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Sunil Rao
Arizona State University
9Publications
2H-index
8Citations
Publications 9
Newest
Published on May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
Uday Shankar Shanthamallu2
Estimated H-index: 2
(ASU: Arizona State University),
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University)
+ 3 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
Machine Learning (ML) and Artificial Intelligence (AI) algorithms are enabling several modern smart products and devices. Furthermore, several initiatives such as smart cities and autonomous vehicles utilize AI and ML computational engines. The current and emerging applications and the growing industrial interest in AI necessitate introducing ML algorithms at the undergraduate level. In this paper, we describe a series of activities to introduce ML in undergraduate digital signal processing (DSP...
Sunil Rao2
Estimated H-index: 2
,
Andreas Spanias25
Estimated H-index: 25
,
Cihan Tepedelenlioglu26
Estimated H-index: 26
Published on Jul 1, 2018
Farib Khondoker (ASU: Arizona State University), Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University)
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
When collecting solar energy via photovoltaic (PV) panel arrays, one common issue is the potential occurrence of faults. Faults arise from panel short-circuit, soiling, shading, ground leakage and other sources. Machine learning algorithms have enabled data-based classification of faults. In this paper, we present an Internet-based PV array fault monitoring simulation using the Java-Dsp(j-Dsp)simulation environment. We first develop a solar array simulation in J-DSP and then form appropriate gra...
Published on Jun 23, 2018
Abhinav Dixit (ASU: Arizona State University), Uday Shankar Shanthamallu2
Estimated H-index: 2
(ASU: Arizona State University)
+ 9 AuthorsPhotini Spanias3
Estimated H-index: 3
(ASU: Arizona State University)
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...
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...
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
Published on Apr 1, 2016 in MELECON (Mediterranean Electrotechnical Conference)
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University),
David Ramirez Dominguez3
Estimated H-index: 3
(ASU: Arizona State University)
+ 8 AuthorsYoshitaka Morimoto1
Estimated H-index: 1
Monitoring utility-scale solar arrays was shown to minimize cost of maintenance and help optimize the performance of the array under various conditions. In this paper, we describe the design of an 18 kW experimental facility that consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchan...
Published on Mar 1, 2016 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
S. Adithya1
Estimated H-index: 1
(UCSD: University of California, San Diego),
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University)
+ 3 AuthorsV. Ramasubramanian12
Estimated H-index: 12
(PES University)
We address the problem of automatic segmentation of the unit database in unit-selection based TTS and propose template based forced alignment segmentation in the one-pass dynamic programming (DP) framework with several variants: i) multi-template representation derived by modified K-means (MKM) algorithm, ii) context-independent and context-dependent templates for reduced multi-template representation, iii) segmental K-means algorithm with MKM modeling of phone classes, as a template-based equiv...
12