Abstract: A novel approach for computing efficiently quantum based signal autocorrelations includes example designs associated with quantum circuits for computing the quantum autocorrelation of the signal. Importantly, to compensate for unique challenges associated with quantum signal processing (particularly regarding probabilistic measurements that result from QFT and IQFT operations), normalization and denormalization steps ensure that quantum measurement results are comparable to ranges that would be obtained with classical autocorrelation computation methods. In addition, because probabilistic measurements resulting from QFT and IQFT operations lose important phase information, lost phase information can be restored following measurement using QFT and IQFT.

Abstract: A system for time series analysis using attention models is disclosed. The system may capture dependencies across different variables through input embedding and may map the order of a sample appearance to a randomized lookup table via positional encoding. The system may capture capturing dependencies within a single sequence through a self-attention mechanism and determine a range of dependency to consider for each position being analyzed. The system may obtain an attention weighting to other positions in the sequence through computation of an inner product and utilize the attention weighting to acquire a vector representation for a position and mask the sequence to enable causality. The system may employ a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation and a linear layer to obtain logits from the single vector representation. The system may use a type dependent final prediction layer.

Abstract: A system implements a modeling (ClassGP) and an optimization (ClassBO) framework that models heterogeneous functions with knowledge of individual partitions within classes, i.e., heterogeneous functions including non-stationary functions which can be divided into locally stationary functions over partitions of input space with an active stationary function in each partition. The framework constructs a class likelihood for the class by combining log marginal likelihoods associated with each partition of the plurality of partitions within the class. The framework aims to improve analysis of systems that are characterized by a discrete and finite number of “classes” of behaviors.

Abstract: Quantum circuits for QFT and Inverse QFT (IQFT) are disclosed that can be applied for use in signal and speech, analysis synthesis and compression. A unique method perceptual selection of QFT components is also outlined.

Abstract: Various embodiments of a system and associated method for detecting and classifying faults in a photovoltaic array using graph-based signal processing.

Abstract: A system for Positive and Unlabeled (PU) learning is tailored specifically for a deep learning framework. The system incorporates an adaptive asymmetric loss function based on Modified Logistic Regression paired with a simple linear transform of an output. When only positive and unlabeled images are available for training, the system results in an inductive classifier where no estimate of the class prior is required.

Abstract: Tracking-based motion deblurring via coded exposure is provided. Fast object tracking is useful for a variety of applications in surveillance, autonomous vehicles, and remote sensing. In particular, there is a need to have these algorithms embedded on specialized hardware, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), to ensure energy-efficient operation while saving on latency, bandwidth, and memory access/storage. In an exemplary aspect, an object tracker is used to track motion of one or more objects in a scene captured by an image sensor. The object tracker is coupled with coded exposure of the image sensor, which modulates photodiodes in the image sensor with a known exposure function (e.g., based on the object tracking). This allows for motion blur to be encoded in a characteristic manner in image data captured by the image sensor. Then, in post-processing, deblurring is performed using a computational algorithm.

Abstract: Various embodiments of a system and methods for audio source separation via multi-scale feature learning are disclosed.

Abstract: Tracking-based motion deblurring via coded exposure is provided. Fast object tracking is useful for a variety of applications in surveillance, autonomous vehicles, and remote sensing. In particular, there is a need to have these algorithms embedded on specialized hardware, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), to ensure energy-efficient operation while saving on latency, bandwidth, and memory access/storage. In an exemplary aspect, an object tracker is used to track motion of one or more objects in a scene captured by an image sensor. The object tracker is coupled with coded exposure of the image sensor, which modulates photodiodes in the image sensor with a known exposure function (e.g., based on the object tracking). This allows for motion blur to be encoded in a characteristic manner in image data captured by the image sensor. Then, in post-processing, deblurring is performed using a computational algorithm.

Abstract: A system for time series analysis using attention models is disclosed. The system may capture dependencies across different variables through input embedding and may map the order of a sample appearance to a randomized lookup table via positional encoding. The system may capture capturing dependencies within a single sequence through a self-attention mechanism and determine a range of dependency to consider for each position being analyzed. The system may obtain an attention weighting to other positions in the sequence through computation of an inner product and utilize the attention weighting to acquire a vector representation for a position and mask the sequence to enable causality. The system may employ a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation and a linear layer to obtain logits from the single vector representation. The system may use a type dependent final prediction layer.

Abstract: Various embodiments of a system and associated method for audio source separation based on generative priors trained on individual sources. Through the use of projected gradient descent optimization, the present approach simultaneously searches in the source-specific latent spaces to effectively recover the constituent sources. Though the generative priors can be defined in the time domain directly, it was found that using spectral domain loss functions leads to good-quality source estimates.

Abstract: Various embodiments of systems and methods for attention models with random features for multi-layered graph embeddings are disclosed.

Abstract: A system estimates spectral radius and leverages local updates from neighboring nodes in a wireless network to iteratively update state values of each node in the network and estimate a spectral radius of the network with guaranteed convergence. A method associated with the system method is a distributed method that efficiently converges to an invertible function of the spectral radius based only on local communications of the network for digital communication models in the presence and/or absence of packet loss, as opposed to conventional centralized methods.

Abstract: A system reconfigures a photovoltaic array used in solar energy based on observed shading conditions to determine an optimal topology of the photovoltaic array to maximize power output. Specifically, the system is designed to reconfigure a photovoltaic array when the photovoltaic array is partially shaded. The system uses a neural network model to determine a topology that maximizes power output of the photovoltaic array based on irradiance data obtained from the photovoltaic array.

Abstract: A system for time series analysis using attention models is disclosed. The system may capture dependencies across different variables through input embedding and may map the order of a sample appearance to a randomized lookup table via positional encoding. The system may capture capturing dependencies within a single sequence through a self-attention mechanism and determine a range of dependency to consider for each position being analyzed. The system may obtain an attention weighting to other positions in the sequence through computation of an inner product and utilize the attention weighting to acquire a vector representation for a position and mask the sequence to enable causality. The system may employ a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation and a linear layer to obtain logits from the single vector representation. The system may use a type dependent final prediction layer.

Abstract: Various embodiments of a cyber-physical system for providing cloud prediction for photovoltaic array control are disclosed herein.

Abstract: Solar array fault detection, classification, and localization using deep neural nets is provided. A fault-identifying neural network uses a cyber-physical system (CPS) approach to fault detection in photovoltaic (PV) arrays. Customized neural network algorithms are deployed in feedforward neural networks for fault detection and identification from monitoring devices that sense data and actuate each individual module in a PV array. This approach improves efficiency by detecting and classifying a wide variety of faults and commonly occurring conditions (e.g., eight faults/conditions concurrently) that affect power output in utility scale PV arrays.

Abstract: Various embodiments for a connection topology reconfiguration technique for photovoltaic (PV) arrays to maximize power output under partial shading and fault conditions using neural networks are disclosed herein.

Abstract: Various embodiments of a system and associated method for detection of COVID-19 and other respiratory diseases through classification of audio samples are disclosed herein. The system utilizes features directly extracted from the coughing audio and develops automated diagnostic tools for COVID-19. In particular, the present application discusses a novel modification of a deep neural network architecture by using log-mel spectrograms of the audio excerpts and by optimizing a combination of binary cross-entropy and focal loss parameters. One embodiment of the system achieved an average validation AUC of 82.23% and a test AUC of 78.3% at a sensitivity of 80.49%.

Abstract: A method including receiving an input data set. The input data set can include one of a feature domain set or a kernel matrix. The method also can include constructing dense embeddings using: (i) Nyström approximations on the input data set when the input data set comprises the kernel matrix, and (ii) clustered Nyström approximations on the input data set when the input data set comprises the feature domain set. The method additionally can include performing representation learning on each of the dense embeddings using a multi-layer fully-connected network for each of the dense embeddings to generate latent representations corresponding to each of the dense embeddings. The method further can include applying a fusion layer to the latent representations corresponding to the dense embeddings to generate a combined representation. The method additionally can include performing classification on the combined representation. Other embodiments of related systems and methods are also disclosed.