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Hybrid Fuzzy Data Aggregation and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

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Informatica (Slovenia) 2024年第20期48卷 95-106页

作者： Alhijaj, Asaad A. Marzook, Ali K. Hussein, Dheyaa Mezaal Alkenani, Jawad Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq University of Basrah College of Engineering Department of Electrical Engineering Basrah Iraq University of Technology Office of the President of the University Baghdad Iraq Department of Computer Science Shatt Al-Arab University College Basra Iraq

Wireless sensor networks (WSNs) are networks with many sensor nodes that are utilized for various purposes, including the military and medical. In hazardous circumstances, precise data aggregation and routing are essential, and the energy consumption of the sensors needs to be closely controlled. Nonetheless, there is a significant chance of redundant data because of external factors and nearby sensors. A multitude of information can be found in large datasets, some of it unnecessary and others useful. This redundancy negatively impacts performance in terms of redundant transmission and computing costs. However, data aggregation might help a network get rid of unnecessary data. In this work, we present a hybrid protocol called fuzzy data aggregation with fuzzy spider monkey optimization routing protocol (FDA-FSMORP) that represents an intelligent approach to collecting sensor data in HWSNs considering energy consumption. The results indicated that the suggested method beat in minimizing data latency our approach reduced energy consumption by 73% using energy more effectively when compared to our simulated outcomes. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Sensor nodes

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Faithful geometric measures for genuine tripartite entanglement

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Physical Review A 2024年第1期110卷 L010402-L010402页

作者： Xiaozhen Ge Lijun Liu Yong Wang Yu Xiang Guofeng Zhang Li Li Shuming Cheng Department of Control Science and Engineering Department of Applied Mathematics The Department of Mathematics and Computer Science State Key Laboratory for Mesoscopic Physics School of Physics Frontiers Science Center for Nano-optoelectronics and Collaborative Innovation Center of Quantum Matter Shanghai Institute of Intelligent Science and Technology Institute for Advanced Study

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation Ei|jkα≤Ej|ikα+Ek|ijα holds for all subadditive bipartite entanglement measure E, all permutations under parties i,j,k, all α∈[0,1], and all pure tripartite states. Then, we rigorously prove that the nonobtuse triangle area, enclosed by side Eα with 0<α≤1/2, is a measure for genuine tripartite entanglement. Finally, it is significantly strengthened for qubits that given a set of subadditive and nonsubadditive measures, some state is always found to violate the triangle relation for any α>1, and the triangle area is not a measure for any α>1/2. Our results pave the way to study discrete and continuous multipartite entanglement within a unified framework.

关键词： Entanglement measures Quantum correlations in quantum information Quantum correlations, foundations & formalism Resource theories

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Adaptive Particle Swarm Optimization-Based Tuning of Fractional Order PID for Moisture control in an Industrial Rotary Dryer System 11th

Adaptive Particle Swarm Optimization-Based Tuning of Fractio...

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11th EAI International Conference on Advancement of science and Technology, ICAST 2023

作者： Bosera, Adisu Safo Sori, Raji Ababe Bayesa, Ashebir Berhanu Department of Electrical Power and Control Engineering Adama Science and Technology University Adama Ethiopia

ISBN: (纸本)9783031641503

In this paper, a dynamic model of an industrial rotary dryer was formulated, and the model of the system was linearized using the Jacobian method. Adaptive particle swarm and gray wolf optimization-based tuning of parameters of fractional order proportional, integral, and derivative (FOPID) and conventional Ziegler-Nichols proportional, integral, and derivative (ZN-PID) was assessed with simulation for a solid moisture control rotary dryer system. The main feature of fractional order PID is that it may have a greater degree of freedom compared to conventional PID. Here, particle swarm and gray wolf optimization algorithms will be used for tuning the parameters of the FOPID controller against the kinds of disturbances that exist in this plant. The performance of the controller is compared to that of typical PID and gray wolf optimizer (GWO) tuning of fractional order PID controllers (FOPID). The performance of the developed controller is evaluated by assessing various time domain metrics. Using the application of the PSO-FOPID control, a satisfactory result was obtained with an integral time square error (ITSE) of 0.0032 compared to 0.0039 and 0.0366 in the cases of GWO-FOPID and ZN-FOPID, returning the expected and desired output moisture. In order to determine the robustness of the proposed control system, random linear density of solids and drying air, inlet temperature of solids, inlet moisture of solids, and linear velocity of solids are subjected to the controller. Therefore, from the simulation result, the performance of the PSO-FOPID controller will show good time domain specifications, such as an overshoot of 16.14%, a rise time of 0.1378 s and a settling time of 5.39 s. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Moisture control

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Deep learning-based human activity recognition using CNN, ConvLSTM, and LRCN

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 259-268页

作者： Uddin, Md. Ashraf Talukder, Md. Alamin Uzzaman, Muhammad Sajib Debnath, Chandan Chanda, Moumita Paul, Souvik Islam, Md. Manowarul Khraisat, Ansam Alazab, Ammar Aryal, Sunil School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Centre for Artificial Intelligence and Optimization Torrens University Australia Department of Electrical and Computer Engineering University of Ottawa Canada

Human activity recognition (HAR) plays a crucial role in assisting the elderly and individuals with vascular dementia by providing support and monitoring for their daily activities. This paper presents a deep learning (DL)-based approach to HAR, leveraging convolutional neural network (CNN), convolutional long short-term memory (ConvLSTM), and long-term recurrent convolutional network (LRCN) architectures. These models are designed to extract spatial features and capture temporal dependencies in video data, enhancing the accuracy of activity classification. We conducted experiments on the UCF50 and HMDB51 video datasets, encompassing diverse human activities. Our evaluation demonstrates that the ConvLSTM model achieves an accuracy of 82% on UCF50 and 68% on HMDB51, while the LRCN model gives accuracies of 93.44% and 71.55%, respectively. Finally, the CNN model outperforms with an accuracy rate of 99.58% for the UCF50 and 92.70% for the HMDB51 datasets. These significant improvements showcase the effectiveness of integrating convolutional and recurrent neural networks for HAR tasks. Our research contributes to advancing HAR systems with potential applications in healthcare, assisted living, and surveillance. By accurately recognizing human activities, our models can assist in remote patient monitoring, fall detection, and public safety initiatives. These findings underscore the importance of DL in enhancing the quality of life and safety for individuals in various contexts. © 2024 The Authors

关键词： Long short-term memory

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Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms

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Biomimetic Intelligence & Robotics 2024年第4期4卷 76-91页

作者： Timothy Sellers Tingjun Lei Chaomin Luo Zhuming Bi Gene Eu Jan Department of Electrical and Computer Engineering Mississippi State UniversityStarkville 39762USA Department of Civil and Mechanical Engineering Purdue University Fort WayneFort Wayne 46805USA Department of Computer Science Asia UniversityTaichung 413305TaiwanChina

In the field of autonomous robots,achieving complete precision is challenging,underscoring the need for human intervention,particularly in ensuring *** Autonomy Teaming(HAT)is crucial for promoting safe and efficient human-robot collaboration in dynamic indoor *** paper introduces a framework designed to address these precision gaps,enhancing safety and robotic interactions within such *** to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram(GVD)with spatio-temporal graphs,effectively combining human feedback,environmental factors,and key *** integral component of this system is the improved Node Selection Algorithm(iNSA),which utilizes the revised Grey Wolf Optimization(rGWO)for better adaptability and ***,an obstacle tracking model is employed to provide predictive data,enhancing the efficiency of the *** insights play a critical role,from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental *** simulation and comparison tests confirm the reliability and effectiveness of our proposed model,highlighting its unique advantages in the domain of *** comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.

关键词： Human autonomy teaming(HAT) Robot path planning Generalized Voronoi diagram(GVD) Spatio-temporal graphs Bio-inspired algorithms

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Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

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computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

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Investigation of a New Stator Permanent Magnet with the effects of Flux-Reversal and Flux-Switching 16

Investigation of a New Stator Permanent Magnet with the effe...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Sarshar, Mohammad Reza Kondelaji, Mohammad Amin Jalali Mirsalim, Mojtaba Montazeri, Seyed Hamid Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tehran Iran Department of Electrical Engineering and Computer Science Cambridge United States

ISBN: (纸本)9798331533946

This article studies a new stator-permanent magnet (PM) motor with flux-switching (FS) and flux-reversal (FR) effects synergies. The proposed structure benefits from the splitting stator pole PMs and consequent-pole FRPM, which reduces the PM utilization compared to the conventional ones. Also, the proposed structure is optimized by a random search multiobjective algorithm to attain the structure with the highest output torque and minimum torque ripple. Additionally, the proposed motor is compared to a conventional FSPM to investigate the advantages of the main motor. Using finite element analysis (FEA) studies, the flux density distribution results show that the FR effect lowers the saturation risk compared to FSPM. Also, the back-EMF generation of the proposed motor has improved by 135% compared to its benchmark. Correspondingly, fullload results show that the proposed topology has enhanced the average torque by 75% and deteriorated the ripple by about 55% compared to FSPM. Therefore, the proposed structure offers a profitable for high torque density applications. © 2025 IEEE.

关键词： Permanent magnets

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Comparative Analysis of Diverse Architectures for Accurate Blood Cancer Cell Classification

Comparative Analysis of Diverse Architectures for Accurate B...

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2024 International Conference on computer, electrical and Communication engineering, ICCECE 2024

作者： Md. Muttakin, G.K. Yesmin, Farhana Alam, Syeda Sadia Ashraf, Md Sadi Akuthota, Vishwanath Quader, Montaser Abdul Rajshahi University of Engineering and Technology Department of Electrical and Computer Engineering India Metropolitan University Department of Computer Science and Engineering United Kingdom Brac University Department of Computer Science and Engineering Bangladesh Drpinnacle Hyderabad India Green University of Bangladesh Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350386479

Blood cancer cell diagnosis is crucial in medical diagnostics. It demands accurate classification of blood cell images. Proper classification of blood cancer cells is fundamental for accurately diagnosing the specific type and subtype of blood cancer as well as efficient treatment planning. It provides specific information, helping healthcare professionals predict the prognosis, survival rates, and the potential for disease recurrence. On this basis, deep learning models have demonstrated remarkable performance. In this paper, we have introduced a comprehensive research into blood cancer cell classification, employing a diverse dataset encompassing various blood cell types. We explore the effectiveness of VGG19 with batch normalization, Vision Transformer (ViT), Ensemble Adversarial Inception-ResNetV2, DenseNet201, and ResNeXt50 architectures in this challenging task. In order to enhance the model performance, we integrate essential data preprocessing techniques, including resizing, cropping, and normalization. Additionally, novel data augmentation strategies, such as random cropping, and flipping are introduced to augment the training dataset and improve model generalization. Remarkably, VGG19 with batch normalization has shown tremendous success by achieving 99.86% accuracy. Moreover, the DenseNet201 model has performed brilliantly by achieving an accuracy of 98.55%. The ResNeXt50 model shows excellent performance with an accuracy of 98.31%. The Vision Transformer (ViT) achieves a solid accuracy of 98.91%. Lastly, Ensemble Adversarial Inception-ResNet V2 also performed no-tably achieving 96.38%. In this context, VGG19 with batch normalization is able to show more excellent performance than other models by achieving the highest accuracy. © 2024 IEEE.

关键词： Cancer cells

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Enhanced Vehicle Detection by Optimized Image Compression in NextG Wireless Network Autonomous Vehicles System 100

Enhanced Vehicle Detection by Optimized Image Compression in...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Monir, Md Fahad Hossain, Md Junayed Barkatullah, Mohammad Hoque, Md Mozammal Hassan, Zoheb Ahmed, Tarem Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering United States Department of Computer Science & Engineering Bangladesh University of Houston Department of Engineering Technology United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798331517786

Autonomous Vehicle System (AVS) is rapidly advancing and is expected to completely transform the transportation industry, bringing about a new era of mobility. As digital data proliferation strains network resources, the demand for performing resource-intensive edge-assisted Deep Learning tasks in AVS with limited computational resources becomes increasingly challenging. The advent of 5G and future cellular networks (NextG) offers the promise of facilitating the seamless execution of these tasks. This research aims to diminish dataset size, in harmony with the integration capabilities of the Semantic and Flexible Open Radio Access Network (SEMO-RAN) framework, which is anticipated to reduce latency and refine resource allocation in vehicle image processing. The focus of this study is on optimizing image compression without compromising the accuracy of vehicle classification, leveraging esteemed Convolutional Neural Network models like YOLOv5. By employing Generative Adversarial Network compression and Wavelet Image Compression methods, our study achieves an impressive 81.82% reduction in data size while maintaining a 96.97% accuracy in classification tasks. This underscores the potential for significant efficiency gains in AVS through improved data management, supported by Digital Twins (DT), Integrated Sensing and Communication (ISAC), and O-RAN. © 2024 IEEE.

关键词： Radio access networks

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An Adaptive control Method Based on Radial Basis Function Neural Network for Variable Stiffness Actuator 25

An Adaptive Control Method Based on Radial Basis Function Ne...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Wu, Xiujun Wang, Chen Xu, Changxian Liu, Keping Wang, Gang Sun, Zhongbo Changchun University Of Technology Department Of Control Science And Engineering Changchun China School Of Electrical And Information Engineering Jilin Engineering Normal University Department Of Control Science And Engineering Changchun China

ISBN: (纸本)9798350340266

Rehabilitation robots based on variable stiffness elastic actuators exhibit strong human-robot interaction char-acteristics due to the specific features of the actuators, including flexible drive and variable stiffness output capabilities. However, to ensure the robot can achieve rehabilitation actions smoothly, safely, and precisely, accurate modeling and control of the variable stiffness elastic actuators becomes a burning question. In order to address this challenge, a dynamic model is established considering the actuator's own gravity factor and the influence of disturbances, and a nominal dynamic model is designed. Accurate tracking of output location and stiffness is reached by designing a radial basis function (RBF) network to approximate the actual dynamic model. Through simulation experiments, it is verified that the block approximation RBF network adaptive controller designed in this article has lower actuator tracking error and higher control accuracy than the proportional integral derivative (PID) controller. © 2024 IEEE.

关键词： Radial basis function networks

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