Harish Chintakunta

Works (4)

Updated: July 5th, 2023 15:42

2016 journal article

DISCOVERING THE WHOLE BY THE COARSE A topological paradigm for data analysis

IEEE SIGNAL PROCESSING MAGAZINE, 33(2), 95–104.

By: H. Krim n, T. Gentimis* & H. Chintakunta*

TL;DR: The increasing interest in big data applications is ushering in a large effort in seeking new, efficient, and adapted data models to reduce complexity, while preserving maximal intrinsic information. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2014 conference paper

Computing persistent features in big data: A distributed dimension reduction approach

International conference on acoustics speech and signal processing.

By: A. Wilkerson n, H. Chintakunta n & H. Krim n

TL;DR: A simplicial collapse algorithm called the selective collapse is developed by representing the previously developed strong collapse as a forest and uses that forest data to improve the speed of both the strong collapse and of persistent homology. (via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018

2014 journal article

Distributed Localization of Coverage Holes Using Topological Persistence

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 62(10), 2531–2541.

By: H. Chintakunta n & H. Krim n

author keywords: Algebraic topology; distributed algorithms; graph theory; sensor networks
TL;DR: This work uses algebraic topological methods to define a coverage hole, and develops provably correct algorithm to detect a hole, which is then partitioned into smaller subnetworks, ensuring that the holes are preserved, and checking for holes in each. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2014 conference paper

Real time detection of harmonic structure: A case for topological signal analysis

International conference on acoustics speech and signal processing.

By: S. Emrani n, H. Chintakunta n & H. Krim n

TL;DR: Using delay embeddings, the timedomain signal is transformed into a point cloud, whose topology reflects the periodic behavior of the signal, and the Euler characteristic provides for a fast computation of topology of the resulting manifold. (via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2025) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.