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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Sagar, D. Unni, Anuroop M. S. Ramaiah University of Applied Sciences Computer Science Engineering Department Bengaluru560058 India

ISBN: (纸本)9781665421683

Lithium-ion battery tends to be the most common choice for the use in hybrid vehicles (HEVs) and Electrical vehicle (EVs) in today's world because of its high-power density, energy density with overall efficiency compared other conventional cells. However, they too come with their own problems which makes it difficult for end user. Lithiumion cells are prone to temperature changes, overcharging and discharging process. The present condition of the EV application the batteries efficiency deteriorates over period of prolonged usage, as a result it hits the end of life (EOL) earlier than expectedTo avoid such situation, the battery should be monitored in real time for reliability, evaluation and restoration. Battery packs should be analysed and diagnosed with respect to the designed simulation model and experimental model to avoid inconsistency in parametersThis thesis proposes a test model for a lithium Ferro phosphate battery pack that is implemented as both a hardware test rig and a corresponding MATLAB/Simulink model test rig. Two discharging tests known as Continuous Discharging Test (CDT) and Pulse Discharging Test (PDT) is used to test the battery pack to Analyse different parameter related inconsistency between the actual model and designed model © 2022 IEEE.

关键词： Battery management systems

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A comprehensive review of deep learning power in steady-state visual evoked potentials

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Neural Computing and Applications 2024年第27期36卷 16683-16706页

作者： Al-Qaysi, Z.T. Albahri, A.S. Ahmed, M.A. Hamid, Rula A. Alsalem, M.A. Albahri, O.S. Alamoodi, A.H. Homod, Raad Z. Shayea, Ghadeer Ghazi Duhaim, Ali M. Tikrit Iraq Technical College Imam Ja’afar Al-Sadiq University Baghdad Iraq Baghdad Iraq Department of Management Information Systems College of Administration and Economics University of Mosul Mosul Iraq Australian Technical and Management College Melbourne Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Applied Science Research Center Applied Science Private University Amman Jordan Department of Oil and Gas Engineering Basra University of Oil and Gas Basra1004 Iraq Ministry of Education Thi-Qar Education Directorate Thi Qar Iraq MEU Research Unit Middle East University Amman Jordan Baghdad Iraq

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of science, PubMed, scienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, include

关键词： Deep learning

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Dynamic Vehicular Diagnostic Tool for Identifying Random Errors & Real-Time Solution Providers using Android Based Multi-Variant End User Profiles 7

Dynamic Vehicular Diagnostic Tool for Identifying Random Err...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Joy, Jiss Rahul, V. Sagar, D. M. S. Ramaiah University of Applied Sciences Computer Science Engineering Department Bengaluru560058 India

ISBN: (纸本)9781665421683

Due to underwhelming growth towards road side assistance have exponential increased the difficulties and inconsistency in providing the vehicles, road side assistance in an adequate manner. Hence, a new approach is a need of the hour. In these paper we discuss about the Android platform based solution to mitigate the under lying problem in service providence for road side *** lack of a proper assistance system for people who become stranded on the road with vehicular difficulty and are unable to find help is a looming issue in the near future. Android is the go-to platform for the majority of people, hence, making an Android-based system to build a network of competent professional service providers to give speedy assistance is a necessary tool for everyone who wants to drive their car anywhere without hesitation. The system validates the service providers, and their information is saved for user reference, providing high-quality and safe services. The system, which has a minimalist layout, provides a clean, straightforward, and efficient interface for consumers to seek help quickly. Despite the fact that there are several roadside assistance teams, they exclusively provide service in well-developed cities. This leaves a large chunk of the country unrepresented. Creating a nationwide network of service providers allows customers to receive reliable assistance anywhere and at any time. © 2022 IEEE.

关键词： Random errors

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An Intelligent Fuzzy Inference System for High Priority Vehicles in Vehicular Named Data Networks

An Intelligent Fuzzy Inference System for High Priority Vehi...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Anu Kaushik Bobbinpreet Kaur Rasmeet S Bali Gautam Srivastava Computer Science & Engineering Department Chandigarh University India Department of Math and Computer Science Brandon University Canada

With the tremendous increase in the number of on-road vehicles, travel time for High Priority vehicles is touching alarming levels. In case of an emergency, this can lead to loss of life. To cater or propose a solution to this problem an efficient model can be developed which focuses on selecting vehicles obstructing the path of high-priority vehicles (HPV) and making communication with selected vehicles. This paper proposes a solution based on a hybrid approach using communication handled by Vehicular Named Data Networks (V-NDN) and decision support through a fuzzy inference system. Considering the basic V-NDN capabilities of transmitting packets and developing communication with vehicles in the periphery, a model is developed which will send Interest Packets to all vehicles in range and prioritize vehicles using directional values to find out which vehicle is obstructing the path of HPV. The decision support is provided through the fuzzy inference system which utilizes “If-Then” rules to implement directional identification of vehicles. Simulations have been run on the ndnSim simulator to verify the suggested scheme's performance. The results have been analyzed using the ndnSim simulator on different scenarios and their efficiency has been observed in terms of average delay, data retrieval performance and data delivery efficiency.

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An Improved Lightweight Framework for Handwritten Chinese Text Recognition Based on CRNN

An Improved Lightweight Framework for Handwritten Chinese Te...

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2022 International Symposium on Intelligent Robotics and Systems, ISoIRS 2022

作者： Shen, Lu Tang, Su-Kit Mirri, Silvia Macao Polytechnic University Faculty of Applied Sciences China Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (纸本)9781665455213

Robots with computer vision and text recognition functions are widely used in industrial production, especially in highly automated factories. However, most robots have an excellent ability to recognize printed characters and show low accuracy in recognition of handwritten characters. Therefore, this paper considers recognizing handwritten text in the intelligent processing of handwritten documents. Its high accuracy prediction results are closely related to the effectiveness of manuscript input, intelligent translation, and intelligent scoring. Handwritten text is more difficult to recognize because it contains sequential information, and the images are more complex than single-character images. This paper proposes a new handwritten Chinese text recognition (HCTR) framework based on existing classical convolutional neural network (CNN) and recurrent neural network (RNN) algorithms. We use a handwritten Chinese text dataset from CASIA-HWDB containing numbers and symbols close to real application scenarios to train the model and compare the performance of various models, such as MobileNetV1 and MobileNetV2, with the proposed model. From the analysis of experimental results, it can be found that the proposed method can achieve higher performance with fewer parameters. In addition, we optimize the dropout rates of input blocks and obtain the best CER of our method is 6.11%. © 2022 IEEE.

关键词： Convolution

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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An IoT based Low Cost E-Parking System in Smart Cities 2

An IoT based Low Cost E-Parking System in Smart Cities

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Pradeepkumar, G. Monika, T. Gowrishankar, C. Bhat, C. Rohith Gowrishankar, V. Senthilkumar, M. Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Jkkn College of Engineering and Technology Department of Electrical and Electronics Engineering Kumarapalayam India Kongunadu College of Engineering and Technology Department of Electrical and Electronics Engineering Tholurpatti India Simats Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Psg Institute of Technology and Applied Research Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350346640

The concerned workers ensure that vehicles are parked in the appropriate spaces. Employees must repeatedly poll their coworkers via personal surveys or the company's internal phone system to ensure that everything is correct. If there is an available parking space, the driver will manually move the vehicle there, regardless of any obstacles. If there is no available space, the car must return and try again later. The proposed intelligent parking system, if implemented, would solve all parking problems. This will take less time and fuel than other options. Intelligent parking solutions will fundamentally alter automobile-centric cities. It may make parking more convenient by bringing order to the chaos. People are always concerned that finding a parking spot will take too long, particularly in densely populated cities. This study focuses on developing a new system in which residents in high-traffic areas can earn extra money by renting out their unused parking spaces to those in need. This strategy could help malls to manage parking more effectively during peak shopping hours. A customer can save time by reserving a parking space ahead of time. © 2023 IEEE.

关键词： Costs

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Hybrid DF and SIR Forwarding Strategy in Cooperative Communications and Higher Order Modulations

Hybrid DF and SIR Forwarding Strategy in Cooperative Communi...

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International Conference on Communications and Networking, ComNet

作者： Slim Chaoui Madallah Alruwaili Haifa Harrouch Omar Alruwaili Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University KSA Computer Science Department Applied College University of Hail KSA

ISBN: (数字)9798331520793

ISBN: (纸本)9798331520809

In this paper we propose a hybrid decode and forward and soft information relaying (HDFSIR) strategy to address the issue of error-propagation in coded cooperative communications. We also introduce a novel method for mapping the conveyed bits' log likelihood ratios onto higher order modulated soft symbols and provide a model characterizing the relationship between the estimated complex soft symbol and the actual high order modulation symbol. A closed form expression for the symbol error rate of the coded cooperative communication with HDFSIR strategy is derived. Monte Carlo simulations are presented to demonstrate the validity of the analytical results.

关键词： Protocols Closed-form solutions Monte Carlo methods Cooperative communication Symbols Modulation Probability Power system reliability Relays Signal to noise ratio

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Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

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computers, Materials & Continua 2022年第10期73卷 899-915页

作者： Ashit Kumar Dutta Yasser Albagory Abdul Rahaman Wahab Sait Ismail Mohamed Keshta Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,*** the interesting applications of autonomous systems,their applicability in agricultural sector becomes *** unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop *** spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the *** recently developed deep learning(DL)models have exhibited effective performance in several data classification *** this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)*** proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target ***,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing ***,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of *** the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is *** design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.

关键词： Autonomous systems object detection precision agriculture unmanned aerial vehicles deep learning parameter tuning

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A Fuzzy Optimized Route Selection Framework for Autonomous Vehicles using V-NDN

A Fuzzy Optimized Route Selection Framework for Autonomous V...

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IEEE International Conference on Big Data

作者： Anu Kaushik Rasmeet S Bali Gautam Srivastava Computer Science & Engineering Department Chandigarh University India Department of Math and Computer Science Brandon University Canada

Rapid mobility and frequent disconnection in vehicular networks makes multi-hop data delivery challenging. To address this concern, adaptive data forwarding is applied over vehicular networks in a named data networking environment. We have proposed An Interest Chain based Forwarding Mechanism (ICFM), which includes a chain based forwarding mechanism by employing fuzzy logic to evaluate the next chain member. Autonomous vehicles forward a packet to the next promising vehicle and in this way a chain is formed to satisfy interest with the requested data. The proposed fuzzy-based interest chain mechanism is used to forward a packet to destination and receive a corresponding data packet with decreased data delivery delay. Experimental analysis has been performed on ndnSIM to verify the performance of the suggested scheme. The scheme runs on different scenarios and efficiency has been observed in terms of interest satisfaction ratio, average number of interest packets, average number of data packets and average delay.

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