Multiple target recognition and position identification circuit based on memristor

被引：4

作者：

Sun, Junwei ^{[1
]}

Xiao, Xiao ^{[1
]}

Liu, Peng ^{[1
]}

Wang, Yanfeng ^{[1
]}

机构：

[1] Zhengzhou Univery Light Ind, Zhengzhou 450002, Peoples R China

来源：

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS | 2022年 / 151卷

基金：

中国国家自然科学基金;

关键词：

Memristor; Multiple target recognition; Position identification; Target preprocessing; FULLY CONVOLUTIONAL NETWORK; NEURAL-NETWORK; MACHINE; IMPLEMENTATION; STABILITY; DESIGN; MODEL;

D O I：

10.1016/j.aeue.2022.154223

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Traditional target recognition has low precision and the data processing is complicated when recognizing multiple targets. A multiple target recognition and position identification circuit based on memristor is designed in this paper, which can simultaneously realize the recognition for multiple targets with different shapes. The dual recognition is used in the designed recognition circuit, including position identification and size recognition, which will improve the recognition accuracy. Integral holding module and double threshold detection module with variable threshold based on memristor can flexibly construct the circuit according to different requirements, and the application range of the circuit is expanded. The signal processing module is designed in this paper to effectively filter out redundant targets and improve the reliability of the circuit. Different from the existing target recognition technologys, the multiple target recognition circuit based on memristor adopts the method of analog circuit to provide reference for the further development of target recognition.

引用

页数：12

共 68 条

[1] Memory analysis for memristors and memristive recurrent neural networks [J].

Bao, Gang ;

Zhang, Yide ;

Zeng, Zhigang .

IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2020, 7 (01) :96-105

[2] Non-ideal memristor synapse-coupled bi-neuron Hopfield neural network: Numerical simulations and breadboard experiments [J].

Chen, Chengjie ;

Bao, Han ;

Chen, Mo ;

Xu, Quan ;

Bao, Bocheng .

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, 2019, 111

[3] Global Mittag-Leffler stability and synchronization of memristor-based fractional-order neural networks [J].

Chen, Jiejie ;

Zeng, Zhigang ;

Jiang, Ping .

NEURAL NETWORKS, 2014, 51 :1-8

[4] MEMRISTOR - MISSING CIRCUIT ELEMENT [J].

CHUA, LO .

IEEE TRANSACTIONS ON CIRCUIT THEORY, 1971, CT18 (05) :507-+

[5] Machine vision technology for the forest products industry [J].

Conners, RW ;

Kline, DE ;

Araman, PA ;

Drayer, TH .

COMPUTER, 1997, 30 (07) :43-&

[6] Direct synthesis approach for designing high selectivity microstrip distributed bandpass filters combined with deep learning [J].

Dai, Xinyue ;

Yang, Qian ;

Du, Hao ;

Li, Jianxing ;

Guo, Cheng ;

Zhang, Anxue .

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, 2021, 131

[7] Exploiting Multi-View SAR Images for Robust Target Recognition [J].

Ding, Baiyuan ;

Wen, Gongjian .

REMOTE SENSING, 2017, 9 (11)

[8] Identification of structural dynamic characteristics based on machine vision technology [J].

Dong, C. Z. ;

Ye, X. W. ;

Jin, T. .

MEASUREMENT, 2018, 126 :405-416

[9] Sparse fully convolutional network for face labeling [J].

Dong, Minghui ;

Wen, Shiping ;

Zeng, Zhigang ;

Yan, Zheng ;

Huang, Tingwen .

NEUROCOMPUTING, 2019, 331 :465-472

[10] A Mixed Mode Neural Network Circuitry for Object Recognition Application [J].

Erkmen, Burcu ;

Vural, Revna Acar ;

Kahraman, Nihan ;

Yildirim, Tulay .

CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2013, 32 (01) :29-46

← 1 2 3 4 5 6 7 →