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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Wu, Muxing Iam, Io-Wa Gong, Cheng Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China Institute of Microelectronics University of Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781665464543

Currently, electric vehicle (EV) battery platforms typically vary in standard, commonly rated at either 800V or 400V. Conventional wireless charging system (WCS) are limited to single voltage level charging, necessitating wireless communication or additional complex circuits for achieving multi-voltage level charging, which can compromise operational stability and overall efficiency. This article proposes a WCS based on the concept of bipolar partial power conversion. By incorporating reversing series switches on the secondary of the DC/DC converter, regulation voltage control enables switching between positive and negative polarity to supply power to either 800V or 400V battery for EV. This approach ensures high power transmission efficiency, without relying on wireless communication, thereby ensuring stable charging process. Furthermore, this article also presents various combinations of bipolar topologies that can be selectively employed based on application requirements. Finally, simulation results validate the feasibility of the proposed 3.2KW WCS. © 2024 IEEE.

关键词： State of charge

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A Deep Learning Approach for Detecting Diabetic Retinopathy

A Deep Learning Approach for Detecting Diabetic Retinopathy

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International Conference on computer and Applications (ICCA)

作者： Mohamed Hossam Hassan Nabil Laila Hammam Hany Ayad Bastawrous Gamal A. Ebrahim Electrical Engineering Department The British University in Egypt Cairo Egypt Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlans Computer and Systems Engineerig Department Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The World Health Organization (WHO) reports that diabetic retinopathy affects one-third of diabetics, regardless of their stage of the disease. Several research efforts are focused on its automated detection and diagnosis. Identifying diabetic retinopathy is crucial due to the damage that occurs to the blood vessels of the eye retina, leading to vision blur or even complete blindness. Thus, an annual checkup is needed for people with diabetes. Moreover, uncontrolled sugar levels for diabetes patients could worsen the current stage of diabetic retinopathy. Consequently, automated detection can greatly contribute to the treatment of disease. This can be carried out through several algorithms, including deep learning models and support vector machines, in addition to transfer learning. This contribution proposes a new approach for diabetic retinopathy automated detection based on convolutional neural network (CNN) models. The proposed model provides both binary and multi-class detection. Both scenarios have shown promising results, where the training accuracies of both the binary classification and the multi-class are 92% and 94%, respectively.

关键词： Deep learning Training Support vector machines Diabetic retinopathy Computational modeling Transfer learning Retina Convolutional neural networks Sugar Diseases

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Deep Learning-Based Classification of Fruit Diseases:An Application for Precision Agriculture

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computers, Materials & Continua 2021年第2期66卷 1949-1962页

作者： Inzamam Mashood Nasir Asima Bibi Jamal Hussain Shah Muhammad Attique Khan Muhammad Sharif Khalid Iqbal Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC UniversityTaxila47040Pakistan Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47080Pakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttockPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanSouth Korea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityBeirutLebanon

Agriculture is essential for the economy and plant disease must be *** recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time *** intelligence can be used to extract fruit color,shape,or texture data,thus aiding the detection of ***,the convolutional neural network(CNN)techniques show a massive success for image classification *** extracts more detailed features and can work efficiently with large *** this work,we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases.A fine-tuned,pretrained deep learning model(VGG19)was retrained using a plant dataset,from which useful features were ***,contour features were extracted using pyramid histogram of oriented gradient(PHOG)and combined with the deep features using serial based *** the fusion process,a few pieces of redundant information were added in the form of ***,a“relevance-based”optimization technique was used to select the best features from the fused vector for the final *** the use of multiple classifiers,an accuracy of up to 99.6%was achieved on the proposed method,which is superior to previous ***,our approach is useful for 5G technology,cloud computing,and the Internet of Things(IoT).

关键词： Agriculture deep learning feature selection feature fusion fruit classification

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Precision Health: Maximizing Well-being with IAHN Integration

Precision Health: Maximizing Well-being with IAHN Integratio...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： Shaik Jumlesha S. Hrushikesava Raju S. Adinarayna U.Sesadri Nabanita Choudhury Vijaya Chandra Jadala Department of CSE Annamacharya Institute of Technology and Science Tirupati AP Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Department of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam Department of CSE Vardhaman College of Engineering Hyderabad Telangana Faculty of Computer Technology Assam Down Town University Guwahati Assam School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Precision health is emerging as a significant trend in healthcare, emphasizing the customization of medical care and interventions to align with the unique characteristics of each patient, ultimately maximizing health outcomes and well-being. Traditional approaches to personalized care are often time-consuming and lack the necessary focus on individual patient needs, leading to inadequate monitoring of disease progression and insufficient patient engagement. To address these challenges, the proposed approach employs an Improved Adaptive Health Navigator (IAHN) that continuously monitors each patient and formulates eco-friendly medications tailored to their specific symptoms. This innovative process incorporates various factors such as lifestyle choices, genomic data, family history, and other relevant parameters to inform precision medicine recommendations. The resulting precision medicine approach is designed to enhance recovery speed and improve patient outcomes. By adopting a proactive strategy, this model facilitates early detection of health issues, reduces the incidence of illnesses, enhances quality of life, and optimizes resource allocation. The IAHN plays a crucial role in the ongoing development of personalized medicine by ensuring continuous health monitoring, empowering healthcare providers, and enabling diagnostic tools to make informed decisions for disease prevention.

关键词： Renewable energy sources Adaptive systems Navigation Reviews Precision medicine Medical services Resource management Medical diagnostic imaging Diseases Testing

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UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detection 14

UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detec...

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14th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis, WASSA 2024

作者： Šmíd, Jakub Přibáň, Pavel Král, Pavel Department of Computer Science and Engineering University of West Bohemia Faculty of Applied Sciences Czech Republic NTIS – New Technologies for the Information Society Univerzitní 2732/8 Pilsen301 00 Czech Republic

ISBN: (纸本)9798891761568

This paper presents our system built for the WASSA-2024 Cross-lingual Emotion Detection Shared Task. The task consists of two subtasks: first, to assess an emotion label from six possible classes for a given tweet in one of five languages, and second, to predict words triggering the detected emotions in binary and numerical formats. Our proposed approach revolves around fine-tuning quantized large language models, specifically Orca 2, with low-rank adapters (LoRA) and multilingual Transformer-based models, such as XLM-R and mT5. We enhance performance through machine translation for both subtasks and trigger word switching for the second subtask. The system achieves excellent performance, ranking 1st in numerical trigger words detection, 3rd in binary trigger words detection, and 7th in emotion detection. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Automated Door to Prevent COVID-19 using Fuzzy Logic 4

Automated Door to Prevent COVID-19 using Fuzzy Logic

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4th International Conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Khokhar, Salah-Ud-Din Sohaib, Rana Muneeb, Abdul Bibi, Irfana Noor, Muhammad Yasir Imran, Muhammad School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Shandong Jinan China Department of Computer Science G.C. University Lahore Punjab Lahore Pakistan Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan Department of Electronics G.C. University Lahore Punjab Lahore Pakistan

ISBN: (纸本)9798350369694

Due to the vagueness and uncertainties due to the coronavirus, it is crucial to implement the standard operating procedures (SOPs) issued by the World Health Organization and the authorities. Therefore an effective control system is required that minimizes the vagueness and uncertainties of people who are on the front line during the period of this pandemic checking the entering persons in an indoor area where it is necessary to maintain a distance of six feet from one another to avoid the spread of coronavirus. In this research work, a robust and efficient system has been proposed that takes protective mask and feverishness values as input and applies them using the fuzzy logic algorithm. If these input values are according to the rules defined in the system, then the door will open as an output. Otherwise, the door will not open. It is, finally, analyzing the output values. This fuzzy model had been implemented using a microcontroller. The genuine difference in the duty cycle of the microcontroller’s output pulse was measured using a 100 MHz digital oscilloscope. Our proposed system was compared with different systems reported in the literature. This work reduces the human effort as well as the negligence of people and builds up the management system without the labor in a mint. © 2023 IEEE.

关键词： Fuzzy logic

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FPGA design and implementation for montgomery multiplication algorithm using MATLAB HDL coder

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Bulletin of the National Research Centre 2024年第1期48卷 1-16页

作者： Elsayed, Ghada Abass, Eman S. Computer Science and Informatics Faculty The British University in Egypt Cairo Egypt Electrical Engineering Department Faculty of Engineering Modern University for Technology and information (MTI University) Cairo Egypt

Modular multiplication for large numbers is especially important in cryptography algorithms such as RSA and elliptic curves. The Montgomery algorithm is the most famous and efficient one for calculating it. Hardware implementation for cryptography co-processors is better than software implementation in terms of speed and security. Many FPGA designs for the Montgomery multiplication algorithm was published based on hardware description languages like VERILOG and VHDL. This paper proposes the FPGA design and implementation using MATLAB HDL Coder. The algorithm is modified such that it can fit any small/large FPGA by introducing scaling factor. The design is configurable in both modulus length and the scaling factor. This paper performs a comparison between the synthesizing results for different scales and for different modulus lengths. The synthesizing is performed up to 8K bit modulus length, and it can be increased easily. In this paper, implementation of different modulus lengths with different frequencies and with different area utilization can be easily achieved. The design utilizes different area resources for each configuration. The target is xc7vx330t-2ffg1157 Virtex-7 Xilinx FPGA. The maximum frequency is 80.81 MHz for 4096-bit modulus length with 8-bit data width and 2 for serialization factor. The minimum area utilization is achieved for minimum configurations, i.e., 1024-bit modulus length with 8-bit data width and for unity serialization factor. This paper proposes a scalable and configurable FPGA design for Montgomery multiplication co-processor-based HDL coder design.

关键词：

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The Smart Optimization and Estimating the Performance of Encapsulating Security Payload in Wireless Network Security 3

The Smart Optimization and Estimating the Performance of Enc...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sripriya, T. Borade, Monali R. Deepaware, Neeta Kumar, Jambi Ratna Raja Khalid, Md. Saifullah Singh, K. Ranjith Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Management Jabalpur India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350319125

The Encapsulating Security Payload (ESP) is the primary issue of safety structure for wireless networks. It inspires to make specific cozy switches of records across a wireless network by offering encrypted messages, information integrity guarantee, and authentication of communications. ESP can also provide excellent selective filtering of community site visitors and provides levels of authentication required for ease of network access. Due to the advances in the wireless era, the overall performance of ESP in Wi-Fi networks is becoming increasingly essential. Consequently, intelligent optimization and estimating the overall performance of ESP in wireless networks is an important task. Clever optimization of ESP in Wi-Fi networks determines the most reliable parameters for ESP to maximize its performance and reduce the network overhead that may arise while looking to take care of records transmission and information protection. Traditional optimization techniques, including Genetic Algorithms, can be used for this project, as they give more accurate outcomes and might effortlessly be implemented in the community infrastructure. Clever optimization can't only decorate the performance of ESP but can also reduce the variety of false alarms in the machine. Moreover, developing an accurate estimation for the overall performance of ESP in Wi-Fi networks is essential to get the most out of its talents. The usage of mathematical models, including a Markov Chain modeling technique, to symbolize various. © 2023 IEEE.

关键词： authentication encapsulating optimization

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Advancements in Sustainable Charging Infrastructure: Integrating Solar Energy and IoT for Smart E-Vehicle Charging Stations

Advancements in Sustainable Charging Infrastructure: Integra...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sivasankar, C. Saravanan, G. Pradeepa, H. Arun, V. Ganesh, E.N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

An effective and environmentally friendly charging infrastructure is essential due to the rising popularity of Electric Vehicles (EVs). The proposed system offers an efficient approach to this problem by suggesting a solar charging station for EV incorporating the Internet of Things (IoT). In order to encourage the use of renewable energy and lessen the environmental impact, the system uses solar energy to power EV charging stations. IoT technologies improve the charging station's performance by allowing realtime monitoring and control, remote access and data analytics. Considerations for solar panel layout optimization and energy storage are central to this study's emphasis on the design and execution of a solar-powered EV charging station. In addition, the station can easily respond to changes in demand and grid circumstances thanks to dynamic load control made possible by IoT. This research proves the feasibility and efficiency of the proposed Solar with IoT Charging Station for Electric Vehicles via practical assessment and simulations. The results provide a scalable and long-term answer that promotes a cleaner and greener transportation environment by aiding the development of electric mobility and integrating renewable energy. © 2023 IEEE.

关键词： Charging Station Dynamic Load Management Electric Vehicle Internet of Things Solar Energy

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Analysis of the Possibility of Using Exploration and Learning Algorithms in the Production of Castings

Analysis of the Possibility of Using Exploration and Learnin...

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75th World Foundry Congress, WFC 2024

作者： Wilk-Kolodziejczyk, Dorota Witkowski, Mateusz Jaśkowiec, Krzysztof Malysza, Marcin Bitka, Adama Marcjan, Lukasz Department of Applied Computer Science and Modelling Faculty of Metals Engineering and Industrial Computer Science AGH University of Krakow Czarnowiejska 66 Krakow30-054 Poland Lukasiewicz – Krakow Institute of Technology Zakopiańska 73 Kraków30-418 Poland

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