{"id":914,"date":"2025-05-05T20:20:30","date_gmt":"2025-05-05T20:20:30","guid":{"rendered":"https:\/\/els-journal.net\/wp\/about\/"},"modified":"2025-05-05T20:20:37","modified_gmt":"2025-05-05T20:20:37","slug":"29103-hardware-acceleration-of-singular-spectral-analysis-a-case-study-on-nqr-spectroscopy","status":"publish","type":"page","link":"https:\/\/els-journal.net\/wp\/?page_id=914","title":{"rendered":"29103 Hardware Acceleration of Singular Spectral Analysis: A Case Study on NQR Spectroscopy"},"content":{"rendered":"\n\n\n

Vol. 29, No. 1 – June 2025<\/h3>\n

Hardware Acceleration of Singular Spectral Analysis: A Case Study on NQR Spectroscopy<\/h3>\n

https:\/\/doi.org\/10.53314\/ELS2529022V<\/h5>\n
K. Thulasiram Varma, Rangababu Peesapati, and Ch. V. Rama Rao<\/h5>\n
Abstract<\/b><\/h5>\n
Singular Spectral Analysis (SSA) is a computationally intensive approach to denoise and detect a time-series signal. <\/span>It requires the evaluation of eigenvalues and eigenvectors of a<\/span><\/h5>
covariance matrix, which is the computationally intensive step in the SSA algorithm. The current work presents a feasible <\/span>approach to implement the algorithm in embedded hardware <\/span>using a PYNQ-Z2 Field Programmable Gate Array (FPGA) <\/span>board. We implemented the algorithm using both the Processing <\/span>System (PS) and the Programmable Logic (PL) of the PYNQ-Z2 <\/span>System on Chip (SoC) with the help of a High-Level Synthesis <\/span>(HLS) tool. A case study is carried out on a Nuclear <\/span>Quadruple Resonance (NQR) signal. The implementation result <\/span>demonstrates a hardware acceleration of 15.48x with respect to <\/span>the equivalent software implementation of the algorithm on the  <\/span>ARM Cortex-A9 processor.<\/span><\/h5>\n
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