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Efficient pipelined flow classification for intelligent data processing in IoT

Digital Communications and Networks 2022年第4期8卷 561-575页

作者： Seyed Navid Mousavi Fengping Chen Mahdi Abbasi Mohammad R.Khosravi Milad Rafiee Department of Computer Engineering Engineering FacultyBu-Ali Sina UniversityHamedanIran Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang262700China Telecommunications Group Department of Electrical and Electronic EngineeringShiraz University of TechnologyShirazIran

The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things(IoT).In recent years,researchers have tried to develop hardware-based solutions for the classification of Internet *** to higher throughput and shorter delays,these solutions are considered as a major key to improving the quality of *** of these efforts have attempted to implement a software algorithm on the FPGA to reduce the processing time and enhance the *** proposed architectures,however,cannot reach a compromise among power consumption,memory usage,and throughput *** view of this,the architecture proposed in this paper contains a pipelinebased micro-core that is used in network processors to classify *** this end,three architectures have been implemented using the proposed *** first architecture performs parallel classification based on header *** second one classifies packets in a serial *** last architecture is the pipeline-based classifier,which can increase performance by nine *** proposed architectures have been implemented on an FPGA *** results are indicative of a reduction in memory usage as well as an increase in speedup and *** architecture has a power consumption of is 1.294w,and its throughput with a frequency of 233 MHz exceeds 147 Gbps.

关键词： Efficiency Intelligent flow processing IoT Packet classification Pipeline

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An Efficient Soil Image Classification Framework Using Transfer Learning Models for Intelligent Geotechnical Applications 2nd

An Efficient Soil Image Classification Framework Using Trans...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Hemdan, Ezz El-Din Al-Atroush, M.E. Structure and Materials Research Lab Prince Sultan University Riyadh Saudi Arabia Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menoufia Egypt Department of Engineering Management College of Engineering Prince Sultan University P.O. Box 66833 Riyadh Saudi Arabia

ISBN: (纸本)9789819787111

Recently, the application of deep learning approaches has significantly contributed to the success of soil investigations, particularly in the categorization of soil images. This, in turn, facilitates informed decision-making in geotechnical engineering projects. This paper introduces an efficient soil classification method utilizing transfer learning models based on Convolutional Neural Networks (CNNs) to categorize soil images. Various pre-trained networks, including Alexnet, Vgg16, Googlenet, Resnet50, Mobilenetv2, and Squeezenet, are employed in this study. The highest accuracy, reaching 90.32%, is achieved by Googlenet, while Alexnet follows closely with an accuracy of 89.24%. Conversely, Squeezenet yields the lowest accuracy at 79.56%. This proposed framework has the potential for integration into Internet of Things (IoT) systems, leading to the development of an innovative, portable IoT-based device for soil type recognition at remote geo-sites, catering to the needs of geotechnical engineers. The results obtained highlight that the Googlenet-based framework outperforms others, offering a superior performance in soil classification. This framework serves as a valuable tool for implementing an intelligent soil image classification system, supporting real-time decision-making in geotechnical engineering applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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BAT Optimized SMC for Continuous Stirred Tank Reactor 8

BAT Optimized SMC for Continuous Stirred Tank Reactor

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8th IEEE Information Technology and Mechatronics engineering Conference, ITOEC 2025

作者： Haroon, Husam E. Elfadel Huang, Congzhi North China Electric Power University School of Control and Computer Engineering Beijing102206 China Omdurman Islamic University Faculty of Engineering Department of Electrical and Electronic Engineering Khartoum Sudan

ISBN: (纸本)9798331517915

This topic proposed a Sliding Mode Control (SMC) strategy utilizing the BAT Algorithm (BAT-SMC) for regulating concentration and temperature in a Continuous Stirred Tank Reactor (CSTR) system. The SMC is designed to maintain concentration (CA) and temperature (T) within desired setpoints by adapting sliding surfaces in the presence of disturbances and system uncertainties. The BAT Algorithm enhances the SMC by optimizing control parameters, reducing chattering effects, and improving tracking accuracy. Simulations validate the efficacy of BAT-SMC, showing improved dynamic performance for both concentration and temperature control compared to other controllers. © 2025 IEEE.

关键词： Bat Algorithm Continuous stirred tank reactor Slide Mode Control

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Performance enhancement of text-independent speaker recognition in noisy and reverberation conditions using Radon transform with deep learning

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International Journal of Speech Technology 2022年第3期25卷 679-687页

作者： El-Moneim, Samia Abd El-Mordy, Eman Abd Nassar, M.A. Dessouky, Moawad I. Ismail, Nabil A. El-Fishawy, Adel S. El-Dolil, Sami El-Dokany, Ibrahim M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt High Institute of Engineering and Technology Tanta Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Recognition (ASR) in mismatched conditions is a challenging task, since robust feature extraction and classification techniques are required. Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) is an efficient network that can learn to recognize speakers, text-independently, when the recording circumstances are similar. Unfortunately, when the recording circumstances differ, its performance degrades. In this paper, Radon projection of the spectrograms of speech signals is implemented to get the features, since Radon Transform (RT) has less sensitivity to noise and reverberation conditions. The Radon projection is implemented on the spectrograms of speech signals, and then 2-D Discrete Cosine Transform (DCT) is computed. This technique improves the system recognition accuracy, text-independently with less sensitivity to noise and reverberation effects. The ASR system performance with the proposed features is compared to that of the system that depends on Mel Frequency Cepstral Coefficients (MFCCs) and spectrum features. For noisy utterances at 25 dB, the recognition rate with the proposed feature reaches 80%, while it is 27% and 28% with MFCCs and spectrum, respectively. For reverberant speech, the recognition rate reaches 80.67% with the proposed features, while it reaches 54% and 62.67% with the MFCCs and spectrum, respectively. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Feature extraction

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Tangentially-Magnetized-Slot-PM-Assisted Magnet Saturation Relieving Design for Doubly Salient Reluctance Machine Position Sensorless Drive Electric Propulsion Applications 6

Tangentially-Magnetized-Slot-PM-Assisted Magnet Saturation R...

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6th International Conference on Electrical engineering and Control Technologies, CEECT 2024

作者： Wang, Weiyu Zhang, Xiaodong Niu, Shuangxia Zhao, Xing Pan, Jianfei Fu, Weinong Great Bay University School of Engineering Dongguan China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Hong Kong University of York Department of Electronic Engineering York United Kingdom Shenzhen University College of Mechatronics and Control Engineering Shenzhen China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen China

ISBN: (纸本)9798331528096

For doubly salient reluctance machines, the magnetic saturation effect easily occurs, thereby the saturated inductance with complex analytical expression brings difficulties to the inductance modeling procedure during position sensorless control. To tackle this issue, a direct solution from electrical machine design is introduced to regulate magnetic flux distribution. The key is to insert tangentially-magnetized-slot-permanent-magnet into stator slots, thus relieving the magnetic saturation in the stator teeth. Consequently, unsaturated inductance characteristics can be acquired through the magnetic saturation relieving effect, thereby the inductance modeling effort can be significantly decreased, and such design is friendly for doubly salient reluctance machine position sensorless drive application. © 2024 IEEE.

关键词： Stators

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Joint Covert and Secure Communication for SWIPT Assisted CNOMA Systems

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IEEE Internet of Things Journal 2025年第12期12卷 20406-20419页

作者： Huang, Gaojian Lei, Yuxin Li, Xingwang Khan, Wali Ullah Wang, Gongpu Nallanathan, Arumugam Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China City University of Macau Faculty of Data Science 999078 China Henan Polytechnic University School of Surveying and Land Information Engineering Jiaozuo454003 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Luxembourg City1855 Luxembourg Beijing Jiaotong University Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing100044 China Institute of Internet of Things Wuxi214111 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

With the rapid advancement of physical-layer security technology, the covert and secure communication has become crucial in safeguarding wireless communication systems. In this paper, we propose a joint covert and secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) assisted cooperative non-orthogonal multiple access (CNOMA) systems. In the CNOMA system, a greedy relay transmits the confidential information to the far user (Carol), with the assistance of the near user (Bob). Meanwhile, as a SWIPT node, Bob is self-sustained by harvesting energy from relay. What's more, a warden (Alice) and non-colluding eavesdroppers (Eves) always attempt to detect and capture the confidential information, respectively. To counteract the attacks from Alice and Eves, a jamming-assisted scheme is employed. For the proposed system model, we derive closed-form expressions for the detection error probability (DEP) and the average minimum detection error probability (AMDEP) of Alice. Additionally, closed-form expressions for the outage probability (OP) of users and the intercept probability (IP) of Eves are obtained. Furthermore, to maximize the effective covert rate (ECR) of Carol, an optimization problem is formulated, subject to covertness and security constraints. Numerical results are provided to demonstrate the impact of the system parameters on covert and secure performance, with the results showing perfect agreement with the theoretical analysis. © 2014 IEEE.

关键词： Inductive power transmission

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Artificial Neural Network-Based Inverter Control and MPPT for Enhanced Performance of Hybrid Microgrids With PV and Battery Storage 15

Artificial Neural Network-Based Inverter Control and MPPT fo...

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15th International Renewable Energy Congress, IREC 2025

作者： Rajab, Z. Elrashid, M. Elhashane, M. Gomez-Romero, J. Khalil, A. Seliman, A. Mohamed, F. Benghazi University Faculty if Engineering Electrical and Electronic Department Libya University of Granada Department of Computer Science and Artificial Intelligence Spain Technical University of Denmark Dtu Engineering Technology Denmark Omar Al-Mukhtar University Faculty of Engineering Electrical Department Libya Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (纸本)9798331516772

Today's power system network has become more complex, with greater responsibilities and challenges in providing a secure, reliable, and high-quality energy supply to communities. A smaller entity of the electrical network, known as a Microgrid (MG), has become more popular in recent times to enhance the reliability and security of energy supply during any energy crisis in the utility network. Microgrids (MG) can provide clean energy through renewable resources such as photovoltaic (PV) systems, wind, and energy storage, offering an effective solution for power supply in both remote and urban residential communities. To ensure the stable operation of a standalone microgrid based on a photovoltaic system, it is essential to implement various mechanisms and control strategies simultaneously for optimal efficiency. This paper introduces a complete model of a hybrid microgrid consisting of a PV source, an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) system, a storage unit, a DC to AC converter with an ANN controller, and both DC and AC loads. The performance of the controller under various ambient conditions and load variations is tested. The ANN controller demonstrates good performance in different situations. © 2025 IEEE.

关键词： Hybrid power

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Compact Physical and Electrical Patch Antenna Engineered for 5G Mobile Devices and Multiband Systems

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Progress in Electromagnetics Research B 2025年 111卷 71-81页

作者： Jemaludin, Nazrin Haziq Al-Gburi, Ahmed Jamal Abdullah Abdulhameed, Muhannad K. Hashim, Sarah R. Mohammed, Dunya Zeki Saeidi, Tale Gupta, Anupma Zakaria, Zahriladha Nurhayati, Nurhayati Elabd, Rania Hamdy Jalan Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Computer Scince Department University of Kerbala Karbala Iraq Air Conditioning Engineering Department Faculty of Engineering Warith Al-Anbiyaa University Iraq Department of Cyber Security Al-Zahraa University for Women Karbala Iraq Department of Electronic and Communications Engineering Gilgamesh University Baghdad Iraq Co. Donegal LetterkennyF92 FC93 Ireland Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamilnadu Chennai602105 India Department of Electrical Engineering Universitas Negeri Surabaya Surabaya60231 Indonesia Department Electronic and Communication Higher Institute of Engineering and Technology in New Damietta Damietta Egypt

This article presents a compact multi-band microstrip patch antenna designed for 5G, Ku, and K-band applications. The antenna operates at 3.5 GHz and 15.6 GHz, supporting 5G communications (3.3–3.6 GHz) and satellite applications (15.6–20 GHz). Fabricated on an FR4 substrate (Εr = 4.3, tan δ = 0.025) with dimensions of 13 × 10 × 1.6 mm3 (0.15λo × 0.12λo × 0.02λo), where λo represents the wavelength at the lower frequency of 3.5 GHz, the antenna achieves return losses of −19 dB and −39 dB at the respective frequencies, with peak gains of −2.8 dBi and 3.7 dBi. The design’s consistency is validated through a comparative analysis with recent studies. The antenna was placed near the ear and mouth area of a human head phantom model to perform a comprehensive SAR analysis. SAR analysis confirms compliance with safety standards, maintaining SAR levels below 2 W/kg. The proposed design demonstrates promising performance for modern communication systems. © (2024), (Electromagnetics Academy). All Rights Reserved.

关键词： Slot antennas

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Scenario-Oriented Multi-Cut Generalized Benders Decomposition-Based Distributed OPF for AC/DC Hybrid Grids

Scenario-Oriented Multi-Cut Generalized Benders Decompositio...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Li, Hai-Xiao Ergun, Hakan Van Hertem, Dirk Lekic, Aleksandra Delft University of Technology Faculty of Electrical Engineering Mathematics & Computer Science Delft Netherlands Chongqing University of Technology School of Electrical and Electronic Engineering Chongqing China Ku Leuven Department of Electrical Engineering Leuven Belgium Etch-EnergyVille Genk Belgium

ISBN: (纸本)9789531842976

In this paper, a distributed optimal power flow (OPF) for the AC/DC hybrid grid, composed of the AC grid, renewable energy system (RES), and voltage source converter (VSC)-based multi-terminal DC (MTDC) grid, is presented. Firstly, we apply a series of linear approximations/convex relaxations to construct a mixed-integer convex AC/DC OPF model. To address the uncertainties from RESs, we then illustrate scenario-oriented decision making for the constructed AC/DC OPF model. Additionally, we verify that considering all possible scenarios can be replaced by a small set of extreme scenarios if the OPF model is (mixed-integer) convex. Furthermore, we develop a scenario-oriented multi-cut generalized Benders decomposition to achieve more efficient distributed problem solving for the constructed AC/DC OPF model. Finally, numerical results are provided to validate the effectiveness of our constructed AC/DC OPF model and our developed distributed problem-solving approach. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Keeping Drivers Alert: A Solution for Monitoring Driver Attention in Assisted-Driving Vehicles 46

Keeping Drivers Alert: A Solution for Monitoring Driver Atte...

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46th ICT and electronics Convention, MIPRO 2023

作者： Jutresa, Robert Peer, Peter Emersic, Ziga Kim, Jihun Faculty of Computer and Information Science Computer Vision Laboratory Ljubljana Slovenia Kyungpook National University School of Electronic and Electrical Engineering Daegu Korea Republic of

ISBN: (纸本)9789532331042

The advent of the new industrial revolution has led to a surge in the number of self-driving and assisted-driving vehicles on the roads. Although this development enhances the overall quality of life, it also poses new challenges and risks. One such difficulty is the need for driver attention in the event of system errors. To overcome this issue, a driver monitoring system is necessary to ensure that the driver remains focused and can take control if necessary, while also sending alerts in case of driver distraction. To address this challenge, we propose a method that employs a convolutional neural network based on the ResNet architecture. This system can recognize selected types of driver distractions in captured infrared images. We experimented with different depths of the ResNet architecture to determine the most optimal results. The best model achieved an accuracy rate of 94% in detecting various forms of driver distraction. © 2023 MIPRO Croatian Society.

关键词： Network architecture

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