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Human Identification by Mean of Optoelectronic Reservoir Computing
Zhejiang University, China.
Zhejiang University, China.
Zhejiang University, China.
Zhejiang University, China.
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2022 (English)In: Proceedings of SPIE - The International Society for Optical Engineering, SPIE , 2022Conference paper, Published paper (Refereed)
Abstract [en]

As an improvement of the traditional recurrent neural networks (RNN), the reservoir computing (RC) only needs to train one output connection weight matrix linearly, which greatly reduces the number of machine learning network calculations. The optoelectronic RC can be realized with a delay feedback loop composed of optical and electrical devices. It has the advantages of lower power consumption and faster speed than the all-electric RC scheme. At the same time, it is easier to be controlled than the all-optical RC scheme. In this paper, we propose to employ the optoelectronic RC to process radar signals to distinguish different persons in the indoor environment. The radar signal required for the simulation is referred from the IDRad data set, which contains the echo signals of the frequency modulated continuous wave (FMCW) radar, and five persons of different ages are free to move around in the room, which is close to the real scene. First, the echo signal is processed and the micro-Doppler features are extracted, and each frame corresponds to a row vector. Then, this vector is used as the input signal of the optoelectronic RC. We numerically studied the impact of parameters such as the size of the RC and the regularization coefficient in the system. Finally, the classification accuracy of five targets reaches 87%.

Place, publisher, year, edition, pages
SPIE , 2022.
Keywords [en]
Delay feedback loop, Human identification, Optoelectronic, Reservoir computing
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:ri:diva-58898DOI: 10.1117/12.2625789Scopus ID: 2-s2.0-85125558651ISBN: 9781510651845 OAI: oai:DiVA.org:ri-58898DiVA, id: diva2:1648429
Conference
13th International Photonics and OptoElectronics Meetings, POEM 2021, 6 November 2021 through 8 November 2021
Note

Funding details: 2020LC0AD01; Funding details: National Natural Science Foundation of China, NSFC, 61771424, 62101483; Funding details: Vetenskapsr├ądet, VR, 2019-05197_VR, P109599; Funding details: Natural Science Foundation of Zhejiang Province, ZJNSF, LQ21F010015, LZ18F010001; Funding details: Latvijas Zin─ütnes Padome, lzp-2021/1-0062; Funding details: National Key Research and Development Program of China, NKRDPC, 2020YFB1805700; Funding details: Fundamental Research Funds for the Central Universities, 2020QNA5012; Funding text 1: The National Key Research and Development Program of China (2020YFB1805700), in part by the Natural National Science Foundation of China under Grant 61771424 and 62101483, the Natural Science Foundation of Zhejiang Province under Grant LZ18F010001 and LQ21F010015, the Swedish Research Council 2019-05197_VR, RISE SK funded project Optical Neural Networks (P109599), the Latvian Council of Science funded ONN-AI project No. lzp-2021/1-0062, State Key Laboratory of Advanced Optical Communication Systems and Networks of Shanghai Jiao Tong University, the Fundamental Research Funds for the Central Universities 2020QNA5012 and Zhejiang Lab (no. 2020LC0AD01).

Available from: 2022-03-30 Created: 2022-03-30 Last updated: 2023-06-02Bibliographically approved

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