Patent ID: 12226182

DETAILED DESCRIPTION

In the following description, reference is made to the accompanying drawings that form a part thereof, and in which is shown by way of illustration specific exemplary embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the concepts disclosed herein, and it is to be understood that modifications to the various disclosed embodiments may be made, and other embodiments may be utilized, without departing from the scope of the present disclosure. The following detailed description is, therefore, not to be taken in a limiting sense.

Reference throughout this specification to “one embodiment,” “an embodiment,” “one example,” or “an example” means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” “one example,” or “an example” in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, databases, or characteristics may be combined in any suitable combinations and/or sub-combinations in one or more embodiments or examples. In addition, it should be appreciated that the figures provided herewith are for explanation purposes to persons ordinarily skilled in the art and that the drawings are not necessarily drawn to scale.

Embodiments in accordance with the present disclosure may be embodied as an apparatus, method, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware-comprised embodiment, an entirely software-comprised embodiment (including firmware, resident software, micro-code, etc.), or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” or “system.” Furthermore, embodiments of the present disclosure may take the form of a computer program product embodied in any tangible medium of expression having computer-usable program code embodied in the medium.

Any combination of one or more computer-usable or computer-readable media may be utilized. For example, a computer-readable medium may include one or more of a portable computer diskette, a hard disk, a random-access memory (RAM) device, a read-only memory (ROM) device, an erasable programmable read-only memory (EPROM or Flash memory) device, a portable compact disc read-only memory (CDROM), an optical storage device, a magnetic storage device, and any other storage medium now known or hereafter discovered. Computer program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages. Such code may be compiled from source code to computer-readable assembly language or machine code suitable for the device or computer on which the code can be executed.

Embodiments may also be implemented in cloud computing environments. In this description and the following claims, “cloud computing” may be defined as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned via virtualization and released with minimal management effort or service provider interaction and then scaled accordingly. A cloud model can be composed of various characteristics (e.g., on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service), service models (e.g., Software as a Service (“SaaS”), Platform as a Service (“PaaS”), and Infrastructure as a Service (“IaaS”)), and deployment models (e.g., private cloud, community cloud, public cloud, and hybrid cloud).

The flow diagrams and block diagrams in the attached figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams or block diagrams may represent a module, segment, or portion of code, which includes one or more executable instructions for implementing the specified logical function(s). It is also noted that each block of the block diagrams and/or flow diagrams, and combinations of blocks in the block diagrams and/or flow diagrams, may be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. These computer program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flow diagram and/or block diagram block or blocks.

Aspects of the invention described herein describe systems and methods that implement wireless biological measurement systems using chaotic waveforms.

Chaotic signals can be described as “deterministic noise”, which is to say that these signals resemble noise signals in the time and frequency domains, but are generated by nonlinear deterministic systems. These nonlinear systems are very sensitive to initial conditions, with very different temporal waveforms being produced with slight changes in initial conditions. Furthermore, each chaotic system can produce a very large ensemble of uncorrelated chaotic waveforms. These can be generated either using analog flows, such as the Chua Double Scroll Attractor, or discrete-time chaotic maps. Analog circuitry generates analog chaotic signals that are solutions to nonlinear differential equations, whereas discrete-time nonlinear state equations generate discrete-time chaotic sequences. Two examples of discrete-time chaotic function generators are:
xk+1=1−2xk2Logistic map:
xk+1=4xk3−3kkCubic map:

Some of the properties of chaotic waveforms are:Chaotic waveforms are generated by nonlinear dynamic systems, in the continuous-time domain (via nonlinear differential equations) and in the discrete-time domain (via nonlinear discrete-time recursions—difference equations).Chaotic waveforms resemble noise in the time- and frequency domains, but are deterministic in nature.Chaotic systems are very sensitive to initial conditions—even a slight change in initial conditions produces a waveform that is uncorrelated with the original waveform. On the other hand, for a given chaotic system and initial condition, a chaotic waveform is repeatable.Continuous-time chaotic waveforms are aperiodic—chaotic waveforms of arbitrarily long lengths can be generated.Discrete-time chaotic waveforms are substantially aperiodic for substantially infinite numerical precision (i.e., analog value) per sample; reducing the numerical precision (e.g. limited-width fixed-point numerical representation) results in periodic orbits.Chaotic waveform ensembles show strong autocorrelations and weak cross-correlations, a property referred to as quasi-orthogonality.Chaotic signals are inherently wideband, and therefore are good candidate waveforms for spread-spectrum communication systems. The wideband nature of these signals also makes them resistant to noise and interference from other signals.A large ensemble of quasi-orthogonal waveforms can be generated for each cubic map, a property referred to as waveform diversity.A large number of chaotic waveforms can be transmitted simultaneously with minimal inter-waveform interference—this is the code-division multiple access (CDMA) property associated with spread-spectrum communication systems.

A result of these properties is that the number of degrees of freedom for the system designer now increases—rather than being limited by waveform and temporal length constraints, the system designer is now able to construct a relatively large quasi-orthogonal waveform ensemble of any arbitrary length (subject to numerical precision constraints). When used in a communication system, a chaotic signal provides a layer of security at the waveform level, in the sense that only a party with a priori knowledge of the chaotic waveform ensemble and a corresponding codebook can decipher the information being transmitted using the chaotic waveform ensemble. This property is especially useful in designing secure waveform families—in addition to digital data encryption, chaotic waveforms offer an additional layer of security at the waveform level.

An additional advantage of chaotic signals, a class of spread-spectrum signals, is their anti-jam capability as well as their ability to operate below the noise floor. It is not possible to determine, in general, any characteristics of the underlying signal using conventional spectral analysis techniques such as a spectroscope or a frequency analyzer.

For designing medical sensing systems using chaotic waveforms, one goal is to replace a wired connection between a transducer and a computing system or processing system by a chaotic communication link. In order to do so, the following properties of chaotic waveforms may be used:Quasi-orthogonality: The strong autocorrelation and low cross-correlation properties of chaotic waveforms enable multiple waveforms to be transmitted simultaneously and demodulated without ambiguity at the receiver. In this way, multiple wireless-enabled transducers (or monitoring devices) function simultaneously on a patient, and parallel channels of chaotically-modulated data can be unambiguously streamed wirelessly to a digital processing back end. Assuming a unique set of chaotic waveforms associated with each monitoring device, a monitoring device may be identified based on a received waveform ensemble. This property resolves any node identification ambiguity.Operation with power levels below the noise floor: With suitable system design, chaotic waveforms can be transmitted below the noise floor, thereby requiring reduce transmit power. Furthermore, transmitting signals at a lower power level reduces any disturbance from these waveforms to other wireless systems.Robust performance: Chaotic waveforms also possess excellent anti jam properties that enable these waveforms to be functional even in the presence of external interference.Novel chaotic modulation schemes for increased spectral efficiency: Chaotic waveform modulation schemes allow for low-power, high data rate communication links to be established and maintained.

FIG.1is a block diagram depicting an example computer architecture of a biological measurement system100. As depicted, biological measurement system100(also referred to as a “biological function monitoring system”) includes a plurality of monitoring devices—a monitoring device108, a monitoring device110, a monitoring device112, a monitoring device114, a monitoring device116, and a monitoring device118attached to a biological entity120. In an aspect, biological entity120may be a human being or an animal (e.g., a dog). Each of monitoring device108through monitoring device118may include a biomedical transducer such as an ECG/EKG transducer.

In an aspect, each of monitoring device108through monitoring device118may be configured to measure one or more biological function measurements. For example, biological entity120may be a human being, and each of monitoring device108through monitoring device118may be associated with an ECG/EKG measurement. In an aspect, each of monitoring device108through monitoring device118is configured to modulate a data symbol associated with the respective biological function measurement onto multiple, distinct chaotic waveforms from a chaotic waveform ensemble, to generate a composite chaotic waveform. Each of monitoring device108through monitoring device118may be configured to transmit the associated composite chaotic waveform over a communication channel106, to a waveform receiver102. In an aspect, communication channel106may be a wireless communication channel. In an aspect, each of monitoring device108through monitoring device118may store a distinct chaotic waveform ensemble, where each chaotic waveform ensemble associated with a monitoring device is a unique set of chaotic waveforms that is distinct from all other chaotic waveform ensembles associate with the other monitoring devices.

Waveform receiver102may be configured to receive a composite chaotic waveform from each of monitoring device108through monitoring device118, demodulate the composite chaotic waveform, and extract a data symbol associated with the biological function measurement. Waveform receiver102may further process each data symbol to compute the biological function measurement.

The biological function measurement computed by receiver102may be transmitted to one or more output device(s)104for display to a user of biological measurement system100. Examples of output device(s)104include visual display monitors (e.g., LCD, LED, or CRT displays), graphical plotting devices, audio devices such as loudspeakers, and other visual display devices such as LED lamps or bulbs.

An example application of biological measurement system100is an ECG/EKG measurement system, where each of monitoring device108through monitoring device118may include an ECG/EKG transducer. Each ECG/EKG transducer in each monitoring device may be configured to measure an ECG/EKG biological signal at an appropriate part of the human body, digitize the ECG/EKG biological signal, modulate the digitized ECG/EKG biological signal by combining multiple chaotic waveforms, and transmit the modulated chaotic waveforms to waveform receiver102. Waveform receiver102demodulates the received modulated chaotic waveforms by performing operations such as one or more correlations, and extracts one or more data symbols from each demodulation (e.g., correlation) process. In an aspect, each data symbol corresponds to an ECG/EKG transducer measurement, and may be similar to a measurement gathered by a contemporary wired ECG/EKG device. Waveform receiver102may analyze the data symbols, and generate an ECG/EKG signal based on the data symbols. This ECG/EKG signal may be displayed to a user on output devices104.

Other applications where the chaotic waveform-based modulation scheme may be used to implement wireless biological measurement systems include:CardiologyPulse oximetryBlood pressure (arterial, venous)Neuromodulation during spine surgeryTemperatureEchocardiographyPost-op intensive care unit recordingPacemaker function diagnosisNeurologyElectroencephalogramTumor diagnosisBrain clot (trauma) detection

FIG.2is a block diagram depicting an example computer architecture of a monitoring device200. The architecture of monitoring device200may be used to implement any, some, or all of monitoring device108through monitoring device118. As depicted, monitoring device200includes a sensor212, a clock220, an analog-to-digital converter ADC214, a processor216, a memory224, a digital-to-analog converter DAC218, an RF front end RFFE202, a transmit antenna210, and a power supply226. RFFE202may further include an upconverting mixer206, a local oscillator LO204, and a power amplifier PA208. In an aspect, processor216may be implemented on a suitable processing device such as a field-programmable gate array (FPGA), a microcontroller, a digital signal processor (DSP), or a customized integrated circuit.

In an aspect, sensor212may be a biomedical transducer such as an ECG/EKG transducer, or some other transducer configured to measure a biological signal. Sensor212may be configured to measure (i.e., sense) a biological function measurement during monitoring a biological function (e.g., heart rate). Sensor212may output an analog signal based on the biological function measurement. This analog signal may be digitized by ADC214to generate a digital data symbol (i.e., a digital data sample) corresponding to the biological function measurement. The digital data symbol may be input to processor216.

In an aspect, memory224may be configured to store an ensemble of (i.e., two or more) chaotic waveforms of a given temporal length, such as, for example, chaotic waveform ensemble (CWA)234. These chaotic waveforms may be written to memory224from an external source, or may be generated by processor216and written to memory224. In an aspect, the chaotic waveforms stored in memory224may be discrete-time, digitized chaotic waveforms generated in the digital domain using nonlinear recursions.

In an aspect, processor216may be configured to read (e.g., two or more) chaotic waveforms from a chaotic waveform ensemble, for example, CWA234, stored in memory224, and combine the chaotic waveforms based on the digital data symbol read from ADC214, to generate a composite chaotic waveform. This process is a chaotic modulation process, where two or more chaotic waveforms are modulated in accordance with the digital data symbol.

In an aspect, processor216outputs the composite chaotic waveform (a digital waveform modulated with the digital data symbol) to DAC218. DAC218converts the (digital) composite chaotic waveform into an analog waveform. In an aspect, ADC214, processor216, and DAC218function using one or more clock signals generated by clock220. For example, clock220may generate clock signals CLK228input to ADC214, CLK230input to processor216, and CLK232input to DAC218.

The analog waveform generated by DAC218is input to upconverting mixer206, where the analog waveform is mixed with a sinusoidal signal from LO204, and upconverted to a waveform at a frequency suitable for transmission over communication channel106. PA208receives the upconverted signal and amplifies the power of the signal, and then transmits the amplified signal to transmit antenna210for wireless transmission over communication channel106.

In an aspect, the different components of monitoring device226may be powered by electrical power generated by power supply226. Power supply226may be a disposable or rechargeable battery, or a power harvesting circuit that may derive electrical power from, for example, heat generated by biological entity120. The mixed-signal blocks associated with monitoring device200(i.e., ADC214and DAC218) may include additional amplification and filtering components as required for appropriate signal conditioning.

In an aspect, monitoring device200may be integrated and packaged into an appropriate format and form factor appropriate to the desired application (e.g., an adhesive transducer to be applied to a patient's skin, or an oxygen sensor attached to a patient's finger).

FIG.3is a timing diagram depicting a relationship300between an ADC clock and a DAC clock. Chaotic waveforms are spread-spectrum waveforms; each data sample measured by sensor212and digitized by ADC214modulates a chaotic waveform that is N samples long, where N>1. Examples of N include lengths of 100 samples, 200 samples, 512 samples, 1024 samples, and so on, of chaotic waveform lengths. Relationship300shows a sample1302and a sample2304generated by sensor212at a rate associated with an ADC clock (e.g., CLK228). Each of sample1302and sample2304is mapped to a composite chaotic waveform, as indicated in relationship300. For a composite chaotic waveform that is N samples long, CLK232associated with DAC218may be a clock that has a frequency that is N-times that of CLK228. This DAC clock (e.g., CLK232) may be generated or derived from CLK228using, for example a clock multiplier.

Relationship300shows sample1302being chaotically modulated to generate a composite chaotic waveform comprising a sample1306, a sample2308, through a sample N310, clocked at a rate corresponding to the DAC clock frequency. Similarly, sample2304is chaotically modulated to generate a composite chaotic waveform comprising a sample1312, a sample2314, through a sample N316, clocked at a rate corresponding to the DAC clock frequency.

FIG.4is a timing diagram depicting a one-sample delay400. Relationship300depicts an ideal (theoretical) relationship between an ADC clock and a DAC clock. The representation of relationship300assumes that digitized data is available at the output of ADC instantaneously. In a practical system, every ADC is associated with a finite conversion time; hence, there is some latency between an analog input to ADC214, and a digitized output from ADC214. One-sample delay400represents this latency, where a sample1402read at the ADC clock frequency is mapped to a sample1411, a sample2412, through a sample N414of a composite chaotic sequence.

A digitized data sample generated before sample1402(not shown inFIG.4) is mapped to samples of an earlier composite chaotic sequence, i.e., a sample1406, a sample2408, through a sample N410. A sample2404that is subsequent to sample1402is mapped to a composite chaotic waveform subsequent to the composite chaotic waveform (not shown inFIG.4) that sample1402is mapped to. During the mapping process, processor216may be in the process of reading in a subsequent digitized data sample from ADC214. In general, a composite chaotic sequence corresponding to an nthdata sample is output when the (n+1)thdata sample is being read in by processor216.

FIG.5is a block diagram depicting an example computer architecture of a waveform receiver500. Waveform receiver500may be configured to receive and demodulate one or more composite chaotic waveforms generated by one or more monitoring devices such as monitoring device108through monitoring device118. In an aspect, waveform receiver500may be identical to waveform receiver102.

As depicted, waveform receiver500may include a receive antenna510, an RF front end RFFE502, an analog-to-digital converter ADC514, a chaotic demodulator512, a clock516, and a data processor520. RFFE502may further include a power amplifier PA508, a local oscillator LO504, and a downconverting mixer506.

In an aspect, receive antenna510may be configured to receive one or more composite chaotic waveforms over communication channel106from each of monitoring device108through monitoring device118. PA508may appropriately amplify and filter the received composite chaotic waveforms, which are downconverted to a suitable intermediate frequency (IF) by downconverting mixer506. Downconverting mixer506may use a locally generated sinusoidal function generated by LO504to accomplish the downconversion process.

In an aspect, the downconverted signal at an intermediate frequency (IF) is received from the output of the downconverting mixer by ADC514. ADC514digitizes the received analog signal and outputs the digitized signal to chaotic demodulator512. Chaotic demodulator512may demodulate the received digitized composite chaotic waveforms and extract one or more data symbols from demodulating each digitized composite chaotic waveform. In an aspect, a demodulation process may be implemented as a correlation with locally-stored replica copies of the chaotic waveforms. All data symbols extracted from the demodulation processes are output by chaotic demodulator512to data processor520. Data processor520may be configured to collectively processes all received data symbols and compute a biological function measurement. This biological function measurement may be, for example, an ECG/EKG measurement as measured by monitoring device108through monitoring device118. In an aspect, data processor520and all functions of data processor520may be integrated into chaotic demodulator512.

In an aspect, clock516provides a clock signal CLK518that is routed to ADC514, chaotic demodulator512, and data processor520for maintaining timing and synchronization.

FIG.6is a block diagram depicting an example computer architecture of a chaotic demodulator600. In an aspect, chaotic demodulator600may be similar to chaotic demodulator512. As depicted, chaotic demodulator600may include tracking loops604, a correlator ensemble612, a symbol decoder614, and a memory616. Tracking loops604may further include carrier tracking loops606and code tracking loops608. Correlator ensemble612may be further comprised of a plurality of correlators—C11, C12through C1K, C21, C22through C2K, and CJ1, CJ2through CJK. Correlators C11through CJK may be configured as a correlator array. In an aspect, chaotic demodulator600may be implemented on a suitable processing device such as a field-programmable gate array (FPGA), a microcontroller, a digital signal processor (DSP), a personal computer, a customized integrated circuit-based processing system, or any other suitable processing device.

In an aspect, chaotic demodulator600receives digital data in602. Digital data in602may be digitized data received from ADC514. Digital data602may be routed to tracking loops604and to correlator ensemble612. Tracking loops604comprising carrier tracking loops606and code tracking loops608perform carrier tracking and code tracking functions, respectively, on one or more chaotic waveforms received from each of monitoring device108through118as digital data in602. Carrier tracking loops606and code tracking loops608track and maintain a lock on timing and synchronization on any received composite chaotic waveform. Carrier tracking loops606function by comparing a sinusoidal carrier signal associated with each received composite chaotic waveform and a locally-generated or locally-stored sinusoidal signal. Code tracking loops608function by comparing a digitized composite chaotic waveform and one or more locally-generated or locally-stored chaotic waveforms. In an aspect, the locally-generated/locally-stored sinusoidal signal and chaotic waveforms may be stored in memory616, and retrieved by carrier tracking loops606and code tracking loops608as necessary. In an aspect carrier tracking loops606may include one or more phase-locked loops (PLLs), while code tracking loops608may include one or more delay-locked loops (DLLs).

In an aspect, each of C11through CJK within correlator ensemble612may be configured to perform a correlation operation associated with a distinct chaotic waveform. Each composite chaotic waveform received by chaotic demodulator600may be correlated against a distinct locally-generated/locally-stored chaotic waveform replica, in each of C11through CJK. In an aspect, the correlations are performed using a parallel-processing approach. In one aspect, each of C11through CJK may be dedicated to performing a single correlation operation. In another aspect, each of C11through CJK may perform multiple correlation operations. Each locally-generated/locally-stored chaotic waveform replica may be stored in memory616.

In an aspect, one or more synchronization signals SYNC610may be used to maintain timing and synchronization between tracking loops604and correlator ensemble612. Each of C11through CJK is configured to perform one or more correlations and output the results of the correlations to symbol decoder614. Symbol decoder614may be configured to derive a data symbol from each of the correlation results. The data symbols derived by symbol decoder614may be output as decoded output data616. This decoded output data616may be further processed by data processor520to compute a biological function measurement (e.g., an ECG/EKG measurement).

In an aspect, correlator ensemble612may be implemented using multi-core processing platforms such as multi-correlator-configured FPGAs, multi-core processing architectures, GPU arrays, or other massively-parallel computing architectures.

FIG.7is a block diagram depicting an example computer architecture of a waveform receiver700. Waveform receiver700may perform functions similar to waveform receiver102. As depicted, waveform receiver700may include a signal front end704, a signal interface708, and a processing system702. Processing system702may further include a memory710, a DMA controller712, a processor714, a GPU array716, and a user I/O interface718. A data bus720may communicatively couple the components of processing system702.

In an aspect, signal front end704may be configured to perform functions of receive antenna510, RFFE502, and ADC514. Signal front end704may be configured to wirelessly receive one or more composite chaotic waveforms from monitoring device108through monitoring device118, downconvert the composite chaotic waveforms, and digitize the downconverted composite chaotic waveforms to generate a digitized signal. The digitized signal may be communicated to processing system702via signal interface708as digital data in706. Signal interface708may also support exchange of timing, handshaking and other signals between signal front end704and processing system702. Examples of signal interface708include USB, serial interface(s), serial port(s), parallel ports, LVDS signaling interface(s), and so on. In an aspect, signal front end704may be designed and configured in the form of a USB dongle.

In an aspect, processing system702may be configured to receive the downconverted composite chaotic waveforms, and perform functions similar to chaotic demodulator512and data processor520. In an aspect, GPU array716may be used to implement functions of correlator ensemble612. Processor714may be configured to implement tracking loops604, symbol decoder614, and data processor520. Some correlation operations associated with correlator ensemble612may be implemented on processor714if processor714is a multi-core processor. Some embodiments of processing system702may not include GPU array716, and may instead implement functionalities associated with correlator ensemble612on processor714.

In an aspect, memory710may be configured to store local replica copies of chaotic waveforms and sinusoidal carrier waveforms, in a manner similar to memory616. Memory710may also be configured to store digital data in706. DMA controller712may be configured to coordinate data transfer between various components of processing system702without intervention of processor714, over data bus720.

In an aspect, user I/O interface718may be used to interface processing system702(and waveform receiver700) with output device(s)104. Examples of protocols supported by user I/O interface may include DVI, HDMI, mouse/keyboard interfaces, and so on.

FIG.8is a timing diagram depicting an effect800of clock non-synchronization. In an aspect, each of monitoring device108through monitoring device118may have an independent clock source (e.g., clock220associated with monitoring device200). In an aspect, all monitoring devices associated with biological measurement system100may be configured with clock sources that share identical characteristics (e.g., frequency, jitter, etc.). However, due to manufacturing tolerances, the characteristics of the clock sources (e.g., frequency) might not be exactly equal. Such phenomena have important ramifications that need to be taken account during system design.

One clock-related phenomenon that may need to be considered during system design is clock synchronization. For example, consider a first and a second monitoring device that are configured to capture a biological function measurement. The clock signals generated by the respective clock sources may not be synchronized. This effect is depicted as effect800.

Effect800depicts a sample1802and a sample2804from a monitoring device1(e.g., monitoring device108), and a sample1806and a sample2808from a monitoring device2(e.g., monitoring device110). These data samples may be data samples similar to data samples output by ADC214. Assuming that the clock signals of monitoring device1and monitoring device2are not aligned, data sample1802will not be aligned with data sample1806, data sample2804will not be aligned with data sample2808, and so on. For example, using the timing of monitoring device1as a reference, if data sample1802is read at t=0 and if the two clock signals are apart by 1 microsecond, data sample1806is read at t=1 microsecond. Due to this reason, the measurements of the biological function from monitoring device1and monitoring device2are non-synchronous. This may lead to errors when the biological function measurement is computed by waveform receiver102.

FIG.9is a timing diagram depicting an effect900of clock bias. Clock bias occurs when the frequency of a clock signal deviates from a nominally specified frequency by a fixed offset. For example, for a specified clock frequency of 1 MHz, an actual frequency of a clock signal as measured may be 1.001 MHz. Over a period of time, the associated frequency deviation may result in a large time skew for a digital clocked with the 1.001 MHz clock as compared to a 1 MHz clock. This may lead to errors in constructing the biological function measurement at waveform receiver102. Effect900illustrates an effect of clock bias.

Effect900depicts a sample1902and a sample2904from a monitoring device1(e.g., monitoring device108), and a sample1906and a sample2908from a monitoring device2(e.g., monitoring device110). These data samples may be data samples similar to data samples output by ADC214. The clock signal associated with monitoring device2has a higher frequency as compared to the clock signal associated with monitoring device1. Due to a shorter time interval between samples from monitoring device2as compared to monitoring device1, a time skew is introduced between the waveforms. This time skew is visible in effect900.

For each of monitoring device108through monitoring device118, effects such as clock non-synchronization, clock bias and other clock effects can cause time skews between received composite chaotic waveforms from the monitoring devices. This, in turn, can cause errors in computing the associated biological measurement function by waveform receiver102.

FIG.10is an example computer architecture of a biological measurement system1000that accounts for clock non-synchronization and clock bias. As depicted, biological measurement system1000includes a waveform receiver1010, a primary monitoring device1002, a secondary monitoring device1004, a secondary monitoring device1006, through a secondary monitoring device1008. In an aspect, each of primary monitoring device1002may have an architecture similar to that of monitoring device200. Secondary monitoring device1004through secondary monitoring device1008may each have an architecture similar to that of monitoring device200, with the only difference being that each of secondary monitoring device1004through secondary monitoring device1008does not include a clock source; instead, each of secondary monitoring device1004through secondary monitoring device1008receives a clock signal CLK1012from primary monitoring device1002.

In an aspect, CLK1012may be generated by primary monitoring device1002, and routed to each of secondary monitoring device1004through secondary monitoring device1008. CLK1012may also be used as a system clock by primary monitoring device1002. In this way, primary monitoring device1002, and secondary monitoring device1004through secondary monitoring device1008are common-clocked and time-synchronized. Each of primary monitoring device1002, and secondary monitoring device1004through secondary monitoring device1008transmits a composite chaotic waveform to waveform receiver1010for demodulation.

FIG.11is an example computer architecture of a biological measurement system1100that includes a clock distribution scheme. As depicted, biological measurement system1100includes a waveform receiver1110, a primary monitoring device1102, a secondary monitoring device1104, a secondary monitoring device1106, through a secondary monitoring device1108. In an aspect, each of primary monitoring device1102may have an architecture similar to that of monitoring device200. Secondary monitoring device1104through secondary monitoring device1108may each have an architecture similar to that of monitoring device200, with the only difference being that each of secondary monitoring device1104through secondary monitoring device1108does not include a clock source; instead, each of secondary monitoring device1104through secondary monitoring device1108receives a clock signal CLK1112from primary monitoring device1002.

In an aspect, CLK1112may be generated by primary monitoring device1102, and routed to each of secondary monitoring device1104through secondary monitoring device1108. CLK1112may also be used as a system clock by primary monitoring device1102. In this way, primary monitoring device1102, and secondary monitoring device1104through secondary monitoring device1108are common-clocked and time-synchronized. Each of primary monitoring device1102, and secondary monitoring device1104through secondary monitoring device1108transmits a composite chaotic waveform to primary monitoring device1102as data. For example, secondary monitoring device1104transmits data1114to primary monitoring device1102; secondary monitoring device1106transmits data1116to primary monitoring device1102; and so on, with secondary monitoring device1108transmitting data1118to primary monitoring device1102. Primary monitoring device1102assembles all the received composite chaotic waveforms along with the composite chaotic waveform generated by primary monitoring device1102, and transmits all the composite chaotic waveforms collectively to waveform receiver1110for demodulation.

FIG.12is a waveform diagram depicting a composite chaotic waveform1200. Composite chaotic waveform1200may be generated by monitoring device200. As depicted, composite chaotic waveform1200includes a reference chaotic waveform1202of length N concatenated (i.e., combined) with an auxiliary chaotic waveform1204of length N. In an aspect, data received from ADC214may be modulated onto auxiliary chaotic waveform1204. Specifically, auxiliary chaotic waveform1204may be temporally phase-shifted by k samples in accordance with a data sample received from ADC214. For example, suppose N=1024, and suppose ADC214is a 10-bit ADC. If a data sample of 0 is received, then there is no phase shift (k=0). If a data sample of 1 is received, then k=1, and so on, to a data sample of 1023 corresponding to a phase shift of 1023.

At the receiver, waveform receiver102receives composite chaotic waveform1200, and separately computes separate correlation functions for reference chaotic waveform1202and auxiliary chaotic waveform1204. The correlation function associated with reference chaotic waveform1202will have a correlation peak at a time index of zero, while the correlation function associated with auxiliary chaotic waveform1204will have a correlation peak at a time index of k. Waveform receiver102compares the two correlation functions, determines the temporal difference k, and maps this difference to the associated data symbol. An advantage of this temporal phase shifting approach is that it improves a spectral efficiency associated with communication using chaotic (and other spread-spectrum) waveforms.

In an aspect, reference chaotic waveform1202and auxiliary chaotic waveform1204may be selected from an ensemble of chaotic waveforms stored in memory224. Each of monitoring device108through monitoring device118may be associated with a unique (i.e., distinct) chaotic waveform ensemble.

FIG.13is a waveform diagram depicting a composite chaotic waveform1300. Composite chaotic waveform1300may be generated by monitoring device200. As depicted, composite chaotic waveform1300includes a reference chaotic waveform1302of length N combined with an auxiliary chaotic waveform1304of length N. The combination may include a mathematical operation such as pointwise addition or multiplication. In an aspect, data received from ADC214may be modulated onto auxiliary chaotic waveform1304in a manner similar to the modulation of auxiliary chaotic waveform1204. To construct composite chaotic waveform1300, reference chaotic waveform1302may be combined with auxiliary chaotic waveform1304by a mathematical operation such as pointwise addition or multiplication.

In an aspect, composite chaotic waveform1300may be transmitted to waveform receiver102. Waveform receiver1302may compute a correlation function for each of reference chaotic waveform1302and auxiliary chaotic waveform1304, and perform the data demodulation process in a manner similar to that described for composite chaotic waveform1200.

In an aspect, reference chaotic waveform1302and auxiliary chaotic waveform1304may be selected from an ensemble of chaotic waveforms stored in memory224. Each of monitoring device108through monitoring device118may be associated with a unique (i.e., distinct) chaotic waveform ensemble.

FIG.14is a waveform diagram depicting a composite chaotic waveform1400. As depicted, composite chaotic waveform1400is comprised of a reference chaotic waveform RCW1, concatenated with an auxiliary chaotic waveform ACW2, an auxiliary chaotic waveform ACW3, an auxiliary chaotic waveform ACW4, an auxiliary chaotic waveform ACW5, through an auxiliary chaotic waveform ACWN. Each of ACW2through ACWN may be independently modulated by distinct data samples received from ADC214, using the temporal phase shifting process described for auxiliary chaotic waveform1204. Composite chaotic waveform1400may be demodulated at waveform receiver102using independent correlation functions for each of RCW1, and ACW2through ACWN. A correlation peak associated with a correlation function for each of ACW2through ACWN is compared with the correlation peak associated with a correlation function for RCW1, and the corresponding phase shifts are appropriately mapped to demodulated data symbols. In an aspect, RCW1, and ACW2thorough ACWN may be selected from an ensemble of chaotic waveforms stored in memory224. Each of monitoring device108through monitoring device118may be associated with a unique (i.e., distinct) chaotic waveform ensemble.

FIG.15is a waveform diagram depicting a composite chaotic waveform1500. As depicted, composite chaotic waveform1500is comprised of a reference chaotic waveform RAW1, combined with an auxiliary chaotic waveform AAW2, an auxiliary chaotic waveform AAW3, an auxiliary chaotic waveform AAW4, an auxiliary chaotic waveform AAW5, through an auxiliary chaotic waveform AAWN, via pointwise addition or multiplication. Each of AACW2through AAWN may be independently modulated by distinct data samples received from ADC214, using the temporal phase shifting process described for auxiliary chaotic waveform1204. Composite chaotic waveform1500may be demodulated at waveform receiver102using independent correlation functions for each of RAW1, and AAW2through AAWN. A correlation peak associated with a correlation function for each of AAW2through AAWN is compared with the correlation peak associated with a correlation function for RAW1, and the corresponding phase shifts are appropriately mapped to demodulated data symbols. In an aspect, RAW1, and AAW2thorough AAWN may be selected from an ensemble of chaotic waveforms stored in memory224. Each of monitoring device108through monitoring device118may be associated with a unique (i.e., distinct) chaotic waveform ensemble.

A direct application of biological measurement system100is an ECG/EKG monitoring system. An example system may include six monitoring devices attached at different points on a human body. A typical bandwidth required for ECG/EKG data is 125 Hz. This relates to a 250 sps sample rate by ADC214. Assuming 10-bit A/D conversion by ADC214, this translates to a 2.5 kbps required transmission data bitrate for each monitoring device. To achieve this, a N=1024 may be chosen for the chaotic waveform length. Each monitoring device may generate a composite chaotic waveform is constructed in a manner similar to composite chaotic waveform1500. In an aspect, all monitoring devices may be common-clocked (e.g., biological measurement system1000), and may sleep for a time period corresponding to 1024 samples between transmissions.

The chaotic waveforms may be clocked at a 300 kHz data rate. The selected sample rate for ADC214may be defined as 2*300,000/2048=292.97 Hz. This is greater than the required sample rate for ADC214. In other words, within a single composite chaotic waveform transmission period, ADC214produces 2 ECG/EKG samples per monitoring device. These two 10-bit data samples are each modulated onto a distinct 1024-point auxiliary chaotic waveform. These two modulated auxiliary chaotic waveforms are combined with (e.g., added to) a 1024-point reference chaotic waveform, and transmitted to waveform receiver102. This is done for each monitoring device. Waveform receiver102receives and demodulates the received composite chaotic waveforms, performs the requisite ECG/EKG processing, and outputs an ECG waveform onto a suitable display device.

Another aspect to consider for an ECG application is common-mode noise rejection. Contemporary wired ECG systems oftentimes use a leg drive circuit, where a portion of the ECG measurements are fed back to the patient's body via a transducer to reduce noise. With a wireless system, using any kind of feedback system will involve designing a full chaotic receiver in a monitoring system. This will make the monitoring system inefficient on power, and large and heavy due to the computing resources required. Alternatives to leg drive circuitry for common mode noise rejection include digital post processing methods such as digital filtering (e.g., FIR filtering). Since ECG data is digitized directly at the source rather than being processed collectively by analog circuitry, all processing needs to be performed in the digital domain by waveform receiver (specifically, data processor520) for wireless transducer applications.

FIG.16is a flow diagram depicting a method1600to generate a composite chaotic waveform. Referring briefly and concurrently back toFIG.2, method1600will be described with respect to the components of monitoring device200. Method1600includes sensing a biological function measurement monitoring a biological function (1602). For example, sensor212may sense an ECG/EKG signal while monitoring an ECG/EKG function.

Method1600includes deriving a data symbol representing the biological function measurement (1604). For example, ADC214may derive a digital data symbol by converting an analog signal sensed by sensor212.

Method1600includes accessing the data symbol (1606). For example, processor216may access (i.e., read in or receive) the digital data symbol from ADC214.

Method1600includes combining two or more chaotic waveforms from a chaotic waveform ensemble generating a composite chaotic waveform (1608). For example, processor216may combine two or more chaotic waveforms from a chaotic waveform ensemble stored in memory224to generate a composite chaotic waveform (e.g., composite chaotic waveform1500). In an aspect, a combination of chaotic waveforms is used to represent the digital data symbol in a composite chaotic waveform that may take the form of composite chaotic waveform1300, composite chaotic waveform1400, composite chaotic waveform1500, or any other composite chaotic waveform.

Method1600includes transmitting the composite chaotic waveform to a receiver (1610). For example, the composite chaotic waveform may be transmitted by216to DAC218, by DAC218to RFFE202, by RFFE202to transmit antenna210, and then to waveform receiver102.

FIG.17Ais a flow diagram depicting a method1700to compute a biological function measurement. Referring briefly and concurrently back toFIG.5, method1700will be described with respect to the components of waveform receiver500. Method1700may include receiving and downconverting a chaotic signal (1702). In an aspect, the chaotic signal may be comprised of multiple composite chaotic waveforms, with each composite chaotic waveform being transmitted by a monitoring device (e.g., monitoring device108). The chaotic signal may be received by receive antenna510and downconverted by RFFE512. Method1700may include digitizing the downconverted chaotic signal (1704). In an aspect, the digitizing may be performed by ADC514.

Method1700may include extracting a first reference chaotic waveform from the digitized signal by computing a first reference correlation function (1706). For example, chaotic demodulator512may be configured to perform a correlation on the digitized signal. The correlation may use a locally-stored copy of a first reference chaotic waveform that enables chaotic demodulator512to extract the first reference chaotic waveform from the digitized signal by computing a first reference correlation function. For example, the first reference chaotic waveform may be a part of a composite chaotic waveform transmitted by monitoring device108.

Method1700may include extracting a second reference chaotic waveform from the digitized signal by computing a second reference correlation function (1708). For example, chaotic demodulator512may be configured to perform a correlation on the digitized signal. The correlation may use a locally-stored copy of a second reference chaotic waveform that enables chaotic demodulator512to extract the second reference chaotic waveform from the digitized signal by computing a second reference correlation function. For example, the second reference chaotic waveform may be a part of a composite chaotic waveform transmitted by monitoring device110.

Method1700may include extracting a first auxiliary chaotic waveform from the digitized signal by computing a first auxiliary correlation function (1710). For example, chaotic demodulator512may be configured to perform a correlation on the digitized signal. The correlation may use a locally-stored copy of a first auxiliary chaotic waveform that enables chaotic demodulator512to extract the first auxiliary chaotic waveform from the digitized signal by computing a first auxiliary correlation function. For example, the first auxiliary chaotic waveform may be a part of a composite chaotic waveform transmitted by monitoring device108.

Method1700may include extracting a second auxiliary chaotic waveform from the digitized signal by computing a second auxiliary correlation function (1712). For example, chaotic demodulator512may be configured to perform a correlation on the digitized signal. The correlation may use a locally-stored copy of a second auxiliary chaotic waveform that enables chaotic demodulator512to extract the second auxiliary chaotic waveform from the digitized signal by computing a second auxiliary correlation function. For example, the second auxiliary chaotic waveform may be a part of a composite chaotic waveform transmitted by monitoring device110.

Method1700then goes to A, with a continued description provided in the description ofFIG.17B.

FIG.17Bis a flow diagram depicting a continuation of method1700. Starting from A, method1700may include performing a first comparison between the first reference correlation function and the first auxiliary correlation function (1714). For example, chaotic demodulator512may perform a first comparison between the first reference correlation function and the first auxiliary correlation function.

Method1700may include extracting a first data symbol from the first comparison, the first data symbol being associated with a biological function measurement (1716). For example, chaotic demodulator512may extract a first data symbol from the first comparison.

Method1700may include performing a second comparison between the second reference correlation function and the second auxiliary correlation function (1718). For example, chaotic demodulator512may perform a second comparison between the second reference correlation function and the second auxiliary correlation function.

Method1700may include extracting a second data symbol from the second comparison, the second data symbol being associated with the biological function measurement (1720). For example, chaotic demodulator512may extract a second data symbol from the second comparison.

Method1700may include processing the first data symbol and the second data symbol (1722). For example, data processor520may process the first data symbol and the second data symbol.

Method1700may include computing the biological function measurement responsive to the processing (1722). For example, data processor520may compute the biological function measurement (e.g., an ECG/EKG measurement) responsive to processing the first data symbol and the second data symbol.

Although the present disclosure is described in terms of certain example embodiments, other embodiments will be apparent to those of ordinary skill in the art, given the benefit of this disclosure, including embodiments that do not provide all of the benefits and features set forth herein, which are also within the scope of this disclosure. It is to be understood that other embodiments may be utilized, without departing from the scope of the present disclosure.