Band-Limited Proximal FISTA for Efficient Sparse Harmonic Recovery on MCU

Compressed sensing (CS) enables signal reconstruction from fewer measurements when the signal is sparse in a transform domain. However, executing ℓ1-regularized recovery on MCU-class hardware is challenging due to limited compute resources and the cost of repeated forward and adjoint operator evaluations. This paper presents a band-limited proximal variant of FISTA that enforces known spectral support during thresholding, restricting the effective optimization domain without changing the measurement model. We implement a complete CS reconstruction pipeline on an STM32F407 (Cortex-M4) using CMSIS-DSP FFT/IFFT kernels and evaluate it using ECG waveforms acquired through an AD8232 front end as benchmark signals. With M=340 measurements (33% of uniform sampling), the embedded implementation achieves a PRDN of 24.38%, closely matching MATLAB references (CVX: 22.64%, FISTA: 22.39%) under identical hyperparameters. Cycle-accurate profiling shows that FFT/IFFT-based forward/adjoint operators dominate the per-iteration runtime. Under a 60 Hz band-limited setting, the required iterations are reduced from 30 to 16 with an acceptable PRDN, demonstrating a practical trade-off between reconstruction accuracy and computational cost on MCU-class devices.

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Publication Details

Journal
Big Data and Cognitive Computing
Published
2026-06-25
DOI
https://doi.org/10.3390/bdcc10070205
Primary Topic
Sparse and Compressive Sensing Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Band-Limited Proximal FISTA for Efficient Sparse Harmonic Recovery on MCU

김민중, Daejin Park, Seongho Cho
Big Data and Cognitive Computing
Sparse and Compressive Sensing Techniques
article

Band-Limited Proximal FISTA for Efficient Sparse Harmonic Recovery on MCU

김민중, Daejin Park, Seongho Cho
article en

Abstract

Compressed sensing (CS) enables signal reconstruction from fewer measurements when the signal is sparse in a transform domain. However, executing ℓ1-regularized recovery on MCU-class hardware is challenging due to limited compute resources and the cost of repeated forward and adjoint operator evaluations. This paper presents a band-limited proximal variant of FISTA that enforces known spectral support during thresholding, restricting the effective optimization domain without changing the measurement model. We implement a complete CS reconstruction pipeline on an STM32F407 (Cortex-M4) using CMSIS-DSP FFT/IFFT kernels and evaluate it using ECG waveforms acquired through an AD8232 front end as benchmark signals. With M=340 measurements (33% of uniform sampling), the embedded implementation achieves a PRDN of 24.38%, closely matching MATLAB references (CVX: 22.64%, FISTA: 22.39%) under identical hyperparameters. Cycle-accurate profiling shows that FFT/IFFT-based forward/adjoint operators dominate the per-iteration runtime. Under a 60 Hz band-limited setting, the required iterations are reduced from 30 to 16 with an acceptable PRDN, demonstrating a practical trade-off between reconstruction accuracy and computational cost on MCU-class devices.

Big Data and Cognitive ComputingVol. 10(7)
Kyungpook National University (KR)
National Research Foundation, National Research Foundation of Korea, IC Design Education Center, Iran Telecommunication Research Center, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion, Division of Human Resource Development
Industry, innovation and infrastructure
Openalex Percentile: Top 7%
Sparse and Compressive Sensing Techniques
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