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19th International conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2022

作者： Quero, Carlos Alexander Osorio Durini, Daniel Rangel-Magdaleno, Jose De Jesus Martinez-Carranza, Jose Ramos-Garcia, Ruben Electronics Department Puebla Mexico Computer Science Department Puebla Mexico Optical Department Puebla Mexico

ISBN: (数字)9781665455084

ISBN: (纸本)9781665455084

In recent years, it had been an evolution in the sensors used by vision systems. Different technologies from RGB camera, IR-camera, LIDAR, RADAR, or sensor combinations can work in different spectrum bands. A new alternative to the vision system is the single-pixel camera (SPC), an architecture based on the compressive sensing theory for 2D reconstruction images. SPC's feature is used as the single detector that measures the linear combinations of pixels through patterns projection of structured light over the object that would like to photograph instead of using an array of pixels as in RGB sensors or cameras. These architectures offer the advantage that they can be adapted to different spectrum bands as near-infrared (NIR), and could operate in scenarios with low visibility or under rain, mist, or fog conditions. Currently, there is little information that discusses the problem of spatial resolution in SPC in scattering conditions for which we defined a methodology to evaluate the spatial resolution SPC's in terms of point spread function (PSF), and we analyzed how it affected the spatial resolution with the distance and under foggy conditions, for an SPC with active illumination at the wavelength at 1550 nm. © 2022 IEEE.

关键词： Graphics processing unit

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Automated Optimization Algorithm for 16-channel Silicon WDM System Using 4-Level Cascaded Mach-Zehnder Interferometers

Automated Optimization Algorithm for 16-channel Silicon WDM ...

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Asia Communications and Photonics conference and Exhibition (ACP)

作者： Yichen Wu Zihan Tao Qipeng Yang Bitao Shen Ming Jin Haowen Shu Bowen Bai Xingjun Wang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Frontier Science Center for Nano-Optoelectronics Peking University Beijing China Peking University Yangtze Delta Institute of Optoelectronics Nantong China Peng Cheng Laboratory Shenzhen China

On-chip Wavelength Division Multiplexing (WDM) devices have been widely used in optical communications and signal processing to increase data throughput. As the number of wavelength channels increases, the performance of WDM devices is susceptible to various types of errors from fabrication, design and environmental changes. To address this issue, we demonstrate an automated optimization control method based on the Gradient-Descent algorithm and examine the performance of applying this algorithm to a silicon photonic 16-channel WDM device utilizing 4-level cascaded tunable Mach-Zehnder interferometers. The extinction ratio and crosstalk of all 16 channels are successfully optimized with different initial temperatures in simulation. This procedure provides a general approach to automated control and optimize complex photonic systems.

关键词： Performance evaluation Mach-Zehnder interferometers Fabrication Signal processing algorithms Crosstalk Wavelength division multiplexing Extinction ratio

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G-SM3: High-Performance Implementation of GPU-based SM3 Hash Function

G-SM3: High-Performance Implementation of GPU-based SM3 Hash...

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International conference on Parallel and Distributed systems (ICPADS)

作者： Jiankuo Dong Sheng Lu Pinchang Zhang Fangyu Zheng Fu Xiao School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Chinese Academy of Sciences Institute of Information Engineering Beijing China

Hash is one of the most important algorithms of cryptography, it is widely used in cryptographic primitives, such as digital signature, key exchange and so on. Further, hash cryptography is also the core operation of blockchain technology. With the explosive growth of the number of IoT devices and the rapid development of blockchain technology, the computing performance of hash has received widespread attention. The GPU high-performance computing platforms with a number of arithmetic cores are widely used in cryptographic optimization and acceleration. In this paper, we propose an efficient parallel accelerated framework of SM3 cryptography hash function based on GPU parallel computing devices, short for GPU-based SM3 (G-SM3). Our G-SM3 optimizes the implementation of the hash cryptographic algorithm from three aspects: parallelism, memory access and instructions. On the desktop GPU NVIDIA Titan V, the peak performance of G-SM3 reaches 23 GB/s, which is more than 7.5 times the performance of OpenSSL on a top-level server CPU (E5-2699V3) with 16 cores. On the embedded GPU which consumes less than 40 W, the SM3 throughput reaches 3.8 GB/s, which is even better than the performance of the serverlevel CPU. Based on the same GTX 1080, our performance is 1.12 times that of the fastest known GPU implementation, and the latency is reduced by more than 95%. Compared to other platforms, our G-SM3 has a huge advantage.

关键词： Performance evaluation Hash functions Instruction sets Graphics processing units Parallel processing Throughput Blockchains

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基于非线性超表面的多任务光电混合神经网络(特邀)

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光学学报 2024年第10期44卷 314-321页

作者：罗栩豪董思禹魏泽勇王占山程鑫彬同济大学物理科学与工程学院精密光学工程技术研究所上海200092 先进微结构材料教育部重点实验室上海200092 上海市数字光学前沿科学研究基地上海200092 上海市全光谱高性能光学薄膜器件及应用专业技术服务平台上海200092

提出一种新型的光电混合神经网络计算模型,通过非线性超表面对线性和非线性光波进行多维度光场调控,并行处理多个通道的信息。非线性超表面采用U型纳米天线,基于自旋和几何相位对基频和倍频光波的相位进行编码和复用,结合后端的浅层电... 详细信息

提出一种新型的光电混合神经网络计算模型,通过非线性超表面对线性和非线性光波进行多维度光场调控,并行处理多个通道的信息。非线性超表面采用U型纳米天线,基于自旋和几何相位对基频和倍频光波的相位进行编码和复用,结合后端的浅层电子神经网络分别实现对手写数字图像输入的高精度分类(盲测准确率为95.53%)和高质量重建(平均峰值信噪比大于26 dB)。所提方法为发展光学、光电、电子模拟和数字计算技术提供了新思路,从而实现大规模并行、高通量和更广泛的人工智能系统。

关键词：物理光学光电计算人工智能非线性超表面并行处理数字分类图像重建

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optical Character Recognition Using Optimized Convolutional Networks*

Optical Character Recognition Using Optimized Convolutional ...

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International conference on Fog and Mobile Edge Computing (FMEC)

作者： Anum Nawaz Muhammad Irfan Tomi Westerlund Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Turku Finland

optical Character Recognition (OCR) has been a prominent area of research in pattern recognition for several decades, owing to its broad application potential in smart living. To improve offline OCR on mobile devices with limited computing resource, we have optimized Convolutional Neural Networks (CNNs) to efficiently detect text using minimal resources. To achieve this, we employed two distinct pretrained CNN models, namely AlexNet and Inception-V3, for feature extraction. Leveraging these models' unique characteristics and capabilities to extract diverse features, we aimed to enhance the classifier's accuracy. This, in turn, facilitates the development of an efficient edge-device application for faster and higher-quality OCR. Experimental results demonstrate that our proposed optimized algorithm outperforms existing CNN-based methods in the field of OCR, particularly in the categorization and detection of handwritten digits and character recognition. The conducted research yielded impressive accuracy results, with up to 97% accuracy on the MNIST dataset and 95.5% accuracy on the NIST dataset.

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FASENet: A Two-Stream Fall Detection and Activity Monitoring Model Using Pose Keypoints and Squeeze-and-Excitation Networks 14th

FASENet: A Two-Stream Fall Detection and Activity Monitoring...

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14th Asian conference on Intelligent information and Database systems (ACIIDS)

作者： Suarez, Jessie James P. Orillaza, Nathaniel S. Naval, Prospero C., Jr. Univ Philippines Diliman Dept Comp Sci Quezon City Philippines Univ Philippines Manila Coll Med Manila Philippines

ISBN: (纸本)9783031219665;9783031219672

Numerous frameworks have already been proposed for vision-based fall detection and activity monitoring. These works have leveraged state-of-the-art algorithms such as 2D and 3D convolutional neural networks in order to analyze and process video data. However, these models are computationally expensive which prevent their use at scale for low-resource devices. Moreover, previous works in literature have not considered modelling features for simple and complex actions given a video segment. This information is crucial when trying to identify actions for a given task. Hence, this work proposes an architecture called FASENet, a 1D convolutional neural network-based two-stream fall detection and activity monitoring model using squeeze-and-excitation networks. By using pose keypoints as inputs for the model instead of raw video frames, it is able to use 1D convolutions which is computationally cheaper than using 2D or 3D convolutions, thereby making the architecture more efficient. Furthermore, FASENet primarily has two streams to process pose segments, a compact and dilated stream which aims to extract features for simple and complex actions, respectively. Furthermore, squeeze-and-excitation networks are used in between these streams to recalibrate features after their combination based on their importance. The network was evaluated in three publicly available datasets, the Adhikari Dataset, the UP-Fall Dataset, and the UR-Fall Dataset. Through the experiments, FASENet was able to outperform prior state-of-the-art work on the Adhikari Dataset for accuracy, precision, and F1. The model was shown to have the best precision on the UP-Fall and UR-Fall Datasets. Finally, it was also observed that FASENet was able to reduce false positive rates compared to a previously related study.

关键词： Computer vision Activity monitoring Deep learning Video processing

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Edge-Based Technology for the Design of Bridges Alerting System: A Practical Case

Edge-Based Technology for the Design of Bridges Alerting Sys...

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AFRICON conference

作者： Stefano Rinaldi Alessandro Depari Paolo Ferrari Alessandra Flammini Emiliano Sisinni Department of Information Engineering University of Brescia Brescia Italy

The Internet of Things (IoT) paradigm has significantly changed many industries recently, including building and construction sectors. Since information is now more readily available and is better organized, new innovative services that were previously unimaginable have been created. The widespread adoption of cloud technology then made it possible also for devices with limited resources to access the computing power needed to develop sophisticated algorithms, enabling the realization of these services. However, there are some drawbacks to cloud computing, including high latency, issues with data security, low reliability, and demand for high bandwidth. Edge computing technology has been proposed recently in order to solve these issues. The target of the research work is to demonstrate the design of an edge-based real-time alerting system for bridges. It is possible to monitor the condition of bridges in real time by using edge devices with sensors and processing capabilities placed on or near the bridge. This can provide early warning of any changes in the structure or behavior of the bridge, allowing for timely maintenance or repairs. The proposed system has been installed and tested on a bridge in the Italian town of Bassano Bresciano. The obtained results have allowed us to demonstrate the benefits that this technology can provide to Structural Health Monitoring (SHM) systems.

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MEMS Gas Sensor Array Fault Diagnostic Unit for Microsystem Applications

MEMS Gas Sensor Array Fault Diagnostic Unit for Microsystem ...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International conference on

作者： Jie Fu Jian Yang Hongshuo Fu Bing Liu Wenbin Zheng Ping Fu Harbin Institute of Technology School of Electronics and Information Engineering Harbin China No.703 Research Institute of CSSC Harbin China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

With the rapid development of the Internet of Things and environmental sensing systems, intelligent microsystems based on MEMS (Micro-Electro-Mechanical systems) sensor arrays for environmental sensing have broad application prospects and advantages in fields such as environmental monitoring, medical diagnostics, and battlefield perception. These systems, particularly those aimed at machine olfaction, integrate MEMS gas sensor arrays with pattern recognition algorithms to monitor gases in the environment in real-time. The performance of these systems heavily depends on the accuracy of the MEMS gas sensor array signals. However, due to the characteristics of the sensitive materials and integrated manufacturing processes, environmental changes can cause failures in the MEMS gas sensor arrays, leading to abnormal operation of the entire microsystem. Therefore, this paper designs a fault diagnosis unit for MEMS gas sensor arrays tailored for microsystem applications, measuring 19mm x 19mm x 3mm. This unit can identify five types of sensor faults: shock, bias, constant output, power loss, and precision degradation. It also features fault isolation and localization capabilities, providing a basis for data recovery in case of MEMS gas sensor failures. The fault diagnosis unit employs a multi-task fault diagnosis algorithm based on 1D CNN-LSTM, achieving a fault identification accuracy of 99.93%, fault isolation accuracy of 99.43%, and fault localization accuracy of 98.12%. It occupies 194k of FLASH and 20k of SRAM, with an average running time of 234ms and a power consumption of 11.58mW.

关键词： Micromechanical devices Fault diagnosis Location awareness Accuracy Power demand Array signal processing Random access memory Sensors Gas detectors Sensor arrays

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A Novel Virtual Node-Based Multi-Controller Management Architecture for LEO Mega-Constellation Satellite Networks

A Novel Virtual Node-Based Multi-Controller Management Archi...

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International conference on Wireless Communications and Signal processing (WCSP)

作者： Yunyao Dong Xiaofan Xu Yueyue Zhang Siming Wang Ping Du Du Xu Xiaoning Zhang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan China Shanghai Key Laboratory of Satellite Network Shanghai Satellite Network Research Institute Co. Ltd Shanghai China

The development of 6G communication has made low earth orbit (LEO) satellite networks to be a critical component of global communication systems. The high density of LEO satellites enables mobile devices to have continuous communication. However, managing satellites in mega-constellation LEO satellite networks poses a scalability challenge due to the large number of LEO satellite nodes. Besides, due to the high mobility of LEO satellites, inter-satellite handover frequently occurs, which leads to the degradation of traffic QoS (Quality of Service). To address these issues, in this paper, we propose a novel virtual node-based multi-controller management architecture for LEO-satellite constellation networks, that can effectively shield the dynamics of satellite network topology. In specific, we first redefine the virtual node as a geographical area with clear boundaries. Then, we set up domain controllers in each virtual node to manage the satellites. Besides, we propose a handover algorithm to reduce delay jitter and delay by jointly considering the probability of handover and the cost of rerouting. We conduct simulations on Starlink Phase I and the results show that compared with benchmark algorithms, the proposed architecture can reduce the delay jitter by more than 50% and the delay by 10%.

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Cognitive Internet of things (CIoT) a success for data collection 6

Cognitive Internet of things (CIoT) a success for data colle...

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6th International conference on Image information processing, ICIIP 2021

作者： Fayaz, Fayaz Ahmad Malik, Arun Yatoo, Arshad Ahmad National Institute of Electronics Information Technology India Lovely Professional University Jalandhar India

ISBN: (纸本)9781665433617

In conjunction with data generated by intelligent machines, cognitive IoT uses cognitive computing technology and the actions these devices can accomplish. The Cognitive Internet of Things (CIoT) is seen as the new IoT is combined with mental and mutual frameworks to facilitate success and intelligence. This leading research area has recently emerged as intelligent sensing. Researchers examine the sensing data performance problems with Smarter technologies, in which people usually use smart gadgets, contribute training datasets towards the Cognitive Internet of things collected by sensors. Moreover, Cognitive Intent of Things (CIOT), shortcomings in the scope of sensing data, contribute to the loss of human life and civil instability. To answer this problem, we propose a new metric in this article, called the Quality of information Coverage (QIC), which will personify information distribution and data sensing incentives to leverage the QIC. In addition, a market-based compensation system is being developed to pledge the QIC. To produce optimum kickbacks for CIoT and news outlets, we evaluate the optimal business solution and examine an acceptable representation. Then, by detailed computations, the results of a competition reward system are studied. The findings suggest that the way the method of reward management hits the balance point with a greater QIC than most current systems. The QIC told a system in this work guarantees that, relative to existing algorithms, the sample variance number obtained datasets for specific regions decreases by approximately less than 40 to 55 percent since these data sets are calibrated. Compared to these non-QIC-Aware algorithms, the average sale price is Sensing proposed should be less than 17 to 18 percent. © 2021 IEEE.

关键词： Internet of things

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