Abstract: A fault detection system including one or more sensors onboard a vehicle, the one or more sensors being configured to detect a predetermined characteristic of the vehicle and generate a plurality of sensor signals corresponding to the predetermined characteristic, and a processor onboard the vehicle and in communication with the one or more sensors, the processor being configured to generate an analysis model for the predetermined characteristic, the analysis model being trained by the processor with a training data set of fast Fourier transform vectors that are generated from the plurality of sensor signals obtained under normal operating conditions of the predetermined characteristic, and determine a health of a vehicle component corresponding to the predetermined characteristic with the analysis model.

Abstract: Described is a neuromorphic processor for signal denoising and separation. The neuromorphic processor generates delay-embedded mixture signals from an input mixture of pulses. Using a reservoir computer, the delay-embedded mixture signals are mapped to reservoir states of a dynamical reservoir having output layer weights. The output layer weights are adapted based on short-time linear prediction, and a denoised output of the mixture of input signals us generated. The denoised output is filtered through a set of adaptable finite impulse response (FIR) filters to extract a set of separated narrowband pulses.

Abstract: Described is a cognitive signal processor for signal denoising and blind source separation. During operation, the cognitive signal processor receives a mixture signal that comprises a plurality of source signals. A denoised reservoir state signal is generated by mapping the mixture signal to a dynamic reservoir to perform signal denoising. At least one separated source signal is identified by adaptively filtering the denoised reservoir state signal.

Abstract: Described is a system for discriminant localization of objects. During operation, the system causes one or more processors to perform an operation of identifying an object in an image using a multi-layer network. Features of the object are derived from the activations of two or more layers of the multi-layer network. The image is then classified to contain one or more object classes, and the desired object class is localized. A device can then be controlled based on localization of the object in the image. For example, a robotic arm can be controlled to reach for the object.

Abstract: Described is a system for understanding machine-learning decisions. In an unsupervised learning phase, the system extracts, from input data, concepts represented by a machine-learning (ML) model in an unsupervised manner by clustering patterns of activity of latent variables of the concepts, where the latent variables are hidden variables of the ML model. The extracted concepts are organized into a concept network by learning functional semantics among the extracted concepts. In an operational phase, a subnetwork of the concept network is generated. Nodes of the subnetwork are displayed as a set of visual images that are annotated by weights and labels, and the ML model per the weights and labels.

Abstract: Described is a system for classifying objects and scenes in images. The system identifies salient regions of an image based on activation patterns of a convolutional neural network (CNN). Multi-scale features for the salient regions are generated by probing the activation patterns of the CNN at different layers. Using an unsupervised clustering technique, the multi-scale features are clustered to identify key attributes captured by the CNN. The system maps from a histogram of the key attributes onto probabilities for a set of object categories. Using the probabilities, an object or scene in the image is classified as belonging to an object category, and a vehicle component is controlled based on the object category causing the vehicle component to perform an automated action.

Abstract: Described is system for mapping user behavior to brain regions of interest. Using a cognitive-behavior model, a behavioral task is selected that is suited for a desired brain state. Using a functional-anatomical model coupled to the cognitive-behavior model, a set of high-definition neurostimulations is selected to be applied to the user during performance of the selected behavioral task. The selected set of high-definition neurostimulations targets specific regions of the user's brain. Changes in the user's brain state are sensed during application of the set of high-definition neurostimulations and performance of the selected behavioral task using at least one brain monitoring technique. The coupled functional-anatomical and cognitive-behavior models are adapted until the desired brain state is reached.

Abstract: A method for determining a vehicle system prognosis includes detecting a predetermined characteristic of a vehicle with one or more sensors, obtaining a plurality of sensor signals corresponding to the predetermined characteristic, receiving the plurality of sensor signals from the one or more sensors and determining an input time series of data based on the sensor signals, generating, a matrix of time series data based on the input time series of data, clustering the matrix of time series data based on predetermined clustering criteria into a predetermined number of clusters, generating a sparse temporal matrix based on the predetermined number of clusters, extracting extracted features that are indicative of an operation of a vehicle system from the sparse temporal matrix and determining an operational status of the vehicle system based on the extracted features, and communicating the operational status of the vehicle system to an operator or crew member of the vehicle.

Abstract: A method of processing a plurality of time-varying signals received at a sensor communicatively coupled to a signal data processor to identify at least one parameter of at least one of the plurality of time-varying signals is provided. The method includes receiving, at a plurality of blind source separation (BSS) modules of the signal data processor, signals derived from the plurality of time-varying signals, each BSS module of the plurality of BSS modules including a filtering subsystem having a pipelined architecture and a parallelized architecture. The method also includes generating a plurality of blind source separated signals, and transmitting at least one pulse descriptor word (PDW) parameter vector signal to a computing device of the signal data processor. The method further includes identifying the at least one parameter from the at least one PDW parameter vector signal, and outputting the at least one parameter from the signal data processor.

Abstract: Described is a cognitive signal processor for signal denoising and blind source separation. During operation, the cognitive signal processor receives a mixture signal that comprises a plurality of source signals. A denoised reservoir state signal is generated by mapping the mixture signal to a dynamic reservoir to perform signal denoising. At least one separated source signal is identified by adaptively filtering the denoised reservoir state signal.

Abstract: A method for generating pulse descriptor words (PDWs) including frequency and/or bandwidth data from time-varying signals received by a sensor includes filtering, at a plurality of blind source separation (BSS) modules, signals derived from the time-varying signals, each BSS module including a filtering subsystem having a plurality of filter modules. Each filter module has a frequency filter coefficient (?) and is parameterized by a center frequency (f). The method also includes transmitting at least one blind source separated signal from the BSS modules to a PDW generation module communicatively coupled to the filtering subsystem. The method further includes generating, using the PDW generation module and based on the blind source separated signal, at least one PDW parameter vector signal containing the frequency data. The method also includes updating, upon generating and based on the PDW parameter vector signal, values of ? and/or f for each filter module.

Abstract: A method of processing a plurality of time-varying signals received at a sensor communicatively coupled to a signal data processor to identify at least one parameter of at least one of the plurality of time-varying signals is provided. The method includes receiving, at a plurality of blind source separation (BSS) modules of the signal data processor, signals derived from the plurality of time-varying signals, each BSS module of the plurality of BSS modules including a filtering subsystem having a pipelined architecture and a parallelized architecture. The method also includes generating a plurality of blind source separated signals, and transmitting at least one pulse descriptor word (PDW) parameter vector signal to a computing device of the signal data processor. The method further includes identifying the at least one parameter from the at least one PDW parameter vector signal, and outputting the at least one parameter from the signal data processor.

Abstract: A neural-Model Predictive Control (MPC) controller is described to control a dynamical system (i.e., “plant”). The MPC controller receives, in a base controller, a measurement of a current state of a plant and generates a control signal based on the measurement of the current state of the plant. A forward module receives the measurement of the current state of the plant and the control signal to generate a forward module prediction. A forward module corrector receives the measurement of the current state of the plant and the control signal from the base controller to generate an additive correction to the forward module prediction to generate a predicted plant state. Control sequences of length L of pairs of control signals and corresponding predicted plant states are generated until Ns control sequences have been generated. A next plant control signal is generated based on the Ns control sequences.

Abstract: Described is a cognitive blind source separator (CBSS). The CBSS includes a delay embedding module that receives a mixture signal (the mixture signal being a time-series of data points from one or more mixtures of source signals) and time-lags the signal to generate a delay embedded mixture signal. The delay embedded mixture signal is then linearly mapped into a reservoir to create a high-dimensional state-space representation of the mixture signal. The state-space representations are then linearly mapped to one or more output nodes in an output layer to generate pre-filtered signals. The pre-filtered signals are passed through a bank of adaptable finite impulse response (FIR) filters to generate separate source signals that collectively formed the mixture signal.

Abstract: In general, one aspect of the subject matter described can be embodied in a method that includes, obtaining a plurality of search results responsive to an initial search query, the search results including a first search result that identifies a first resource; determining, using a document-to-query-to-document model, that the first resource is relevant to a first suggested query different from the initial search query; generating a presentation of the search results responsive to the initial search query; and providing the presentation of the search results in response to the initial search query. Each search result in the presentation includes a link to a respective resource, wherein the first search result in the presentation includes a link that, upon a selection by a user, can cause the first suggested query to be submitted to a search engine.

Abstract: A method for detecting vehicle system faults includes receiving, with a processor, a plurality of sensor signals from one or more sensors; thresholding, with the processor, the plurality of sensor signals for each respective sensor substantially in real time; and generating, with the processor, abnormal derivative frequency values for each of the plurality of thresholded sensor signals in real time and determining an operational status of at least each of the one or more sensors based on the abnormal derivative frequency values.

Abstract: A method for detecting vehicle system faults includes receiving, with a processor, a plurality of sensor signals from one or more sensors; thresholding, with the processor, the plurality of sensor signals for each respective sensor substantially in real time; and generating, with the processor, abnormal derivative frequency values for each of the plurality of thresholded sensor signals in real time and determining an operational status of at least each of the one or more sensors based on the abnormal derivative frequency values.

Abstract: In general, one aspect of the subject matter described can be embodied in a method that includes, for a first document that is included in first search results responsive to a first user-submitted query, selecting a plurality of previously submitted queries for which the first document was a responsive search result. The method can further include determining whether second documents that are relevant to the previously submitted query have at least a threshold level of diversity in comparison to the first search results, wherein second documents are determined to be relevant to the previously submitted query based on data that is indicative of user behavior. The method can additionally include identifying one or more queries from the selected previously submitted queries to provide as first suggested queries, and providing the one or more identified queries as first suggested queries with the first search results for the first user-submitted query.

Abstract: An insulation portion for an electrochemical cell having a plurality of slots into which bent electrode tabs are slid through during an assembly process of an electrochemical cell. A latch is disposed adjacent the slots and is movable from a resting position to a biased position to engage the electrode tabs. Attachment of the insulation portion to the electrochemical cell and bending of the electrode tabs can be robotically performed, such that controllers of an attachment device and bending device are in communication with each other.

Abstract: An electrochemical cell includes components that are welded from an external source after the components are assembled in a cell canister. The cell canister houses electrode tabs and a core insert. An end cap insert is disposed opposite the core insert. An external weld source, such as a laser beam, is applied to the end cap insert, such that the end cap insert, the electrode tabs, and the core insert are electrically coupled by a weld which extends from the end cap insert to the core insert.