Abstract: Described is a bio-inspired vision system for object recognition. The system comprises an attention module, an object recognition module, and an online labeling module. The attention module is configured to receive an image representing a scene and find and extract an object from the image. The attention module is also configured to generate feature vectors corresponding to color, intensity, and orientation information within the extracted object. The object recognition module is configured to receive the extracted object and the feature vectors and associate a label with the extracted object. Finally, the online labeling module is configured to alert a user if the extracted object is an unknown object so that it can be labeled.

Abstract: The present invention relates to a hierarchical system for multi-objective shape control of variable stiffness structures. The system operates by initializing a hierarchical hybrid Gur-game controller that is affixed to a variable stiffness structure. The hierarchical hybrid Gur-game controller includes a hierarchy of Gur-games being utilized, at each level of the hierarchy, to control at least two angular rotations and at least one stiffness setting of the variable stiffness structure. The Gur-game controller is utilized to optimize variables, including angular rotations and stiffness settings of the variable stiffness structure to morph into a desired shape. Finally, the variable stiffness structure is morphed into the desired shape according to the optimized angular rotation and stiffness settings.

Abstract: The present invention relates to a method for optimizing the design of a shape morphing structure using a genetic algorithm. The method includes defining design parameters of a surface having variable properties into a chromosome. The variable properties of the chromosome are the actual properties of the chromosome. The chromosome has a total of Nmax genes, where each gene corresponds to a variable property element in the surface. Additionally, each gene has n design parameters, wherein the design parameters are incremental changes to the actual properties of the chromosome. A genetic algorithm is employed to optimize the genes until a fitness level for at least one chromosome has been exceeded. When the fitness value for any chromosome in the population is above a predetermined threshold, then the design process is terminated and the final design solution[s] are the design parameters of the chromosome[s] that exceed the predetermined threshold value.

Abstract: Described is a fault-tolerant electro-mechanical system that is able to saccade to a target by training and using a signal processing technique. The invention enables tracking systems, such as next generational cameras, to be developed for autonomous platforms and surveillance systems where environment conditions are unpredictable. The invention includes at least one sensor configured to relay a signal containing positional information of a stimulus. At least one actuator is configured to manipulate the sensor to enable the sensor to track the stimulus. A processing device is configured to receive positional information from each sensor and each actuator. The processing device sends a positional changing signal to at least one actuator and adjusts at least one positional changing signal according to the information from each sensor and each actuator to enable the actuator to cause the sensor to track the stimulus.

Abstract: A method and system for characterizing, detecting, and predicting or forecasting multiple target events from a past history of these events includes compressing temporal data streams into self-organizing map (SOM) clusters, and determining trajectories of the temporal streams via the clusters to predict the multiple target events. The system includes an evolutionary multi-objective optimization (EMO) module for processing the temporal data streams, which are obtained from a plurality of heterogeneous domains; a SOM module for characterizing the temporal data streams into self-organizing map clusters; and a target event prediction (TEP) module for generating prediction models of the map clusters. The SOM module employs a vector quantization method that places a set of vectors on a low-dimensional grid in an ordered fashion. The prediction models each include trajectories of the temporal data streams, and the system predicts the multiple target events using the trajectories.

Abstract: The present invention relates to an anti-tamper system. The system comprises a circuit pathway having a unique, programmable, evolved chip in the pathway. The chip has logic units with transistors having fixed parameters. The chip has route lengths and connections between the logic units that are formed in an evolutionary formation of the chip such that the evolutionary formation changes the route lengths and connections between the logic units to create feedback and delay changes. The changes in the route lengths and connections cause the transistors to operate in intermediate analog states. In chip formation, a search and optimization algorithm explores, in a clock-less environment, various route lengths and connections such that the chip can behave in a desired fashion to provide a desired output for a given input. Through use of the evolved chip, the anti-tamper system provides a security benefit of utilizing a unique chip in its pathway.

Abstract: Described is a navigation system. The navigation system comprises a route planning module and a route guidance module. The route planning module is configured to receive a request from a user for guidance to a particular destination. Based on a starting point, the route planning module determines a route from the starting point to the particular destination. The route guidance module is configured to receive the route, and based on the route and current location of the user, provide location-specific instructions to the user. The location-specific instructions include reference to specific visible objects within the vicinity of the user.

Abstract: Described is a system for temporal prediction. The system includes an extraction module, a mapping module, and a prediction module. The extraction module is configured to receive X(1), . . . X(n) historical samples of a time series and utilize a genetic algorithm to extract deterministic features in the time series. The mapping module is configured to receive the deterministic features and utilize a learning algorithm to map the deterministic features to a predicted {circumflex over (x)}(n+1) sample of the time series. Finally, the prediction module is configured to utilize a cascaded computing structure having k levels of prediction to generate a predicted {circumflex over (x)}(n+k) sample. The predicted {circumflex over (x)}(n+k) sample is a final temporal prediction for k future samples.

Abstract: A method for enhancing the quality of a digital image by using a single user-defined parameter. A virtual image is created based on the single user-defined parameter and the original digital image. An adaptive contrast enhancement algorithm operates on a logarithmically compressed version of the virtual image to produce adaptive contrast values for each pixel in the virtual image. A dynamic range adjustment algorithm is used to generate logarithmic enhanced pixels based on the adaptive contrast values and the pixels of the logarithmically compressed version of the virtual image. The logarithmic enhanced pixels are exponentially expanded and scaled to produce a compensated digital image.

Abstract: Anomaly prediction of battery parasitic load includes processing input data related to a state of charge for a battery and a durational factor utilizing a machine learning algorithm and generating a predicted start-up state of charge. Warnings are issued if the predicted start-up state of charge drops below a threshold level within an operational time.

Abstract: A system and method for determining events in a system or process, such as predicting fault events. The method includes providing data from the process, pre-processing data and converting the data to one or more temporal spike trains having spike amplitudes and a spike train length. The spike trains are provided to a dynamical neural network operating as a liquid state machine that includes a plurality of neurons that analyze the spike trains. The dynamical neural network is trained by known data to identify events in the spike train, where the dynamical neural network then analyzes new data to identify events. Signals from the dynamical neural network are then provided to a readout network that decodes the states and predicts the future events.

Abstract: A vision-based system for automatically detecting the type of object within a specified area, such as the type of occupant within a vehicle is presented. The type of occupant can then be used to determine whether an airbag deployment system should be enabled or not. The system extracts different features, including wavelet features and/or a disparity map from images captured by image sensors. These features are then processed by classification algorithms to produce class confidences for various occupant types. The occupant class confidences are fused and processed to determine occupant type. In a preferred embodiment, image features from image edges, wavelet features, and disparity are used. Various classification algorithms may be implemented to classify the object. Use of the disparity map and/or wavelet features provides greater computational efficiency.

Abstract: Described is an active learning system for fingerprinting an object identified in an image frame. The active learning system comprises a flow-based object segmentation module for segmenting a potential object candidate from a video sequence, a fixed-basis function decomposition module using Haar wavelets to extract a relevant feature set from the potential object candidate, a static classifier for initial classification of the potential object candidate, an incremental learning module for predicting a general class of the potential object candidate, an oriented localized filter module to extract features from the potential object candidate, and a learning-feature graph-fingerprinting module configured to receive the features and build a fingerprint of the object for tracking the object.

Abstract: Described is a system for diagnosis and prognosis of a component. The system is configured to receive a signal from a component. The signal is representative of a current health observation of the component. The system also computes a present likelihood of the component failure based on the signal. Additionally, the system computes a future likelihood of failure of the component for a given future mission. Through diagnosis, a user can determine the present health of the component, and based on the present health and future mission, determine whether or not the component will fail in the future mission.

Abstract: The present invention provides an automatic color balancing method for digital images by essentially performing adaptive weighting of surface reflectance and illuminant spectra components of the image. The adaptive weighting mechanism is derived from fuzzy logic based inference methods, taking advantage of its ability to perform inferences from data by providing a computational framework for knowledge that is in linguistic form. It also makes it amenable for implementation on hardware because of the commercially available fuzzy logic chips that already exist in real-world systems such as camcorders for image stabilization, washing machines, etc. The present invention enables much more efficient and robust color segmentation that forms core components for several computer vision algorithms.

Abstract: A method, an apparatus, and a computer program product for three-dimensional shape estimation using constrained disparity propagation are presented. An act of receiving a stereoscopic pair of images of an area occupied by at least one object is performed. Next, pattern regions and non-pattern regions are detected in the images. An initial estimate of ?patial disparities between the pattern regions in the images is generated. The initial estimate is used to generate a subsequent estimate of the spatial disparities between the non-pattern regions. The subsequent estimate is used to generate further subsequent estimates of the spatial disparities using the disparity constraints until there is no change between the results of subsequent iterations, generating a final estimate of the spatial disparities. A disparity map of the area occupied by at least one object is generated from the final estimate of the three-dimensional shape.

Abstract: A system, method, and apparatus for signal characterization, estimation, and prediction comprising an integrated search algorithm that cooperatively optimizes several data mining sub-tasks, the integrated search algorithm including a machine learning model, and the method comprising processing the data for data embedding, data embedding the processed data for searching for patterns, extracting time and frequency patterns, and training the model to represent learned patterns for signal characterization, estimation, and prediction.

Abstract: Anomaly prediction of battery parasitic load includes processing input data related to a state of charge for a battery and a durational factor utilizing a machine learning algorithm and generating a predicted start-up state of charge. Warnings are issued if the predicted start-up state of charge drops below a threshold level within an operational time.

Abstract: Described is a system for temporal prediction. The system includes an extraction module, a mapping module, and a prediction module. The extraction module is configured to receive X(1), . . . X(n) historical samples of a time series and utilize a genetic algorithm to extract deterministic features in the time series. The mapping module is configured to receive the deterministic features and utilize a learning algorithm to map the deterministic features to a predicted {circumflex over (x)}(n+1) sample of the time series. Finally, the prediction module is configured to utilize a cascaded computing structure having k levels of prediction to generate a predicted {circumflex over (x)}(n+k) sample. The predicted {circumflex over (x)}(n+k) sample is a final temporal prediction for k future samples.

Abstract: Object detection and tracking operations on images that may be performed independently are presented. A detection module receives images, extracts edges in horizontal and vertical directions, and generates an edge map where object-regions are ranked by their immediacy. Filters remove attached edges and ensure regions have proper proportions/size. The regions are tested using a geometric constraint to ensure proper shape, and are fit with best-fit rectangles, which are merged or deleted depending on their relationships. Remaining rectangles are objects. A tracking module receives images in which objects are detected and uses Euclidean distance/edge density criterion to match objects. If objects are matched, clustering determines whether the object is new; if not, a sum-of-squared-difference in intensity test locates matching objects.