no code implementations • 1 Apr 2024 • Di Fan, Ayan Biswas, James Paul Ahrens
In our research, we conducted a thorough assessment of various machine learning algorithms for both classification and regression tasks relevant to predicting wildfires.
no code implementations • 9 Sep 2023 • Ayan Biswas, Supriya Dhabal, Palaniandavar Venkateswaran
The proposed DSP and DL-based music genre classification algorithm and deployment architecture demonstrate a promising approach for music genre classification.
no code implementations • 11 Jan 2023 • Derek DeSantis, Ayan Biswas, Earl Lawrence, Phillip Wolfram
In this study, we propose a new method for combining in situ buoy measurements with Earth system models (ESMs) to improve the accuracy of temperature predictions in the ocean.
no code implementations • 5 Aug 2022 • Jingyi Shen, Haoyu Li, Jiayi Xu, Ayan Biswas, Han-Wei Shen
We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications.
no code implementations • 27 Sep 2021 • Tuhin Subhra Roy, Mintu Nandi, Ayan Biswas, Pinaki Chaudhury, Suman K Banik
We present an information-theoretic formalism to study signal transduction in four architectural variants of a model two-step cascade with increasing input population.
no code implementations • 31 Aug 2020 • Subhashis Hazarika, Ayan Biswas, Phillip J. Wolfram, Earl Lawrence, Nathan Urban
With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing.
no code implementations • 8 Dec 2019 • Qun Liu, Subhashis Hazarika, John M. Patchett, James Paul Ahrens, Ayan Biswas
Data modeling and reduction for in situ is important.
no code implementations • 13 Jun 2018 • Baibhab Chatterjee, Priyadarshini Panda, Shovan Maity, Ayan Biswas, Kaushik Roy, Shreyas Sen
In this work, we will analyze, compare and contrast existing neuron architectures with a proposed mixed-signal neuron (MS-N) in terms of performance, power and noise, thereby demonstrating the applicability of the proposed mixed-signal neuron for achieving extreme energy-efficiency in neuromorphic computing.