Plain Sight Systems, Inc.

Yale University

Other

Thetaray Ltd.

Ronald R Coifman

William G Fateley

Frank Geshwind

Andreas C Coppi

Richard A Deverse

Amir Averbuch

Gil David

Hitten P Zaveri

Mladen V Wickerhauser

Frederick J Warner

Yves Meyer

Kevin N Sheth

Gustave L Davis

Richard Andrew DeVerse

Alon Schclar

Amit Bermanis

Ali Haddad

Yosi Keller

Ronen Talmon

Mauro M Maggioni

A real-time automated method to diagnose and/or detect stroke and engage the patient, care-takers, emergency medical system and stroke neurologists in the management of this condition includes the steps of continuously measuring natural limb activity, conveying the measurements to a cloud based real-time data processing system, identifying patient specific alert conditions, and determining solutions for acting upon needs of the patient. The system by which the method is implemented includes at least one body worn sensor continuously measuring natural limb activity and a patient worn data transmission device conveying the measurements to a cloud based real-time data processing system that identities patient specific alert conditions and determines solutions for acting upon needs of the patient.

System and method for diagnosing and notification regarding the onset of a stroke

A real-time automated method to diagnose and/or detect stroke and engage the patient, care-takers, emergency medical system and stroke neurologists in the management of this condition includes the steps of continuously measuring natural limb activity, conveying the measurements to a cloud based real-time data processing system, identifying patient specific alert conditions, and determining solutions for acting upon needs of the patient. The system by which the method is implemented includes at least one body worn sensor continuously measuring natural limb activity and a patient worn data transmission device conveying the measurements to a cloud based real-time data processing system that identifies patient specific alert conditions and determines solutions for acting upon needs of the patient.

Detection of abnormalities in multi-dimensional data is performed by processing the multi-dimensional data to obtain a reduced dimension embedding matrix, using the reduced dimension embedding matrix to form a lower dimension (of at least 2D) embedded space, applying an out-of-sample extension procedure in the embedded space to compute coordinates of a newly arrived data point and using the computed coordinates of the newly arrived data point and Euclidean distances to determine whether the newly arrived data point is normal or abnormal.

Anomaly detection in dynamically evolving data and systems

The present invention relates to a system for suppressing transient interference from a signal. The system includes a modeling system, wherein the modeling system constructs a model of transient interference from a first signal, and a filtering system, wherein the filtering system suppresses transient interference from a second signal by applying the model to the second signal.

System and method for anomaly detection and extraction

A method for classification of a newly arrived multidimensional data point (MDP) in a dynamic data uses multi-scale extension (MSE). The multi-scale out-of-sample extension (OOSE) uses a coarse-to-fine hierarchy of the multi-scale decomposition of a Gaussian kernel that established the distances between MDPs in a training set to find the coordinates of newly arrived MDPs in an embedded space. A well-conditioned basis is first generated in a source matrix of MDPs. A single-scale out-of-sample extension (OOSE) is applied to the newly arrived MDP on the well-conditioned basis to provide coordinates of an approximate location of the newly arrived MDP in an embedded space. A multi-scale OOSE is then applied to the newly arrived MDP to provide improved coordinates of the newly arrived MDP location in the embedded space.

Method for classification of newly arrived multidimensional data points in dynamic big data sets

A method for hierarchical clustering comprises multi-level partitioning of data points into non-overlapping LDFs and formation of sets of such LDFs, wherein original data points may reside in different LDFs. The sets of LDFs are then hierarchically processed into sets of super-LDFs, which are further processed up the hierarchy. In some embodiments, the further processing ends with the construction of a single super-LDF at the root of the hierarchy. The partitioning of the data points and the hierarchical processing of the sets of LDFs and super-LDFs uses respective local affinity matrices.

Clustering and classification via localized diffusion folders

A hyper-spectral imaging system comprises imaging foreoptics () to focus on a scene or object of interest () and transfer the image of said scene or object () onto the focal plane of a spatial light modulator (), a spatial light modulator () placed at a focal plane of said imaging foreoptics (), an imaging dispersion device () disposed to receive an output image of the spatial light modulator (), and an image collecting device disposed to receive the output of the imaging dispersion device ().

Hyper-spectral imaging methods and devices

An apparatus and method for multispectral imaging comprising an array of filters in a mosaic pattern, a sensor array and an acquisition and processing module. The sensor array being disposed to receive an image that has been filtered by the array of filters. The acquisition and processing module processes the output of the sensor array (or mosaic acquired data) to provide a processed image. The acquisition and processing module processes the mosaic acquired data by performing first interpolation on the mosaic acquired data by the sensor array to provide a first approximation and performing second interpolation on the values of the first approximation to provide a second approximation.

Systems, methods and devices for multispectral imaging and non-linear filtering of vector valued data

An improved method and system for classifying tissue samples comprises determining a tissue type classification based on spectral data of training samples of known target classification. Denoised spectral data is generated from the spectral data based on the tissue type classification. A classifier is then trained using the denoised spectral data to classify the tissue samples.

System and method for hyper-spectral analysis

A spectral measurement device comprising an entrance aperture for receiving an electromagnetic energy and a mask located at the entrance aperture in the form of a two-dimensional encodement pattern. An optical element conditions the electromagnetic energy received from the mask for presentation to the spectral dispersion element and the and a spectral dispersion element disperses the electromagnetic energy in one or more dimensions. Additionally, the optical element conditions the dispersed electromagnetic energy onto an array of detector elements.

Devices and method for spectral measurements

A hyper-spectral analysis method for characterizing or distinguishing diverse elements within hyper-spectral images. A plurality of patches of pixels from within the hyper-spectral images are extracted as being patches around pixels of the elements to be characterized or distinguished. The statistics of spectra for each patch of pixels are computed. A first classifier is computed from frequency-wise standard deviation of the spectra in each patch and a set of second classifiers are computed from principal components of the spectral in each patch. A combined classifier is computed based on the output of the first classifier and at least one of the second classifiers. The elements are characterized or distinguished based on the output of at least one of the classifiers, preferably the combined classifier.

Encoded spatio-spectral information processing is performed using a system having a radiation source, wavelength dispersion device and two-dimensional switching array, such as digital micro-mirror array (DMA). In one aspect, spectral components from a sample are dispersed in space and modulated separately by the switching array, each element of which may operate according to a predetermined encoding pattern. The encoded spectral components can then be detected and analyzed. In a different aspect, the switching array can be used to provide a controllable radiation source for illuminating a sample with radiation patterns that have predetermined characteristics and separately encoded components. Various applications are disclosed.

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North Haven, CT, United States of America

Ronald R Coifman