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Uday Shankar Shanthamallu
Arizona State University
11Publications
2H-index
11Citations
Publications 11
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...
Published on Oct 2, 2018in arXiv: Machine Learning
Uday Shankar Shanthamallu2
Estimated H-index: 2
,
Jayaraman J. Thiagarajan10
Estimated H-index: 10
+ 1 AuthorsAndreas Spanias25
Estimated H-index: 25
Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships, in many scenarios a single graph is rarely sufficient to succinctly represent all interactions, and hence multi-layered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutio...
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...
Published on Jul 1, 2018
Farib Khondoker (ASU: Arizona State University), Trevor Thornton22
Estimated H-index: 22
(ASU: Arizona State University)
+ 1 AuthorsUday Shankar Shanthamallu2
Estimated H-index: 2
(ASU: Arizona State University)
Internet of Things (IoT) has enabled several applications related to data analytics. In this paper, an intuitive method for optimizing activity detection data is presented. Further applications include exploring detection accuracies of physical activities such as walking intensity and movement on stairs. This method utilizes different Microcontroller Units (MCUs) with embedded sensors which are used for activity detection. Additionally, this method also incorporates supervised learning - more sp...
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 Jun 23, 2018
Andreas Spanias25
Estimated H-index: 25
,
Andreas Spanias1
Estimated H-index: 1
+ 8 AuthorsErica Forzani20
Estimated H-index: 20
Published on Jun 23, 2018
Andreas Spanias25
Estimated H-index: 25
,
Jennifer Blain Christen8
Estimated H-index: 8
+ 8 AuthorsWendy M. Barnard
Published on Jan 1, 2018in arXiv: Learning
Uday Shankar Shanthamallu2
Estimated H-index: 2
,
Jayaraman J. Thiagarajan10
Estimated H-index: 10
,
Andreas Spanias25
Estimated H-index: 25
Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields. Graph attention networks (GAT), a recent addition to the broad class of feature learning models in graphs, utilizes the attention mechanism to efficiently learn continuous vector representations for semi-supervised learning problems. In this paper, we perform a ...
Published on Jan 1, 2018in arXiv: Machine Learning
Uday Shankar Shanthamallu2
Estimated H-index: 2
,
Jayaraman J. Thiagarajan10
Estimated H-index: 10
+ 1 AuthorsAndreas Spanias25
Estimated H-index: 25
Modern data analysis pipelines are becoming increasingly complex due to the presence of multi-view information sources. While graphs are effective in modeling complex relationships, in many scenarios a single graph is rarely sufficient to succinctly represent all interactions, and hence multi-layered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutio...
12