{"id":781,"date":"2024-08-10T14:16:04","date_gmt":"2024-08-10T14:16:04","guid":{"rendered":"https:\/\/els-journal.net\/wp\/about\/"},"modified":"2025-04-27T08:37:09","modified_gmt":"2025-04-27T08:37:09","slug":"28103-a-novel-separable-convolution-architecture-for-image-processing-applications-clone","status":"publish","type":"page","link":"https:\/\/els-journal.net\/wp\/?page_id=781","title":{"rendered":"28103 Accelerated Curve Fitting Approach for Denoised NQR Signal Parameter Estimation"},"content":{"rendered":"\n\n\n

Vol. 28, No. 1 – June 2024<\/h3>\n

Accelerated Curve Fitting Approach for Denoised NQR Signal Parameter Estimation<\/h3>\n

https:\/\/doi.org\/10.53314\/ELS2428022V<\/h5>\n
K. Thulasiram Varma and Rangababu Peesapati<\/h5>\n
Abstract<\/b><\/h5>\n
Signal parameters such as amplitude, frequency, decay constant, and phase play an important role in identifying the signal\u2019s origin in many real-life applications. Standard gradient-based curve-fitting approaches are computationally complex to implement on hardware because of large matrix multiplication and partial derivatives. The current work demonstrates a feasible approach to extract parameters from exponential decay Nuclear Quadrupole Resonance (NQR) signals using derivative-free optimization approaches on a PYNQ-Z2 FPGA board. The hardware implementation results indicate 0.12 seconds is required to extract the amplitude, frequency, decay constant, and phase from 5×10-3<\/sup><\/h5>
sec signals with the residual error in the order of 10-5<\/sup>.<\/h5>\n
Full text: <\/a><\/h5>\n\n\n\n\n\"cited<\/a>\n\n\n\n\nGoogle Scholar Citations <\/a>\n\n\n\n\n
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