neuromorphic vision
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2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Mani Teja Vijjapu ◽  
Mohammed E. Fouda ◽  
Agamyrat Agambayev ◽  
Chun Hong Kang ◽  
Chun-Ho Lin ◽  
...  

AbstractNeuromorphic vision sensors have been extremely beneficial in developing energy-efficient intelligent systems for robotics and privacy-preserving security applications. There is a dire need for devices to mimic the retina’s photoreceptors that encode the light illumination into a sequence of spikes to develop such sensors. Herein, we develop a hybrid perovskite-based flexible photoreceptor whose capacitance changes proportionally to the light intensity mimicking the retina’s rod cells, paving the way for developing an efficient artificial retina network. The proposed device constitutes a hybrid nanocomposite of perovskites (methyl-ammonium lead bromide) and the ferroelectric terpolymer (polyvinylidene fluoride trifluoroethylene-chlorofluoroethylene). A metal-insulator-metal type capacitor with the prepared composite exhibits the unique and photosensitive capacitive behavior at various light intensities in the visible light spectrum. The proposed photoreceptor mimics the spectral sensitivity curve of human photopic vision. The hybrid nanocomposite is stable in ambient air for 129 weeks, with no observable degradation of the composite due to the encapsulation of hybrid perovskites in the hydrophobic polymer. The functionality of the proposed photoreceptor to recognize handwritten digits (MNIST) dataset using an unsupervised trained spiking neural network with 72.05% recognition accuracy is demonstrated. This demonstration proves the potential of the proposed sensor for neuromorphic vision applications.


Author(s):  
Yousheng Zou ◽  
Yuqing Song ◽  
Xiaobao Xu ◽  
Yuanzhou Zhang ◽  
Zeyao Han ◽  
...  

As an artificial perception system, neuromorphic vision sensing system can imitate the complex image sensing and processing functions of the human visual neural network. In order to stimulate the nervous...


2021 ◽  
pp. 2104632
Author(s):  
Xuanyu Shan ◽  
Chenyi Zhao ◽  
Xinnong Wang ◽  
Zhongqiang Wang ◽  
Shencheng Fu ◽  
...  

2021 ◽  
Vol 15 ◽  
Author(s):  
Yihan Lin ◽  
Wei Ding ◽  
Shaohua Qiang ◽  
Lei Deng ◽  
Guoqi Li

With event-driven algorithms, especially spiking neural networks (SNNs), achieving continuous improvement in neuromorphic vision processing, a more challenging event-stream dataset is urgently needed. However, it is well-known that creating an ES-dataset is a time-consuming and costly task with neuromorphic cameras like dynamic vision sensors (DVS). In this work, we propose a fast and effective algorithm termed Omnidirectional Discrete Gradient (ODG) to convert the popular computer vision dataset ILSVRC2012 into its event-stream (ES) version, generating about 1,300,000 frame-based images into ES-samples in 1,000 categories. In this way, we propose an ES-dataset called ES-ImageNet, which is dozens of times larger than other neuromorphic classification datasets at present and completely generated by the software. The ODG algorithm implements image motion to generate local value changes with discrete gradient information in different directions, providing a low-cost and high-speed method for converting frame-based images into event streams, along with Edge-Integral to reconstruct the high-quality images from event streams. Furthermore, we analyze the statistics of ES-ImageNet in multiple ways, and a performance benchmark of the dataset is also provided using both famous deep neural network algorithms and spiking neural network algorithms. We believe that this work shall provide a new large-scale benchmark dataset for SNNs and neuromorphic vision.


Nano Energy ◽  
2021 ◽  
pp. 106439
Author(s):  
Jianyu Du ◽  
Donggang Xie ◽  
Qinghua Zhang ◽  
Hai Zhong ◽  
Fanqi Meng ◽  
...  

2021 ◽  
Author(s):  
Ranajay Medya ◽  
Sai Sukruth Bezugam ◽  
Dwijay Bane ◽  
Manan Suri

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4320
Author(s):  
Bowen Du ◽  
Weiqi Li ◽  
Zeju Wang ◽  
Manxin Xu ◽  
Tianchen Gao ◽  
...  

Nowadays, our lives have benefited from various vision-based applications, such as video surveillance, human identification and aided driving. Unauthorized access to the vision-related data greatly threatens users’ privacy, and many encryption schemes have been proposed to secure images and videos in those conventional scenarios. Neuromorphic vision sensor (NVS) is a brand new kind of bio-inspired sensor that can generate a stream of impulse-like events rather than synchronized image frames, which reduces the sensor’s latency and broadens the applications in surveillance and identification. However, the privacy issue related to NVS remains a significant challenge. For example, some image reconstruction and human identification approaches may expose privacy-related information from NVS events. This work is the first to investigate the privacy of NVS. We firstly analyze the possible security attacks to NVS, including grayscale image reconstruction and privacy-related classification. We then propose a dedicated encryption framework for NVS, which incorporates a 2D chaotic mapping to scramble the positions of events and flip their polarities. In addition, an updating score has been designed for controlling the frequency of execution, which supports efficient encryption on different platforms. Finally, extensive experiments have demonstrated that the proposed encryption framework can effectively protect NVS events against grayscale image reconstruction and human identification, and meanwhile, achieve high efficiency on various platforms including resource-constrained devices.


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