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IEEE Conference on Wireless Communications and Networking

作者： Chi Jin Zheng Chang Fengye Hu Mingan Luan Timo Hämäläinen Faculty of Information Technology University of Jyväskylä Finland School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Communication Engineering Jilin University China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Physical layer security (PLS) in symbiotic radio (SR) systems is primarily considered for passive eavesdropping scenarios. However, overlooking the impact of proactive eavesdroppers poses significant risks. In this paper, we focus on secure transmission in SR systems under proactive eavesdropping conditions. A PLS strategy is investigated for a full-duplex facultative symbiotic radio (FD-FSR) system. First, we introduce an innovative FSR protocol. It allows backscatter devices (BDs) to dynamically switch between cognitive and symbiotic patterns. Next, we develop a multi-device scheduling method. It adaptively assigns BDs as transmitters, cooperators, or jammers to enhance the secrecy rate. We formulate the pattern switching and BD scheduling as a mixed integer programming problem (MIP). To solve this, we first decompose it into binary decision-making and multi-variable optimization sub-problems. Then, a low-complexity two-stage optimization strategy is employed. Numerical results demonstrate that our proposed strategy significantly outperforms existing schemes.

关键词： Symbiosis Decision making Switches Full-duplex system Physical layer security Security Jamming Optimization Eavesdropping Backscatter

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Deep Learning Based Phase Current Prediction Model in Three-Phase IGBT Two-Level Inverter for Electrical Drives

Deep Learning Based Phase Current Prediction Model in Three-...

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Compatibility in Power electronics (CPE)

作者： Murat Akin Yavuz Canbay Cemil Ocak Faculty of Engineering Gazi University Ankara Türkiye Computer Engineering Faculty of Engineering and Architecture Sutcu Imam University Kahramanmaras Türkiye Electrical-Electronic Engineering Faculty of Technology Gazi University Ankara Türkiye

ISBN: (数字)9798350318265

ISBN: (纸本)9798350318272

Power electronics circuits are useful for changing the waveform of a power supply from DC to AC, and these electronic circuits, of which the power inverter is one of them, play a crucial role when it comes to the management of renewable energy sources and the conversion of power supply waveforms. Research for artificial intelligence applications to predict the non-linear behavior of power inverters has garnered prominence recently. This study proposes a novel bidirectional long-short-term memory (BiLSTM) based deep learning model to predict the current value of the inverter based on the duty cycle, voltage values, motor rotation speed, and mean phase voltage of the inverter for the first time. The proposed model was compared to mainly used recurrent neural network (RNN) variations such as long-short term memory (LSTM) and gated recurrent unit (GRU). According to the experimental findings, it was observed that the proposed model yielded lower mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and higher r-squared (R2) results for predicting the current values of three phases. Therefore, it can be inferred that the proposed model is effective for current prediction.

关键词： Deep learning Temperature measurement Temperature sensors Recurrent neural networks Power supplies Predictive models Inverters

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Enhanced Physical Layer Security for Full-Duplex Facultative Symbiotic Radio: A Pattern Switching and Multi-Device Scheduling Strategy

Enhanced Physical Layer Security for Full-Duplex Facultative...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Jin, Chi Chang, Zheng Hu, Fengye Luan, Mingan Hämäläinen, Timo University of Jyväskylä Faculty of Information Technology Finland School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Communication Engineering Jilin University China

ISBN: (纸本)9798350368369

关键词： Integer programming

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Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

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computers, Materials & Continua 2022年第1期70卷 715-738页

作者： Shidrokh Goudarzi Seyed Ahmad Soleymani Mohammad Hossein Anisi Domenico Ciuonzo Nazri Kama Salwani Abdullah Mohammad Abdollahi Azgomi Zenon Chaczko Azri Azmi Centre for Artificial Intelligent(CAIT) Universiti Kebangsaan MalaysiaBangi43600Malaysia School of Computer Engineering Iran University of Science and TechnologyResalat Sq.16846-13114TehranIran School of Computing Faculty of Engineering Universiti Teknologi MalaysiaJohorMalaysia School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUnited Kingdom Department of Electrical Engineering and Information Technologies University of Naples“Federico II”Naples80125Italy Razak Faculty of Technology and Informatics Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G ***,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network *** address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood *** light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river *** framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a ***,an algorithm hybridized with Group Method Data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative *** proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River *** performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.

关键词： Unmanned aerial vehicles wireless sensor networks group method data handling particle swarm optimization river flow prediction

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital 6

Value Stream Mapping for Smart Pharmaceutical Management in ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Boonsothonsatit, Ganda Silapunt, Rardchawadee Vongbunyong, Supachai Kaemarungsi, Kamol Chanpuypetch, Wirachchaya Chonsawat, Nilubon Graduate School of Management and Innovation King Mongkut's University of Technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut's University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut's University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： electronic health record

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Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

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computer Systems Science & engineering 2023年第7期46卷 303-319页

作者： Imran Ali Zohaib Mushtaq Saad Arif Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Department of Mechanical Engineering National Taiwan University of Science and TechnologyTaipei City106335TaiwanChina Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Mechanical Engineering HITEC University TaxilaTaxila Cantt.47080Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia

Hyperspectral imaging is gaining a significant role in agricultural remote sensing *** data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third *** classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral *** this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral *** dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each *** data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation *** this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in *** preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired *** the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral *** resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational *** support vector machines classifier was employed for multiclass classification of target crops using *** classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa *** proposed scheme achieved greater than 90%results for all the performance evaluation *** PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR *** proposed scheme

关键词： Hyperspectral imaging visible and near-infrared edge preserving feature dimensionality reduction crop classification

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Optimizing Resource Allocation in Mobile Edge Computing for IIoT through Reinforcement Learning and Multiobjective Particle Swarm Optimization 2

Optimizing Resource Allocation in Mobile Edge Computing for ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Avv, Sudhakar Mohan, M. Maharajan, K. Habelalmateen, Mohammed I. Naga Saranya, N. SR University Department of Electrical and Electronics Engineering Telangana India Nitte Meenakshi Institute of Technology Department of ISE Bengaluru India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Bangalore India Universiti Tun Hussein Onn Malaysia Parit Raja Faculty of Electrical and Electronic Engineering Johor Batu Pahat Malaysia College of Technical Engineering the Islamic University Department of Computer Technical Engineering Najaf Iraq Saveetha College of Liberal Arts and Science SIMATS Department of Computer Applications Chennai India

ISBN: (纸本)9798350317558

Mobile networks are flexible enough to support a range of resource allocation models and service-based choices in computing domains, which affects both virtual reality and the Industrial Internet of Things (IIOT). Virtual resource management is made easier by the Mobile Edge Computing (MEC) paradigm, which also makes edge connectivity between data terminals and execution in the core network under heavy load possible. Meeting customer requirements efficiently is achieved through thoughtful planning, aided by cognitive agents. User data, incorporating behavioral patterns, is amalgamated to cater to IIOT service types. Neural caching for memory during task execution is made easier by the use of swarm intelligence and reinforcement learning techniques. Prediction strategies optimize business operations and caching to reduce execution delays. Predictive models evaluate performance by taking into account variables like nearby user equipment and mobile edge computing resources. This method's efficacy is demonstrated by a cognitive agent model that manages resource distribution and creates networks of communication to improve service quality. For accurate resource distribution among end users, reinforcement learning techniques - more especially, Multi-Objective Particle Swarm Optimization (MOPSO) algorithms - are used. This includes building cost mapping tables and optimizing allocation in MEC. The proposed method outperforms existing algorithms like Task-Offloading and Resource Allocation Strategy and achieves a better throughput value of 785.32. © 2024 IEEE.

关键词： Communication Industrial Internet of Things Mobile Edge Computing Optimization Quality of Service Reinforcement Learning

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Integrated Polarity Optimization of MPRM Circuits Based on Improved Multi-objective Particle Swarm Optimization

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Chinese Journal of electronics 2020年第5期29卷 833-840页

作者： FU Qiang WANG Pengjun TONG Nan WANG Mingbo ZHONG Caiming College of Science and Technology Ningbo University Faculty of Electronic Engineering and Computer Science Ningbo University College of Electrical and Electronic Engineering Wenzhou University

Aiming at the multi-objective polarity design of Mixed-polarity Reed-Muller(MPRM) circuit,such as small area and low power consumption, an integrated polarity optimization scheme based on improved Multi-objective particle swarm optimization(MOPSO) is proposed. In the Improved MOPSO(IMOPSO) algorithm,particles in the external archive can be actively evolved through self-learning operations to find better circuit polarity. The particles in the population achieve selflearning fractals by comparing the differences between their own states and individuals in external archive to enhance the evolutionary level of the population. A multiobjective decision model of area and power consumption is established according to the characteristics of MPRM circuit. The tabular technique and the IMPOPO algorithm are combined to obtain the Pareto optimal polarity set of the MPRM circuit for area and power consumption. The MCNC Benchmark circuit is used to test the performance of the algorithm. The results verify the effectiveness of the proposed algorithm.

关键词： multiobjective polarity design low-power electronics evolutionary computation self-learning fractals MPRM circuit Polarity Self-learning fractal particle swarm optimisation self-learning operations multiobjective decision model logic circuits learning (artificial intelligence) integrated polarity optimization scheme Multi-objective particle swarm optimization (MOPSO) circuit polarity tabular technique Area-power trade-off power consumption multiobjective particle swarm optimization IMPOPO algorithm logic design Improved MOPSO algorithm MCNC benchmark circuit Pareto optimal polarity set MPRM circuits Mixed-polarity Reed-Muller circuit Pareto optimisation

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Improving WiFi Fingerprint Database With Irregular Distribution of Reference Points Using Interpolation Methods

Improving WiFi Fingerprint Database With Irregular Distribut...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Lee, Seon-Woo Thi, Luong Nguyen Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Dalat University Faculty of Mathematics and Informatics Dalat Viet Nam Hallym University Division of Software Chuncheon Korea Republic of Dalat University Faculty of Information Technology Dalat Viet Nam Dalat University Department of Electronics and Telecommunications Dalat Viet Nam

ISBN: (纸本)9798350353983

Using WiFi Fingerprinting for indoor positioning is becoming increasingly popular due to its cost-effectiveness. However, to ensure the system can operate with high accuracy and stability, this technique requires a burden of data collection tasks from a dense distribution of reference points (RPs). To decrease the cost of data collection, as well as to improve the WiFi fingerprint database with irregular distribution of RPs, this study applies and compares different interpolation methods. Evaluating these methods in a public dataset including multiple sub-datasets with irregular distributions of RPs, the results highlight the Linear Barycentric interpolation (LB) as the most favorable method due to its low complexity and stable performance. The LB-based fingerprint database achieves an average interpolation error of less than 3 dBm compared to the original one. When applying the interpolated database to positioning tasks, it can help reduce acquisition costs by up to 50% while improving positioning accuracy by up to 11%. The study reveals the feasibility of interpolation in optimizing WiFi fingerprint data collection, especially in large and complex environments where regular distribution of RPs cannot be deployed. © 2024 IEEE.

关键词： Interpolation

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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