USA As Represented by Secretary of the Navy

International Business Machines Corporation

Chung Tien Nguyen

Francis J O'Brien, Jr

Bruce J Bates

Sherry E Hammel

Steven C Nardone

Michael Wayne Brown

Kai F Gong

Chidambar Ganesh

Margaret Marafino

A solution for managing information using an identification tag is provided. For example, information relating to an item and/or one or more transfers of the item can be managed using an identification tag associated with the item. The identification tag can store and transmit an item identifier, such as an electronic product code (EPC), corresponding to the item, which can be obtained and stored in a data store. Access information for retrieving the item identifier and identification information for a party to the transfer can be used to generate a custom identifier that can be provided for storage on the identification tag in place of the item identifier. The custom identifier can subsequently be used to acquire information on the item and/or the transfer event.

Information management using a custom identifier stored on an identification tag

A method is provided for automatically characterizing data sets containing data points described by d-dimensional vectors obtained by measurements, such as with sonar arrays, as either random or non-random. The data points are located by the d-dimensional vectors in a d-dimensional Euclidean space which may comprise any number d of dimensions and may comprise more than three dimensions. Large or small sets of data may be analyzed. A virtual volume is determined which contains data points from the maximum and minimums of the d-dimensional vectors. The virtual volume is then partitioned. The probability of each partition containing at least one data point for a random distribution is compared to a measurement of the number of partitions actually containing at least one data point whereby the data set is characterized as either random or non-random.

Method for classifying a random process for data sets in arbitrary dimensions

Disclosed is a system, method, and program for generating a compiler to map a code set to object code capable of being executed on an operating system platform. At least one neural network is trained to convert the code set to object code. The at least one trained neural network can then be used to convert the code set to the object code.

Method, system, and program for converting code to executable code using neural networks implemented in a software program

Provided is a compiler to map application program code to object code capable of being executed on an operating system platform. A first neural network module is trained to generate characteristic output based on input information describing attributes of the application program. A second neural network module is trained to receive as input the application program code and the characteristic output and, in response, generate object code. The first and second neural network modules are used to convert the application program code to object code.

Method, system, and program for converting application program code to executable code using neural networks based on characteristics of the inputs

A signal processing system provides and processes a digital signal, converted from to an analog signal, which includes a noise component and possibly also an information component comprising small samples representing four mutually orthogonal items of measurement information representable as a sample point in a symbolic Cartesian four-dimensional spatial reference system. An information processing sub-system receives said digital signal and processes it to extract the information component. A noise likelihood determination sub-system receives the digital signal and generates a random noise assessment of whether or not the digital signal comprises solely random noise, and if not, generates an assessment of degree-of-randomness. The information processing system is illustrated as combat control equipment for undersea warfare, which utilizes a sonar signal produced by a towed linear transducer array, and whose mode operation employs four mutually orthogonal items of measurement information.

System and method for stochastic characterization of sparse, four-dimensional, underwater-sound signals

Disclosed is a an integrated circuit method and system for generating a compiler to map a code set to object code capable of being executed on an operating system platform. The integrated circuit is encoded with logic including at least one neural network. The at least one neural network in the integrated circuit is trained to convert the code set to object code. The at least one trained neural network is then used to convert the code set to object code.

Method and system for converting code to executable code using neural networks implemented in a very large scale integration (VLSI) integrated circuit

A method and apparatus are provided for automatically characterizing the spatial arrangement among the data points of a three-dimensional time series distribution in a data processing system wherein the classification of said time series distribution is required. The method and apparatus utilize grids in Cartesian coordinates to determine (1) the number of cubes in the grids containing at least one input data point of the time series distribution; (2) the expected number of cubes which would contain at least one data point in a random distribution in said grids; and (3) an upper and lower probability of false alarm above and below said expected value utilizing a discrete binomial probability relationship in order to analyze the randomness characteristic of the input time series distribution. A labeling device also is provided to label the time series distribution as either random or nonrandom, and/or random or nonrandom within what probability, prior to its output from the invention to the remainder of the data processing system for further analysis.

System and apparatus for the detection of randomness in three dimensional time series distributions made up of sparse data sets

Enhanced model identification in signal processing using arbitrary exponential functions

A method and apparatus are provided for automatically characterizing the spatial arrangement among the data points of a time series distribution in a data processing system wherein the classification of said time series distribution is required. The method and apparatus utilize a grid in Cartesian coordinates to determine (1) the number of cells in the grid containing at least-one input data point of the time series distribution; (2) the expected number of cells which would contain at least one data point in a random distribution in said grid; and (3) an upper and lower probability of false alarm above and below said expected value utilizing a discrete binomial probability relationship in order to analyze the randomness characteristic of the input time series distribution. A labeling device also is provided to label the time series distribution as either random or nonrandom, and/or random or nonrandom.

System and apparatus for the detection of randomness in time series distributions made up of sparse data sets

The present invention relates to a system and a method for signal classification. The system comprises a sensor array for receiving a series of input signals such as acoustic signals, pixel-based image signal (such as from infrared images detectors), light signals, temperature signals, etc., a wavelet transform module for transforming the input signals so that characteristics of the signals are represented in the form of wavelet transform coefficients and an array of hybrid neural networks for classifying the signals into multiple distinct categories and generating a classification output signal. The hybrid neural networks each comprise a location neural network for processing data embedded in the frequency versus time location segment of the output of the transform module, a magnitude neural network for processing magnitude information embedded in the magnitude segment of the output of the transform module, and a classification neural network for processing the outputs from the location and magnitude neural networks. A method for processing the signal using the system of the present invention is also described.

Wavelet-based hybrid neurosystem for classifying a signal or an image

A system and a method for recognizing patterns comprises a first stage for xtracting features from inputted patterns and for providing topological representations of the characteristics of the inputted patterns and a second stage for classifying and recognizing the inputted patterns. The first stage comprises two one-layer neural networks and the second stage comprises a feedforward two-layer neural network. Supplying signals representative of a set of inputted patterns to the input layers of the first and second neural networks, training the first and second neural networks using a competitive learning algorithm, and generating topological representations of the input patterns using the first and second neural networks The method further comprises providing a third neural network for classifying and recognizing the inputted patterns and training the third neural network with a back-propagation algorithm so that the third neural network recognizes at least one interested pattern.

Hybrid neural network for pattern recognition

A signal processing system provides and processes a digital signal, generd in response to an analog signal, which includes a noise component and possibly also an information component representing four mutually orthogonal items of measurement information representable as a sample point in a symbolic four-dimensional hyperspatial reference system. An information processing and decision sub-system receives said digital signal and processes it to extract the information component. A noise likelihood determination sub-system receives the digital signal and generates a random noise assessment of whether or not the digital signal comprises solely random noise, and if not, generates an assessment of degree-of-randomness. The noise likelihood determination system controls whether or not the information processing and decision sub-system is used, in response to one or both of these generated outputs. One prospective practical application of the invention is the performance of a triage function upon signals from sonar receivers aboard naval submarines, to determine suitability of the signal for feeding to a subsequent contact localization and motion analysis (CLMA) stage.

System and method for stochastic characterization of a signal with four

A system and method for signal processing using wavelets are disclosed. The method includes the steps of generating a wavelet decomposition tree modeling the signal. The tree includes a plurality of levels of wavelet coefficients representative of the signal. The method further includes determining the actual number of wavelet coefficients available at each of the plurality of levels; measuring mean energy for each of the plurality of levels; and determining a desired number of wavelet coefficients for each of the plurality of levels based on the mean energy determined in the step of measuring. The mean energy in the step of measuring for each of the plurality of levels is indicative of the desired number of wavelet coefficients selected from each of the plurality of level. The method further includes the step of retaining the desired number of wavelet coefficients and forming a compressed representation of the signal.

Energy-based wavelet system and method for signal compression and

A method for finding a probability density function (PDF) and its statistical moments for an arbitrary exponential function of the form g(x)=.alpha.x.sup.m e.sup.-.beta.x.spsp.n,0&lt;x&lt;.infin., where .alpha., .beta., n&gt;0, m&gt;-1 are real constants in one-dimensional distributions and g(x.sub.1,x.sub.2, . . . ,x.sub.l) in the hyperplane. Non-linear regression analyses are performed on the data distribution and a root-mean-square (RMS) is calculated and recorded for each solution set until convergence. The basis function is reconstructed from the estimates in the final solution set and a PDF is obtained. The moment generating function (MGF), which characterizes any statistical moment of the distribution, is obtained using a novel function derived by the inventors and the mean and variance are obtained in standard fashion. Simple hypotheses about the behavior of such functional forms may be tested statistically once the empirical least squares methods have identified an applicable model derived from actual measurements.

Model identification and characterization of error structures in signal

A chaotic signal processing system receives an input signal provided by a nsor in a chaotic environment and performs a processing operation in connection therewith to provide an output useful in identifying one of a plurality of chaotic processes in the chaotic environment. The chaotic signal processing system comprises an input section, a processing section and a control section. The input section is responsive to input data selection information for providing a digital data stream selectively representative of the input signal provided by the sensor or a synthetic input representative of a selected chaotic process. The processing section includes a plurality of processing modules each for receiving the digital data stream from the input means and for generating therefrom an output useful in identifying one of a plurality of chaotic processes. The processing section is responsive to processing selection information to select one of the plurality of processing modules to provide the output. The control module generates the input data selection information and the processing selection information in response to inputs provided by an operator.

Growing community of inventors

Austin, TX, United States of America

Chung Tien Nguyen