The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kertas kerja ini menunjukkan reka bentuk dan pelaksanaan sistem pengesanan getaran berasaskan imej pada tatasusunan gerbang boleh diprogramkan medan (FPGA), yang bertujuan untuk aplikasi untuk penindasan gegaran untuk sistem bantuan pembedahan mikro. Sistem ini boleh mengekstrak komponen getaran dalam jalur frekuensi yang ditentukan pengguna daripada imej bergerak dalam masa nyata. Untuk pengesanan yang pantas dan mantap, kami menggunakan pendekatan statistik menggunakan aliran optik padat untuk memperoleh komponen getaran, dan mereka bentuk perkakasan tersuai berdasarkan kaedah Lucas-Kanade (LK) untuk mengira aliran optik. Dan untuk penapisan laluan jalur tanpa kelewatan fasa, kami melaksanakan penggabung linear Fourier berbilang jalur terhad (BMFLC), sejenis penapis laluan jalur suai yang boleh menyusun semula isyarat input sebagai campuran isyarat sinusoidal dengan berbilang frekuensi dalam tempoh yang ditentukan jalur, tanpa kelewatan fasa. Keseluruhan sistem dilaksanakan sebagai saluran paip yang mendalam pada Xilinx Kintex-7 XC7K325T FPGA tanpa menggunakan sebarang memori luaran. Kami menggunakan aritmetik titik tetap untuk mengurangkan penggunaan sumber sambil mengekalkan ketepatan hampir dengan aritmetik titik terapung berketepatan dua. Eksperimen empirikal mendedahkan bahawa sistem yang dicadangkan mengekstrak komponen gegaran frekuensi tinggi daripada gerakan tangan, dengan gerakan frekuensi rendah yang disengajakan berjaya ditapis keluar. Sistem ini boleh memproses imej bergerak VGA pada 60fps, dengan kelewatan kurang daripada 1 µs untuk BMFLC, mencadangkan keberkesanan seni bina saluran paip dalam. Selain itu, kami merancang untuk menyepadukan sistem segmentasi berasaskan CNN untuk meningkatkan ketepatan pengesanan dan menunjukkan hasil penilaian perisian awal.
Taito MANABE
Nagasaki University
Kazuya UETSUHARA
Nagasaki University
Akane TAHARA
Nagasaki University
Yuichiro SHIBATA
Nagasaki University
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Salinan
Taito MANABE, Kazuya UETSUHARA, Akane TAHARA, Yuichiro SHIBATA, "FPGA Implementation and Evaluation of a Real-Time Image-Based Vibration Detection System with Adaptive Filtering" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 12, pp. 1472-1480, December 2020, doi: 10.1587/transfun.2020VLP0002.
Abstract: This paper shows design and implementation of an image-based vibration detection system on a field-programmable gate array (FPGA), aiming at application to tremor suppression for microsurgery assistance systems. The system can extract a vibration component within a user-specified frequency band from moving images in real-time. For fast and robust detection, we employ a statistical approach using dense optical flow to derive vibration component, and design a custom hardware based on the Lucas-Kanade (LK) method to compute optical flow. And for band-pass filtering without phase delay, we implement the band-limited multiple Fourier linear combiner (BMFLC), a sort of adaptive band-pass filter which can recompose an input signal as a mixture of sinusoidal signals with multiple frequencies within the specified band, with no phase delay. The whole system is implemented as a deep pipeline on a Xilinx Kintex-7 XC7K325T FPGA without using any external memory. We employ fixed-point arithmetic to reduce resource utilization while maintaining accuracy close to double-precision floating-point arithmetic. Empirical experiments reveal that the proposed system extracts a high-frequency tremor component from hand motions, with intentional low-frequency motions successfully filtered out. The system can process VGA moving images at 60fps, with a delay of less than 1 µs for the BMFLC, suggesting effectiveness of the deep pipelined architecture. In addition, we are planning to integrate a CNN-based segmentation system for improving detection accuracy, and show preliminary software evaluation results.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020VLP0002/_p
Salinan
@ARTICLE{e103-a_12_1472,
author={Taito MANABE, Kazuya UETSUHARA, Akane TAHARA, Yuichiro SHIBATA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={FPGA Implementation and Evaluation of a Real-Time Image-Based Vibration Detection System with Adaptive Filtering},
year={2020},
volume={E103-A},
number={12},
pages={1472-1480},
abstract={This paper shows design and implementation of an image-based vibration detection system on a field-programmable gate array (FPGA), aiming at application to tremor suppression for microsurgery assistance systems. The system can extract a vibration component within a user-specified frequency band from moving images in real-time. For fast and robust detection, we employ a statistical approach using dense optical flow to derive vibration component, and design a custom hardware based on the Lucas-Kanade (LK) method to compute optical flow. And for band-pass filtering without phase delay, we implement the band-limited multiple Fourier linear combiner (BMFLC), a sort of adaptive band-pass filter which can recompose an input signal as a mixture of sinusoidal signals with multiple frequencies within the specified band, with no phase delay. The whole system is implemented as a deep pipeline on a Xilinx Kintex-7 XC7K325T FPGA without using any external memory. We employ fixed-point arithmetic to reduce resource utilization while maintaining accuracy close to double-precision floating-point arithmetic. Empirical experiments reveal that the proposed system extracts a high-frequency tremor component from hand motions, with intentional low-frequency motions successfully filtered out. The system can process VGA moving images at 60fps, with a delay of less than 1 µs for the BMFLC, suggesting effectiveness of the deep pipelined architecture. In addition, we are planning to integrate a CNN-based segmentation system for improving detection accuracy, and show preliminary software evaluation results.},
keywords={},
doi={10.1587/transfun.2020VLP0002},
ISSN={1745-1337},
month={December},}
Salinan
TY - JOUR
TI - FPGA Implementation and Evaluation of a Real-Time Image-Based Vibration Detection System with Adaptive Filtering
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1472
EP - 1480
AU - Taito MANABE
AU - Kazuya UETSUHARA
AU - Akane TAHARA
AU - Yuichiro SHIBATA
PY - 2020
DO - 10.1587/transfun.2020VLP0002
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2020
AB - This paper shows design and implementation of an image-based vibration detection system on a field-programmable gate array (FPGA), aiming at application to tremor suppression for microsurgery assistance systems. The system can extract a vibration component within a user-specified frequency band from moving images in real-time. For fast and robust detection, we employ a statistical approach using dense optical flow to derive vibration component, and design a custom hardware based on the Lucas-Kanade (LK) method to compute optical flow. And for band-pass filtering without phase delay, we implement the band-limited multiple Fourier linear combiner (BMFLC), a sort of adaptive band-pass filter which can recompose an input signal as a mixture of sinusoidal signals with multiple frequencies within the specified band, with no phase delay. The whole system is implemented as a deep pipeline on a Xilinx Kintex-7 XC7K325T FPGA without using any external memory. We employ fixed-point arithmetic to reduce resource utilization while maintaining accuracy close to double-precision floating-point arithmetic. Empirical experiments reveal that the proposed system extracts a high-frequency tremor component from hand motions, with intentional low-frequency motions successfully filtered out. The system can process VGA moving images at 60fps, with a delay of less than 1 µs for the BMFLC, suggesting effectiveness of the deep pipelined architecture. In addition, we are planning to integrate a CNN-based segmentation system for improving detection accuracy, and show preliminary software evaluation results.
ER -