Arizona State University

Lawrence Livermore National Security, LLC

Andreas Spanias

Cihan Tepedelenlioglu

Jayaraman Jayaraman Thiagarajan

Huan Song

Pavan Turaga

Sameeksha Katoch

Karthikeyan Natesan Ramamurthy

Suren Jayasuriya

Visar Berisha

Mahesh K Banavar

Gowtham Muniraju

Vivek Sivaraman Narayanaswamy

David H Frakes

Xue Zhang

Uday Shanthamallu

Deepta Rajan

Odrika Iqbal

Rajapandian Ayyanar

Rushil Anirudh

Robert Santucci

Megan Willi

Girish Kalyanasundaram

Prasanna Sattigeri

Harish Krishnamoorthi

Divya Mohan

Sunil Srinivasa Manjanbail Rao

Sai Zhang

Sunil Rao

Jie Fan

Henry Braun

Jayaraman J Thiagarajan

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.

Tracking-based motion deblurring via coded exposure

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.

Systems and methods for time series analysis using attention models

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

Systems and methods for fault classification in photovoltaic arrays using graph signal processing

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

Systems and methods for audio source separation via multi-scale feature learning

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.

Systems and methods for unsupervised audio source separation using generative priors

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

Systems and methods for a supra-graph based attention model for multi-layered embeddings and deep learning applications

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.

Systems and methods for consensus-based distributed graph spectral radius estimation

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

Systems and methods for skyline prediction for cyber-physical photovoltaic array control

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.

Solar array fault detection, classification, and localization using deep neural nets

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.

Systems and methods for connection topology optimization in photovoltaic arrays using neural networks

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) Nystr&#xf6;m approximations on the input data set when the input data set comprises the kernel matrix, and (ii) clustered Nystr&#xf6;m 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.

Systems and methods for customizing kernel machines with deep neural networks

Systems and methods for a supra-fusion graph attention model for multi-layered embeddings and deep learning applications

Various embodiments of systems and methods for robust max consensus for wireless sensor networks in the presence of additive noise by determining and removing a growth rate estimate from state values of each node in a wireless sensor network are disclosed.

Growing community of inventors

Tempe, AZ, United States of America

Andreas Spanias