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Face Recognition Based on Fusion of Zonal Images 9

Face Recognition Based on Fusion of Zonal Images

引用

9th International Electrical engineering Congress, iEECON 2021

作者： Nualtim, Warinthorn Suwansantisuk, Watcharapan Kumhom, Pinit King Mongkut's University of Technology Thonburi Faculty of Engineering Department of Computer Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Faculty of Engineering Department of Electronic and Telecommunication Engineering Bangkok Thailand

ISBN: (纸本)9781728195841

Partial face recognition from videos has practical applications to surveillance and threats monitoring, but is difficult to perform accurately due to an absence of facial information and due to environmental effects, such as a poor resolution, noise, blur, and variation in light-intensity, in the input images. This research aims to design a method for partial face recognition based on a technique called zonal-image fusion. The proposed method uses different pose images from two videos as a source and recognizes the images against frontal faces in a database. The method handles misalignment and the absence of facial information in input images by fusing the input images and arriving at a single combined image. The fused image is partition into 3 × 3 sub-blocks. We compare each sub-block in the fused image to the corresponding sub-block in a database image, to obtain a measure of similarity. The database image that is most similar to the fused image is taken to an output image from face recognition. We compare the proposed method to existing methods by using publicly available dataset. The comparison shows that the proposed method is promising, although the face recognition rates have an enormous room for improvement. © 2021 IEEE.

关键词： Face recognition

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Dual-loop joint torque control based on lower limb exoskeleton robots 18

Dual-loop joint torque control based on lower limb exoskelet...

引用

18th IEEE Conference on Industrial electronics and Applications, ICIEA 2023

作者： Zhang, Meitong Zhang, Chi Yi, Feng Shu, Xindong Yuan, Ting Li, Zheng Hebei University of Science and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences School of Electrical Engineering Ningbo315201 China Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo315201 China University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Science Faculty of Electrical Engineering and Computer Science Ningbo315201 China Hebei University of Science and Technology School of Electrical Engineering Shijiazhuang050000 China

ISBN: (纸本)9798350312201

In the framework of impedance control, the torque trajectory of the joint is unpredictable due to nonlinear factors during the movement of the exoskeleton. To precisely predict command torque trajectory and compensate for the disturbances, we propose a dual-loop joint torque control system including predictive feedforward and a disturbance observer. A predictive feedforward module is adopted as the control outer loop based on the feedback control, and a general compensation module is used to compensate for the prediction error. The inner loop adopts a disturbance observer to estimate and compensate for low-frequency external disturbances. The simulation, which utilizes the actual torque of hip and knee joints as a reference, is conducted to prove the effectiveness of the proposed method. The comparison results demonstrate that the proposed control method can realize high bandwidth for exoskeleton joint control. © 2023 IEEE.

关键词： Torque control

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Deep Reinforcement Learning-enabled Dynamic UAV Deployment and Power Control in Multi-UAV Wireless Networks

Deep Reinforcement Learning-enabled Dynamic UAV Deployment a...

引用

IEEE International Conference on Communications (ICC)

作者： Yu Bai Zheng Chang Riku Jäntti Department of Information and Communications Engineering Aalto University Espoo Finland School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyväskylä Jyvaskyla Finland

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Using Unmanned Aerial Vehicles (UAVs) as aerial base stations for providing services to ground users has received growing research interest in recent years. The dynamic deployment of UAVs represents a significant research direction within UAV network studies. This paper introduces a highly adaptable UAV wireless network that accounts for the mobility of UAVs and users, the variability in their states, and the tunable transmission power of UAVs. The objective is to maximize energy efficiency while ensuring the minimum number of unserved online users. This dual objective is achieved by jointly optimizing the states, transmission powers, and movement strategies of UAVs. To address the variable state challenges posed by the dynamic environment, user and UAV data is encapsulated within a multi-channel map. A Convolutional Neural Network (CNN) then processes this map to extract key features. The deployment and power control strategy are determined by an agent trained by the Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) algorithm. Simulation results demonstrate the effectiveness of the proposed strategy in enhancing energy efficiency and reducing the number of unserved online users.

关键词： Wireless networks Heuristic algorithms Simulation Power control Autonomous aerial vehicles Feature extraction Energy efficiency

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A Comparative Analysis of Rainfall Forecasting in Cox’s Bazar using SARIMA, LSTM and GRU

A Comparative Analysis of Rainfall Forecasting in Cox’s Baz...

引用

International Conference on computer and Information technology (ICCIT)

作者： Md. Eyakub Ali Panna Shil Md. Shadman Shakib Anirban Chanda Robin Khisa Md. Azizul Baten Ferdousi Mayoa Department of Electrical and Electronic Engineering Noakhali Science & Technology University Noakhali Bangladesh Department of Statistics Shahjalal University of Science And Technology Sylhet Bangladesh Faculty of Computer Science and Engineering Sylhet International University Sylhet Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Evaluating climate change, particularly rainfall fluctuations, is critical for regions prone to extreme weather, like Cox's Bazar, Bangladesh. This study employs both traditional time-series models and deep learning techniques to forecast rainfall patterns. An Exploratory Data Analysis (EDA) was performed on the monthly average rainfall data (1981-2022) from the Bangladesh Meteorological Department, revealing significant trends and seasonal patterns that guided model selection. ARIMA (0,0,1)(2,0,1)12, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models were developed to predict future rainfall. Among them, the GRU model achieved the lowest training errors (RMSE: 5.61, MAE: 3.48), while LSTM excelled in test data performance, producing lower errors (RMSE: 7.79, MAE: 4.68). Additionally, LSTM demonstrated the smallest percentage difference between training and testing errors, highlighting its superior generalization capabilities compared to SARIMA and GRU. These findings underscore the effectiveness of LSTM in capturing complex rainfall patterns, contributing to improved flood risk assessment and water resource management strategies.

关键词： Training Rain Predictive models Market research Data models Floods Risk management Forecasting Long short term memory Water resources

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Increasing Degree of Ternary Bent Functions

Increasing Degree of Ternary Bent Functions

引用

International Symposium on Multiple-Valued Logic

作者： Milena Stanković Radomir S. Stanković Claudio Moraga Jaakko T. Astola Department of Computer Science Faculty of Electronic Engineering Niš Serbia Mathematical Institute of SASA Belgrade Serbia Faculty of Computer Science Technical University of Dortmund Dortmund Germany Department of Signal Processing Tampere University of Technology Tampere Finland

ISBN: (数字)9798350343083

ISBN: (纸本)9798350343090

Binary bent functions are often used as a basis for deriving cryptographically interesting sequences. For instance, such sequences are used to secure safe communication among unsafe channels. Ternary bent functions are a mathematically interesting object defined as a direct extension of the concept by referring to definition of binary bent functions in terms of Walsh spectral coefficients. The degree of ternary bent functions is defined as the largest sum of powers of variables in a term in their Generalized Positive-polarity Reed-Muller expressions for ternary functions. In cryptography, the corresponding expression for the binary case is also called the Algebraic Normal Form (ANF). It is supposed that bent functions with larger degrees are more suitable for practical applications. We present a way to increase the degree of ternary bent functions by using a particular class of FFT-like permutation matrices implementing certain well suited substitutions of variables.

关键词： Transforms Matrices Cryptography Logic

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An Overview of Machine Learning Applications in Blockchain for Green Energy Exchanges

An Overview of Machine Learning Applications in Blockchain f...

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Power engineering Conference (SAUPEC), Southern African Universities

作者： Pfano Nemakonde Fhulufhelo Nemangwele Mukovhe Ratshitanga Dept. of Physics Faculty Science Engineering and Agriculture University of Venda Thohoyandou South Africa Dept. of Electrical Electronic and Computer Engineering Cape Peninsula University of Technology Bellville South Africa

ISBN: (数字)9798331535162

ISBN: (纸本)9798331535179

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges.

关键词： Green energy Power engineering Reviews Machine learning Smart grids Peer-to-peer computing Blockchains Predictive analytics Forecasting Monitoring

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Computation Offloading and Resource Allocation for Digital Twin-empowered Mobile Edge Computing

Computation Offloading and Resource Allocation for Digital T...

引用

International Symposium on Wireless Communication Systems (ISWCS)

作者： Wenxin Wu Zheng Chang Zhuangzhuang Cui Tero Bodström Timo Hämäläinen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Department of Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

Mobile-edge computing (MEC) has been introduced as a promising paradigm to provide computing resources to resource-limited devices. Currently, most of the research consider static computation offloading and resource allocation in MEC, while ignoring the impact of mobility of mobile users (MUs). In this paper, we propose an algorithm to optimize the task offloading and resource allocation in Digital Twin (DT)-empowered MEC system in two steps. First, to obtain stable offloading choices, we propose an innovative algorithm for predicting stable association of MU and edge node (EN) pairs assisted by DT to enhance users’ experience. Second, taking into account the contention for resources and the constraints of offloading tasks, we construct an incentive mechanism based on reverse-auction and design an unit-revenue-based algorithm to maximize the overall revenue of MUs’ side. Simulation results demonstrate that the proposed algorithm can improve the task offloading success rate and the overall system utility, thus show the the effectiveness of our algorithm.

关键词： Wireless communication Multi-access edge computing Simulation Prediction algorithms Stability analysis Digital twins Resource management

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Model Predictive Control Method for Nonisolated Universal Battery Charger 32

Model Predictive Control Method for Nonisolated Universal Ba...

引用

32nd IEEE International Symposium on Industrial electronics, ISIE 2023

作者： Guler, Naki Fesli, Ugur Komurcugil, Hasan Bayhan, Sertac Gazi University Technology Faculty Department of Electrical-Electronic Engineering Ankara Turkey Qatar Environment and Energy Research Institute Doha Qatar Gazi University Technical Science Vocational School Electrical and Energy Department Ankara Turkey Eastern Mediterranean University Famagusta Department of Computer Engineering Via Mersin 10 Turkey

ISBN: (纸本)9798350399714

This paper proposes a Model Predictive Control (MPC) method for a universal bridgeless non-isolated wide-output voltage range onboard battery charger. The onboard battery charger has two different (boost and buck) operation modes in one cycle of the grid voltage. While the boost mode occurs twice in each half cycle, the buck mode takes place once in every half cycle. Contrary to the existing control methods, the proposed MPC method is based on grid current control and does not need a mode detection algorithm. Aside from this feature, the proposed method offers easy implementation, fast dynamic response, and no need for complex modulation schemes. The grid current reference is generated by a proportional-integral (PI) controller depending on the battery's current or voltage, since the battery should be charged in constant current (CC) and constant voltage (CV) modes. Simulation studies are conducted to investigate the effectiveness of the proposed control strategy for non-isolated wide-output voltage range bridgeless onboard battery chargers. Simulation studies are conducted using two different battery models having nominal voltages of 48V and 350V. The results reveal that the proposed method successfully charges the two batteries in both CV and CC modes. Moreover, the grid current is maintained at unity power factor and total harmonic distortion (THD) is smaller than the limits defined in international standards. © 2023 IEEE.

关键词： Model predictive control

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Design, Analysis and Fabrication of Dual Band Microstrip Patch Antenna for (L2) Band GPS and WiFi Applications

Design, Analysis and Fabrication of Dual Band Microstrip Pat...

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2022 International Conference for Advancement in technology, ICONAT 2022

作者： Oo, Wai Mar Tun, Hla Myo Nway, Tint May Pradhan, Devasis Sahu, Prasanna Kumar Naing, Zaw Min Yangon Technological University Department of Electronic Engineering Yangon Myanmar Yangon Technological University Faculty of Electrical and Computer Engineering Yangon Myanmar Acharya Institute of Technology Department of Electronics and Communication Engineering Bengaluru India National Institute of Technology Department of Electrical Engineering Odisha Rourkela India Ministry of Science and Technology Department of Research and Innovation Yangon Myanmar

ISBN: (纸本)9781665425773

Dual-frequency antennas are popular for many applications in communication systems. This work proposes a dual-band microstrip patch antenna design based on two-layer stacked patch for GPS and WiFi purposes. The targeted antenna is created to maneuver at the frequency range of 1.227GHz GPS L2 band (1.215GHz to 1.239GHz) and the 2.4GHz WiFi band frequency (2.4GHz to 2.48GHz). The antenna is fabricated on substrate of FR4 which dielectric constant 4.4 and the substrate material's height is 1.6mm. The created two-layer stacked patch antenna is compared with a conformist single patch antenna and analyzed the parameters of performance specifications for return loss (RL), bandwidth, VSWR, gain and pattern of radiation. It is recognized that the performance of the fabricated antenna improves when compared to the single-layer conventional antenna. © 2022 IEEE.

关键词： Global positioning system

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Multi-scale Context Intertwining for Panoramic Renal Pathology Segmentation

Multi-scale Context Intertwining for Panoramic Renal Patholo...

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

作者： Ye Zhang Xianchao Guan Hengrui Li Xiangming Yan Ziyue Wang Yongbing Zhang School of Computer Science and Technology Harbin Institute of Technology Shenzhen Leibniz-Institut für Analytische Wissenschaften ISAS - e.V. Pengcheng Laboratory Shenzhen Faculty of Computing Harbin Institute of Technology Department of Electronic and Computer Engineering National University of Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Panoramic segmentation of renal pathological tissues plays a crucial role in diagnosing renal carcinoma and other kidney-related diseases. The multi-scale nature of kidney tissues, which requires different magnification levels for accurate analysis, presents a significant challenge for segmentation models. In this work, we propose a Multi-scale Context Intertwining Network (MCINet) to address this issue. Our approach utilizes an auxiliary interaction network to enhance feature interaction between different scales and generate pseudo-labels for unannotated structures. By incorporating exponential moving average strategies, we ensure seamless feature integration across scales. Extensive experiments demonstrate that MCINet outperforms state-of-the-art models in key metrics such as Dice and Hausdorff Distance, proving its efficacy in renal tissue segmentation tasks.

关键词： Measurement Image segmentation Pathology Analytical models Signal processing Acoustics Speech processing Optimization Kidney Diseases

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