Abstract: Systems and methods for detecting anomalies in antenna systems (e.g., air traffic control surveillance systems), include a processor receiving antenna status information. A variational autoencoder receives and optimizes the antenna status information and determines whether it qualifies as an anomaly. Optimized antenna status information is compared to either non-anomalous or anomalous antenna status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the antenna status information by generating a plurality of probabilistic models of the antenna status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the antenna status information prior to optimization.

Abstract: Methods of training a variational autoencoder (VAE) to recognize anomalous data in a distributed system are provided. Input image data representative of devices/processes in a distributed system are provided to an encoder of a VAE on a processor. The input image data is compressed, via the processor, using a first plurality of weights with the encoder. A normal distribution of the compressed image data is created in a latent space of the VAE. The compressed image data from the latent space is decompressed using a second plurality of weights with a decoder of the VAE. The decompressed image data from the decoder is optimized. At least the first and second plurality of weights are updated, via the processor, based on the loss detected in the optimized decompressed image data. The above steps are iterated until the decompressed image data possesses substantially the same statistical properties as the input image data.

Abstract: Systems and methods for detecting anomalies in aviation data communication systems (e.g., air traffic control surveillance systems), include a processor receiving device status information. A variational autoencoder receives and optimizes the device status information and determines whether it qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the device status information prior to optimization.

Abstract: Network security anomaly detection systems and methods include a processor, in communication with the network, receiving network device status information. A variational autoencoder receives the device status information, optimizes the device status information, and determines whether the device status information qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space.

Abstract: A method for predicting hydrocarbon recovery in a subterranean formation may include generating pre-heating data associated with fracturing in a sample from the subterranean formation, determining a first fractal pattern based upon the pre-heating data, and heating the sample with RF power to cause additional fracturing in the sample. The method may include generating post-heating data associated with additional fracturing in the sample after heating with RF power, determining a second fractal pattern based upon the post-heating data, detecting change between the first fractal pattern and the second fractal pattern, and predicting hydrocarbon recovery from the subterranean formation based upon the detected change.

Abstract: Systems (100) and methods (500) for searching electronic documents. The methods comprise: using first electronic documents to derive topics respectively defined by sets of words; using the topics to transform a format of known subject reviews and written pieces from a textual format to a numerical format in which each of the subject reviews and written pieces is expressed as a topic vector containing a plurality of first numbers respectively corresponding to the topics; generating concept vectors by transforming a domain of the topic vectors from a first numerical domain to a second different numerical domain; and determining similarities between the known subject reviews and the written pieces by comparing first concept vectors that are associated with the known subject reviews to second concept vectors concept vectors that are associated with the written pieces.

Abstract: A method for predicting hydrocarbon recovery in a subterranean formation may include generating pre-heating data associated with fracturing in a sample from the subterranean formation, determining a first fractal pattern based upon the pre-heating data, and heating the sample with RF power to cause additional fracturing in the sample. The method may include generating post-heating data associated with additional fracturing in the sample after heating with RF power, determining a second fractal pattern based upon the post-heating data, detecting change between the first fractal pattern and the second fractal pattern, and predicting hydrocarbon recovery from the subterranean formation based upon the detected change.

Abstract: A method for predicting hydrocarbon recovery in a subterranean formation may include generating pre-heating data associated with fracturing in a sample from the subterranean formation, determining a first fractal pattern based upon the pre-heating data, and heating the sample with RF power to cause additional fracturing in the sample. The method may include generating post-heating data associated with additional fracturing in the sample after heating with RF power, determining a second fractal pattern based upon the post-heating data, detecting change between the first fractal pattern and the second fractal pattern, and predicting hydrocarbon recovery from the subterranean formation based upon the detected change.

Abstract: A method for predicting hydrocarbon recovery in a subterranean formation may include generating pre-heating data associated with fracturing in a sample from the subterranean formation, heating the sample with RF power to cause additional fracturing in the sample, and generating post-heating data associated with additional fracturing in the sample after heating with RF power. The method may also include detecting change between the pre-heating data and post-heating data, and predicting hydrocarbon recovery from the subterranean formation based upon the detected change.

Abstract: Systems (100) and methods (300, 400) for efficient comparative non-spatial image data analysis. The methods involve ranking a plurality of non-spatial images (1011, 1050, 1231, 1539, 0001, 0102, 0900, 1678, 0500, 0020, 0992, 1033, 1775, 1829) based on at least one first attribute thereof; generating a screen page (1102-1106) comprising an array (1206) defined by a plurality of cells (1208) in which at least a portion of the non-spatial images are simultaneously presented; and displaying the screen page in a first GUI window (802) of a display screen. Each cell comprises only one non-spatial image. The non-spatial images are presented in an order defined by the ranking thereof.

Abstract: Systems (100) and methods (300) for efficient video analysis. The methods involve: automatically identifying features of at least one feature class which are contained in a first video stream; simultaneously generating a plurality of first video chips (904) using first video data defining the first video stream; displaying an array comprising the first video chips within a graphical user interface window; and concurrently playing the first video chips. Each of the first video chips comprises a segment of the first video stream which includes at least one identified feature.

Abstract: Systems (100) and methods (300) for efficient spatial feature data analysis. The methods involve simultaneously generating chip images using image data defining at least a first image and video chips using video data defining at least a first video stream. Thereafter, an array is displayed which comprises grid cells in which at least a portion of the chip images is presented, at least a portion of the video chips is presented, or a portion of the chip images and a portion of the video chips are presented. Each chip image comprises a panned-only view, a zoomed-only view, or a panned-and-zoomed view of the first image including a visual representation of at least one first object of a particular type. Each of the video chips comprises a segment of the first video stream which include a visual representation of at least one second object of the particular type.

Abstract: A method for predicting hydrocarbon recovery in a subterranean formation may include generating pre-heating data associated with fracturing in a sample from the subterranean formation, heating the sample with RF power to cause additional fracturing in the sample, and generating post-heating data associated with additional fracturing in the sample after heating with RF power. The method may also include detecting change between the pre-heating data and post-heating data, and predicting hydrocarbon recovery from the subterranean formation based upon the detected change.

Abstract: A method for predicting hydrocarbon recovery in a subterranean formation may include generating pre-heating data associated with fracturing in a sample from the subterranean formation, determining a first fractal pattern based upon the pre-heating data, and heating the sample with RF power to cause additional fracturing in the sample. The method may include generating post-heating data associated with additional fracturing in the sample after heating with RF power, determining a second fractal pattern based upon the post-heating data, detecting change between the first fractal pattern and the second fractal pattern, and predicting hydrocarbon recovery from the subterranean formation based upon the detected change.

Abstract: Systems (100) and methods (300) for efficiently and accurately detecting changes in feature data. The methods generally involve: determining first vectors for first features extracted from a first image using pixel information associated therewith; comparing the first vectors with second vectors defined by spatial feature data; classifying the first features into a plurality of classes based on the results of the vector comparisons; and analyzing the first image to determine if any one of the first features of at least one of the plurality of classes indicates that a relevant change has occurred in relation to an object represented thereby.

Abstract: Systems (100) and methods (300) for efficient spatial feature data analysis. The methods involve simultaneously generating two or more chip images (904) using image data defining a first image (508); concurrently displaying an array (906) comprising all or a portion of the chip images in a plug-in window (702); and displaying in the plug-in window information relating to an attribute (a1, a2) of a feature (A5) contained in a selected one of the displayed chip images (1002). The chip images are generated in response to a user selection of a feature (A1) contained in at least a portion of the first image displayed in an application window (504). Each of the chip images comprises a panned view, a zoomed view, or a panned-and-zoomed view of the first image including one or more features of a user-selected feature class.

Abstract: Method includes calculating a mean z coordinate value for points within the point cloud. An initial set of points is selected which have z coordinate values which deviate from the mean by at least an initial value. Thereafter, a triangulated irregular network (TIN) is constructed using the initial set of points. The method continues by determining if there is a significant point that exists among the points contained within an x, y extent of each triangle. If so, the TIN is updated to include the initial set of points and any significant points determined to exist within the triangles that form the TIN. Thereafter, the method continues by repeating the determining and the updating steps until there are no additional significant points found within the triangles.

Abstract: Systems (100) and methods (300) for efficient feature data analysis. The methods involve: determining a first number of screen pages needed to verify that each of a plurality of clusters of detected features comprises only detected features which were correctly identified during feature extraction/detection operations as being of the same feature class as a selected feature of an image; determining a second number of screen pages needed to verify that each of a plurality of singular detected features was correctly identified during the feature extraction/detection operations as being of the same feature class as the selected feature of the image; selecting one of a plurality of different validation processes based on values of the first number of screen pages and the second number of screen pages; and performing the selected validation process to verify that each of the detected features does not constitute a false positive.

Abstract: Method for improving the quality of a set of a three dimensional (3D) point cloud data representing a physical surface by detecting and filling null spaces (606). The method includes analyzing (206) the data to identify the presence of a plurality of level 1 fractals (401), each defined by a plurality of voxels (400) containing data points arranged in one of a plurality of three-dimensional patterns. The method also includes selectively filling (212) voxels in the 3D point cloud data with a first predetermined limited number of data points to increase a number of instances where level 2 fractals (604) can be used for representing the 3D point cloud data. Each level 2 fractal is defined as a common plurality of the level 1 fractals having a common three-dimensional pattern, where the common plurality of level 1 fractals are also arranged in accordance with the common three dimensional pattern.

Abstract: Systems (100) and methods (300) for efficient video analysis. The methods involve: automatically identifying features of at least one feature class which are contained in a first video stream; simultaneously generating a plurality of first video chips (904) using first video data defining the first video stream; displaying an array comprising the first video chips within a graphical user interface window; and concurrently playing the first video chips. Each of the first video chips comprises a segment of the first video stream which includes at least one identified feature.