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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2020

09.01.2020

Memristor Crossbar Array Based ACO For Image Edge Detection

verfasst von: Yongbin Yu, Quanxin Deng, Liyong Ren, Nyima Tashi

Erschienen in: Neural Processing Letters | Ausgabe 2/2020

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Abstract

Memristor provides an available way to design and deploy swarm intelligence. As a typical swarm intelligence algorithm, ant colony optimization is implemented by the memristor crossbar array to make image edge detection in this paper. Firstly, a non-linear voltage-controlled memristor model with a relaxation term is proposed. Then, an improved ant colony optimization with padding strategy is designed. Thirdly, a memristor crossbar array with external control circuits is designed to implement ant colony optimization for image edge detection, which offers high device density and parallel computing. In the course of ant colony optimization based image edge detection deployed by memristor crossbar array, the threshold to generating edges can be directly chosen as the mean of the final conductance matrix. On the one hand, experiment results show that more delicate edges can be detected by proposed method compared to holistically-nested edge detection based on neural networks. On the other hand, Figure of merit of proposed method is better than that of Sobel operator.
Vorheriger Artikel Performance Evaluation of a New BP Algorithm for a Modified Artificial Neural Network
Nächster Artikel Feature Enhancement for Multi-scale Object Detection

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Literatur
1.
Pereira F, Tavares J (2009) Bio-inspired algorithms for the vehicle routing problem. Springer, BerlinCrossRef
2.
Das TK, Venayagamoorthy GK, Aliyu UO (2008) Bio-inspired algorithms for the design of multiple optimal power system stabilizers: SPPSO and BFA. IEEE Trans Ind Appl 44(5):1445–1457CrossRef
3.
4.
5.
Baterina A, Oppus C (2010) Image edge detection using ant colony optimization. WSEAS Trans Signal Process 6:58–67
6.
Tian J, Weiyu Y, Chen L, Ma L (2011) Image edge detection using variation-adaptive ant colony optimization. Trans Comput Collective Intell 5:27–40
7.
Chua L (1971) Memristor-the missing circuit element. IEEE Trans Circuit Theory 18(5):507–519CrossRef
8.
Chua LO (2012) The fourth element. Proceedings IEEE 100(6):1920–1927CrossRef
9.
Chua LO (2018) How we predicted the memristor. Nat Electron 1(5):322–322CrossRef
10.
Zidan MA, Strachan JP, Lu WD (2018) The future of electronics based on memristive systems. Nat Electron 1(1):22–29CrossRef
11.
Lee J, Wei L (2017) On-demand reconfiguration of nanomaterials: when electronics meets ionics. Adv Mater 30:1702770CrossRef
12.
Kim K-H, Jo S, Gaba S, Wei L (2010) Nanoscale resistive memory with intrinsic diode characteristics and long endurance. Appl Phys Lett 96:053106–053106CrossRef
13.
Govoreanu B, Kar G, Chen YY, Paraschiv V, Kubicek S, Fantini A, Radu I, Goux L, Clima S, Degraeve R, Jossart N, Richard O (2011) \(10\times 10~\text{nm}^{2}\) hf/hfox crossbar resistive ram with excellent performance, reliability and low-energy operation. In: 2011 IEEE international electron devices meeting (IEDM)
14.
Torrezan A, Strachan JW, Medeiros-Ribeiro G, Williams S (2011) Sub-nanosecond switching of a tantalum oxide memristor. Nanotechnology 22:485203CrossRef
15.
Choi BJ, Torrezan A, Strachan JW, Kotula P, Lohn A, Marinella M, Li Z, Williams S, Yang JJ (2016) High-speed and low-energy nitride memristors. Adv Funct Mater 26(29):5290–5296CrossRef
16.
Yang JJ, Strukov D, Stewart D (2013) Memristive devices for computing. Nat Nanotechnol 8:13–24CrossRef
17.
Zhou J, Cai F, Wang Q, Chen B, Gaba S, Wei L (2016) Very low programming-current RRAM with self-rectifying characteristics. IEEE Electron Device Lett 37:1–1CrossRef
18.
Xia Q, Yang JJ, Wei W, Li X, Williams S (2010) Self-aligned memristor cross-point arrays fabricated with one nanoimprint lithography step. Nano Lett 10:2909–14CrossRef
19.
Zidan MA, Omran H, Naous R, Sultan A, Fahmy HAH, Lu WD, Salama KN (2016) Single-readout high-density memristor crossbar. Sci Rep 6(1):18863CrossRef
20.
21.
Alibart F, Zamanidoost E, Strukov DB (2013) Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat Commun 4(1):2072CrossRef
22.
Prezioso M, Merrikh-Bayat F, Hoskins B, Adam G, Likharev K, Strukov D (2014) Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521:61CrossRef
23.
Sheridan P, Cai F, Chao D, Ma W, Zhang Z, Wei L (2017) Sparse coding with memristor networks. Nat Nanotechnol 12:784CrossRef
24.
Choi S, Shin J, Lee J, Sheridan P, Wei L (2017) Experimental demonstration of feature extraction and dimensionality reduction using memristor networks. Nano Lett 17:3113–3118CrossRef
25.
Pershin Y, Di Ventra M (2016) Memcomputing implementation of ant colony optimization. Neural Process Lett 44:265–277CrossRef
26.
Pajouhi Z, Roy K (2018) Image edge detection based on swarm intelligence using memristive networks. IEEE Trans Comput Aided Des Integr Circuits Syst 37(9):1774–1787 09CrossRef
27.
28.
Keysers D, Deselaers T, Gollan C, Ney H (2007) Deformation models for image recognition. IEEE Trans Pattern Anal Mach Intell 29(8):1422–1435 08CrossRef
29.
30.
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 779–788
31.
32.
33.
Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670MathSciNetCrossRef
34.
Yu J, Zhang B, Kuang Z, Lin D, Fan J (2017) iprivacy: image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Trans Inf Forensics Secur 12(5):1005–1016 05CrossRef
35.
Yu J, Tao D, Wang M, Rui Y (2015) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45(4):767–779CrossRef
36.
Zhang J, Jun Y, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction. IEEE Trans Image Process 27:1MathSciNetCrossRef
37.
Ding L, Goshtasby A (2000) On the canny edge detector. Pattern Recognit 34:721–725CrossRef
38.
Gao W, Zhang X, Yang L, Liu H (2010) An improved sobel edge detection 5:67–71
39.
40.
Pande S, Bhadouria V, Ghoshal D (2012) A study on edge marking scheme of various standard edge detectors. Int J Comput Appl 44:33–37
Metadaten
Titel
Memristor Crossbar Array Based ACO For Image Edge Detection
verfasst von
Yongbin Yu
Quanxin Deng
Liyong Ren
Nyima Tashi
Publikationsdatum
09.01.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10179-6

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