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Sameeksha Katoch
Arizona State University
8Publications
1H-index
5Citations
Publications 8
Newest
Published on May 2, 2019in Synthesis Lectures on Signal Processing
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Pavan Turaga1
Estimated H-index: 1
(ASU: Arizona State University)
+ 3 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
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Published on Jan 1, 2019in arXiv: Learning
Vivek Sivaraman Narayanaswamy (ASU: Arizona State University), Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University)
+ 2 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
Modern audio source separation techniques rely on optimizing sequence model architectures such as, 1D-CNNs, on mixture recordings to generalize well to unseen mixtures. Specifically, recent focus is on time-domain based architectures such as Wave-U-Net which exploit temporal context by extracting multi-scale features. However, the optimality of the feature extraction process in these architectures has not been well investigated. In this paper, we examine and recommend critical architectural chan...
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...
1 Citations Source Cite
Published on Jun 23, 2018
Abhinav Dixit (ASU: Arizona State University), Uday Shankar Shanthamallu2
Estimated H-index: 2
(ASU: Arizona State University)
+ 9 AuthorsAndrew Strom1
Estimated H-index: 1
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...
1 Citations Source Cite
Published on Oct 1, 2017 in FIE (Frontiers in Education Conference)
Abhinav Dixit , Sameeksha Katoch1
Estimated H-index: 1
+ 3 AuthorsAndreas Spanias25
Estimated H-index: 25
Several web-based signal processing simulation packages for education have been developed in a Java environment. Although this environment has provided convenience and accessibility using standard browser technology, it has recently become vulnerable to cyber-attacks and is no longer compatible with secure browsers. In this paper, we describe our efforts to transform our award-winning J-DSP online laboratory by rebuilding it on an HTML5 framework. Along with a new simulation environment, we have...
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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...
3 Citations Source Cite
Published on Jul 1, 2017
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University),
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University)
+ 5 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
Source Cite
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