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Sameeksha Katoch
Arizona State University
10Publications
3H-index
28Citations
Publications 10
Newest
Last.Farib KhondokerH-Index: 1
view all 13 authors...
#1Henry Braun (ASU: Arizona State University)H-Index: 6
#2Pavan Turaga (ASU: Arizona State University)H-Index: 24
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 6 authors...
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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#1Vivek Sivaraman Narayanaswamy (ASU: Arizona State University)H-Index: 1
#2Sameeksha Katoch (ASU: Arizona State University)H-Index: 3
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 28
view all 5 authors...
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...
Oct 1, 2018 in ICIP (International Conference on Image Processing)
#1Sameeksha Katoch (ASU: Arizona State University)H-Index: 3
#2Pavan Turaga (ASU: Arizona State University)H-Index: 24
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 4 authors...
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...
2 CitationsSource
#1Abhinav Dixit (ASU: Arizona State University)H-Index: 1
#2Uday Shankar Shanthamallu (ASU: Arizona State University)H-Index: 2
Last.Huan SongH-Index: 4
view all 12 authors...
1 Citations
#1Abhinav DixitH-Index: 1
Last.Huan SongH-Index: 4
view all 12 authors...
#1Sameeksha Katoch (ASU: Arizona State University)H-Index: 3
#2Gowtham Muniraju (ASU: Arizona State University)H-Index: 3
Last.Devarajan SrinivasanH-Index: 5
view all 8 authors...
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...
7 CitationsSource
Oct 1, 2017 in FIE (Frontiers in Education Conference)
#1Abhinav DixitH-Index: 1
#2Sameeksha KatochH-Index: 3
Last.Andreas SpaniasH-Index: 28
view all 6 authors...
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...
3 CitationsSource
#1Sunil Rao (ASU: Arizona State University)H-Index: 4
#2Sameeksha Katoch (ASU: Arizona State University)H-Index: 3
Last.Devarajan SrinivasanH-Index: 5
view all 11 authors...
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...
10 CitationsSource
#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 3
#2Sunil Rao (ASU: Arizona State University)H-Index: 4
Last.Devarajan SrinivasanH-Index: 5
view all 8 authors...
5 CitationsSource
1