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Pavan Turaga
Arizona State University
4Publications
1H-index
1Citations
Publications 4
Newest
Anirudh Som1
Estimated H-index: 1
,
Hongjun Choi + 2 AuthorsPavan Turaga1
Estimated H-index: 1
Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and computer vision applications. Key bottlenecks to their large scale adoption are computational expenditure and difficulty in incorporating them in a differentiable architecture. We take an important step in this paper to mitigate these bottlenecks by proposing a novel one-step approach to generate PIs directly from the i...
Published on May 28, 2019
Eduardo Salazar (UCSF: University of California, San Francisco), Mayank Gupta1
Estimated H-index: 1
(ASU: Arizona State University)
+ 4 AuthorsMatthew P. Buman (UCSF: University of California, San Francisco)
Suhas Lohit4
Estimated H-index: 4
(ASU: Arizona State University),
Kuldeep Kulkarni6
Estimated H-index: 6
(ASU: Arizona State University)
+ 2 AuthorsAmit Ashok12
Estimated H-index: 12
(UA: University of Arizona)
Traditional algorithms for compressive sensing recovery are computationally expensive and are ineffective at low measurement rates. In this paper, we propose a data-driven noniterative algorithm to overcome the shortcomings of earlier iterative algorithms. Our solution, ReconNet , is a deep neural network, which is learned end-to-end to map block-wise compressive measurements of the scene to the desired image blocks. Reconstruction of an image becomes a simple forward pass through the network an...
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...
Suhas Lohit4
Estimated H-index: 4
,
Rajhans Singh1
Estimated H-index: 1
+ 1 AuthorsPavan K. Turaga21
Estimated H-index: 21
In resource-constrained environments, one can employ spatial multiplexing cameras to acquire a small number of measurements of a scene, and perform effective reconstruction or high-level inference using purely data-driven neural networks. However, once trained, the measurement matrix and the network are valid only for a single measurement rate (MR) chosen at training time. To overcome this drawback, we answer the following question: How can we jointly design the measurement operator and the reco...
1