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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Rajesh Kannan Megalingam Sakthiprasad Kuttankulangara Manoharan Avinash Hegde Kota Department of Electronics and Communication Engineering Humanitarian Technology (HuT) Labs Amrita Vishwa Vidyapeetham Amritapuri India Electrical and Computer Engineering Department Digital Design Environment Laboratory (DDEL) University of Cincinnati Cincinnati OH USA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

Annapoorna is a tele-operated robot to deliver food, water, and medicines to COVID patients in isolation wards. It is equipped with a telemedicine facility for doctors and nurses to interact with patients remotely. This greatly reduces direct contact of doctors, nurses, and food service staff with patients during COVID or other pandemic times, thereby reducing infection risk. The battery powered robot reaches each bedside to serve food and medicines to the patients in isolation wards. The robot can be controlled by smartphone app or wireless joystick controller. The tablet attached at the top of the robot serves dual purpose. The doctors and nurses can interact with the patient with any online meeting portals like Skype, Google Hangout, Zoom etc. In addition, the operator can control the robot using the tablet camera there by saving additional cost for the camera.

关键词： COVID-19 Wireless communication Navigation Tuberculosis Telemedicine Robot vision systems Cameras

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Using Swarm Intelligence Algorithms for Optimization in IoT Applications 10

Using Swarm Intelligence Algorithms for Optimization in IoT ...

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10th IEEE Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Roshanzamir, Mohamad Nematollahi, Mohammad Ali Darbandy, Mohammad Tayarani Roshanzamir, Mahdi Alizadehsani, Roohallah Shoeibi, Afshin Ahmadian, Davood Fasa University Faculty of Engineering Department of Computer Engineering Fasa74617-81189 Iran Fasa University Faculty of Science Department of Computer Sciences Fasa74617-81189 Iran Islamic Azad University Taft School of Architecture Taft*** Iran University of Tabriz Department of Electrical and Computer Engineering Tabriz Iran Victoria Australia K. N. Toosi University of Technology FPGA Laboratory Faculty of Electrical Engineering Tehran Iran University of Tabriz Faculty of Mathematics Statistics and Computer Sciences Tabriz Iran

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

Internet of Things (IoT) is considered as the next industrial revolution in the years ahead. Many devices will have been connected to the Internet and more and more devices were added to them. The number of these types of devices is increased rapidly. So, management of them needs advanced technologies. Optimization of different problems in IoT is an essential subject of many researches and Swarm Intelligence algorithms (SI) are one of the most successful methods in this field. They were inspired by the collective behavior of simple creatures which communicate directly and indirectly with each other. Although they are considered as simple agents, their collective behavior is smart. They can overcome most problems in their life using collective behavior. In this research, we investigated some of the most common optimization problems in IoT and the SI algorithms which are used to solve them. © 2022 IEEE.

关键词： Internet of things

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From Circuits to SoC Processors: Arithmetic Approximation Techniques & Embedded Computing Methodologies for DSP Acceleration

arXiv

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arXiv 2023年

作者： Leon, Vasileios K. National Technical University of Athens School of Electrical and Computer Engineering Division of Computer Science Microprocessors and Digital Systems Laboratory 9 Heroon Polytechniou Zografou Campus Athens15780 Greece

The recent end of Dennard’s Scaling and the declining Moore’s Law have signified a new era for computing systems. Power efficiency has now become a critical factor for both cloud and edge computing. Concurrently, the rapid growth of compute-intensive applications from the digital Signal Processing (DSP) and Artificial Intelligence (AI) domains challenges the resources of computing systems. As a result, the computing industry is forced to find alternative design approaches and computing platforms to sustain increased power efficiency, while providing sufficient performance. Among the examined solutions, Approximate Computing, Hardware Acceleration, and Heterogeneous Computing have gained great momentum. Approximate Computing is a novel design paradigm that exploits the inherent error resilience of DSP/AI applications to deliver gains in power, area, and/or performance by reducing the quality of the results. Hardware Acceleration refers to the execution of demanding computational tasks on specialized hardware, such as the Application-Specific Integrated Circuits (ASICs) and the Field-Programmable Gate Arrays (FPGAs), rather than general-purpose processors. Finally, Heterogeneous Computing refers to versatile processing architectures, such as the Vision Processing Units (VPUs), which integrate more than one type of processor and various memory technologies. In this Dissertation, we introduce design solutions and methodologies, built on top of the preceding computing paradigms, for the development of energy-efficient DSP and AI accelerators. In particular, we adopt the promising paradigm of Approximate Computing and apply new approximation techniques in the design of arithmetic circuits. Based on our methodology, these arithmetic approximation techniques are then combined with hardware design techniques to implement approximate ASIC- and FPGA-based DSP and AI accelerators. Moreover, we propose methodologies for the efficient mapping of DSP/AI kernels on distinctive embe

关键词： digital signal processing

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Scattering exceptional point in the visible

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Light(Science & Applications) 2023年第10期12卷 2228-2235页

作者： Tao He Zhanyi Zhang Jingyuan Zhu Yuzhi Shi Zhipeng Li Heng Wei Zeyong Wei Yong Li Zhanshan Wang Cheng-Wei Qiu Xinbin Cheng MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai 200092China Institute of Precision Optical Engineering School of Physics Science and EngineeringTongji UniversityShanghai 200092China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 200092China Shanghai Frontiers Science Center of Digital Optics Shanghai 200092China Shanghai Professional Technical Service Platform for Full-Spectrum and High-Performance Optical Thin Film Devices and Applications Shanghai 200092China Department of Electronic Science and Technology Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Institute of Acoustics School of Physics Science and EngineeringTongji UniversityShanghai 200092China

Exceptional point (EP) is a special degeneracy of non-Hermitian systems. One-dimensional transmission systems operating at EPs are widely studied and applied to chiral conversion and sensing. Lately, two-dimensional systems at EPs have been exploited for their exotic scattering features, yet so far been limited to only the non-visible waveband. Here, we report a universal paradigm for achieving a high-efficiency EP in the visible by leveraging interlayer loss to accurately control the interplay between the lossy structure and scattering lightwaves. A bilayer framework is demonstrated to reflect back the incident light from the left side ( | r_(−1) | >0.999) and absorb the incident light from the right side ( | r_(+1) | < 10^(–4)). As a proof of concept, a bilayer metasurface is demonstrated to reflect and absorb the incident light with experimental efficiencies of 88% and 85%, respectively, at 532 nm. Our results open the way for a new class of nanoscale devices and power up new opportunities for EP physics.

关键词： visible exceptional incident

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Few-Shot Learning Based on Information Geometry

Few-Shot Learning Based on Information Geometry

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2023 China Automation Congress, CAC 2023

作者： Chen, Jiacheng Fu, Yao Wang, Tian Liu, Deyuan Wang, Jian Snoussi, Hichem Institute of Artificial Intelligence Beihang University Beijing China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China Institute of Artificial Intelligence Sklsde Beihang University Zhongguancun Laboratory Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science Wuhan University Wuhan China University of Technology of Troyes Laboratory of Computer Science and Digital Society Troyes France

ISBN: (纸本)9798350303759

Few-shot classification, as the name suggests, is a very challenging task where only a few training examples are available for each category. Extensive research has been done to solve the problem of the high cost of labeling a large number of samples. A recent research strategy is to find similarity measures between query sets and support sets for judgment. Recently, information geometry techniques have been applied in deep learning research, which explores the relationships and properties between probability distributions by treating them as geometric objects and using geometric tools. Based on information geometry, this paper characterizes differences in the geometric properties of images as differences in probability distributions. Assume that the data satisfies a multivariate Gaussian distribution and that different images are considered as different points on the manifold according to the parameters that characterize the probability distribution of the images. We measure the similarity between different images by the geodesic and validate three methods for estimating the geodesic on a neural network model with 64 neurons at four levels and improve the model performance by Firth bias. The GM, GMF, KM, and KMF methods are validated in the resnet-12 and resnet-18 frameworks using a meta-learning approach on the miniImageNet and CUB datasets, respectively. The experiments show that the models trained by the above four algorithms have good performance and have advantages over the classical algorithms. © 2023 IEEE.

关键词： Probability distributions

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Multi-Objective Optimized Generative Adversarial Networks For Video Anomaly Detection

Multi-Objective Optimized Generative Adversarial Networks Fo...

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2023 China Automation Congress, CAC 2023

作者： Cao, Zhexiao Fu, Yao Wang, Tian Liu, Deyuan Wang, Jian Snoussi, Hichem Institute of Artificial Intelligence Beihang University Beijing China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China Institute of Artificial Intelligence Sklsde Beihang University Zhongguancun Laboratory Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science Wuhan University Wuhan China University of Technology of Troyes Laboratory of Computer Science and Digital Society Troyes France

ISBN: (纸本)9798350303759

The objective of video anomaly detection is to distinguish events within videos that deviate from expected normal behavior. An effective anomaly detection model requires strong spatio-temporal feature extraction capabilities to capture both appearance and motion information from the video. We utilize a generative adversarial networks model to perform anomaly detection by predicting future frame. The model is trained with multi-objective loss function for appearance and motion constraints. We introduce multi-objective optimization algorithm to guarantee the convergence of training objectives, including intensity loss, gradient loss for appearance constraints, optical flow loss for motion constraints and adversarial loss for adversarial training. By ensuring corresponding generative outcome for both normal events to confirm to expectation and anomaly events not to do so, we obtain an anomaly detection model with satisfying experimental result on video anomaly detection datasets, showing the success of the proposed training strategy. © 2023 IEEE.

关键词： Multiobjective optimization

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High Sensitivity Strain and Temperature Sensing With Cascaded Sagnac Interferometer Based on Harmonic Vernier Effect

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Photonic Sensors 2025年第3期15卷 1-14页

作者： Ding, Xin Liu, Shen Wang, Mengjie Chen, Nan Zhang, Weiguan Wang, Yiping Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education/Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China Guangdong and Hong Kong Joint Research Centre for Optical Fiber Sensors Shenzhen University Shenzhen China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China School of Electrical Engineering Nantong University Nantong China

To further improve sensor sensitivity, a strain and temperature sensor based on the harmonic Vernier effect with cascaded Sagnac interferometers (SIs) is proposed. Through a combination of simulation and experimentation, it is shown that the basic Vernier effect can be realized when the lengths of the polarization-maintaining fiber (PMF) in two SIs are slightly different. Furthermore, the first-order harmonic Vernier effect can be achieved when the lengths of two PMFs are approximately integer multiples. This sensor, leveraging the harmonic Vernier effect, demonstrates higher sensitivity. Compared to a single SI, the strain sensitivity based on the basic Vernier effect is improved to 61.93 pm/με with a magnification factor of 7.6, and the temperature sensitivity is improved to 14.29 nm/°C with a magnification factor of 9.4. For the first-order harmonic Vernier effect, the strain sensitivity increases to 146.35 pm/με with a magnification factor of 18, and the temperature sensitivity increases to 24.92 nm/°C with a magnification factor of 16.5. Additionally, the sensor based on the harmonic Vernier effect exhibits good stability in strain and temperature measurement. Unlike the basic Vernier effect, the harmonic Vernier effect does not require strict control of the reference and sensing interferometer lengths, further increasing sensitivity. Due to its simple structure and low cost, the proposed sensor shows significant potential for applications in high-precision measurement engineering and medical treatment.

关键词： Cascade Sagnac interferometers harmonic Vernier effect strain and temperature measurement polarization-maintaining fiber

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Resource Allocation for Semantic Segmentation Tasks in Autonomous Driving: A Likelihood Active Inference Approach

Resource Allocation for Semantic Segmentation Tasks in Auton...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinxin Zhu F. Richard Yu Ying He Biao He College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The latest Segment Anything Model enables realtime scene annotation and understanding for autonomous driving systems, enhancing driving safety. However, effectively allocating resources for real-time performance and accuracy remains challenging in edge-cloud architectures. Traditional reinforcement learning struggles with poor generalization and the explorationexploitation dilemma, making it difficult to define clear reward functions. To address this, we propose a likelihood active inference approach to optimize resource allocation and improve system resource utilization. We use "intelligence" as a high-level indicator to quantify the efficiency of cognition in active inference, evaluating the difference between predicted and actual states during policy exploration. Experimental results show our algorithm outperforms mainstream deep reinforcement learning algorithms, improving sample efficiency and suitability for dynamically changing task workloads.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Signal processing Prediction algorithms Inference algorithms Real-time systems Resource management Speech processing Autonomous vehicles

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Graph Convolutional Neural Network for Human Action Recognition: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第2期2卷 128-145页

作者： Ahmad, Tasweer Jin, Lianwen Zhang, Xin Lai, Songxuan Tang, Guozhi Lin, Luojun School of Electronics and Information Engineering South China University of Technology Guangzhou510006 China Electrical and Computer Engineering Department COMSATS University Islamabad Islamabad45550 Pakistan Guangdong Artificial Intelligence and Digital Economy Laboratory Guangzhou510335 China School of Mathematics and Computer Science Fuzhou University Fuzhou350122 China

Video-based human action recognition is one of the most important and challenging areas of research in the field of computer vision. Human action recognition has found many pragmatic applications in video surveillance, human–computer interaction, entertainment, autonomous driving, etc. Owing to the recent development of deep learning methods for human action recognition, the performance of action recognition has significantly enhanced for challenging datasets. Deep learning techniques are mainly used for recognizing actions in images and videos comprising of Euclidean data. A recent development in deep learning methods is the extension of these techniques to non-Euclidean data or graph data with many nodes and edges. Human body skeleton resembles a graph, therefore, the graph convolutional network (GCN) is applicable to the non-Euclidean body skeleton. In the past few years, GCN has emerged as an important tool for skeleton-based action recognition. Therefore, we conduct a survey using GCN methods for action recognition. Herein, we present a comprehensive overview of recent GCN techniques for action recognition, propose a taxonomy for the categorization of GCN techniques for action recognition, carry out a detailed study of the benchmark datasets, enlist relevant resources and open-source codes, and finally provide an outline for future research directions and trends. To the best of authors’ knowledge, this is the first survey for action recognition using GCN techniques. Impact Statement— Graph convolutional neural networks have marked a great progress in recent years. There is a similarity between body skeleton and a graph;therefore, GCNs have been widely used for skeleton-based action recognition. In this article, we summarize recent graph-based action recognition techniques, provide a deeper insight of these methods, list source-codes and available resources. This article will help the researchers to develop a basic understanding of graph convolutional methods fo

关键词： Convolution

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Is it necessary to calculate young’s modulus in afm nanoindentation experiments regarding biological samples?

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Micro and Nanosystems 2021年第1期13卷 3-8页

作者： Kontomaris, Stylianos-Vasileios Malamou, Anna Stylianou, Andreas Faculty of Architecture Engineering & Built Environment Athens Metropolitan College Athens Greece Radar Systems and Remote Sensing Lab School of Electrical & Computer Engineering National Technical University Athens Greece Cancer Biophysics Laboratory Department of Mechanical and Manufacturing Engineering Faculty of Engineering University of Cyprus Nicosia Cyprus

Background: The determination of the mechanical properties of biological samples using Atomic Force Microscopy (AFM) at the nanoscale is usually performed using basic models arising from the contact mechanics theory. In particular, the Hertz model is the most frequently used theoretical tool for data processing. However, the Hertz model requires several assumptions, such as homogeneous and isotropic samples and indenters with perfectly spherical or conical shapes. As it is widely known, none of these requirements are 100 % fulfilled for the case of indentation experiments at the nanoscale. As a result, significant errors arise in the Young’s modulus calculation. At the same time, an analytical model that could account complexities of soft biomaterials, such as nonlinear behavior, anisotropy, and heterogeneity, may be far-reaching. In addition, this hypothetical model would be ‘too difficult’ to be applied in real clinical activities since it would require a very heavy workload and highly specialized personnel. Objective: In this paper, a simple solution is provided to the aforementioned dead-end. A new approach is introduced in order to provide a simple and accurate method for mechanical characterization at the nanoscale. Method: The ratio of the work done by the indenter on the sample of interest to the work done by the indenter on a reference sample is introduced as a new physical quantity that does not require homogeneous, isotropic samples or perfect indenters. Results: The proposed approach provides an accurate solution from not only a physical perspective but also a simpler solution which does not require activities such as the determination of the cantilever’s spring constant and the dimensions of the AFM tip. Conclusion: It has been observed from this opinion paper that the solution aims to provide a significant opportunity to overcome the existing limitations provided by Hertzian mechanics and apply AFM techniques in real clinical activities. © 2021 Bentham

关键词： Indentation

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