x01



else



if (pixel > BlackMax)
// definitely white



     bit = 0x00



else









if ((prev > MidRange) && (next> MidRange))  //prob black



     bit = 0x01



else



if ((prev < MidRange) && (next < MidRange)) //prob white



     bit = 0x00



else



if (pixel < MidRange)



     bit = 0x01



else



     bit = 0x00










From this one can see that using surrounding pixel values can give a good indication of the value of the center dot's state. The scheme described here only uses the dots from the same row, but using a single dot line history (the previous dot line) would also be straightforward as would be alternative arrangements.
Updating Clockmarks for the Next Column
Once the center of the first data dot for the column has been determined, the clockmark values are no longer needed. They are conveniently updated in readiness for the next column after the data has been retrieved for the column.
Since the clockmark direction is perpendicular to the traversal of dots down the dot column, it is possible to use the pixel delta to update the column, and subtract the column delta to update the pixel for both clocks:

    UpperClock.column+=DataDelta.pixel
    LowerClock.column+=DataDelta.pixel
    Upperlock.pixel−=DataDelta.column
    LowerClock.pixel−=DataDelta.column


These are now the estimates for the next dot column.
Timing
The timing requirement will be met as long as DRAM utilization does not exceed 100%, and the addition of parallel algorithm timing multiplied by the algorithm DRAM utilization does not exceed 100%. DRAM utilization is specified relative to Process1, which writes each pixel once in a consecutive manner, consuming 9% of the DRAM bandwidth.
The timing as described in this section, shows that the DRAM is easily able to cope with the demands of the alternative Artcard Reader algorithm. The timing bottleneck will therefore be the implementation of the algorithm in terms of logic speed, not DRAM access. The algorithms have been designed however, with simple architectures in mind, requiring a minimum number of logical operations for every memory cycle. From this point of view, as long as the implementation state machine or equivalent CPU/DSP architecture is able to perform as described in the following sub-sections, the target speed will be met.
Locating the Targets
Targets are located by reading pixels within the bounds of a pixel column. Each pixel is read once at most. Assuming a run-length encoder that operates fast enough, the bounds on the location of targets is memory access. The accesses will therefore be no worse than the timing for Process 1, which means a 9% utilization of the DRAM bandwidth.
The total utilization of DRAM during target location (including Process 1) is therefore 18%, meaning that the target locator will always be catching up to the alternative Artcard image sensor pixel reader.
Processing the Targets
The timing for sorting and checking the target numbers is trivial. The finding of better estimates for each of the two target centers involves 12 sets of 12 pixel reads, taking a total of 144 reads. However the fixing of accurate target centers is not trivial, requiring 2 sets of evaluations. Adjusting each target center requires 8 sets of 20 different 6-entry convolution kernels. Thus this totals 8×20×6 multiply-accumulates=960. In addition, there are 7 sets of 7 pixels to be retrieved requiring 49 memory accesses. The total number per target is therefore 144+960+49=1153, which is approximately the same number of pixels in a column of pixels (1152). Thus each target evaluation consumes the time taken by otherwise processing a row of pixels. For two targets we effectively consume the time for 2 columns of pixels.
A target is positively identified on the first pixel column after the target number. Since there are 2 dot columns before the orientation column, there are 6 pixel columns. The Target Location process effectively uses up the first of the pixel columns, but the remaining 5 pixel columns are not processed at all. Therefore the data area can be located in 2⁄5 time available without impinging on any other process time.
The remaining 3⁄5 of the time available is ample for the trivial task of assigning the ranges for black and white pixels, a task that may take a couple of machine cycles at most.
Extracting Data
There are two parts to consider in terms of timing:

    
    
        Getting accurate clockmarks and border values
        Extracting dot values
    
    


Clockmarks and border values are only gathered every second dot column. However each time a clockmark estimate is updated to become more accurate, 20 different 6-entry convolution kernels must be evaluated. On average there are 2 of these per dot column (there are 4 every 2 dot-columns). Updating the pixel ordinate based on the border only requires 7 pixels from the same pixel scanline. Updating the column ordinate however, requires 7 pixels from different columns, hence different scanlines. Assuming worst case scenario of a cache miss for each scanline entry and 2 cache misses for the pixels in the same scanline, this totals 8 cache misses.
Extracting the dot information involves only 4 pixel reads per dot (rather than the average 9 that define the dot). Considering the data area of 1152 pixels (384 dots), at best this will save 72 cache reads by only reading 4 pixel dots of 9. The worst case is a rotation of 1° which is a single pixel translation every 57 pixels, which gives only slightly savings.
It can then be safely said that, at worst, we will be reading fewer cache lines less than that consumed by the pixels in the data area. The accesses will therefore be no worse than the timing for Process 1, which implies a 9% utilization of the DRAM bandwidth.
The total utilization of DRAM during data extraction (including Process 1) is therefore 18%, meaning that the data extractor will always be catching up to the alternative Artcard image sensor pixel reader. This has implications for the Process Targets process in that the processing of targets can be performed by a relatively inefficient method if necessary, yet still catch up quickly during the extracting data process.
Phase 2—Decode Bit Image
Phase 2 is the non-real-time phase of alternative Artcard data recovery algorithm. At the start of Phase 2 a bit image has been extracted from the alternative Artcard. It represents the bits read from the data regions of the alternative Artcard. Some of the bits will be in error, and perhaps the entire data is rotated 180° because the alternative Artcard was rotated when inserted. Phase 2 is concerned with reliably extracting the original data from this encoded bit image. There are basically 3 steps to be carried out as illustrated in FIG. 48:

    
    
        Reorganize the bit image, reversing it if the alternative Artcard was inserted backwards
        Unscramble the encoded data
        Reed-Solomon decode the data from the bit image
    
    


Each of the 3 steps is defined as a separate process, and performed consecutively, since the output of one is required as the input to the next. It is straightforward to combine the first two steps into a single process, but for the purposes of clarity, they are treated separately here.
From a data/process perspective, Phase 2 has the structure as illustrated in FIG. 49.
The timing of Processes 1 and 2 are likely to be negligible, consuming less than 1/1000th of a second between Process 3 (Reed Solomon decode) consumes approximately 0.32 seconds, making this the total time required for Phase 2.
Reorganize the bit image, reversing it if necessary The bit map in DRAM now represents the retrieved data from the alternative Artcard. However the bit image is not contiguous. It is broken into 64 32 k chunks, one chunk for each data block. Each 32 k chunk contains only 28,656 useful bytes:

    48 bytes from the leftmost Orientation Column
    28560 bytes from the data region proper
    48 bytes from the rightmost Orientation Column
    4112 unused bytes


The 2 MB buffer used for pixel data (stored by Process 1 of Phase 1) can be used to hold the reorganized bit image, since pixel data is not required during Phase 2. At the end of the reorganization, a correctly oriented contiguous bit image will be in the 2 MB pixel buffer, ready for Reed-Solomon decoding.
If the card is correctly oriented, the leftmost Orientation Column will be white and the rightmost Orientation Column will be black. If the card has been rotated 180°, then the leftmost Orientation Column will be black and the rightmost Orientation Column will be white.
A simple method of determining whether the card is correctly oriented or not, is to go through each data block, checking the first and last 48 bytes of data until a block is found with an overwhelming ratio of black to white bits. The following pseudocode demonstrates this, returning TRUE if the card is correctly oriented, and FALSE if it is not:















totalCountL = 0



totalCountR = 0



for (i=0; i<64; i++)



{



blackCountL = 0



blackCountR = 0



currBuff = dataBuffer



for (j=0; j<48; j++)



{



blackCountL += CountBits(*currBuff)



currBuff++



  }



currBuff += 28560



for (j=0; j<48; j++)



{



blackCountR += CountBits(*currBuff)



currBuff++



  }



dataBuffer += 32k



if (blackCountR > (blackCountL * 4))



      return TRUE



  if (blackCountL > (blackCountR * 4))



      return FALSE



  totalCountL += blackCountL



  totalCountR += blackCountR



}



return (totalCountR > totalCountL)










The data must now be reorganized, based on whether the card was oriented correctly or not. The simplest case is that the card is correctly oriented. In this case the data only needs to be moved around a little to remove the orientation columns and to make the entire data contiguous. This is achieved very simply in situ, as described by the following pseudocode:















DATA_BYTES_PER_DATA_BLOCK = 28560



to = dataBuffer



from = dataBuffer + 48)       // left orientation column



for (i=0; i<64; i++)



{



BlockMove(from, to, DATA_BYTES_PER_DATA_BLOCK)



from += 32k



to += DATA_BYTES_PER_DATA_BLOCK



}










The other case is that the data actually needs to be reversed. The algorithm to reverse the data is quite simple, but for simplicity, requires a 256-byte table Reverse where the value of Reverse[N] is a bit-reversed N.












DATA_BYTES_PER_DATA_BLOCK = 28560


to = outBuffer


for (i=0; i<64; i++)


{


from = dataBuffer + (i * 32k)


from += 48               // skip orientation column


from += DATA_BYTES_PER_DATA_BLOCK − 1  // end of block


for (j=0; j < DATA_BYTES_PER_DATA_BLOCK; j++)


{


*to++ = Reverse[*from]


from−−


  }


}









The timing for either process is negligible, consuming less than 1/1000th of a second:

    
    
        2 MB contiguous reads (2048/16×12 ns=1,536 ns)
        2 MB effectively contiguous byte writes (2048/16×12 ns=0.1,536 ns)

Unscramble the Encoded Image

    
    


The bit image is now 1,827,840 contiguous, correctly oriented, but scrambled bytes. The bytes must be unscrambled to create the 7,168 Reed-Solomon blocks, each 255 bytes long. The unscrambling process is quite straightforward, but requires a separate output buffer since the unscrambling cannot be performed in situ. FIG. 49 illustrates the unscrambling process conducted memory
The following pseudocode defines how to perform the unscrambling process:

    groupSize=255
    numBytes=1827840;
    inBuffer=scrambledBuffer;
    outBuffer=unscrambledBuffer;
    for (i=0; i<groupSize; i++)
    
        for (j=i; j<numBytes; j+=groupSize)
        
            outbuffer[j]=*in Buffer++
        
        
    
    


The timing for this process is negligible, consuming less than 1/1000th of a second:

    
    
        2 MB contiguous reads (2048/16×12 ns=1,536 ns)
        2 MB non-contiguous byte writes (2048×12 ns=24,576 ns)
    
    


At the end of this process the unscrambled data is ready for Reed-Solomon decoding.
Reed Solomon Decode
The final part of reading an alternative Artcard is the Reed-Solomon decode process, where approximately 2 MB of unscrambled data is decoded into approximately 1 MB of valid alternative Artcard data.
The algorithm performs the decoding one Reed-Solomon block at a time, and can (if desired) be performed in situ, since the encoded block is larger than the decoded block, and the redundancy bytes are stored after the data bytes.
The first 2 Reed-Solomon blocks are control blocks, containing information about the size of the data to be extracted from the bit image. This meta-information must be decoded first, and the resultant information used to decode the data proper. The decoding of the data proper is simply a case of decoding the data blocks one at a time. Duplicate data blocks can be used if a particular block fails to decode.
The highest level of the Reed-Solomon decode is set out in pseudocode:















// Constants for Reed Solomon decode



sourceBlockLength = 255;



destBlockLength = 127;



numControlBlocks = 2;



// Decode the control information



if(! GetControlData(source, destBlocks, lastBlock))



  return error



destBytes = ((destBlocks−1) * destBlockLength) + lastBlock



offsetToNextDuplicate = destBlocks * sourceBlockLength



// Skip the control blocks and position at data



source += numControlBlocks * sourceBlockLength



// Decode each of the data blocks, trying



// duplicates as necessary



blocksInError = 0;



for (i=0; i<destBlocks; i++)



{



  found = DecodeBlock(source, dest);



if(! found)



  {



duplicate = source + offsetToNextDuplicate



while ((! found) && (duplicate<sourceEnd))



      {



found = DecodeBlock(duplicate, dest)



duplicate += offsetToNextDuplicate



      }



  }



  if (! found)



      blocksInError++



  source += sourceBlockLength



  dest += destBlockLength



}



return destBytes and blocksInError










DecodeBlock is a standard Reed Solomon block decoder using m=8 and t=64.
The GetControlData function is straightforward as long as there are no decoding errors. The function simply calls DecodeBlock to decode one control block at a time until successful. The control parameters can then be extracted from the first 3 bytes of the decoded data (destblocks is stored in the bytes 0 and 1, and lastblock is stored in byte 2). If there are decoding errors the function must traverse the 32 sets of 3 bytes and decide which is the most likely set value to be correct. One simple method is to find 2 consecutive equal copies of the 3 bytes, and to declare those values the correct ones. An alternative method is to count occurrences of the different sets of 3 bytes, and announce the most common occurrence to be the correct one.
The time taken to Reed-Solomon decode depends on the implementation. While it is possible to use a dedicated core to perform the Reed-Solomon decoding process (such as LSI Logic's L64712), it is preferable to select a CPU/DSP combination that can be more generally used throughout the embedded system (usually to do something with the decoded data) depending on the application. Of course decoding time must be fast enough with the CPU/DSP combination.
The L64712 has a throughput of 50 Mbits per second (around 6.25 MB per second), so the time is bound by the speed of the Reed-Solomon decoder rather than the maximum 2 MB read and 1 MB write memory access time. The time taken in the worst case (all 2 MB requires decoding) is thus 2/6.25s=approximately 0.32 seconds. Of course, many further refinements are possible including the following:
The blurrier the reading environment, the more a given dot is influenced by the surrounding dots. The current reading algorithm of the preferred embodiment has the ability to use the surrounding dots in the same column in order to make a better decision about a dot's value. Since the previous column's dots have already been decoded, a previous column dot history could be useful in determining the value of those dots whose pixel values are in the not-sure range.
A different possibility with regard to the initial stage is to remove it entirely, make the initial bounds of the data blocks larger than necessary and place greater intelligence into the ProcessingTargets functions. This may reduce overall complexity. Care must be taken to maintain data block independence.
Further the control block mechanism can be made more robust:

    
    
        The control block could be the first and last blocks rather than make them contiguous (as is the case now). This may give greater protection against certain pathological damage scenarios.
        The second refinement is to place an additional level of redundancy/error detection into the control block structure to be used if the Reed-Solomon decode step fails. Something as simple as parity might improve the likelihood of control information if the Reed-Solomon stage fails.

Data Card Reader

    
    


FIG. 51, there is illustrated one form of card reader 500 which allows for the insertion of Artcards 9 for reading. FIG. 50 shows an exploded perspective of the reader of FIG. 51. Cardreader is interconnected to a computer system and includes a CCD reading mechanism 35. The cardreader includes pinch rollers 506, 507 for pinching an inserted Artcard 9. One of the roller e.g. 506 is driven by an Artcard motor 37 for the advancement of the card 9 between the two rollers 506 and 507 at a uniformed speed. The Artcard 9 is passed over a series of LED lights 512 which are encased within a clear plastic mould 514 having a semi circular cross section. The cross section focuses the light from the LEDs eg 512 onto the surface of the card 9 as it passes by the LEDs 512. From the surface it is reflected to a high resolution linear CCD 34 which is constructed to a resolution of approximately 480 dpi. The surface of the Artcard 9 is encoded to the level of approximately 1600 dpi hence, the linear CCD 34 supersamples the Artcard surface with an approximately three times multiplier. The Artcard 9 is further driven at a speed such that the linear CCD 34 is able to supersample in the direction of Artcard movement at a rate of approximately 4800 readings per inch. The scanned Artcard CCD data is forwarded from the Artcard reader to ACP 31 for processing. A sensor 49, which can comprise a light sensor acts to detect of the presence of the card 13.
The CCD reader includes a bottom substrate 516, a top substrate 514 which comprises a transparent molded plastic. In between the two substrates is inserted the linear CCD array 34 which comprises a thin long linear CCD array constructed by means of semi-conductor manufacturing processes.
Turning to FIG. 52, there is illustrated a side perspective view, partly in section, of an example construction of the CCD reader unit. The series of LEDs eg. 512 are operated to emit light when a card 9 is passing across the surface of the CCD reader 34. The emitted light is transmitted through a portion of the top substrate 523. The substrate includes a portion eg. 529 having a curved circumference so as to focus light emitted from LED 512 to a point eg. 532 on the surface of the card 9. The focused light is reflected from the point 532 towards the CCD array 34. A series of microlenses eg. 534, shown in exaggerated form, are formed on the surface of the top substrate 523. The microlenses 523 act to focus light received across the surface to the focused down to a point 536 which corresponds to point on the surface of the CCD reader 34 for sensing of light falling on the light sensing portion of the CCD array 34.
A number of refinements of the above arrangement are possible. For example, the sensing devices on the linear CCD 34 may be staggered. The corresponding microlenses 34 can also be correspondingly formed as to focus light into a staggered series of spots so as to correspond to the staggered CCD sensors.
To assist reading, the data surface area of the Artcard 9 is modulated with a checkerboard pattern as previously discussed with reference to FIG. 5. Other forms of high frequency modulation may be possible however.
It will be evident that an Artcard printer can be provided as for the printing out of data on storage Artcard. Hence, the Artcard system can be utilized as a general form of information distribution outside of the Artcam device. An Artcard printer can prints out Artcards on high quality print surfaces and multiple Artcards can be printed on same sheets and later separated. On a second surface of the Artcard 9 can be printed information relating to the files etc. stored on the Artcard 9 for subsequent storage.
Hence, the Artcard system allows for a simplified form of storage which is suitable for use in place of other forms of storage such as CD ROMs, magnetic disks etc. The Artcards 9 can also be mass produced and thereby produced in a substantially inexpensive form for redistribution.
Print Rolls
Turning to FIG. 54, there is illustrated the print roll 42 and print-head portions of the Artcam. The paper/film 611 is fed in a continuous “web-like” process to a printing mechanism 15 which includes further pinch rollers 616–619 and a print head 44.
The pinch roller 613 is connected to a drive mechanism (not shown) and upon rotation of the print roller 613, “paper” in the form of film 611 is forced through the printing mechanism 615 and out of the picture output slot 6. A rotary guillotine mechanism (not shown) is utilised to cut the roll of paper 611 at required photo sizes.
It is therefore evident that the printer roll 42 is responsible for supplying “paper” 611 to the print mechanism 615 for printing of photographically imaged pictures.
In FIG. 55, there is shown an exploded perspective of the print roll 42. The printer roll 42 includes output printer paper 611 which is output under the operation of pinching rollers 612, 613.
Referring now to FIG. 56, there is illustrated a more fully exploded perspective view, of the print roll 42 of FIG. 55 without the “paper” film roll. The print roll 42 includes three main parts comprising ink reservoir section 620, paper roll sections 622, 623 and outer casing sections 626, 627.
Turning first to the ink reservoir section 620, which includes the ink reservoir or ink supply sections 633. The ink for printing is contained within three bladder type containers 630–632. The printer roll 42 is assumed to provide full color output inks. Hence, a first ink reservoir or bladder container 630 contains cyan colored ink. A second reservoir 631 contains magenta colored ink and a third reservoir 632 contains yellow ink. Each of the reservoirs 630–632, although having different volumetric dimensions, are designed to have substantially the same volumetric size.
The ink reservoir sections 621, 633, in addition to cover 624 can be made of plastic sections and are designed to be mated together by means of heat sealing, ultra violet radiation, etc. Each of the equally sized ink reservoirs 630–632 is connected to a corresponding ink channel 639–641 for allowing the flow of ink from the reservoir 630–632 to a corresponding ink output port 635–637. The ink reservoir 632 having ink channel 641, and output port 637, the ink reservoir 631 having ink channel 640 and output port 636, and the ink reservoir 630 having ink channel 639 and output port 637.
In operation, the ink reservoirs 630–632 can be filled with corresponding ink and the section 633 joined to the section 621. The ink reservoir sections 630–632, being collapsible bladders, allow for ink to traverse ink channels 639–641 and therefore be in fluid communication with the ink output ports 635–637. Further, if required, an air inlet port can also be provided to allow the pressure associated with ink channel reservoirs 630–632 to be maintained as required.
The cap 624 can be joined to the ink reservoir section 620 so as to form a pressurized cavity, accessible by the air pressure inlet port.
The ink reservoir sections 621, 633 and 624 are designed to be connected together as an integral unit and to be inserted inside printer roll sections 622, 623. The printer roll sections 622, 623 are designed to mate together by means of a snap fit by means of male portions 645–647 mating with corresponding female portions (not shown). Similarly, female portions 654–656 are designed to mate with corresponding male portions 660–662. The paper roll sections 622, 623 are therefore designed to be snapped together. One end of the film within the role is pinched between the two sections 622, 623 when they are joined together. The print film can then be rolled on the print roll sections 622, 625 as required.
As noted previously, the ink reservoir sections 620, 621, 633, 624 are designed to be inserted inside the paper roll sections 622, 623. The printer roll sections 622, 623 are able to be rotatable around stationery ink reservoir sections 621, 633 and 624 to dispense film on demand.
The outer casing sections 626 and 627 are further designed to be coupled around the print roller sections 622, 623. In addition to each end of pinch rollers eg 612, 613 is designed to clip in to a corresponding cavity eg 670 in cover 626, 627 with roller 613 being driven externally (not shown) to feed the print film and out of the print roll.
Finally, a cavity 677 can be provided in the ink reservoir sections 620, 621 for the insertion and gluing of an silicon chip integrated circuit type device 53 for the storage of information associated with the print roll 42.
As shown in FIG. 56, the print roll 42 is designed to be inserted into the Artcam camera device so as to couple with a coupling unit 680 which includes connector pads 681 for providing a connection with the silicon chip 53. Further, the connector 680 includes end connectors of four connecting with ink supply ports 635–637. The ink supply ports are in turn to connect to ink supply lines eg 682 which are in turn interconnected to printheads supply ports eg. 687 for the flow of ink to print-head 44 in accordance with requirements.
The “media” 611 utilised to form the roll can comprise many different materials on which it is designed to print suitable images. For example, opaque rollable plastic material may be utilized, transparencies may be used by using transparent plastic sheets, metallic printing can take place via utilization of a metallic sheet film. Further, fabrics could be utilised within the printer roll 42 for printing images on fabric, although care must be taken that only fabrics having a suitable stiffness or suitable backing material are utilised.
When the print media is plastic, it can be coated with a layer which fixes and absorbs the ink. Further, several types of print media may be used, for example, opaque white matte, opaque white gloss, transparent film, frosted transparent film, lenticular array film for stereoscopic 3D prints, metallised film, film with the embossed optical variable devices such as gratings or holograms, media which is pre-printed on the reverse side, and media which includes a magnetic recording layer. When utilising a metallic foil, the metallic foil can have a polymer base, coated with a thin (several micron) evaporated layer of aluminum or other metal and then coated with a clear protective layer adapted to receive the ink via the ink printer mechanism.
In use the print roll 42 is obviously designed to be inserted inside a camera device so as to provide ink and paper for the printing of images on demand. The ink output ports 635–637 meet with corresponding ports within the camera device and the pinch rollers 672, 673 are operated to allow the supply of paper to the camera device under the control of the camera device.
As illustrated in FIG. 56, a mounted silicon chip 53 is insert in one end of the print roll 42. In FIG. 57 the authentication chip 53 is shown in more detail and includes four communications leads 680–683 for communicating details from the chip 53 to the corresponding camera to which it is inserted.
Turning to FIG. 57, the chip can be separately created by means of encasing a small integrated circuit 687 in epoxy and running bonding leads eg. 688 to the external communications leads 680–683. The integrated chip 687 being approximately 400 microns square with a 100 micron scribe boundary. Subsequently, the chip can be glued to an appropriate surface of the cavity of the print roll 42. In FIG. 58, there is illustrated the integrated circuit 687 interconnected to bonding pads 681, 682 in an exploded view of the arrangement of FIG. 57.
Artcards can, of course, be used in many other environments. For example ArtCards can be used in both embedded and personal computer (PC) applications, providing a user-friendly interface to large amounts of data or configuration information.
This leads to a large number of possible applications. For example, a ArtCards reader can be attached to a PC. The applications for PCs are many and varied. The simplest application is as a low cost read-only distribution medium. Since ArtCards are printed, they provide an audit trail if used for data distribution within a company.

Further, many times a PC is used as the basis for a closed system, yet a number of configuration options may exist. Rather than rely on a complex operating system interface for users, the simple insertion of a ArtCards into the ArtCards reader can provide all the configuration requirements.

While the back side of a ArtCards has the same visual appearance regardless of the application (since it stores the data), the front of a ArtCards is application dependent. It must make sense to the user in the context of the application.

It can therefore be seen that the arrangement of FIG. 59 provides for an efficient distribution of information in the forms of books, newspapers, magazines, technical manuals, etc.
In a further application, as illustrated in FIG. 60, the front side of a ArtCards 80 can show an image that includes an artistic effect to be applied to a sampled image. A camera system 81 can be provided which includes a cardreader 82 for reading the programmed data on the back of the card 80 and applying the algorithmic data to a sampled image 83 so as to produce an output image 84. The camera unit 81 including an on board inkjet printer and sufficient processing means for processing the sampled image data. A further application of the ArtCards concept, hereinafter called “BizCard” is to store company information on business cards. BizCard is a new concept in company information. The front side of a bizCard as illustrated in FIG. 61 and looks and functions exactly as today's normal business card. It includes a photograph and contact information, with as many varied card styles as there are business cards. However, the back of each bizCard contains a printed array of black and white dots that holds 1–2 megabytes of data about the company. The result is similar to having the storage of a 3.5′′ disk attached to each business card.
The information could be company information, specific product sheets, web-site pointers, e-mail addresses, a resume . . . in short, whatever the bizCard holder wants it to. BizCards can be read by any ArtCards reader such as an attached PC card reader, which can be connected to a standard PC by a USB port. BizCards can also be displayed as documents on specific embedded devices. In the case of a PC, a user simply inserts the bizcard into their reader. The bizCard is then preferably navigated just like a web-site using a regular web browser.

Simply by containing the owner's photograph and digital signature as well as a pointer to the company's public key, each bizcard can be used to electronically verify that the person is in fact who they claim to be and does actually work for the specified company. In addition by pointing to the company's public key, a bizcard permits simple initiation of secure communications.

A further application, hereinafter known as “TourCard” is an application of the ArtCards which contains information for tourists and visitors to a city. When a tourcard is inserted into the ArtCards book reader, information can be in the form of:

    
    
        Maps
        Public Transport Timetables
        Places of Interest
        Local history
        Events and Exhibitions
        Restaurant locations
        Shopping Centres

TourCard is a low cost alternative to tourist brochures, guide books and street directories. With a manufacturing cost of just one cent per card, tourCards could be distributed at tourist information centres, hotels and tourist attractions, at a minimum cost, or free if sponsored by advertising. The portability of the bookreader makes it the perfect solution for tourists. TourCards can also be used at information kiosk's, where a computer equipped with the ArtCards reader can decode the information encoded into the tourCard on any web browser.

It is interactivity of the bookreader that makes the tourCard so versatile. For example, Hypertext links contained on the map can be selected to show historical narratives of the feature buildings. In this way the tourist can embark on a guided tour of the city, with relevant transportation routes and timetables available at any time. The tourcard eliminates the need for separate maps, guide books, timetables and restaurant guides and creates a simple solution for the independent traveller. Of course, many other utilizations of the data cards are possible. For example, newspapers, study guides, pop group cards, baseball cards, timetables, music data files, product parts, advertising, TV guides, movie guides, trade show information, tear off cards in magazines, recipes, classified ads, medical information, programmes and software, horse racing form guides, electronic forms, annual reports, restaurant, hotel and vacation guides, translation programmes, golf course information, news broadcast, comics, weather details etc.

    
    


For example, the ArtCards could include a book's contents or a newspaper's contents. An example of such a system is as illustrated in FIG. 59 wherein the ArtCards 70 includes a book title on one surface with the second surface having the encoded contents of the book printed thereon. The card 70 is inserted in the reader 72 which can include a flexible display 73 which allows for the folding up of card reader 72. The card reader 72 can include display controls 74 which allow for paging forward and back and other controls of the card reader 72.

## Claims

1. A data storage device which comprises
a data carrier having at least one planar surface; and
an array of detectable items positioned on the planar surface and detectable with a sensing device, the array being configured to represent a two-dimensional code that defines at least executable instructions and redundancy encoding to impart fault tolerant characteristics to the code, the executable instructions including image processing algorithms, wherein the redundancy encoding includes reed-solomon fault correction encoding.

2. A data storage device as claimed in claim 1, in which the data carrier is a card such that the array of detectable items are positioned on a fwsc surface of the card and a visual depiction representing the code is positioned on a second opposite surface of the card.

3. A data storage device as claimed in claim 2, in which the card is of a plastics material that is coated with a hydrophilic dye-fixing layer.

4. A data storage device as claimed in claim 2, in which the detectable items arc in the form of dots printed on the first surface of the card.

5. A data storage device as claimed in claim 4 in which the array of dots are printed with a 1600 dpi resolution.

6. A data storage device as claimed in claim 4, in which the dots define at least one target which is detectable by the sensor, the target being associated with at least one data area defined by the dots.

7. A data storage device as claimed in claim 6, in which the targets are oriented to permit reading of the card in at least two directions.