WO2004102456A2 - Active interferometric signal analysis in software - Google Patents
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- WO2004102456A2 WO2004102456A2 PCT/US2003/021525 US0321525W WO2004102456A2 WO 2004102456 A2 WO2004102456 A2 WO 2004102456A2 US 0321525 W US0321525 W US 0321525W WO 2004102456 A2 WO2004102456 A2 WO 2004102456A2
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
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- the invention generally relates to active signal analysis systems, and in particular, interferometric signal analysis systems.
- Interferometric analysis has proven useful for performing signal analysis, particularly for detecting events of interest within signal patterns, such as within a two- dimensional image.
- One example of interferometric analysis involves irradiating a moving object, such as an aircraft, with radar pulses, that interact with the object and interfere with one another and which are then reflected back to a sensing device such as an antenna. Signals received by the antenna are compared against signals originally transmitted in an effort to detect a unique spectral signature of the object so as to identify the object.
- This form of interferometric analysis is referred to as "active analysis", because signals used to irradiate the event of interest interact with the object and with other signals to actively modify the resulting interference pattern. In other words, the system being analyzed is actively modified. In the case of radar, electromagnetic fields surrounding the event of interest are actively modified.
- passive analysis An alternative type of signal analysis is "passive analysis" wherein signals that have already been detected are processed in an effort to identify events of interest therein. Examples include filtering images in an attempt to enhance features therein. These techniques are deemed to be passive because no noise or other signals are added into the system being analyzed. More specifically, passive interferometric analysis typically involves the manipulation of observed intensity patterns or phase information without the addition of noise or other signals. Often, passive analysis systems exploit parametric methods such as matched filter techniques, maximum a-posteriori (MAP) techniques, maximum likelihood estimator (MLE) techniques, singular value decomposition (SND), classical Fourier analysis, parametric distributional clustering (PCA), or median filter variants. ⁇ on-parametric methods may be employed as well.
- MAP maximum a-posteriori
- MLE maximum likelihood estimator
- SND singular value decomposition
- PCA parametric distributional clustering
- quantitation and clustering techniques may be employed such as "soft-computing" and its variants which include neural networks, fuzzy logic systems, and Bayesian inferencing systems.
- passive analysis techniques each requires that the observed signal have an intensity greater than the intensity of the background signal in which it appears.
- the analysis system must properly estimate the background signal and to properly estimate any variability in the background signal.
- spread spectrum (S-S) amplitude correlation is provided with background subtraction, and scaling and normalization is employed to compensate for detector nonlinearities.
- ASP active signal processing
- SQUID superconducting quantum interferometric device
- IMINT imagery intelligence
- SIGINT signals intelligence
- ELINT electronic intelligence
- active signal processing has been applied to femto-lasers. While these hardware-based systems provide enhanced signals in some circumstances, such systems are expensive to build and operate and can only physically manipulate or generate very specific types of signals.
- Another exemplary active system or technique that has been implemented in software is stochastic resonance wherein noise is applied to an image or a system being analyzed to improve at output signal-to-noise ratio (SNR).
- SNR signal-to-noise ratio
- a detector is modeled in terms of a bistable nonlinear dynamic system, which is enhanced by applying additive random noise element and a periodic sinusoidal forcing function. Stochastic resonance occurs when the SNR passes through a maximum as the noise level is increased. By properly selecting the amount of noise being applied, events of interest can be enhanced so that signals below background can be detected.
- stochastic resonance is employed using a classical noise source such as a standard Gaussian noise pattern.
- stochastic resonance may employed using some form of quantum noise.
- QSR quantum stochastic resonance
- an active signal processing system implemented in software capable of detecting events of interest in spatial, temporal or spatio-temporal systems within a combinatorial state space wherein the state space is discrete and continuous and wherein background noise is nonstationary.
- the system would be capable of analyzing signals received from a wide variety of arrayed platform detector configurations including linear, arrayed, spectral, and time-varying and can handle N-attribute systems.
- the improved system would be capable of analyzing signals with a signal to background ratio of 10 "4 .
- the system or technique would ideally be capable of detecting and quantitating specific hybridization (e.g., genomic events) and specific binding events (e.g., proteomic events).
- specific hybridization e.g., genomic events
- specific binding events e.g., proteomic events
- the system should be capable of processing data from a high-density gene expression microarrays (i.e. array is providing 10 to 10 spots per array) and which represents discrete, spatially indexed systems having high probe densities (i.e. from 10 6 to 10 9 probes per spot).
- the techniques are provided for performing active interferometric signal analysis in software.
- the techniques provide for both detection and quantitation analysis by exploiting constructive or destructive interferometric analysis (or a combination of constructive and destructive interferometric analysis) using reverberant convergence to detect resonance events.
- the techniques achieve software emulation of wave-particle interactions and wave-wave interactions and can operate in either the frequency domain or the phase domain.
- the techniques may be used for analyzing static spatial systems, static data from arrayed measurement platforms, dynamical systems, spatio-temporal systems or plasma systems.
- the techniques exploit expressor functions designed to reject any interfering noise or background clutter from possible events of interest within arrayed data to be analyzed. More specifically, the expressor functions are designed so as to extract spectral invariants of possible events of interest associated with the array device used to detect the arrayed data. Various aspects of the invention are directed to the generation of the expressor functions. Typically, the expressor functions are designed to incorporate some form of non-Gaussian noise, such as quantum noise, pseudorandom noise, polycyclostationary noise, cyclostationary noise, stationary noise, non-stationary noise or a systemic bias.
- non-Gaussian noise such as quantum noise, pseudorandom noise, polycyclostationary noise, cyclostationary noise, stationary noise, non-stationary noise or a systemic bias.
- the array prior to the application of the expressor function to arrayed data, the array is pre-conditioned so as to convert the arrayed data to a spectral domain in which spectral harmonics parameterize events of interest to a pre-determined dynamical system.
- Preconditioning may be performed by applying a preconditioning function incorporating, e.g., a 1-D Fourier function, a 2-D Fourier function, an N-D Fourier function, a time division multiplexing (TDM) function, a wavelength division multiplexing (WDM) function, a frequency division multiplexing (FDM) function, a radial bias function, a wavelet kernel function, a fractal function, or a soliton function.
- TDM time division multiplexing
- WDM wavelength division multiplexing
- FDM frequency division multiplexing
- the arrayed data itself may be the form of a spatial 1-D array, a spatial 2-D array, a spatial point emitter array, a temporal point emitter array, a spatio-temporal point emitter array, a spectral point emitted array or a virtual array constructed by combining spatial separate point emitters.
- a technique for actively analyzing a signal pattern representative of arrayed data to identify events of interest therein.
- a signal pattern representative of arrayed data is input and resonance patterns are generated based on interference between synthetic noise and the signal pattern.
- resonances within the resonance patterns associated with events of interest are detected.
- the signal pattern is preconditioned prior to the step of applying synthetic noise to the signal pattern.
- a system for analyzing an arrayed signal pattern generated by an arrayed platform device to identify events of interest within the signal pattern.
- the system includes an expressor function input unit for inputting previously generated expressor functions capable of extracting spatial, spatio-temporal or spectral invariants of events of interest associated with the particular arrayed platform device being used.
- the system also includes a preconditioning unit for preconditioning the arrayed signal pattern.
- An active interferometric coupler operates to convolve the preconditioned arrayed signal pattern and the expressor functions so as to interferometrically enhance portions of the preconditioned pattern associated with events of interest, if any, present within preconditioned pattern.
- the system also includes a resonance marker detector for identifying the occurrence of events of interest within the convolved signal pattern.
- the system for analyzing an arrayed signal pattern includes an expressor function input unit for inputting previously-generated expressor functions and an adaptive interferometric coupler for preconditioning the arrayed signal pattern so as to convert the arrayed signal pattern to a spectral domain while simultaneously convolving the signal pattern and the expressor functions so as to interferometrically enhance portions of the signal pattern to identify events of interest, if any, within the signal pattern.
- the system includes a preconditioner for preconditioning the arrayed signal pattern and an expressor function adaptation unit for generating preconditioned expressor functions based on input canonical expressor functions (i.e. general expressor function not associated with the particular platform being used) and based on a preconditioned signal pattern while interferometrically enhancing portions the preconditioned signal pattern so as to identify events of interest, if any, within the signal pattern.
- a preconditioner for preconditioning the arrayed signal pattern
- an expressor function adaptation unit for generating preconditioned expressor functions based on input canonical expressor functions (i.e. general expressor function not associated with the particular platform being used) and based on a preconditioned signal pattern while interferometrically enhancing portions the preconditioned signal pattern so as to identify events of interest, if any, within the signal pattern.
- the system includes an expressor function input unit for inputting previously-generated expressor functions derived for the platform being used, a preconditioner for preconditioning the arrayed signal pattern and a iterative convolution coupler.
- the system also includes an adaptive controller unit for controlling the coupler to iteratively and selectively convolve the expressor functions to a preconditioned signal pattern until a predetermined degree of convergence is achieved so as to identify events of interest within the enhanced signal pattern.
- FIG. 1 is a block diagram providing an overview of system components of the first exemplary embodiment of the invention (the open loop interferometric system), which provides separate expressor function generation unit, preconditioner, active interferometric coupler and resonant marker detector components;
- the open loop interferometric system which provides separate expressor function generation unit, preconditioner, active interferometric coupler and resonant marker detector components;
- FIG. 2 is a flowchart providing an overview of method steps for the first exemplary embodiment of the invention
- FIG. 3 is a block diagram providing an overview of system components of the second exemplary embodiment of the invention (the goal-directed interferometric system), which provides an adaptive interferometric coupler;
- FIG. 4 is a flowchart providing an overview of method steps for the second exemplary embodiment of the invention.
- FIG. 5 is a block diagram providing an overview of system components of the third exemplary embodiment of the invention (the self-organizing interferometric system), which provides a expressor function adaptation unit;
- FIG. 6 is a flowchart providing an overview of method steps for the third exemplary embodiment of the invention.
- FIG. 7 is a block diagram providing an overview of system components of the fourth exemplary embodiment of the invention (the iterative interferometric system), which provides a adaptive controller for iteratively controlling a separate iterative interferometric coupler;
- FIG. 8 is a flowchart providing an overview of method steps for the fourth exemplary embodiment of the invention.
- FIG. 9 is a graph illustrating combinations of arrayed data signal patterns that may be processed by any of the embodiments of the invention.
- FIG. 10 is a graph illustrating combinations of preconditioning functions and expressor functions that may be used in connection with any of the embodiments of the invention.
- FIG. 11 is a graph illustrating an exemplary platform array detector and active interferometric analysis system that may exploit the invention
- FIG. 12 is a graph illustrating various platform array detectors that may be used in connection with any of the embodiments of the invention.
- FIG. 13 is a graph illustrating the structure of an exemplary microarray platform that may be used to generate arrayed data for use by any of the embodiments of the invention.
- FIG. 14 is a flowchart summarizing the overall method of the invention, at least in accordance with an exemplary embodiment thereof.
- FIG. 1 illustrates, at a high-level, exemplary system components for the first exemplary implementation of the invention. Components employed only during a design phase are shown on the left; components employed during the actual analysis of an input signal are shown on the right.
- expressor function generation unit 100 generates one or more designer expressor functions based upon the specific characteristics of a platform array detector 102 to be used to detect signal patterns to be processed and also based on calibration data derived from platform array 102.
- the calibration data is generated based on known events of interest. That is, input signals or physical samples containing known events of interest are applied to the detector to generate the calibration data.
- the platform array detector is a genomic biochip/microarray and the calibration data is derived from a biological sample containing known gene expressions.
- the characteristics of the platform array detector that are input by the expressor function generation unit include both the layout of the array as well as its coherent noise or background signal characteristics.
- Components employed during the actual operational phase of the system include the aforementioned platform array detector 102 that generates a detected signal pattern and a preconditioner unit 104 that preconditions the detected signal pattern so as to convert the signal pattern to a spectral domain (for an example of a technique for transforming the signal pattern to a spectral domain, please see U.S. Pat. App. Ser. No. 10/430,664 entitled “Method and System for Characterizing Microarray Output Data” filed on May 5, 2003 which is hereby fully incorporated by reference) in which spectral harmonics parameterize events of interest to a predetermined dynamical system (for which the applicable characteristics are input into the preconditioner unit.
- the operational phase components also include an active interferometric coupler 106 that convolves the preconditioned signal pattern and the previously generated expressor functions.
- the convolution is performed so as to interferometrically enhance portions of the preconditioned signal pattern associated with events of interest, if any, that are present within the preconditioned signal pattern.
- the convolution is performed via reverberant convergence so as to emulate active interferometric enhancement.
- the system also includes a resonant marker detector 108 that processes the convolved signal pattern so as to identify particular events of interest, if any, appearing within the convolved signal pattern.
- each component may be a computer product operative to perform the functions described.
- each component is a software module operating within a single generally programmable computer.
- the software modules are implemented using separate microprocessors or application specific integrated chips (ASICs).
- ASICs application specific integrated chips
- some of the components are implanted in software whereas others are completely hard- wired.
- step 110 a signal pattern derived from a particular platform array detector is input and, at step 112, expressor functions designed to extract spectral invariants for events of interest detectable by the particular platform array are input.
- the input signal pattern is preconditioned to convert the signal pattern to the spectral domain in which spectral harmonics parameterize events of interest to the aforementioned predetermined dynamical system.
- the preconditioned signal pattern and the expressor functions are then convolved using reverberant convergence so as to emulate an active interferometric enhancement of portions of the signal pattern associated with events of interest thus yielding a convolved signal pattern (also referred to herein as an enhanced signal pattern.
- the convolved signal pattern is examined to identify events of interest therein. It should be noted that a list of specific events of interest need not be input at step
- the technique operates to detect possible events of interest present in the input pattern based solely on the input pattern and the previously-generated expressor functions.
- the technique of FIG. 2 is not merely a diagnostic technique that seeks to determine whether particular pre-determined events of interest are present in the input signal pattern. This will be more readily apparent from the detailed examples which follow.
- expressor function generation unit 100 of FIG. 1 inputs platform array detector characteristics represented mathematically as: ⁇ [ ⁇ (N, M,T,A)]
- ⁇ (N,M,T,A) denotes a preconditioned extraction core corresponding to one or more events of interest.
- the indices N (0:n), M(0:m), T(l:k), ⁇ (0:p) respectively refer to the spatial, temporal and spectral dimensions of the physical or virtual arrayed platform described in FIG 11. Details of preconditioning the extraction core corresponding to event of interest are given in U.S. Pat. App. Ser. No. 10/430,664 entitled "Method and System for Characterizing Microarray Output Data” filed on May 5, 2003.
- the Expressor Function Generation unit also inputs calibrated data (j exemplars) represented mathematically as:
- QEF Quantum Expressor Functions
- Hamiltonians are utilized as they provide a mathematical basis for determining the dynamical behavior of a system at each coordinate and at each momentum. Hamiltonian systems are further characterized by kinetic and potential energy. A key property of Hamiltonian systems is that they are conservative and have no dissipation of energy during relaxation. The use of a Spin Boson Hamiltonian, which is a particular type of Hamiltonian systems, permits the exploitation of quantum stochastic resonance phenomenonology. Hamilton's Equations and Hamiltonian functions pertain to well understood concepts in dynamical systems theory and dynamical equations of motion.
- SNR signal to noise ratio
- the power spectrum S( ⁇ ) may be represented by the Fourier
- a custom Hamiltonian couples the above system to an ensemble of harmonic oscillators is given by:
- ⁇ denotes the asymmetric energy
- ⁇ is the tunneling
- An important aspect is to couple the transformed and preconditioned discrete microarray output to a mathematical model for a quantum-mechanical dynamical system with specific properties.
- Specific exemplary parameters for use in calculating the Hamiltonian are those proposed by A.J. Legett et al., Reviews of Modern Physics, 59, 1, 1987 and A.O. Caldiera and A. J.Legett Annals of Physics, 149, 374, 1983.
- the parameters are important for an offline simulation of this Spin Boson system on a digital computer.
- the empirical observables are then collected and used to estimate and compute spectral properties, which are actually used by the method.
- Power Spectral Density is a well understood concept is signal processing and basis for many computational algorithms. For a spectral signature, partitioned into bands of interest, power spectral density is formally as the set of measurements of average power in each spectral band, normalized by bandwidths. Formal definition can be found in "Cyclostationarity in Communications and Signal Processing", Ed. William Gardner, 1994, IEEE Press, NY, pages 46-47.
- the analytic for the external force is given by
- the parameters ⁇ , ⁇ 0 are predetermined and are design specific. Typically, values of
- the QEF is designed by matching the power spectral density (PSD) amplitude of preconditioned, extracted events of interest to that of the Spin
- Boson system described above so that stochastic and deterministic time scales match and so that the time scales couple back to noise statistics and degree of asymmetry.
- the technique employs a fully automated iterative conjugate gradient relaxation method for spectral matching between asymmetric base system and the transformed, preconditioned extraction core.
- the determination of the QEF depends on the specifics of bioelectronics substrates used for actual analysis.
- the method is however generalizable to all arrayed embodiments.
- the method is highly scalable to array dimensions (as the offline design trade-space time does not matter to computational complexity). As the system is an overdetermined coupled system, convergence criteria and stability of relaxation method directly relates to downstream resonance effectiveness.
- the order function (OF) of ground truth is generated as follows.
- the order function (OF) is for ground truth wherein ground truth represents a state wherein a positive signal to noise ratio (SNR) is expected for pixel intensities of selected exemplars used for developing an QEF.
- SNR signal to noise ratio
- a classical Order Function (OF) is used with the following form:
- n( ⁇ ) - ⁇ h k Zmk ⁇ kz k e - ⁇ k e
- Diado integral 1 h k ⁇ J d ⁇ h( ⁇ )e ⁇ 2ltik ⁇ is referred to as the Diado integral.
- QEF are designed from a canonical dynamical system drawn from a family of exponential systems.
- Daido integral is a specific term used to construct an Order Function, which is then used to construct the QEF.
- the OF is calculated using an order match which implies that the variance of the density function for a specific exponential family approaches 0 (within 0.000001 - 0.0001).
- the free energy for the density function is given by
- ⁇ a and ⁇ b represent state vectors.
- X a b represents the random observable in symmetric bilinear form, and ⁇ denotes the characteristic function, calculated using extractions from exemplars with reference objects of interest known (see D.C. Brody and L.P. Hughston, "Geometry of Quantum Statistical Inference", Physics Review Letters, 77(14), pp. 2851-2855, 1996). Determination of Entrainment States
- NS-MRF refers to the nonstationary Markov random field representation.
- the OF of ground truth is modulated to yield the QEF as follows. Under controlled calibration, as stated above, maximal SNR enhancement (optimal resonance) is achieved when OF yields a single peak. It is an important design point for matching PSD of coupling Spin Boson system to the synthetic QEF.
- the specific form of the QEF to be used is the parameters of a generic OF shown above. So the exemplary method exploits two connotations of OF: (a) parametric form for the QEF (that is closer to the classical form) and (b) as exponential attractor for a dissipative system. The two OF's are then recoupled and convolved with spectrally transformed exemplar used for constructing the QEF. The resulting QEF is given by:
- the QEF is represented digitally using a matrix or array having the same number of elements in the spectrally transformed and preconditioned, extraction core to be analyzed.
- this pertains to the preprocessing done to the post- hybridization microarray intensity output to get it into a form where it can be convolved with the Quantum Expressor Function.
- a major limiting restriction in QSR that is avoided by the exemplary method pertains to matching the stochastic and deterministic time scales in "domain system" and the external coupling asymmetric dynamical system, since this has limited applicability to continuous data.
- the signal pattern output by platform array detector 102 is represented mathematically as:
- the Preconditioner unit 104 uses the input signal pattern to perform the mathematical calculations detailed in U.S. Pat. App. Ser. No. 10/430,664 entitled “Method and System for Characterizing Microarray Output Data” filed on May 5, 2003 which is hereby incorporated by reference, to generate the preconditioned output signal pattern represented mathematically as:
- Active interferometric coupler 106 performs the following mathematical calculations using the preconditioned signal pattern:
- / is defined as a vector containing the preconditioned components from an event of interest
- O 1 -* represents is the QEF after / convolutions.
- D denotes a small constant
- f pc denotes the preconditioned spectral vector corresponding to a known event of interest present in the arrayed pattern being analyzed.
- f c refers to the spectral components of the positive control.
- the convolution iteration can be expressed as:
- f nc refers to the spectral components of a canonical negative control, or preconditioned footprint of an event of interest known to be absent in the arrayed image.
- Parseval Avg. from Pos. Con. PM refers to the parseval number for a canonical event of interest known to be present
- Parseval Avg. from Neg. Con. PM refers to the parseval number for a canonical event of interest known to be absent.
- resonance marker detector 108 performs the following mathematical calculations using the convolved signal pattern to identify the events of interest within the convolved signal pattern. The resonant iteration is terminated when
- N e.g. 10 3 iterations
- the resonance interaction is performed digitally by applying a matrix representative of the resonance equation to a matrix representative of the resonance stimulus in combination with a matrix representative of the preconditioned extraction of event of interest.
- the final result of interferometric analysis is the readout of locations wherein resonance has occurred and as identified by row and column number (i ).
- the reverberation convergence performed by the active interferometric coupler to achieve a resonance state is performed independent of preconditioning.
- the reverberations are an open loop process terminated upon detection of a predetermined condition (i.e. observation of a resonance marker.)
- the active interferometric coupler implements either a destructive or a constructive interference of two dynamical systems.
- FIG. 3 illustrates exemplary system components for the second exemplary implementation of the invention.
- components employed only during a design phase are shown on the left and components employed during the actual analysis of an input signal are shown on the right.
- Some of the components of the system of FIG. 3 are the same as those shown in FIG. 1 and hence will be not described again in detail.
- an expressor function generation unit 200 generates expressor functions based upon cahbration data and based on the specific characteristics of a platform array detector 202 and during the operational phase, the platform array detector generates a signal pattern for analysis.
- no separate preconditioner unit is provided.
- any separate resonant maker detector provided.
- a single adaptive interferometric coupler 204 operates to simultaneously precondition the detected signal pattern to convert the arrayed signal pattern to a spectral domain while convolving the signal pattern and the expressor functions to interferometrically enhance of portions of the signal pattern and to identify events of interest, if any, within the preconditioned signal pattern.
- the platform array detector which is a physical hardware component, all other components illustrated in FIG. 3 may be implemented in software, hardware, firmware or some combination thereof.
- adaptive interferometric coupler 204 does not merely perform the operations previously described with respect components 104, 106 and 108 of FIG. 1. Rather, adaptive interferometric coupler 204 performs a different set of operations. Briefly, with the technique of FIG. 3, coupling intelligence is merged with preconditioning and the expressor functions are pre-coupled to the preconditioned functions, as will be apparent from the detailed example below. Convergent reverberation is performed simultaneously with preconditioning, which is now a closed loop process. Hence, with the technique of FIG. 3, preconditioning does not merely transform spatial arrayed data to spectral data but also transforms the data to a pre-determined state of a predetermined dynamical system.
- a signal pattern derived from a particular platform array detector is input and, at step 208, expressor functions designed to extract spectral invariants for events of interest detectable by the particular platform array detector are input.
- the input signal pattern is preconditioned to convert the signal pattern to the spectral domain while convolving the signal with the expressor functions so as to emulate an active interferometric enhancement of portions of the signal pattern so as to identify events of interest therein.
- the technique described above a list of specific events of interest need not be input during the operational phase. Rather, the technique operates to detect possible events of interest present in the input pattern based solely on the input pattern and the previously-generated expressor functions.
- FIG. 5 illustrates exemplary system components for the third exemplary implementation of the invention. Unlike the systems thus far described, no separate design phase is required. Rather, all components operate during the actual analysis of an input signal. Again, some components are the same as those described above and hence will be not described again in detail.
- a platform array detector 302 generates a signal pattern for analysis.
- the signal pattern is preconditioned by a preconditioner unit 304, which, as before, operates to convert the signal pattern to a spectral domain in which spectral harmonics parameterize events of interest to a predetermined dynamical system.
- the preconditioned signal pattern as well as characteristics of the platform array detector, calibration data and a set of canonical expressor functions are then all input by an expressor function adaptation unit 306.
- the canonical expressor functions are generalized expressor functions that have not been tailored to the particular platform being used. No separate design phase expressor function generation unit is provided. Nor is a separate resonant maker detector provided. Rather, expressor function adaptation unit 306 operates to simultaneously generate preconditioned expressor functions based on the preconditioned signal pattern and based on the canonical expressor functions while convolving the preconditioned signal pattern and the preconditioned expressor functions to interferometrically enhance portions of the signal pattern to identify events of interest. As before, with the exception of the platform array detector, which is a physical hardware component, all other components illustrated in FIG. 5 may be implemented in software, hardware, firmware or some combination thereof.
- expressor function adaptation unit 306 does not merely perform the operations previously described with respect to components 100, 106 and 108 of FIG. 1. Rather, with the technique of the self-organizing interferometric system, the coupling intelligence and the reverberation dynamics are pre-built into the expressor functions and so the expressor functions are not merely a representation of a spectral marker as with the technique of FIGS. 1 and 2 and with the technique of FIGS. 3 and 4. This will be more readily apparent from the detailed example below. Also, although the preconditioning unit may be the same as in the technique of FIGS. 1 and 2, the preconditioned array now serves as a preconditioner to the expressor function to thereby produce a properly preconditioned expressor, which itself corresponds to the detection of resonant marker.
- a signal pattern derived from a particular platform array detector platform is input and, at step 310, the signal patterns are preconditioned to convert the signal pattern to the spectral domain in which spectral harmonics parameterize events of interest to the aforementioned predetermined dynamical system.
- preconditioned expressor functions are then generated based on the preconditioned signal pattern and on the characteristics of the platform array detector and based on the events of interest. This is performed using a closed loop process employing reverberant convergence so as to emulate active interferometric enhancement of portions of the original signal pattern associated with the events of interest.
- generation of the preconditioned expressor functions allows immediate detection of the events of interest without further processing. As before, no list of specific events of interest need be input during the operational phase.
- FIG. 7 illustrates exemplary system components for the fourth exemplary implementation of the invention.
- an expressor function generation unit 400 generates expressor functions based upon calibration data and based on the specific characteristics of a platform array detector 402 and, during the operational phase, the platform array detector generates a signal pattern for analysis.
- the signal pattern is preconditioned by a preconditioner unit 404, which, as before, operates to convert the signal pattern to a spectral domain in which spectral harmonics parameterize events of interest to a predetermined dynamical system.
- An iterative interferometric coupler 406 convolves the preconditioned signal pattern and the previously-generated expressor functions so as to interferometrically enhance portions of the preconditioned signal pattern associated with events of interest that are present within the preconditioned signal pattern.
- the iterative interferometric coupler operates under the control of an adaptive controller 408 to sequentially produce an iteratively convolved signal pattern.
- the adaptive controller operates to control the interferometric coupler to iteratively and selectively convolve expressor functions to the preconditioned signal pattern until a predetermined degree of convergence is achieved so as to allow identification of events of interest within the enhanced signal pattern.
- all other components illustrated in FIG. 7 may be implemented in software, hardware, firmware or some combination thereof.
- a signal pattern derived from a particular platform array detector is input and, at step 412, expressor functions designed to extract spectral invariants for events of interest detectable by the particular platform array detector are input. Then, at step 414, the input signal pattern is preconditioned to convert the signal pattern to the spectral domain spectral domain in which spectral harmonics parameterize events of interest to the aforementioned predetermined dynamical system.
- the preconditioned signal pattern and the expressor functions are then iteratively convolved under the control of the controller to achieve a reverberant convergence so as to emulate active interferometric enhancement of the signal pattern associated and to allow detection of events of interest.
- a reverberant convergence so as to emulate active interferometric enhancement of the signal pattern associated and to allow detection of events of interest.
- no list of specific events of interest need be input during the operational phase.
- the equations provided above in connection with the open loop interferometric system may be accordingly adjusted such that multiple QEFs may be employed to achieve a desired resonance level and the most relevant QEF is chosen after each convolution if a desired resonance is not achieved.
- FIG. 9 lists specific examples of types of arrayed data that may be processed. The examples shown include spatial 2-D data, spatial 1-D data, point emitter data, temporal point emitter data, spatio-temporal point emitter data and spectral point emitter data. The different types of arrayed data may be separately processed or, as shown, combined to yield virtual arrays for processing.
- the symbol ⁇ generally represents a virtual array combiner operation, wherein individual spatial values are collected together into a single function or data array.
- FIG. 9 illustrates: (a) spatial data from a 2-D spatial array 500 represented in the form of a 2-D function h(x,y);
- arrayed data ((a) - (f)) may be further combined into a larger virtual array (g) by virtual array combiner 524.
- any of the types of arrayed data ((a) - (g)) may be further filtered via linear or non-linear filters 526, to yield filtered array functions (h).
- the final collection of data is represented in the figure as arrayed data 528.
- arrayed data 528 As can be appreciated, a wide variety of arrayed data may be input, combined and manipulated to yield individual multi-dimensional input functions for co ⁇
- FIG. 10 lists specific examples of types of expressor functions and types of preconditioning functions.
- the types of expressor functions are identified in terms of the type of noise associated with the expressor function.
- Examples of expressor functions include quantum expressor functions (QEFs), classical expressor functions (CEFs), classical statistical noise expressor functions (SCNEFs), pseudorandom noise expressor functions (PRNEFs), systemic bias expressor functions and expressor functions based on porycyclostationary noise (PCS), cyclostationary noise (CS), stationary noise (SN), non-stationary noise (NS).
- preconditioner functions examples include 1-D Fourier functions, 2-D Fourier functions, N-D Fourier functions, a time division multiplexing (TDM) functions, wavelength division multiplexing (WDM) functions, frequency division multiplexing (FD) functions, radial basis functions, wavelet kernel functions, fractal functions and soliton functions.
- TDM time division multiplexing
- WDM wavelength division multiplexing
- FD frequency division multiplexing
- radial basis functions wavelet kernel functions
- fractal functions soliton functions.
- any combination of expressor function type and preconditioner function type illustrated in the figure may be employed.
- FIG. 11 illustrates an exemplary system 700 incorporating a biological platform array 702 that provides arrayed data to a software-based active interferometric analysis system 704.
- the platform array includes a solid support 706 upon which various probes 708 are attached via attachment chemistry portion 710.
- the probes include one or more of cDNA, mRNA or oligoiiucleoti.de probes or proteins, peptides or oligopeptides.
- a detector 712 detects a resulting pattern, which is then forwarded to the analysis system in the form of arrayed data.
- the analysis system exploits one of the general techniques described above to identify events of interest present in the biological sample. In this example, the events of interest are typically output in the form of one or more "gene calls".
- platform array detectors may be used to generate the arrayed data. Examples of platforms detectors are listed in FIG. 12. The examples include optical platforms, biomolecular platforms, ionic platforms, biomechanical platforms, optoelectronic platforms, radio frequency platforms, and other electronic microdevices.
- Exemplary biomolecular spatial array platforms include: hybridized spotted cDNA microarrays, synthesized oligonucleotide arrays, spotted oligonucleotide arrays, peptide nucleotide assays, single nucleotide polymorphism (SNP) arrays, carbohydrate arrays, glycoprotein arrays,, protein arrays, proteomic arrays, tissue arrays, antibody arrays, antigen arrays, bioassays, sequencing microarrays, sequencing by hybridization (SBH) microarrays, siRNA duplexes, RNAi arrays glass-based arrays, nylon membrane arrays, thin film arrays, polymer-substrate arrays, capillary electrophoresis arrays, genospecfral arrays, electronic arrays, bead arrays, quantum dot arrays, glycan arrays, spotted wells, and spotted well plates.
- SNP single nucleotide polymorphism
- any of the foregoing platforms may be implemented as a microarray.
- Components of a typical- microarray are shown in FIG. 13, which include a substrate, a probe portion, a label portion, a bioassay portion, and a detector.
- Exemplary substrates include glass, nylon, thin film, and polymer.
- Exemplary probes CDNA probes, spotted oligonucleotide probes and synthetic oligonucleotide probes.
- Exemplary labels 1- dye labels and 2-dye labels.
- Exemplary bioassay portions include prehybridization buffers, hybridization buffers and wash buffers.
- Exemplary detectors include laser scanners, confocal microscopy detectors, charge coupled devices (CCDs) and electronic readouts.
- the substrate has a certain layout represented by feature size, various controls (positive, negative dye and alignment), and probe density.
- FIG. 14 provides a summary of an exemplary technique of the invention. Briefly, the figure illustrates method for actively analyzing a signal pattern representative of arrayed data to identify events of interest therein.
- a signal pattern representative of arrayed data is input and, at step 702, resonance patterns are generated based on interference between synthetic noise and the signal pattern, in accordance with techniques already described. Thereafter, at step 704, resonances are detected within the resonance patterns associated with events of interest.
- the synthetic noise used at step 702 is preferably in the form of a quantum expressor function, a classical expressor function, classical statistical noise, pseudorandom noise, a systemic bias or some combination thereof.
- the arrayed data may be, as already explained, in the form of a spatial 2-D array, a spatial 1-D array, an N-D array, a temporal point emitter array, a spatio-temporal point emitter array, spectral point emitter array or a virtual array constructed by combining spatial separate point emitters.
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AU2003247960A AU2003247960A1 (en) | 2002-07-09 | 2003-07-09 | Active interferometric signal analysis in software |
EP03816978A EP1532568A2 (en) | 2002-07-09 | 2003-07-09 | Active interferometric signal analysis in software |
CN038191547A CN1705953B (en) | 2002-07-09 | 2003-07-09 | Active interferometric signal analysis in software |
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US44025303P | 2003-01-15 | 2003-01-15 | |
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US8484000B2 (en) | 2004-09-02 | 2013-07-09 | Vialogy Llc | Detecting events of interest using quantum resonance interferometry |
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CN106844824B (en) * | 2016-11-22 | 2020-05-12 | 电子科技大学 | Radio frequency crystal oscillator residual life estimation method based on accelerated vibration condition |
CN106908431B (en) * | 2017-04-05 | 2019-10-11 | 常州工学院 | The method for determining electrophoresis detection optimized parameter based on Capillary Electrophoresis noise analysis |
CN114001816B (en) * | 2021-12-30 | 2022-03-08 | 成都航空职业技术学院 | Acoustic imager audio acquisition system based on MPSOC |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5784162A (en) * | 1993-08-18 | 1998-07-21 | Applied Spectral Imaging Ltd. | Spectral bio-imaging methods for biological research, medical diagnostics and therapy |
US6136541A (en) * | 1999-02-22 | 2000-10-24 | Vialogy Corporation | Method and apparatus for analyzing hybridized biochip patterns using resonance interactions employing quantum expressor functions |
US6142681A (en) * | 1999-02-22 | 2000-11-07 | Vialogy Corporation | Method and apparatus for interpreting hybridized bioelectronic DNA microarray patterns using self-scaling convergent reverberant dynamics |
-
2003
- 2003-07-09 WO PCT/US2003/021525 patent/WO2004102456A2/en not_active Application Discontinuation
- 2003-07-09 CN CN2011101031681A patent/CN102184321A/en active Pending
- 2003-07-09 AU AU2003247960A patent/AU2003247960A1/en not_active Abandoned
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---|---|---|---|---|
US5784162A (en) * | 1993-08-18 | 1998-07-21 | Applied Spectral Imaging Ltd. | Spectral bio-imaging methods for biological research, medical diagnostics and therapy |
US6136541A (en) * | 1999-02-22 | 2000-10-24 | Vialogy Corporation | Method and apparatus for analyzing hybridized biochip patterns using resonance interactions employing quantum expressor functions |
US6142681A (en) * | 1999-02-22 | 2000-11-07 | Vialogy Corporation | Method and apparatus for interpreting hybridized bioelectronic DNA microarray patterns using self-scaling convergent reverberant dynamics |
Cited By (1)
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US8484000B2 (en) | 2004-09-02 | 2013-07-09 | Vialogy Llc | Detecting events of interest using quantum resonance interferometry |
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WO2004102456A3 (en) | 2005-01-27 |
AU2003247960A1 (en) | 2004-12-03 |
EP1532568A2 (en) | 2005-05-25 |
CN1705953A (en) | 2005-12-07 |
CN102184321A (en) | 2011-09-14 |
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