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Mahesh K. Banavar
Clarkson University
83Publications
12H-index
473Citations
Publications 83
Newest
Published on Oct 1, 2018
Abhinav Dixit , Uday Shankar Shanthamallu2
Estimated H-index: 2
+ 2 AuthorsMahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
This work in progress paper describes software that enables online machine learning experiments in an undergraduate DSP course. This software operates in HTML5 and embeds several digital signal processing functions. The software can process natural signals such as speech and can extract various features, for machine learning applications. For example in the case of speech processing, LPC coefficients and formant frequencies can be computed. In this paper, we present speech processing, feature ex...
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Published on Oct 1, 2018
Seema Rivera (Clarkson University), Mahesh K. Banavar12
Estimated H-index: 12
(Clarkson University),
Dana M. Barry5
Estimated H-index: 5
(Clarkson University)
The central focus of this work-in-progress is to investigate the following: (1) What do the lesson plans created by teachers reveal about their understanding of science and engineering practices? (2) Will including programming exercises in all lesson plans improve STEM skills in general, and coding skills in particular? And (3) Will integrating science and engineering practices in high school lesson plans improve student retention in STEM and STEM-related areas? To answer these questions, we dev...
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Published on Oct 1, 2018
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
+ 2 AuthorsMahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
The analysis of a distributed consensus algorithm for estimating the maximum of the node initial state values in a network is considered in the presence of communication noise. Conventionally, the maximum is estimated by updating the node state value with the largest received measurements in every iteration at each node. However, due to additive channel noise, the estimate of the maximum at each node has a positive drift at each iteration and this results in nodes diverging from the true max val...
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 Mar 2, 2018in Synthesis Lectures on Communications
Sai Zhang4
Estimated H-index: 4
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
+ 1 AuthorsMahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
Abstract The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) g...
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Published on Dec 1, 2017
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University),
Sai Zhang4
Estimated H-index: 4
(ASU: Arizona State University)
+ 4 AuthorsRafaela Villalpando-Hernandez1
Estimated H-index: 1
A distributed spectral clustering algorithm to group sensors based on their location in a wireless sensor network (WSN) is proposed. For machine learning and data mining applications in WSN's, gathering data at a fusion center is vulnerable to attacks and creates data congestion. To avoid this, we propose a robust distributed clustering method without a fusion center. The algorithm combines distributed eigenvector computation and distributed K-means clustering. A distributed power iteration meth...
2 Citations Source Cite
Published on Oct 1, 2017 in FIE (Frontiers in Education Conference)
Kevin Mack (Clarkson University), Mahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
This paper describes the use of Bluetooth hardware for localization and signal processing education on Android smart-phones and tablets. The localization algorithm uses the Received Signal Strength Indcation (RSSI) value of transmitting devices in order to triangulate their position. The concepts that are featured in the use of this technology have classroom relevant content such as multilateration (a matrix problem in linear algebra), wave properties and interactions (physics), statistics relat...
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Published on Oct 1, 2017 in FIE (Frontiers in Education Conference)
Mahesh K. Banavar12
Estimated H-index: 12
(Clarkson University),
Houchao Gan (Clarkson University)+ 1 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
This paper describes the use of a space usage determination algorithm for teaching signal processing and machine learning concepts to undergraduate electrical engineering and computer science students. An Android device transmits a high-frequency signal in an unknown space. The device determines the reflective properties of this unknown space by analyzing the received signal. Based on the features extracted from this signal, the app measures distances and determines how the space can be utilized...
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