Patent Publication Number: US-2023153657-A1

Title: Network of intelligent machines

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a Continuation-in-Part application of U.S. patent application Ser. No. 16/741,551 filed on Jan. 13, 2020, which in turn claims the benefit of U.S. patent application Ser. No. 13/843,784 filed on Mar. 15, 2013 (now issued as U.S. Pat. No. 10,534,995). The contents of all of the above patent applications are incorporated by reference herein. 
    
    
     FIELD OF INVENTION 
     The present disclosure relates generally to a system for processing data obtained from a plurality of intelligent machines and particularly for machines that change their internal states based on input that is shared between machines. 
     BACKGROUND 
     Today, computerized machines are used to perform tasks in almost all aspects of life, such as handling purchases at store checkout stands, and taking and tracking orders at Internet shopping sites, packaging and sorting merchandise, keeping track of inventory in warehouses, tracking automobile registration data, medical screening for various conditions, and detecting the presence of certain items or conditions. In some instances, there is a single machine that handles all the transactions or activities for that organization. However, in most cases, there are many machines at different locations handling similar tasks. For example, hospitals may have different campuses with a number of MRI machines in different parts of the campuses. Similarly, grocery store chains may have many stores and warehouses across a large geographical area, each store having a number of checkout registers. Likewise, farmers and orchards may each have their own facilities to automatically sort their produce, like sorting apples into high and low grade. Such sorting machines are often based on the appearance of the product, like in the case where a video camera is used to identify bad fruits based on an automatic classifier. 
     There is an inefficiency stemming from the fact that the different machines are run and updated separately and independently from one another. While a huge amount of data is collected by each machine, the different machines are unable to “coordinate” with each other or learn from each other. Although the machines often have human operators attending to them to deal with any unusual situations or malfunctions, each of the operators only know what is happening with the subset of machines that he is in charge of, and does not benefit from the data in other machines. This lack of communication and shared newly learned features between machines creates inefficiency and redundancy that result in errors. In one instance, a shopper looking for a specific item may have no quick and easy way of knowing which nearby stores carry the item he is looking for. In this kind of situation, much time is wasted by the shopper finding out the phone numbers and calling each of the nearby stores to do a stock check. In another instance, a medical diagnostic machine that has few patients with fractures and utilizes its original core detection algorithm would remain with same detection capability for a long time, keeping it inferior to a diagnostic machine located at a sports medicine center that would continuously get smarter from being exposed to larger samples of such fractures. In yet another instance involving produce classification machines, an operator would have to adjust each machine individually to make sure it weeds out produce with a certain new condition that would be unappealing to customers. In yet another instance involving object detection machines scanning employees&#39; bags for prohibited items (e.g., explosives, weapons, alcohol, cigarettes) bags of an employee from a town whose lunches contain items that are unique to that area might get misinterpreted as a bag with a prohibited content, because the machine at corporate headquarters is unaware of bag content types of other towns. 
     An intelligent system that eliminates the inefficiency and redundancy and increases the accuracy by allowing machines to coordinate, communicate, and learn from each other is desired. 
     SUMMARY 
     In one aspect, the disclosure pertains to a self-updating apparatus in a network of apparatuses that is configured to characterize items or conditions. The apparatus includes: a first processing unit that includes a first object unit that receives an item, a first test unit that applies different types of tests to the item and takes measurements, a first sensor that receives the outcomes from the different types of tests and generates corresponding output signals, a first computation unit that receives the output signals and processes the output signals to generate parameter values; a first memory storing parameter data for the items, wherein the parameter data are useful for categorizing the item based on the measurements taken from the item and characteristics calculated using at least one of the measurements and output signals, and a first processing module including an artificial intelligence program. The first processing module automatically selects a source from which to receive new parameter data, wherein the first processing module selects the new parameter data to receive based on similarity between measurements taken by the first processing unit and measurements that were taken by the source, automatically modifies the parameter data that are stored in the first memory with the new parameter data received from the source to generate modified parameter data, and transmits a subset of the modified parameter data to a recipient. At least one of the source and the recipient is a second processing unit that is configured similarly to the first processing unit. 
     In another aspect, the disclosure pertains to a non-transitory computer-readable storage medium storing instructions for categorizing items, wherein the non-transitory computer-readable storage medium is part of a network of apparatuses. The non-transitory computer-readable storage medium includes: instructions for the first processing unit to receive an item, take measurements of the item, and apply different types of tests to the item, instructions to receive outcomes from the different types of tests and generate corresponding output signals; instructions to process the output signals to generate parameter values, instructions to store parameter data for the item in a first memory, wherein the parameter data are useful for categorizing the item based on at least one of the measurements and output signals, instructions to automatically select a source from which to receive new parameter data, wherein the first processing module selects the new parameter data to receive based on measurements taken by the first processing unit, instructions to automatically modify the parameter data that are stored in the first memory using the new parameter data received from the source to generate modified parameter data, and instructions to transmit a subset of the modified parameter data to a recipient, wherein at least one of the source and the recipient is a second processing unit that is configured similarly to the first processing unit. For example, the apparatuses in the network may include a threat detection machine capable of screening items based on a combination or two or more tests. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG.  1    depicts a machine network system that includes a plurality of machines that communicate with each other and with a central processing unit. 
         FIG.  2    is a detailed depiction of the machines and the central processing unit. 
         FIG.  3    is a flowchart illustrating the parameter updating process. 
         FIG.  4    depicts an example embodiment where each machine is a sorting machine. 
         FIG.  5    depicts one of the machines of  FIG.  4    in more detail. 
         FIG.  6    depicts an optical unit portion of the machine in  FIG.  5   . 
         FIG.  7 A  is a computer image of fruit showing soft puff and crease as detected by the machine of  FIG.  5   . 
         FIG.  7 B  is a histogram of the fruit surface corresponding to the image of  FIG.  7 A . 
         FIG.  8    is a histogram obtained from a surface of a fruit having sour rot. 
         FIG.  9    is a histogram obtained from a surface of a fruit having clear rot. 
         FIG.  10    is a histogram obtained from a surface of a fruit having a pebbled peel. 
         FIG.  11    is a histogram obtained from a surface of a fruit showing soft puff and crease condition. 
         FIG.  12    is a histogram obtained from a surface of a fruit showing a ridge and valley defect. 
         FIG.  13    is a histogram obtained from a fruit having a split or cut in the peel. 
         FIG.  14    is a histogram obtained from a fruit having clear puff and crease condition. 
         FIG.  15    is a block diagram of an exemplary embodiment of the threat detection system. 
         FIG.  16    is a block diagram illustrating the modules of the computation unit for executing a threatening item identification method. 
         FIG.  17    is an exemplary embodiment of the threat detection system including a single test unit and multiple object units. 
         FIG.  18    is a block diagram showing the test unit and the object units. 
         FIG.  19    is another exemplary embodiment of the threat detection system wherein the object is a human being (or any of other animals). 
         FIG.  20    is yet another exemplary embodiment of the threat detection system for testing inanimate objects and human beings. 
         FIG.  21    is a perspective view of an exemplary embodiment of the threat detection system including a single test unit and multiple object units. 
         FIG.  22    is a cross-sectional view of an alternative embodiment of the threat detection system wherein the central unit has a curved outer surface. 
         FIG.  23    depicts an example embodiment of a machine network where each machine is a threat detection machine. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments are described herein in the context of machines that classify fruits according to their grades, and in the context of threat detection machines. However, it is to be understood that the embodiments provided herein are just examples and the scope of the inventive concept is not limited to the applications or the embodiments disclosed herein. For example, the system of the disclosure may be useful for any type of equipment that is capable of automatically learning rules from examples (machine learning algorithms), including but not limited to a machine that employs artificial neural network and is capable of iterative learning, such as medical diagnostic machines, fault testing machines, and object identification machines. 
     As used herein, “remotely located” means located in different forums, companies, organizations, institutions, and/or physical locations. Machines that are located on different floors of the same building, for example, could be remotely located from each other if the different floors host different organizations. A “processing unit,” as used herein, includes both a central processing unit ( 20 ) and a machine ( 30 ) or a group of machines ( 50 ). “Parameters,” as used herein, include central parameters and internal parameters, and include data and sets of data and functions, either static or dynamic. Examples of a “parameter” include but are not limited to color, wavelength, density, effective atomic number, diameter, weight, electrical conductivity, content of a specific chemical or material. A “parameter value” is a measured value, such as “red,” 690 nm, 3 inches, 15 grams, 3.5×10 7  S/m, 10 g of potassium chlorate, etc. “Parameter data” include any conditions or definitions based on parameter values, such as “wavelength between 650 nm and 700 nm” or “density greater than 0.5 g/cm 3 ” and mathematical combinations of parameter values such the sum of wavelengths and color divided by density” An apple, for example, will be associated with a certain set of parameter data or mathematical combinations of parameter values such that when an apple is received by a processing unit, the processing unit will recognize it as an apple based on the item&#39;s parameter values. 
     The system of the disclosure is useful for coordinating information exchange among a plurality of machines. This disclosure discusses a network of machines in communication with each other, which examines the collective body of data from the different machines to generate and modify a set of central parameter data. The machines may be remotely-located from the central processing unit and in different places around the world. By being networked, the different machines can learn from one another and utilize the “knowledge” gained from different machines to teach and improve its counter parts. For example, where the machines are fruit-sorting machines, the machines may learn and adjust to a trend that a new condition affecting citrus fruits is showing up at different locations around the world. The central processing unit may be able to either figure out a way to detect this condition based on this data, or utilize the adjusted updated local central parameter data in that machine, determine which geographical locations are susceptible to this condition, and transmit information and new central parameter data to the machines in these locations that will help detect this new condition so the fruits with the new defect can be rejected. 
     The central processing unit sees and analyzes the data from a high level using a global network of detection machines. Hence, the system of the disclosure allows an intelligent, better-informed understanding of a situation that cannot be provided by individual machines alone. 
       FIG.  1    depicts a machine network  10  that includes a central processing unit  20  in communication with a plurality of machines  30  via a network. The central processing unit  20  is configured to receive and selectively transmit information to the machines  30 . Each machine  30  may be one machine unit or a group of units, and typically includes hardware components for receiving items to be tested. In some cases, a plurality of machines  30  are grouped to form a “group” or “family”  50  of machines  30  that directly share data among themselves without going through the central processing unit  20 . Machines  30  in a family  50  of machines often have a commonality, such as presence in a same general geographic region, configuration to handle specific fruit types (e.g., citrus fruit), or representing the same company. The machines  30  test each item that is received and characterizes the item according to a set of internal parameter data. For example, in the case of fruit-sorting machines, the machine  30  may characterize each fruit as “reject,” “juice,” “Grade B,” and “Grade A.” The machines  30  are configured to transmit data to the central processing unit  20 , which collects data from all the machines  30  and develops its own central parameter data. In one embodiment, the central processing unit  20  initially receives data from the machines  30  to self-train and generate its own set of central parameter data. As more data is received, the central processing unit  20  refines and modifies its central parameter data such that accuracy and breadth of the characterization is enhanced over time. 
     The machine  30  would typically be used to characterize an item, for example by detecting the presence of a condition. The machine  30  may be a fruit sorting machine, a detection machine, a store checkout machine, a medical diagnostic machine, a fault detection machine etc. The inventive concept is not limited to being used with any particular type of machine. For example, the machine  30  could be part of a security check system at the entrance of a high-tech manufacturing facility, in which case it could be used to detect the presence of any digital storage devices that may be used for misappropriating intellectual property or technical data. A machine at the entrance/exits of stores could be used to detect stolen merchandise. A fault detection machine could detect micro cracks in air plane wings, and a medical diagnostic device could detect types of cancer, fractures or other conditions. A fruit sorting machine would detect bruises or damage on the fruit. If the presence of a target item is detected, an alarm will be generated to invite an operator who can confirm the presence of the target item/condition, or to activate an automatic response such as locking in the undesired item, re-directing it to a trash bin, opening a repair ticket, or placing a comment in a medical file. 
     Different machines encounter different items and conditions, are exposed to different information, and may learn and develop different classification/characterization rules. Hence, each machine  30  has a unique set of strengths and weaknesses. Each machine  30  sends data to other machines  30  and the central processing unit  20  and receives information from other machines  30  and the central processing unit  20 . The communication between different machines as well as between the machines  30  and the central processing unit  20  can be achieved through any secure network using a predetermined protocol. 
     A processing unit (e.g., a machine  30 ) determines which data should be sent to which other processing units based on data comparison among the machines  30 . For example, if data comparison reveals that Machine X has encountered items that Machine Y has not encountered yet, Machine X may transmit parameter data for the items that Machine Y has not encountered to Machine Y, so that Machine Y will recognize the item when it first encounters the item. In another example, where a fig sorting machine  30  and an orange sorting machine  30  compare data with each other and other machines  30 , the fig sorting machine and the orange sorting machine may notice that some of the other machines sort items that are generally round and share similar characteristics as figs and oranges. They may transmit data to those machines and perhaps obtain parameter data and values from those machines, so that both sets of machines can distinguish between figs, oranges, and other items. Even if the fig sorting machine has never countered an orange directly, it will be able to recognize an orange if one were to be received by it because it learned the orange parameter data from the orange sorting machine. 
     In another example, a security check machine in Building A may frequently encounter USB devices carried by employees. A security check machine in Building B, on the other hand, may not have encountered USB devices from its employees and customers. Upon comparison of items between the machines at Building A and Building B, the machine at Building A may send parameter data for USB devices to the machine at Building B. If a third machine at Building C already has its own parameter data for USB devices, machines at Buildings A and C may compare their internal parameters and make any updates to further refine the parameter data (e.g., the size range may be broadened to cover different brands, while density may be adjusted for the additional range of size). 
     As explained, the machines and processing units can “learn” from each other by comparing and updating their parameter data. In some cases, parameters that are missing in one processing unit are added by being received from another processing unit. In other cases, parameters that are different yet identify the same item triggers the processing units to modify one or more sets of parameter data to strengthen the characterization capability. 
     In one embodiment, each machine  30  or a group of machines  30  incorporates an artificial intelligence program and is able to learn or change their internal states based on input. For example, the machines  30  may learn about ordinary items as more items pass through it. The machines may, for example, incorporate a neural network. In the beginning, the synaptic weights and thresholds of the neural network are initialized and a first set of items are introduced to the machines to receive an output. For example, where the machines  30  are fruit-sorting machines, the output would be a classification assigned to each fruit (e.g., Grade A, Grade B, juice, reject). A machine trainer will initially feed a randomly mixed batch of fruits to the machine  30  and provide input as to how each fruit is categorized, thereby “training” the machine  30 . This type of machine training is well known. The machine  30 , by using the measurements and the outcomes that each set of measurements was supposed to produce, generates a set of conditions for identifying how a fruit should be categorized. The machine runs tests on the items, makes measurements, and generates a set of parameter data for each item. Each machine has a storage unit that records the parameter values of all the items it encountered. After the initial training with a set of fruits, each machine has a set of internal parameter data that it uses to characterize the next fruit that is received. The more fruits a machine  30  or a group of machines  30  has seen, the more data points it will have in its memory and the more accurate the next characterization will be. The internal parameter data are continually modified to enhance the accuracy of characterization. 
     In one embodiment, each machine  30  transmits the parameter data of all the items it encountered to the central processing unit  20 . The central processing unit  20  maintains a set of central parameter data. The central processing unit  20  processes the data received from the plurality of machines  30  in the system by running each input to generate and modify the central parameter data, which are used to characterize the next fruit. 
     The central processing unit  20  also incorporates an artificial intelligence program. As the central processing unit  20  receives data from all the machines  30  in the network, it will develop a broader set of parameter data that cover all the global possibilities. Furthermore, the central processing unit  20  will be able to analyze regional trends, unlike the machines  30 . Based on the trends and patterns it sees, the central processing unit  20  can prepare certain machines for new parameters or parameter data they will encounter. Alternatively, machines  30  can directly share with each other data that they encountered, effectively “educating” one another. 
     The central processing unit  20  also receives external data  40 , such as intelligence data or any data to be selectively distributed to the machines  30 . The external data  40  may include intelligence information or details about situations in certain regions. For example, suppose a situation where the machines are detection machines. If a valuable painting is stolen in Florence, Italy, the outside data can be used to inform the central processing unit  20  of this situation. The central processing unit  20  can, in response, adjust the parameter data to heighten the sensitivity for paintings (e.g., by adding a range of spectroscopy analysis value) and transmit the adjusted parameter data to the machines so that the machines will almost immediately be “looking for” the stolen painting. Likewise, if a stadium has long lines that are moving slowly, a request can be input to lower the sensitivity level of the machines at the entrance to help move the lines along faster. In another instance involving the produce sorting machine, information regarding expected weather trends in a given geography can alert the system to highten the detection of certain types of damage that are correlated to this weather. In another instance, a turbine safety inspection machine may learn pattern of certain blade damage due to increased feather residues from increased bird migration during certain seasons and geographies and adjust those machines to increase sensitivity for those inspection machines each year at that period and in that region. The external data  50  may be input by the machine trainer  40  or from another source. 
       FIG.  2    depicts one embodiment of the machine  30  and the central processing unit  20 . Each machine  30  has a processing module  32  that employs artificial intelligence and a memory  38  that stores internal parameter data. The processing module  32  and the memory  38  are together referred to as a “processing unit” ( 32 + 38 ). In addition to having a processing unit, the machine  30  is configured to receive items, move the received items, for example with a moving mechanism, and subject each item to one or more tests via a test module  34 . The test may be a simple determination of shape or weight, and may be a more complex form of imaging, as well as any other known tests that would help analyze or detect a condition. Using the test results (e.g., measurements), a characterization module  36  characterizes the item. The test module  34  and the characterization module  36  are together referred to as a “measurement unit” ( 34 + 36 ), and includes physical components for receiving, holding, and testing items. If more information is needed to characterize the item, the machine  30  requests extra information from an external source, such as an operator or additional sensors. In characterizing the item, the machine  30  uses the internal parameter data stored in a memory  38 . The internal parameter data were previously generated by correlating items with different conditions with their characterization. Hence, the characterization includes comparison of the measurements (e.g., parameter values) against the internal parameter data. As more extra information is received, each machine may update or modify its set of internal parameter data. The machine  30  has a receiver/transmitter  39  for exchanging information via the network. 
     The central processing unit  20  includes a processing module  24  that includes an artificial intelligence program, and a memory  22  that includes a machine database for storing central parameters and data about the different machines in the network. The processing module  24  and the memory  22  are together referred to as the “processing unit” ( 24 + 22 ). The central processing unit  20  generates its own set of central parameter data based on the measurement and characterization data it received from the machines  30 . The central parameter data are likely to be much more extensive and inclusive compared to the local internal parameter data on any single machine  30  because while each machine  30  only encounters the items that it directly handles, the central processing unit  20  has a global perspective. The machine database keeps track of all the machines that send information to it. Upon receiving data, the central processing unit  20  may tag the data with a machine ID to track which machine, group of machines, or family of machines that share knowledge, the data came from. This way, the central processing unit  20  can catch any trends such as weather or other external common phenomena, or be on the lookout for a pattern that may be a warning sign. The central processing unit  20  also uses the machine database to determine which machine will benefit from a new update/modification to the parameter data. 
     As shown, the central processing unit  20  and each machine  30  has a receiving portion  26  and a transmitting portion  28  for communicating to other machines  30  and processing units in the network. The receiving portion  26  and the transmitting portion  28  may be one physical component. As mentioned above, the central processing unit  20  also receives external data  40  from a source other than the machines  30 . When the processing module  32  of a machine  30  determines that there is an unusual situation at hand or the situation may need a warning, it generates an alert via the alert generator. Upon receiving the alert, either internal system reactions would take place to trigger an action (such as redirecting the item) or a human operator would be able to assess the situation and respond appropriately. The alert may be some type of audiovisual output to a device accessed by the operator. 
     Although not explicitly shown, both the machines  30  and the central processing unit  20  can include a user interface for communicating with an operator and/or machine trainer. 
       FIG.  3    illustrates the iterative parameter updating process  70  of the machine network system  10 , as well as the iterative data flow between the machines  30  and the central processing unit  20 . The iterative data flow may happen directly between different machines  30 , or between machine groups (each “machine group” includes a plurality of machines). As shown, a machine  30  receives items and subjects each item to a test to obtain measurements (step  71 ). In this flowchart, it is assumed that the machine  30  has already received its initial training and has a preliminary set of parameter data. The measurements are then compared against these parameter data to determine an outcome (step  73 ). If the measurements fit substantially well with the parameter data that is associated with one of the previously encountered items (step  75 —“no”), the machine  30  concludes that no new item/situation is encountered and proceeds to characterize or process the item consistently, the way that it is trained to process items with those parameter values (step  77 ). The machine  30  may store, either in a local storage unit or at the central processing unit  20 , data from the scan regardless of the outcome. If the measurements do not match any previously encountered set of parameter data well enough (step  75 —“yes”), an alert is generated to either trigger an automated response or alert an operator (step  79 ). The operator examines the item, reviews the measurements (parameter values), and subjects the item to additional tests if desired to come up with a characterization. In some embodiments, the machine  30  collects additional information. The operator then provides feedback (extra information) to the machine by inputting his characterization (step  81 ). The machine updates its parameter data to incorporate the extra information that it just received (step  83 ). The measurements that triggered the alert and the operator input are either retained within the machine  30 , and or transmitted to other machines  30 , and or transmitted to the central processing unit  20  (step  85 ). 
     The machines  30 , groups of machines  50 , or the central processing unit  20  receives measurements and characterizations from machines in the machine network  10 , which are often at different locations (step  91 ). The central processing unit  20 , the machines  30 , and/or groups of machines  50  receive data independently of the machines  30  and has its set of central parameter data (step  93 ) from previous training. The training may be based on its own internal data sets or data sets received from other machines  30 , families of machines  50  and/or the central processing unit  20 . As mentioned above, the central processing unit  20  also receives external data (step  95 ). 
     The machines  30  and/or the central processing unit  20  continually modifies its central parameter data  94  based on the measurement data it receives from the machines  30 , families of machines  50 , and the central processing unit  20  and the extra information  81  that pertains to previously un-encountered items/conditions. Central parameter data help the machines  30 , family of machines  50 , and the central processing units  20  to identify which items are being encountered by almost all the machines, so the parameter data for that items can be strengthened. Central parameter data may be used to selectively increase the “resolution” of detection. Once central parameter data are modified, the central processing unit  20 , the machines  30  and/or families of machines  50  identifies, either for its self or other, machines that would benefit from the updated parameter data, e.g. the machines that would encounter the condition/item that is affected by the updated parameter data (step  97 ). For example, where the newly found condition is due to a fruit disease that is only in a certain region, parameter data for the condition would not be sent to machines at other locations. On the other hand, if the parameter data pertain to a condition that is applicable to any location (e.g., a bruise) the parameter data may be sent to all the machines  30 . The updated parameter data are then transmitted to the identified select machines (step  99 ). 
     The machines  30  that receive the modified central parameter data may further modify their own internal parameter data to reflect the modified central parameter data. This way, the machines  30 , other machines  30 , other families of machines  50  and the central processing unit  20  are continually teaching and learning from one another. The machine  30  in the machine network  10  learns from multiple sources: 1) items that pass through the machine, 2) extra information received, e.g. from the local operator, 3) updated parameter data received from the central processing unit  20 , 4) new data and updated parameter data from itself, 5) data and updated parameter data from other machines  30 , or families of machines  50 . 
       FIG.  4    depicts an embodiment where each machine  30  is a sorting machine  100 . The sorting machine  100  may be a fruit sorting machine that incorporates the features described in U.S. Pat. No. 5,845,002 and is enhanced with the processing unit  32 + 38  to include artificial intelligence and internal parameter data. Although the inventive concept is not limited to being used with any particular type of machine, the disclosure is provided to provide a concrete example of how the processing module  32  in machine  30  and or central processing unit  20  may be used. 
       FIG.  5    shows select parts of the sorting machines  100  in more detail. As shown, the sorting machine  100  includes a conventional conveyor line  112  upon which a plurality of items  114  are conveyed. For simplicity of illustration, this particular depiction does not show the processing unit, although the processing unit is part of the sorting machine  100 . This is just one example embodiment, and use of a conveyor line is not a limitation of the inventive concept—items can be examined in static situations or utilizing other movement mechanisms such as robotics. The items  114  are fruit (e.g., citrus fruit) in the context of this disclosure, although this is not a limitation of the inventive concept. The particular sorting machine  100  may be suitable for items that are generally spherical and have a topographic surface texture. In other embodiments, the sorting machines  100  may be replaced by medical examination machines, object screening machines, etc. 
     The conveyor  112  transports the fruit  114  into an optical housing  116 , where the fruit is illuminated at an inspection station  118  within an optical housing  116 . The conveyor  112  transports and orients the fruit  114  to control the presentation of the fruit  114  for imaging. The conveyor is designed to provide a maximum optical exposure of fruit  114  at inspection station  118 . Conveyor system  112  in the illustrated embodiment includes driven spools to rotate the fruit  114 . In the embodiment of  FIG.  4    and  FIG.  5   , the fruit  114  is rotated in a retrograde direction as it moves through the inspection station  118  to at least partially compensate for its forward motion down conveyor  112 . The fruit  114  is rotated so that the same surface tends to remain facing a camera  130  during an extended time exposure to allow complete and reliable imaging. This may, of course, be time-synchronized by means well known in the art. 
     When the fruit  114  is carried by the conveyor  112  into the housing  116  and to inspection station  118 , the fruit  14  is illuminated by a pair of light sources  122 ,  124 . The light sources  122 ,  124  are focused on the fruit  114  from below and may further be provided with conventional optics to assist in providing optimal illumination of the surface of the fruit  114 . 
     The optical sources  22 ,  24  may be optical fibers, or laser beams or light beams formed by LEDs. Alternatively, a single light source may be utilized and may be optically divided into two optical sources  22 ,  24 . The light sources  22 ,  24  (or a single light source) provide the incident light that will be scattered within the fruit to cause it to glow. The frequency or frequency spectrum of the light is selected based on the optical properties of the item to be inspected, to produce the desired scattering within the item, and the resultant projection of that glow through the surface thereof. With citrus fruit, the ordinary visible spectrum may suffice. 
     The camera  130  is coupled to a texture mode computer  134 . The texture mode computer  134  is a personal computer coupled to both a master computer  136  which runs the functions of the conveyor and sorting systems and to an input/output computer  138 , which provides user input and output access to the system  100 . The texture analysis of the fruit  114  is made by the texture mode computer  134 . According to user instructions, input through input/output computer  138  to master remote computer  136  will implement a sorting operation as dictated by texture mode computer  134  at a plurality of sorting stations  140 , which may include solenoid-actuated ejection fingers upon which the fruit  114  rides, and by which the fruit  114  is ejected from the conveyor line  112  into appropriate sorting bins  142  or secondary conveyors. 
     The texture module of the sorting machine  100  is made up of three subsystems that include the lighting and optics (including the optical housing  116 ), imaging as provided by the cameras  30  and mirrors  126   a,    126   b,    128   a,    128   b,  and image processing within the texture mode computer  134 . 
     The central input/output computer  138  and the master remote computer  136  are conventional and are substantially the same as used in prior art classification and sorting apparatus. The central input/output computer  138  provides for system control including providing for all aspects of user interface, selection for input and output of various classification parameters, and for determining conveyor paths in the machine  100  where multiple lanes for the conveyor  112  are provided in a more complex array than the simple linear depiction of  FIG.  4   . 
     For certain applications, it may be desired to use a specific wavelength or spectrum of incident light, so that a desired optical effect may accentuate the particular type of defect in that type of item to be monitored. It is left to the reasonably skilled practitioner, faced with the particular type of item and defect, to determine the correct frequency or spectrum of the incident light. 
     The inspection station  118  is appropriately baffled as desired, either to provide flat black nonreflecting surface to avoid spurious images, or to include reflective surfaces if desired to increase the light intensity incident upon the fruit. In the embodiment illustrated in  FIG.  5   , the glow from light scattered within the fruit  114  and projected through its peel is reflected from lower mirrors  126   a,    126   b,  and from there to upper mirrors  128   a,    128   b.  A CCD matrix or scanning camera  130  has its optics  132  focused on the upper mirrors  128   a,    128   b  to capture, in a single computer image, virtually the entire exterior surface of a hemisphere of fruit  114 . 
     As shown in  FIG.  6   , there are two cameras  130   a,    130   b,  each of which captures an image of one of the two hemispheres of the fruit  114 . For example, the first hemispheric image of the fruit  114  is reflected by the lower right mirror  127   a  to the upper left mirror  129   a  and from there to the first camera  130   a.  The image of that first hemisphere is also reflected by the lower left mirror  127   b  into upper right mirror  129   b  in the first camera  130   a.    
     After the fruit  114  has proceeded down the conveyor  112  and experienced a synchronized rotation to expose its other hemisphere, the image of the second hemisphere of the fruit  114  is reflected by the lower right mirror  127   c  to the upper left mirror  129   c,  and from the lower left mirror  127   d  to the upper left mirror  129   d,  both resultant images being reflected into the other camera  130   b.    
     The lighting system uses two tungsten Halogen projection lamps  122 ,  124  situated on opposite sides of the fruit  114  and below the fruit centerline. The lamps emit enough light of the proper frequency or spectrum incident on the fruit  114  to create a glowing effect transmitted through the peel/skin of the fruit  114  that can be detected by a camera. In other words, the fruit will provide a glowing effect to the camera provided that the positioning, intensity, and frequency/spectrum of the light is such that the light penetration into the peel or rind of fruit  114  occurs and is scattered therewithin to provide a glowing effect through the peel. 
     There is no special filter on the camera  130 , and time exposure of the imaging is electronically controlled. Electronic control of the time exposure compensates for any difference in the intensity of the glow due to differences in fruit size and peel thickness. This can be determined during the initial part of the run and appropriate corrections, either automatic or manual, may be entered through the input/output controller  138 . 
     Automatic control may be effected by user of a photodiode  144  mounted on each camera  130  to generate an output frequency, by a frequency generator (not shown), which depends upon the amount of light sensed by each photodiode. By using the output frequency from the frequency generator controlled by photodiodes  144 , the exposure time on the CCD chip within cameras  30  is controlled. 
     There are a large number of ways in which the fruit  114  may be illuminated, as well as ways in which a computer image may be taken of the fruit  114 , either with the user of one or more cameras and various optical systems and configurations. A substantially complete computer image of each fruit  114  is provided so that texture characterizations as discussed below will not omit any significant portion of the fruit surface. For some applications, an image of one hemisphere only, using a single camera  30  and simplified optics, may be sufficient. 
     The texture mode computer  134  performs image processing and passes the classification information to the rest of the system for final drop-out selection according to means known in the art. 
     Now, processing of the captured image to provide topographic surface texture grading will be described. In the illustrated embodiment, the first step is to drop out invalid information such as reflected light intensities from the light sources  122 ,  124  which do not constitute the glow from light scattered within the fruit  114  and emerging through its peel. Turning to  FIG.  7 A , bright portions  146  of an actual computer image of a smooth fruit peel are depicted. Two images of fruit  114  are shown in  FIG.  7 A , depicting in essence the two hemispherical views of the fruit. Thus, regions  146  of the graphic image, because of their distinctively higher intensity levels, can be eliminated as portions of the graphic information signal carrying no information about the topographic surface texture. 
     A scan of the fruit surface is made to provide maximum, minimum, and standard deviation of the intensity of the entire pixel pattern constituting the image, to provide an indication if there are intensity variations in the image which could constitute surface defects requiring further examination, such as puff and crease, peel, cuts, punctures, etc. 
     A puff in a citrus fruit is an area of the peel which is slightly detached from the underlying meat, and thus will be slightly swollen or “puffed out.” A crease is the reverse, in which a portion of the rind surface has been depressed relative to adjoining areas. 
     If no defects are detected, then the graphic image is checked for high frequency data which, for example, would be indicative of pebbliness of the fruit surface. The data derived from the fruit  114  can then be fed back to the master computer  136  for classification purposes according to predefined criteria. 
     In an instance where global statistical analysis of the fruit surface indicates that peel defects exist, the type of defect can then be determined by applying a series of data filters to identify them. The high pass data filter can be used to search for cuts or punctures. A low pass filter with blob analysis, tracing and aspect ratio of areas of greater intensity is useful to identify puff and crease and to distinguish it from rot. 
     After the puff and crease data is separated, a series of checks to show peak intensities over standard deviation values can be used to identify the degree of defect within a category of defect, such as puff and crease. After this processing is done, the size of the fruit as a whole is compared with the area affected in order to generate a percentage value for the defect of the affected surface. Other defects, such as rot or breaks in the rind may not be subject to a percentage evaluation, but may constitute a cause for immediate rejection of the fruit regardless of the percentage of the affected area of the fruit. 
       FIG.  7 A , in which a computer image of a smooth orange rind is depicted, illustrates the double image from the reflected image provided to the camera. Brightened areas  146  from the illumination source are eliminated as not containing information relevant to the nature of the peel condition. Statistical information is then taken of the entire graphic image to obtain maxima, minima, and standard deviations to characterize the intensity variations of the image pixels. In this case, the statistical deviations which would be returned would indicate that the fruit was smooth and well within the acceptable range. At that point, further statistical analysis would not be performed, and the fruit position tagged within the sorting machine  100  and carried down conveyor  112  to be routed to the appropriate sorting bin  142  or secondary conveyor, or for analysis and classification according to additional methods and criteria. 
     For the purposes of illustration, a typical scan line  148  is taken across one portion of the two hemispherical images in  FIG.  7 A . Scan line intensity is then depicted in the histogram of  FIG.  7 B  where intensity is graphed against the vertical scale and positioned along the scan line along the horizontal scale with end  150  corresponding to the left end of the histogram of  FIG.  7 B  and end  152  of scan line  148  corresponding to the right end of the histogram of  FIG.  7 B . A visual examination of the histogram of  FIG.  7 B  indicates variations of pixel intensity maintained within a range of values with a fairly limited deviation from a mean, to provide a pattern quite different from the histograms depicted in  FIGS.  8 - 14   , wherein various fruit defects are illustrated. Through conventional statistical measures, the histograms can be characterized by meaningful statistical parameters, and through those parameters, sorted into categories to reliably identify the topographic surface texture of the fruit  114 . 
       FIG.  8    depicts an intensity histogram taken from a computer image of a fruit  114  that is blemished by a surface decomposition known as sour rot. As compared to the histogram of  FIG.  7 B , there is a much wider variation between maxima and minima, and deviation from the mean is much greater than in the case of  FIG.  7 B .  FIG.  9    depicts an intensity histogram taken from a computer image of a fruit  114  that is characterized by a clear rot skin blemish. The histogram shows a large peak sharply falling off to an average pixel intensity.  FIG.  10    depicts an intensity histogram taken from a computer image of a fruit  114  that is characterized by high porosity or a pebbled surface that some consumers may dislike.  FIG.  11    depicts an intensity histogram taken from a computer image of a fruit  114  whose surface is blemished by a condition known as soft puff and crease, and  FIG.  12    is a histogram taken from a fruit  114  whose surface is blemished by a defect known as ridge and valley.  FIG.  13    depicts a histogram taken from a fruit  114  with “fracture,” which include splits, cuts, punctures, and scrapes.  FIG.  14    depicts a histogram taken from a fruit  114  with a skin defect called clear puff and crease. 
     Each of the histograms described above may be saved in the memory of the machines as part of predefined conditions for characterizing the fruit. Upon taking a measurement from a newly received fruit, the machine  30  will subject it to the imaging test to produce an image similar to that disclosed in  FIG.  7 A  and generate a histogram. The histogram will be then compared against the stored histograms that indicate certain conditions/defects to make a chracterization. 
     Now, the process  70  of  FIG.  3    can be explained in the context of the sorting machine  100 . Sorting machines  100  may be placed in different facilities such as farms and orchards, possibly in different parts of the world. Each sorting machine  100  would initially be “trained” by its sets of data obtained from samples or an operator who runs a representative sample of fruits through the machine and provides input as to in which bin  142  each fruit in the sample should be placed. The sorting machine  100  develops its own set of parameter data based on the sample fruits and the inputs, and uses these parameter data to categorize the next fruit it encounters. More specifically, in step  71 , the fruit is imaged as two hemispheres, in the manner shown in  FIG.  7 A , with a scan line  148  across one portion of the two hemispherical images. Scan line intensity is then depicted in a histogram, similarly to what is shown in  FIG.  7 B  and  FIGS.  8 - 14   . In step  73 , the machine compares the histogram of the current fruit against its internal parameter data. If there is a substantially close match between the histogram of the current fruit and one of the previously-generated histograms (step  75 —“no”), the current fruit will be sorted or categorized into the same bin  142  as the previous fruit that generated the similar histogram (step  77 ). On the other hand, if the histogram of the current fruit does not resemble any of the previously generated histograms closely enough (step  75 —“yes”), an alert is generated (step  79 ). To the system or to an operator, in response to the alert, the data is used to train and determine how the fruit should be categorized, and tells the machine  100  how it should be categorized (step  81 ). The machine  100  modifies or updates its internal parameter data with this new data (step  83 ) and categorizes the current fruit according to the operator input (step  77 ). 
     The machines  30 , family of machines  50 , and/or central processing units  20  initially receive the scan data and categorization data from selections of machines, and in some cases all the machines in the network (step  91 ), and generates its own central parameter data (step  93 ). The central parameter data may not be exactly the same as the local parameters on any one machine  100  since the machines  30  and or processing units  20  “see” more fruits than any single machine  100  in the network, and is presumably exposed to many more variations and conditions than any single machine  100 . The central parameter data, thus, may be broader in the range of defects that are covered and able to distinguish defects with a higher resolution. The machine  30 , family of machines  50 , and/or the central processing units  20  also receives any external data (step  95 ). For example, the external data might be a request from a Department of Agriculture to report all cases of a specific condition, or local weather conditions. 
     The machine  30 , family of machines  50 , and/or central processing units  20  then identifies the machines  100  that they should receive data from and that should receive the updated/modified central parameter data (step  97 ). For example, if the update/modification to the parameter data pertains to a pebbliness of the fruit skin, this update would be sent to machines  100  whose primary function is to sort fruits to send to various grocery stores. However, the modified parameter data would not be sent to machines at a juicing factory because the texture of the fruit skin would not matter much to the juicing process, which typically happens after the skin is removed. At the same time, the machine  30 , family of machines  50 , and/or central processing units  20  also determines that all the machines  100  in the network should receive the request from the external data. The data and or parameters are then transmitted to the selected machines (step  99 ). 
       FIGS.  15 - 23    describe another example of the machine network  10 , this example incorporating a threat detection machine  200 . The threat detection system of the invention is useful for detecting the presence of various threatening items. A “threatening item” is any substance and or a combination of substances and items that may be of interest to a security system including but not limited to explosives, explosive devices, improvised explosive devices, chemical warfare agents, industrial and other chemicals that are deemed hazardous, biological agents, contraband, drugs, weapons, and radioactive materials. The threat detection machine  200  provides an automated system for performing different types of tests to screen multiple threatening items fast, such that multiple items can be examined in a relatively short period of time. Furthermore, the system of the invention decreases the reliance on human operators, using instead a computation unit that determines a risk factor based on concurrent acquisition and processing of the different test results. Thus, the system provides the much-needed method of increasing the accuracy of a security check test without compromising the throughput. Although the examples of  FIGS.  15 - 23    focus on identification of “threatening item,” it should be understood that the method and system disclosed herein may be adapted to identify any type of target item by defining the parameter and parameter data for the target item, regardless of whether the target item is threatening or not. 
     An “ionized radiation test,” as used herein, is intended to include any form of test that emits ionized radiation such as nuclear, X-ray, or Gamma ray radiation. Examples of X ray methods include standard X-ray transmission, backscatter methods, dual or multi energy methods as well as CT-scan. Examples of nuclear radiation source testing include methods such as Thermal Neutron Analysis, Pulsed fast neutron analysis, backscatter, and terahertz test, among others. A “non-ionizing test” includes methods that use a non-ionizing electromagnetic (EM) radiation source, such as those that expose the material to a pulsed EM field and acquire the return pulse. These methods include use of high-millimeter waves, Nuclear Magnetic Resonance (NMR) spectroscopy, Electron Spin Resonance (ESR) and Nuclear Quadrapole Resonance (NQR), among others. An additional potential non-ionizing source includes Tetrahertz. In addition, “non-ionizing tests” also include methods used in detection of conductive materials that subject an item to electromagnetic fields, either constant or pulsed wave, and detect the corresponding direction of changes in the field. “Chemical analysis” is intended to include methods of substance detection including ion mobility spectrometry (IMS), ion trap mobility spectroscopy (ITMS), capture detection, chemiluminescence, gas chromatography/surface acoustic wave, thermo-redox, spectroscopic methods, selective polymer sensors, and MEM based sensors, among others. 
     A “biological classification” classifies biological threats (e.g., organisms, molecules) according to guidelines indicating the potential hazard level associated with toxins, bioregulators, and epidemically dangerous organisms (such as viruses, bacteria, and fungi). A “biometric classification test” includes standard discrete biometric methods such as finger prints, as well as physio-behavioral parameters indicative of suspect behavior. 
     As used herein, “simultaneously” is intended to mean a partial or a complete temporal overlap between two or more events of the same or different durations. For example, if Event A begins at time 0 and ends at time 10 and Event B begins at time 2 and ends at time 10, Event A and Event B are occurring simultaneously. Likewise, Event C and Event D that both start at time 0 and end at time 7 are also occurring simultaneously. “Sequentially,” on the other hand, indicates that there is no temporal overlap between two or more events. If Event E begins at time 0 and ends at time 6 and Event F begins at time 7 and ends at time 10, Events E and F are occurring sequentially. 
     A “threat determination function,” as used herein, is intended to include a function or sets of functions that define a condition that indicates the presence of a threat. These function(s) can be a static value, sets of static values, or a dynamic calculation. The function(s) can be either rule-based or based on other methods such as neural network. 
     A “risk factor” indicates the likelihood that an item is a threatening item. A “set” of risk factors may include one or more risk factors. 
       FIG.  15    depicts an embodiment of threat detection machine  200  that may be used in the machine network  10  instead of the sorting machine  100 . The threat detection machine  200  may be, but is not limited to, a system such as what is described in U.S. Pat. No. 8,171,810 that may be useful at security check stations. As shown in  FIG.  15   , the threat detection machine  200  includes a test unit  220 , a computation unit  240 , and an object unit  260  that are coupled to one another. The object unit  260  has a mechanism that is designed to hold an object (e.g., a bag or a piece of luggage) that is being examined. The test unit  220  includes various test sources and/or equipment such as a radiation source for an X-ray exam, a chemical analysis unit for a chemical exam, RF coils and or other magnetic field inductions for a non-ionizing exam. The computation unit  240 , which has a processor and a memory, is configured to receive inputs from the test unit  220  and the object unit  260  and process the inputs to generate a risk factor. The risk factor indicates the likelihood of the object unit  260  containing a threatening item. Optionally, there may be a communication unit that may include a user interface unit (not shown) that is coupled to the computation unit  240  so that the risk factor and a corresponding alert can be communicated to an operator of the threat detection machine. 
     The tests that are incorporated into the test unit  220  may be any currently known tests for screening threatening items, and is not limited to the examples mentioned herein. There may also be a plurality of object units coupled to the test unit  220  and the computation unit  240  so that multiple items can be examined almost at the same time. 
     The object unit  260  has one or more doors  261  through which an item  262  can be placed in the object unit  260  to be subjected to various tests. In some embodiments, the item  262  remains stationary on a platform in the object unit  260 . In other embodiments, the item  262  is moved across the object unit  260  through a moving mechanism  267 . The moving mechanism  267  may be coupled to a grasping and/or rotating mechanism  264 , which may be a robotic mechanism that is capable of holding the item  262  and positioning and rotating the item  262  in a desired location at the desired test angle. In the embodiment shown, the moving mechanism  267  is a type of pulley system, an x-y positioner system  265 , a linear motor, or any combination of these systems, and is coupled to the grasping and/or rotating mechanism  264 . In an alternative embodiment, the moving mechanism may be a conveyor belt that carries the item  262  through different test stages. 
     The object unit  260  includes an automated receiver  269  that automatically provides extra information about the owner of the item  262 . In some embodiments, the extra information may include ticketing information. In other embodiments, additional information about the owner, such as his name, citizenship, travel destination, etc. may also be made available by the automated receiver  269 . The automated receiver  269  may be implemented with digital/magnetic tagging, RF tagging, or other smart card scan that identifies the owner/carrier of the item  262 . This automatic correlation between the item  262  and its owner/carrier facilitates identifying the responsible person if a threatening item is found. The object unit  260  has one or more doors  261  through which the item can be removed. In some embodiments, the doors  261  are locked automatically upon the identification of a threatening item as part of the operational security protocols. 
     In this exemplary embodiment, the ionized radiation test unit  220  has an X-ray source subunit  222 , a chemical analysis subunit  230 , and non-ionizing source subunit  236 . The X-ray examination is done by an X-ray source  224  generating a beam and directing it toward the item  262 . The X-ray source  224  is preferably supported by a rotating mechanism  226  that allows the beam to be pointed in different directions, as it may be desirable to adjust the direction of the beam according to the size and the position of the item  262 . A plurality of sensors  266  are located in the object unit  260  and positioned to receive the X-ray beams after they pass through the item  262 . Additional sensors  266  can be positioned to acquire back scatter radiation as well. The beam is received by the sensors  266  after passing through the item  262 . The sensors  266  generate output signals based on the received beam and feed the output signals to the computation unit  240 . Where X-ray is used as one of the tests, the walls of the X-ray subunit  222  and the object unit  260  are shielded to contain the radiation within the object unit  260 . 
     The chemical analysis may be performed by taking a sample from the item  262  and running the sample through the chemical analysis subunit  230 . A path implemented by a flow device such as a rotational flow device  232  connects the grasping and/or rotating mechanism  264  to the chemical analysis subunit  230  so that the sample from the item  262  can be transported to the chemical analysis subunit  230 . The chemical analysis may be based on, for example, ion mobility spectroscopy, or newer methods such as selective polymers or MEMs-based sensors. Where ion mobility spectroscopy is used, the chemical analysis subunit  230  includes an ionization reaction chamber  228 . An air flow is generated by a vacuum pump  233  for obtaining a gas sample from the object unit  260 . The gas sample travels through the adjustable closure pipes  232 , which have particle acquisition pores  263  in proximity to the object  260  for obtaining gas samples. The rotational flow device  232  and the particle acquisition pores  263  provide a means for continuous-contact gas agitation and particle acquisition for continual analysis while the object moves inside the object unit  260  for other tests. The particle acquisition pores  263  may be placed on the grasping and/or rotating mechanism  264  that moves the item  262  across the object unit  260 , such as the robotic arm or the conveyor belt mentioned above. The gas sample enters the chemical analysis subunit  230 . In an exemplary embodiment using the IMS method, the gas sample enters an ionization reaction chamber  228  through the rotational flow device  232  and becomes ionized by an ionization source. The ionized gas molecules are led to a collector plate (not shown) located in the ionization reaction chamber  228  by an electric field within the chamber  228 . The quantity of ions arriving at the collector plate as a function of time is measured and sent to the computation unit  240  in the form of one or more output signals. A microprocessor at the chemical analysis subunit  230  may convert the quantity of ions to a current before sending the current to the computation unit  240 . IMS is a well-established method. 
     Optionally, the chemical analysis subunit  230  contains an interfacing module  235  to a biological detection system. If a biological detection system is incorporated into the test unit  220 , a biological classification of the object can be obtained. A biological detection system that detects molecular materials could utilize one of the chemical analysis methods. A system that is intended to identify an organism, such as Anthrax, would utilize an automated DNA testing based on automated polymerase chain reaction (PCR) according to the current state of technology. 
     The non-ionizing source subunit  236  may contain a radiofrequency (RF) source and/or a magnetic source, such as RF coils  238  and antennae for NQR testing and/or eddy current testing. These tests provide information on the chemical compositions of the object and or information on the existence of metallic and other conductive materials. Magnetic sources may be a plurality of sources that vary in size and strength, so that the location of a threatening item can be detected as well as its presence. Radiofrequency waves and/or a magnetic field is directed at the item  262  and the sensors  266  receive the wave and/or the field after it passes through the item  262 . For example, where the subunit  236  is a metal detector, the metal detector may transmit low-intensity magnetic fields that interrogate the item  262  as it passes through the magnetic fields. A transmitter generates the magnetic field that reacts with the metal objects in its field and the sensors  266  measure the response from this reaction. The sensors  266  send the measurement result to the computation unit  240 . 
     In addition to the X-ray exam, ion mobility spectrometry, and the non-ionizing source test used in the embodiment of  FIG.  15   , any other test may be employed by the threat detection system  200  if considered useful for the particular application. Also, the X-ray exam, the ion mobility spectrometry, and the non-ionizing source test may be substituted by different tests as deemed fit by a person skilled in the art. Preferably, each of the subunits  222 ,  230 ,  236  is designed to be replaceable independent of other subunits. Thus, substituting one test with another will likely be a matter of replacing one subunit with another. 
     The sensors  266  may be a fused-array sensor capable of collecting multiple information either in parallel or in a multiplexed manner. Information collected may include any test results such as X-ray, terahertz ray, gamma ray, RF, chemical, nuclear radiation, and current information. 
     The computation unit  240  includes a processor  242 , a memory  244 , and a power supply  246 . Using a multi-variant method such as the method described below in reference to  FIG.  16   , the computation unit  240  determines the risk factor, which indicates the likelihood that an object will contain a threatening item. The computation unit  240  has a communication interface  250  through which it can send visual and/or audio alerts in any mode of communication, preferably wirelessly, if an item is likely to contain a threatening item. There is also at least one open interface  295  that allows the computation unit  240  to communicate with another apparatus, such as a platform for human portal system or a platform for biometric inputs. The open interface  295  may allow wired or wireless connections to these other apparatuses. 
     The chemical analysis test results may be sent directly from the collector plate in the chemical analysis subunit  230  to the computation unit  240 . If desired, however, the data from the collector plate may be sent to one or more sensors  266  in the object unit  260  and sent to the computation unit  240  indirectly from the sensors  266 . When using other methods such as passive sensors, particles can be routed directly to sensors  266 . Other data, such as X-ray data, are collected by the sensors  266  and sent to the computation unit  240 . As used herein, “sensors” include any type of device that is capable of making a physical or electrical measurement and generating an output signal for the computation unit  240 , such as sensors  266  in the object unit  220  and the collector plate in the chemical analysis subunit  230 . 
     Although  FIG.  15    shows the test unit  220 , the computation unit  240 , and the object unit  260  as three separate components, the division is conceptual and the physical units do not necessarily have to correlate with the conceptual division. For example, all three units may be contained in one housing, or the test unit  220  and the object unit  260  may be contained in the same housing while the computation unit  240  is in a remote location. 
       FIG.  16    is a block diagram illustrating the modules of the computation unit  240  for executing a threatening item identification method. As described above, the computation unit  240  receives inputs from the test unit  220  and/or the object unit  260 . These inputs originate as raw data collected by the sensors  266  and/or the collector plate in ion mobility spectrometry (or another chemical sensor). As shown in the diagram, the method uses a set of functional modules  2116 ,  2118 ,  2120 ,  2122 ,  2124 ,  2126 ,  2128 ,  2206 ,  2208  to process the various inputs from the sensors  266  and the sensor in the test unit  220  (e.g., the collector plate). Using these modules, values are calculated for various parameters such as texture, density, electrical conductivity, molecular classification, location classification, radiation classification, visual classification, biological classification, and biometric classification for the item  262 . Where the item  262  is something like a bag that contains multiple components, the components may be automatically divided according to texture, density, conductivity, etc. so that each component is classified separately. 
     In the particular embodiment of the threatening item identification method that is shown in  FIG.  16   , the active radiation (e.g., X-ray) detection results are used for determination of texture classification, density classification, shape context classification, location classification, and visual classification. The radioactive level of the object may be determined for radiation classification. Current data or induced EM field responses are used for parameter data for texture classification, conductivity classification, and location classification. The magnetic response is used for determining parameter data for molecular classification, density classification, and location classification. Any chemical analysis result is used for molecular classification. Output signals from the sensors  266  and output signals from the chemical analysis subunit  230  are fed to the different modules in parallel, so that the values for all the parameters of the classification areas such as texture, density, etc. can be determined substantially simultaneously. 
     After the parameters based on values and functions for each of these classification areas is determined, the values are collectively processed in a multi-variant data matrix module  2300  to generate a risk factor. The multi-variant data matrix  2300  arranges the plurality of classification parameters from function matrices  2116 ,  2118 ,  2120 ,  2122 ,  2124 ,  2126 ,  2128 ,  2206 ,  2208 ,  2210  into an n-dimensional data matrix. For instance, visual classification function matrix  2124  would yield numerous visualization data [V] as a function of number of (1 . . . n) and measurement and angles (Φ) depending on the number of rotations performed by the grasping and/or rotating mechanism  64 , so one form of data would be V=f(Φ)n. Additionally, a series of visualization data [V] related to density parameters [D] at each angle Φ would yield the set of parameters V=f(D, Φ, n). Another set of parameters fed into the multi-variant data matrix  2300  would be conductivity classifications from the conductivity classification functions matrix  2120  and would similarly yield an array of interrelated parameters, for example conductivity [Z] as having varying intensities (i) as a function of location (l) yielding one set of Z=f(i,l). These three exemplary functions V=f(Φ, n), V=f(D, Φ, n), and Z=f(i,l) would be arranged in the multi variant data matrix  2300  in such a way that provides multiple attributes for particular three-dimensional locations, as well as global attributes, throughout the screened item. More generally, all classification function matrix blocks will produce numerous parameter sets, so that an n-dimensional parameter matrix is produced for processing in block  2310 . 
     The n-dimensional parameter matrix generated in block  2310  enables numerous calculations and processing of dependent and interdependent parameters to be performed in block  2310 . The parameters from the multi-variant data matrix module  2300  is submitted to the threat determination functions, which include running sets of hybrid calculations. Hybrid calculations include combinations of rule-based and other methods (such as neural network or other artificial intelligence (AI)-based algorithms) and comparison of the result against real-world knowledge criteria and conditions (block  2310 ). In some embodiments, an example of a rule-based decision would combine testing some or all of the parameter(s) against thresholds. For example, a condition such as “If texture classification T(Φ,L)n&gt;3, density classification D(Φ,L)n&gt;4, conductivity classification Z(i,l)n&gt;4, location classification&gt;3, and radiation classification&gt;1” could be used as a condition for determining one type of risk factor and possibly generating an alert. Calculations may be any simple or complex combination of the individual parameter values calculated by test block  2310  to determine sets of risk factors. Sets of risk factors represent various categories of threats that are likely to be present in the object. For instance, there may be a category of threat functions associated with the likelihood of a biological event which would produce a risk factor for this category, there may also be a category of threat functions associated with the likelihood of an explosive threat which would produce a risk factor for the explosive category, and yet there may be a category threat functions associated with a general likelihood evoked by a combination of attributes not necessarily specifically to the material type. Different calculations may yield a number of risk factors within each category. The threat functions include test conditions and apply criteria based on pre-existing real world knowledge on signals and combinations of signals identifying threats. 
     If a high-enough risk factor is determined that the preset set of threat thresholds are satisfied, depending on the embodiment, the location, quantity, and type of the threatening item may be estimated (block  2320 ), an alert may also be generated (block  2330 ). Whether a risk factor is high enough to trigger the alert depends on the sensitivity settings within the system, which has a default setting and is reconfigurable by the user. An “alert” may include a visual or audio signal for notifying the operator that a threatening item may have been identified, and may also include taking other operational actions such as closure/locking of the door  261  in the object unit  260 . Optionally, a signal (e.g., a green light) may be generated to indicate that an object is clear of threatening items (block  2325 ). 
       FIG.  17    is a cross-sectional view of an exemplary embodiment of the threat detection machine  200  including a single test unit  220  and multiple object units  260   a - 260   e.    FIG.  21    is a perspective view of the threat detection machine  200 . In this embodiment, the centrally located test unit  220  has flat outer surfaces that interface the object units  260   a - 260   e.  As shown, the test unit  220  is located centrally with respect to the object units  260  so that an object can be tested by the test unit  220  regardless of which object unit it is in. The test unit  220  and the object unit  260  may be made of any material with structural integrity including various metals (e.g., steel) or composite material. Preferably, there is a rotating mechanism in the test unit  220  that allows the direction of the test beam, etc. to be adjusted depending on which object is being tested. Once all the object units are filled, the test unit performs tests on the objects by turning incrementally between each object unit  260  as shown by the arrows. Some tests are performed sequentially. For example, if an X-ray test is performed, the X-ray beam is directed from the test unit  220  to the multiple object units  260   a - 260   e  sequentially, e.g. in a predetermined order. However, other tests are performed simultaneously for the multiple object units  260   a - 260   e.  For example, if a chemical analysis test is performed, a sample of each object in the multiple object units  260   a - 260   e  can be taken simultaneously, as each object unit has its own rotation flow device  232 , grasping and/or rotating mechanism  264 , and particle acquisition pores  263 . Thus, depending on the tests that are included in the particular embodiment, the overall testing may be partly sequential and partly simultaneous for the multiple object units  260   a - 260   e.  All the test data are sent to the computation unit  240 , preferably as soon as they are obtained. 
     The output signals from the sensors  266  (and the collector plate of the chemical analysis subunit  230 , if applicable) may be processed by a single computation unit  240  or a plurality of computation units  240 . Where a single computation unit  240  is used, the computation unit  240  keeps the items separate so that it yields five different results, one for each item  262 . 
     The embodiment of  FIG.  17    allows multiple items to be processed quickly compared to the current security check system where passengers form a single line and one object (e.g., bag) is processed at a time. Therefore, all the tests incorporated into the test unit  220  can be performed for each of the items in the object units  260   a - 260   e  without compromising the traffic flow. 
     The threat detection machine  200  of  FIG.  17    may be designed as a modular unit, so that the number of object units  260  is adjustable. Thus, if a first area is getting heavy traffic while traffic in a second area has slowed down, a few of the object units from the second area can be used for the first area by simply being detached from one test unit  220  and being attached to another test unit  220 . The detaching-and-attaching mechanism may use hook systems and/or a clasping/grasping/latching mechanism. This flexibility results in additional cost savings for public entities that would use the threat detection machine  200 . The object units  260   a - 260   e  are substantially identical to one other. 
     Additionally, the platform on which the item  262  is placed in the object unit  260  may have a sensor, such as a weight or optical sensor, that signals to the test unit  220  whether the particular object unit  260  is in use or not. So, if only object units  260   a,    260   b,    260   d,  and  260   e  are used for some reason, the test unit  220  will not waste time sending test beams and collecting samples from the empty object unit  260   c  and the system  10  will automatically optimize its testing protocols. The threat detection machine  200  may include a processor for making this type of determination. A sensor is placed either in each object unit  260  or in the test unit  220  to detect an output signal indicating that an item in the object unit  260  has been tested. 
     Although the particular embodiment shows the units as having hexagonal shapes for a honeycomb configuration, this is just an example and not a limitation. For example, the test unit  220  may have any polygonal or curved cross section other than a hexagon.  FIG.  22   , for example, shows a cross-sectional view of a threat detection system  200  wherein the test unit  220  has a curved outer surface (as opposed to flat outer surfaces as in the embodiment of  FIG.  17   ). The shapes of the object units  260   a - 260   e  are adapted so they can efficiently and securely latch onto the test unit  220 . Furthermore, the structure allows a resource in a central unit (e.g., the test unit  220 ) to be shared among the surrounding compartments (e.g., object units  260 ) in a fast and space-efficient manner, making the structure useful for various applications other than detection of threatening items. For example, where multiple items need to be encoded with a piece of data, the data source can be placed in the central unit so that items in the surrounding compartments can read the data. In a case of laser etching, items in the compartments could receive data encoding from the central unit. 
       FIG.  18    is a block diagram showing the test unit  220  and the object units  260   a - 260   e.  In the particular embodiment, a single computation unit  240  is used for all the object units  260   a - 260   e.  Each of the object units  260   a - 260   e  contains a moving device, such as a mechanical mechanism, multi axis manipulator, robotic mechanism, a conveyor belt, or any other rotating and linear mechanism and a sensor array, as described above in reference to  FIG.  15   . The moving device allows both linear and rotational movement. The test unit  220  has four subunits: an ionized radiation source subunit, a chemical analysis subunit, a non-ionizing radiation source subunit, and a magnetic field induction subunit. Each of the object units  260   a - 260   e  is coupled to the test unit  220  and the computation unit  240 . 
       FIG.  19    is another exemplary embodiment of the threat detection system  200  wherein the item is a human being (or any of other animals). In the particular embodiment that is shown, the test unit  220  has two object units  260   a,    260   b  attached to it. Naturally, tests involving radiation will be used with caution, by choosing appropriate radiation sources and parameters when the “items” being tested are human beings. If desired, a camera may be installed somewhere in the test unit  220  or the object unit  260   a  and/or  260   b  to obtain images of objects in order to obtain a biometric classification and/or transmit images to an operator. 
       FIG.  20    is yet another exemplary embodiment of the threat detection machine  200  for testing inanimate items and human beings. The particular embodiment has the test unit  220  with five object units  260   a - 260   e  for testing inanimate items and a portal  260   f  for human beings or animals to pass through. The test unit  220  tests items in the object units  260   a - 260   e  and human beings in the object unit  260   f  that are in each of the object units  260   a - 260   f.  However, all the object units and both test units would still feed signals to a single computation unit  240 . 
       FIG.  23    depicts an embodiment of the machine network  10  where each machine  30  includes a threat detection machine  200 . Similarly to the network of sorting machine  100  illustrated in  FIG.  4   , the threat detection machine  200  of the network in  FIG.  23    is enhanced with the processing unit  32 + 38  to include artificial intelligence and internal parameter data. The computation unit  240  of a threat detection machine  200  in the network communicates with the processing module  32  that employs artificial intelligence and a memory  38  that stores internal parameter data. The measurement unit  34 + 36  of the machine  30  in  FIG.  23    would include the test unit  220 , and the object unit  260  would receive objects/items to be characterized. 
     A single, free-standing threat detection machine  200  allows detection of threatening items with increased accuracy compared to the currently available system. Unlike systems that use a sequence of separate equipment, with each equipment using only one test and generating a test result based only on that one test, the system relies on a combination of a plurality of parameters. Thus, while a bomb that has a low level of explosive and a small amount of conductive material may escape detection by the current system because both materials are present in amounts below the threshold levels as measured by separate tests, the item could be caught by the threat detection machine  200  because the presence of a certain combination of indicative materials and vicinity parameters included in the threat determination functions could trigger an alarm. The use of combinations of parameters allows greater flexibility and increased accuracy in detecting the presence of threatening items. 
     Adding the network element to the threat detection machine  200  takes the level of threat detection accuracy even higher. With a single, free-standing threat detection machine  200 , two components that are dangerous only when combined may still pass undetected if the two components are not placed through the same threat detection machine  200 . In a large stadium or venue that hosts thousands of people, putting all the people through a single threat detection machine  200  would slow down the entry process too much. When there are multiple entrances into a venue, the machine network  10  allows a threat detection machine  200  to be positioned at each entrance and communicate with one another. So, if Bad Guy A enters through the front entrance carrying component A and Bad Guy B enters through the side entrance carrying component B that would raise alarm only if combined with component A, the threat detection machines  200  at the two entrances can communicate each other through the network and note that components A and B have been detected going into the same venue. 
     As the machine network  10  is not limited to being within a certain distance, the threat detection capability can be extended beyond a single venue to a jurisdiction of any size, such as a city or any larger geographic area. In one embodiment, the central processing unit  20  is “watching” and processing all the data passing through the machines  30  in the machine network  10 . Upon noticing any unusual activity, whether it be based on items that are passing through the threat detection machines  200  in the network  10 , identities of individuals, or any combination of such data, an alert may be generated. 
     The machine network  10  allows different types of machines to work as a “team.” This is different from the single, free-standing machine that looks for specific items/materials such as explosives, drugs, weapons, etc. being carried by one individual. By detecting an unusual pattern or the presence of a general combination of potentially hazardous materials/conditions, the machine network  10  enables detection of a potentially dangerous situation/devices effectively. 
     Various embodiments of the processing units may be implemented with or involve one or more computer systems. The computer system is not intended to suggest any limitation as to the scope of use or functionality of described embodiments. The computer system includes at least one processor and memory. The processor executes computer-executable instructions and may be a real or a virtual processor. The computer system may include a multi-processing system which includes multiple processing units for executing computer-executable instructions to increase processing power. The memory may be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory, etc.), or combination thereof. In an embodiment of the present disclosure, the memory may store software for implementing various embodiments of the disclosed concept. 
     Further, the computing device may include components such as memory/storage, one or more input devices, one or more output devices, and one or more communication connections. The storage may be removable or non-removable, and includes magnetic disks, magnetic tapes or cassettes, compact disc-read only memories (CD-ROMs), compact disc rewritables (CD-RWs), digital video discs (DVDs), or any other medium which may be used to store information and which may be accessed within the computing device. In various embodiments of the present disclosure, the storage may store instructions for the software implementing various embodiments of the present disclosure. The input device(s) may be a touch input device such as a keyboard, mouse, pen, trackball, touch screen, or game controller, a voice input computing device, a scanning computing device, a digital camera, or another device that provides input to the computing device. The output computing device(s) may be a display, printer, speaker, or another computing device that provides output from the computing device. The communication connection(s) enable communication over a communication medium to another computing device or system. The communication medium conveys information such as computer-executable instructions, audio or video information, or other data in a modulated data signal. A modulated data signal is a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired or wireless techniques implemented with an electrical, optical, RF, infrared, acoustic, or other carrier. In addition, an interconnection mechanism such as a bus, controller, or network may interconnect the various components of the computer system. In various embodiments of the present disclosure, operating system software may provide an operating environment for software&#39;s executing in the computer system, and may coordinate activities of the components of the computer system. 
     Various embodiments of the present disclosure may be described in the general context of computer-readable media. Computer-readable media are any available media that may be accessed within a computer system. By way of example, and not limitation, within the computer system, computer-readable media include memory, storage, communication media, and combinations thereof. 
     It should be understood that the inventive concept can be practiced with modification and alteration within the spirit and scope of the appended claims. For example, although certain embodiments of the machine  100  and the machine  200  are described herein, the system of the inventive concept is not limited to being implemented with only the disclosed embodiments. The system may be implemented, for example, with other types of machines. The description is not intended to be exhaustive or to limit the inventive concept to the precise form disclosed. It should be understood that the disclosed concept can be practiced with modification and alteration.