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Reconstruction-Free Compressive Vision for Surveillance Applications

Published on May 2, 2019in Synthesis Lectures on Signal Processing
· DOI :10.2200/s00914ed2v01y201904spr017
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Pavan K. Turaga21
Estimated H-index: 21
(ASU: Arizona State University)
+ 3 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
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Abstract
Abstract Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors includi...
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References49
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Published on Oct 1, 2018 in ICIP (International Conference on Image Processing)
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University),
Pavan K. Turaga21
Estimated H-index: 21
(ASU: Arizona State University)
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
This paper aims to develop a fast dynamic-texture prediction method, using tools from non-linear dynamical modeling, and fast approaches for approximate regression. We consider dynamic textures to be described by patch-level non-linear processes, thus requiring tools such as delay-embedding to uncover a phase-space where dynamical evolution can be more easily modeled. After mapping the observed time-series from a dynamic texture video to its recovered phase-space, a time-efficient approximate pr...
Published on May 1, 2018
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University),
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University)
+ 5 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
This paper describes three methods used in the development of a utility-scale solar cyber-physical system. The study describes remote fault detection using machine learning approaches, power output optimization using cloud movement prediction and consensus-based solar array parameter estimation. Dynamic cloud movement, shading and soiling, lead to fluctuations in power output and loss of efficiency. For optimization of output power, a cloud movement prediction algorithm is proposed. Integrated f...
Ali Mousavi7
Estimated H-index: 7
(Rice University),
Gautam Dasarathy7
Estimated H-index: 7
(Rice University),
Richard G. Baraniuk82
Estimated H-index: 82
(Rice University)
We develop a novel computational sensing framework for sensing and recovering structured signals. When trained on a set of representative signals, our framework learns to take undersampled measurements and recover signals from these measurements using a deep convolutional neural network. In other words, it learns a transformation from the original signals to a near-optimal number of undersampled measurements and the inverse transformation from measurements to signals. This is in contrast to conv...
Published on Sep 1, 2017 in ICIP (International Conference on Image Processing)
Akshat Dave1
Estimated H-index: 1
(Indian Institute of Technology Madras),
Anil Kumar3
Estimated H-index: 3
(Indian Institute of Technology Madras)
+ 1 AuthorsKaushik Mitra10
Estimated H-index: 10
(Indian Institute of Technology Madras)
Reconstruction of signals from compressively sensed measurements is an ill-posed problem. In this paper, we leverage the recurrent generative model, RIDE, as an image prior for compressive image reconstruction. Recurrent networks can model long-range dependencies in images and hence can handle global multiplexing in compressive imaging. We perform MAP inference with RIDE using back-propagation to the inputs and projected gradient method. We propose an entropy thresholding based approach for pres...
Published on Aug 1, 2017in Renewable & Sustainable Energy Reviews 10.56
Florian Barbieri2
Estimated H-index: 2
(Curtin University),
Sumedha Rajakaruna14
Estimated H-index: 14
(Curtin University),
Ghosh Arindam50
Estimated H-index: 50
(Curtin University)
This paper endeavors to provide the reader with an overview of the various tools needed to forecast photovoltaic (PV) power within a very short-term horizon. The study focuses on the specific application of a large scale grid-connected PV farm. Solar resource is largely underexploited worldwide whereas it exceeds by far humans’ energy needs. In the current context of global warming, PV energy could potentially play a major role to substitute fossil fuels within the main grid in the future. Indee...
Published on Aug 1, 2017
Uday Shankar Shanthamallu2
Estimated H-index: 2
(ASU: Arizona State University),
Andreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
+ 1 AuthorsMike Stanley1
Estimated H-index: 1
(NXP Semiconductors)
This paper provides a brief survey of the basic concepts and algorithms used for Machine Learning and its applications. We begin with a broader definition of machine learning and then introduce various learning modalities including supervised and unsupervised methods and deep learning paradigms. In the rest of the paper, we discuss applications of machine learning algorithms in various fields including pattern recognition, sensor networks, anomaly detection, Internet of Things (IoT) and health m...
Published on Aug 1, 2017
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University),
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University)
+ 8 AuthorsMahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
In this paper, we describe a Cyber-Physical system approach to Photovoltaic (PV) array control. A machine learning and computer vision framework is proposed for improving the reliability of utility scale PV arrays by leveraging video analysis of local skyline imagery, customized machine learning methods for fault detection, and monitoring devices that sense data and actuate at each individual panel. Our approach promises to improve efficiency in renewable energy systems using cyber-enabled senso...
Barbara Bravi4
Estimated H-index: 4
(EPFL: École Polytechnique Fédérale de Lausanne),
Peter Sollich31
Estimated H-index: 31
Published on Nov 10, 2016in Information Sciences 5.52
Jing Liu6
Estimated H-index: 6
(Xi'an Jiaotong University),
Feng Lian7
Estimated H-index: 7
(Xi'an Jiaotong University),
Mahendra Mallick13
Estimated H-index: 13
This paper present a novel distributed compressed sensing based joint detection and tracking approach for multi-static radar system, which reduces the computational load largely, in a centralized fusion framework.In this paper, we consider reconstructing the sparse vector representing the target state space directly.A novel DGSSMP algorithm is proposed to reconstruct the sparse grid reflection vector in distributed compressed sensing, under a general condition when each individual sensing matrix...
Published on Jun 1, 2016in Magnetic Resonance in Medicine 3.86
Benjamin Paul Berman1
Estimated H-index: 1
(UA: University of Arizona),
Abhishek Pandey3
Estimated H-index: 3
(UA: University of Arizona)
+ 7 AuthorsAli Bilgin22
Estimated H-index: 22
(UA: University of Arizona)
Purpose Lung function is typically characterized by spirometer measurements, which do not offer spatially specific information. Imaging during exhalation provides spatial information but is challenging due to large movement over a short time. The purpose of this work is to provide a solution to lung imaging during forced expiration using accelerated magnetic resonance imaging. The method uses radial golden angle stack-of-stars gradient echo acquisition and compressed sensing reconstruction. Meth...
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