USA As Represented by Secretary of the Navy

Francis J O'Brien, Jr

Chung Tien Nguyen

Bruce J Bates

Nathan Johnnie

Sherry E Hammel

Joseph W Robicheau

Steven C Nardone

Helene B Anderson

Marcus L Graham

Aimee M Ross

Lynn T Antonelli

Kai F Gong

Susan E Maloney

A computer-implemented method to characterize a signal structure is provided. The method may receive a first set of time-dependent data vectors; select a sample size of data vectors; determine a minimum number of lags to process to characterize the first set of data vectors; calculate a cross-product term for each data vector in the sample size for each lag, a mean value and a variance value based on the sum of the cross-product term for each lag; calculate a circular correlation function for each lag; determine an upper and a lower correlation bound for the circular correlation based on the mean value, the variance value, and a desired false alarm rate; and characterize the signal structure based on the number of calculated circular correlation functions that exceed the upper or lower correlation bounds so as to generate a signal characterization data signal.

System and method of use for non-parametric circular autocorrelation for signal processing

A device is provided with integrated hardware and software components for measuring and monitoring abnormalities on animal and human tissue and other surfaces. The device includes a display panel and a control panel secured to the upper surface of a housing and a plurality of sensor arrays attached to the lower surface on two scanner belts. A processor receives input from the sensor arrays to create data objects which are stored in an image object database. A retrieval component retrieves the image objects and identifies attributes to display image and quantitative values on a the display panel. A hardware processing component runs at least one algorithm to determine the area of a surface abnormality. Another hardware processing component is provided to receive user input to update images and to select a deformation region for area calculation.

Surface deformation image analyzer

An electrolyte detector is provided that includes two micrometers which slide relative to each other along a frame to adjust the electrolyte detector to a size of the battery. A transmitter rod and a receiver rod support an acoustic transmitter and an acoustic receiver, respectively, and are slidably mounted with respect to the micrometers. The micrometers measure the position of the transmitter and receiver for placement on the side of the battery at a desired level of electrolyte. A transmitter director and receiver director concentrates the transmission and receipt of acoustic energy to locate the electrolyte level. An electronic circuit analyzes the received signal to determine whether the signal is transmitted through air or through an electrolyte.

Battery electrolyte level detector apparatus

A signal processing system and method includes a transducer for receiving an analog signal having a random component and possibly an information component. The analog signal is converted into a digital signal having sample points. A nearest-neighbor calculation component calculates the expected average nearest neighbor distance between the sample points, the actual average distance and an error value. These values are corrected for edge effects. A first randomness assessment compares the actual average distance against the expected average distance with the standard error value. A second randomness assessment compares actual repeated values in the digital signal against expected repeated values. An information processing system continues processing the signal if the assessments indicate the possibility of an information component.

System and method for detection of noise in sparse data sets with edge-corrected measurements

A method is provided for characterizing data sets containing data points. The method can characterize the data sets as random or as non-random. In the method, a convex hull envelope is constructed which contains the data points and passes through at least four non-coplanar data points. The convex hull envelope is partitioned into cells. The method classifies the data set as a sized sample. Based on the classification, a predetermined set of tests is selected for operating on the data set.

Method for detecting a random process in a convex hull volume

A method for locating defects in a target includes subjecting the target to an ultrasonic vibration. A first laser beam is then transmitted to the target and a reflection is received. A vibration signal is produced from the reflection that gives the target's response to the ultrasonic vibration. A digital image is produced of the target that includes the region of the first laser beam reflection. The digital images are overlaid with the vibration signal to provide overlaid data. The overlaid data is tested to determine a probability of the overlaid data being non-random. The probability is compared against a threshold to indicate a potential area of concern that may include defects.

Method of laser vibration defect analysis

A device for recovery of physical objects includes a chain having two ends and a retriever attached to one of the ends. The retriever is configured to locate the object to be recovered and to grasp and retrieve that object once located. The retriever includes transducers that detect acoustic frequencies propagating in a fluid medium. Independent transponders, which can be deployed from the retriever; are provided for deployment in an area to be searched. Each transponder emits a predetermined acoustic frequency that is detectable by the transducers. The transducers signal movement of the recovery device for retrieval of the object.

Device for recovery of physical objects

Linkage assembly

A method is provided for automatically characterizing data sets containing data points, which may be produced by measurements such as with sonar arrays, as either random or non-random. The data points for each data are located within a Cartesian space and a polygon envelope is constructed which contains the data points. The polygon is divided into grid cells by constructing a grid over the polygon. A prediction is made as to how many grid cells would be occupied if the data were merely a random process. The prediction takes one of two forms depending on the sample size. For small sample sizes, an exact Poisson probability method is utilized. For large sample sizes an approximation to the exact Poisson probability is utilized. A third test is utilized to test the adequacy of the Poisson based model is adequate to assess the data set as either random or non-random.

Method for detecting a spatial random process using planar convex polygon envelope

A multi-stage method is provided for automatically characterizing data sets containing data points which are each defined by measurements of three variables as either random or non-random. A three-dimensional Cartesian volume which is sized to contain all of a total number N of data points in the data set which is to be characterized. The Cartesian volume is partitioned into equal sized cubes, wherein each cube may or may not contain a data point. A predetermined route is defined that goes through every cube one time and scores each cube as a one or a zero thereby producing a stream of ones and zeros. The number of runs is counted and utilized to provide a Runs Test which predicts if the N data points in any data set are random or nonrandom. Additional tests are used in conjunction with the Runs Test to increase the accuracy of characterization of each data set as random or nonrandom.

Enhanced randomness assessment method for three-dimensions

A method is provided for characterizing sparse data as either signal or noise. In one embodiment, a two-dimensional area which contains a distribution of data points of the sparse data from a selected time period is divided into equal size cells wherein each cell has an expectation of containing at least one data sample. Based on the expected proportion of a plurality cells which will be nonempty in a random distribution, a Poisson distribution or a Binomial distribution is utilized to determine a data sample mean. A probability of a false signal is determined from the number of cells utilized. A probability is also computed from the sample mean and compared to the probability of a false signal to determine whether to characterize the sparse data as signal or noise.

Method for sparse data two-stage stochastic mensuration

A two-step 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. The method and apparatus utilize a Cartesian grid to determine: the number of cells in the grid containing at least one input data point of the time series distribution; the expected number of cells which would contain at least one data point in a random distribution in said grid; and an upper and lower probability of false alarm bracketing the expected value utilizing a discrete binomial probability relationship in order to analyze the randomness of the input. A statistical test of significance of the sparse data is utilized to determine the existence of noise and signal. The probability of distinguishing noise from signal is increased by comparing the parts of the method.

Enhanced system for detection of randomness in sparse time series distributions

The method includes reading a data set including a sparse number of data points and applying multiple tests wherein the results are evaluated by a decision module to determine whether to classify the data as random or nonrandom. In one preferred embodiment, if any one test determines the data is nonrandom, then the data is labeled nonrandom. The data is labeled and stored prior to beginning the method once again for the next set of data.

Multi-stage planar stochastic mensuration

A two-stage method is provided for automatically characterizing the spatial arrangement among data points of a three-dimensional time series distribution in a data processing system wherein the classification of this time series distribution is required. The invention utilizes two-stage method Cartesian grids 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 statistically determined random distribution in these grids; and (3) an upper and lower probability of false alarm above and below this expected value utilizing a second discrete probability relationship in order to analyze the randomness characteristic of the input time series distribution.

Detection of randomness in sparse data set of three dimensional time series distributions

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.

Growing community of inventors

Newport, RI, United States of America

Francis J O'Brien, Jr