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6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022

作者： Niu, Yitong Habeeb, Fadya A. Mansoor, Muneer Sameer Gheni Gheni, Hassan Muwafaq Ahmed, Saadaldeen Rashid Radhi, Ahmed Dheyaa School of Aeronautical Engineering AnYang University Anyang China College of Education for Women Tikrit University Department of Mathematics Iraq Department of Mobile Communications and Computing Engineering Baghdad Iraq Mustaqbal University College Computer Techniques Engineering Department Hillah Iraq Computer Science Computer Engineering Tikrit University Iraq Karabuk University Turkey College of Pharmacy University of Al-Ameed PO Box 198 Karbala Iraq

ISBN: (数字)9781665470131

ISBN: (纸本)9781665470131

This paper researches and designs an automatic charging control and monitoring system for PV electric vehicle batteries, which can automatically charge the low-voltage and high-voltage batteries by obtaining the operation mode of PV electric vehicles, avoiding battery maintenance to the maximum extent, preventing damage to the battery cell caused by over-discharge, and increasing the range of the whole vehicle. Suppose the PV EV is in parking mode. In that case, it will obtain the power of the low-voltage battery in real-Time and charge the low-voltage battery according to the request, and then it will obtain the power of the high-voltage battery and send a power-up request to charge the high-voltage battery according to the request;if the PV EV is in driving mode, it will obtain the power of the low-voltage battery in real-Time and generate a power-up request according to the power-up request. When the PV electric vehicle is in driving mode, it will obtain the low voltage battery power condition in real-Time and send a request to recharge the low voltage battery. © 2022 IEEE.

关键词： Electric vehicles

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Receiver-Based Data Communication for Wireless Sensor Networks 2

Receiver-Based Data Communication for Wireless Sensor Networ...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Chitra Kiran, N. Parkash, Jai Cheerla, Sreevardhan Shanmugaraja, P. Manohara, M. Madan, Parul Alliance University Department of Electronics and Communication Engineering Karnataka Bangalore560068 India Gulzar Group of Institutes Department of Computer Science and Engineering Punjab Khanna141401 India Koneru Lakshmaiah Education Foundation Department of Electronics and Communications Engineering Andhra Pradesh Vaddeswaram522302 India Sona College of Technology Department of Information Technology Tamil Nadu Salem636005 India Mohan Babu University School of Engineering Andhra Pradesh Tirupati517102 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350315905

As the Web of Things (IoT) has created throughout recent years, unlicensed radio range has become more clogged with brilliant wireless gadgets that are utilized in an assortment of use spaces, prominently the 2.4 GHz ISM band. One of the easiest and most productive answers for energy-compelled networks is the wake-up receiver (WuRx). Rather than utilizing a conventional radio, this innovation empowers energy-independent on-request communication for persistent observing. One of the WSN methods that utilizes the most investment is directing. Consequently, planning an energy-effective steering procedure is fundamental. This exploration proposes Grouped WuRx based on Multicast wake-up (CWM), a progressive Wake-up Receiver-based directing convention that all the while streamlines energy use and time productivity for indoor conditions. In our proposed technique, bunches are made inside the organization. Estimation discoveries show that, in contrast with past directing conventions, our proposed arrangement has much better execution as far as conveyance delay and further developed energy effectiveness. © 2023 IEEE.

关键词： Wireless sensor networks

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Anomaly Transmitter Recognition and Tracking

Anomaly Transmitter Recognition and Tracking

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IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN

作者： Tianyi Zhao Shamik Sarkar Yuan Tian Danijela Cabric Electrical and Computer Engineering Department University of California Los Angeles USA Department of Electronics and Communications Engineering Indraprastha Institute of Information Technology Delhi India

ISBN: (数字)9798350317640

ISBN: (纸本)9798350317657

Device authentication and identification are important to ensure security in spectrum access and management. While prior works have studied radio frequency fingerprinting for such purpose and demonstrated its effectiveness, most works have been focusing on closed-set classification, where only a known set of authorized transmitters appears to be identified. However, unauthorized transmitters can also exist in a designated band. Therefore, recognizing and tracking those unauthorized transmitter behaviors is necessary to protect spectrum security. This work investigates this problem and proposes an Anomaly Recognition and Tracking (ART) framework. The ART framework first learns a closed-set classifier between the authorized transmitters and modifies the classifier to detect anomaly signals. Then, the framework collects the detected anomaly signals and performs unsupervised clustering to assign labels to the predicted anomaly transmitters. Finally, the framework incrementally learns the anomaly transmitter features with limited signals and updates the classifier accordingly. We evaluate our proposed framework with a WiFi dataset and show that with 10 % false alarm rate, it can detect more than 99 % anomaly signals. Moreover, our proposed framework can distinguish between and learn the features of different anomaly transmitters with as few as 10 signals received from each anomaly transmitter. Finally, the framework can update the classifier, track the anomaly transmitters, and classify among all authorized and anomaly transmitters with more than 99 % accuracy.

关键词： Accuracy Heuristic algorithms Radio transmitters Subspace constraints Clustering algorithms Fingerprint recognition Robustness

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments 13

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kirubhakaran Karthikeswaran, D. Arun, M. Chakravarthy, R. Arun Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Surya Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A∗ algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry. © 2024 IEEE.

关键词： Motion planning

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Watermarking and Encryption for Electronic Medical Records (EMR) Security Based on mCloud 2

Watermarking and Encryption for Electronic Medical Records (...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Abdulshaheed, Haider Rasheed Abbas, Yaseen Saadi Tawfeq, Jamal F. Ateeq, Karamath Alnawayseh, Saif. E. A. Ghazal, Taher M. Baghdad College Of Economic Sciences University Computer Engineering Techniques Department Baghdad Iraq College Of Engineering Al-Nahrain University Electronic And Communications Engineering Department Baghdad Iraq Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering Mutah University Department Of Electrical Engineering Al-Karak Jordan Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing 'big EMR data' from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article. © 2024 IEEE.

关键词： Authentication

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Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction

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Annals of Data Science 2022年第2期9卷 393-428页

作者： Emara, Heba M. Elwekeil, Mohamed Taha, Taha E. El-Fishawy, Adel S. El-Rabaie, El-Sayed M. El-Shafai, Walid El Banby, Ghada M. Alotaiby, Turky Alshebeili, Saleh A. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt KACST Riyadh Saudi Arabia King Saud University Riyadh Saudi Arabia Department of Electrical Engineering King Saud University Riyadh11421 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh21974 Saudi Arabia

Seizure detection and prediction are a very hot topics in medical signal processing due to their importance in automatic medical diagnosis. This paper presents three efficient frameworks for applications related to electroencephalogram (EEG) signal processing. The first one is an automatic seizure detection framework based on the utilization of scale-invariant feature transform (SIFT) as an extraction tool. The second one depends on the utilization of the fast Fourier transform (FFT) and an artificial neural network for epileptic seizure prediction. Finally, an automated patient-specific framework for channel selection and seizure prediction is presented based on FFT. The simulation results show the success of the proposed frameworks for automated medical diagnosis. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Fast Fourier transforms

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Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques 22

Investigating Positive and Negative Qualities of Human-in-th...

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2022 CHI Conference on Human Factors in Computing Systems, CHI 2022

作者： Chan, Liwei Liao, Yi-Chi Mo, George B Dudley, John J Cheng, Chun-Lien Kristensson, Per Ola Oulasvirta, Antti Department of Computer Science National Yang Ming Chiao Tung University Taiwan Department of Communications and Networking Aalto University Finland Department of Engineering University of Cambridge United Kingdom

ISBN: (纸本)9781450391573

Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. In HCI, design optimization problems are often exceedingly complex, involving multiple objectives and expensive empirical evaluations. Model-based computational design algorithms assist designers by generating design examples during design, however they assume a model of the interaction domain. Black box methods for assistance, on the other hand, can work with any design problem. However, virtually all empirical studies of this human-in-the-loop approach have been carried out by either researchers or end-users. The question stands out if such methods can help designers in realistic tasks. In this paper, we study Bayesian optimization as an algorithmic method to guide the design optimization process. It operates by proposing to a designer which design candidate to try next, given previous observations. We report observations from a comparative study with 40 novice designers who were tasked to optimize a complex 3D touch interaction technique. The optimizer helped designers explore larger proportions of the design space and arrive at a better solution, however they reported lower agency and expressiveness. Designers guided by an optimizer reported lower mental effort but also felt less creative and less in charge of the progress. We conclude that human-in-the-loop optimization can support novice designers in cases where agency is not critical. © 2022 ACM.

关键词： Multiobjective optimization

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An Intelligent Approach for Intrusion Detection in Industrial Control System

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computers, Materials & Continua 2023年第11期77卷 2049-2078页

作者： Adel Alkhalil Abdulaziz Aljaloud Diaa Uliyan Mohammed Altameemi Magdy Abdelrhman Yaser Altameemi Aakash Ahmad Romany Fouad Mansour Department of Information and Computer Science College of Computer Science and EngineeringUniversity of Ha’ilHa’il81481Saudi Arabia Applied College University of Ha’ilHa’il81481Saudi Arabia College of Education New Valley UniversityEl-Kharga72511Egypt College of Art University of Ha’ilHa’il81481Saudi Arabia School of Computing and Communications Lancaster UniversityLeipzig04109Germany College of Science New Valley UniversityEl-Kharga72511Egypt

Supervisory control and data acquisition(SCADA)systems are computer systems that gather and analyze real-time data,distributed control systems are specially designed automated control system that consists of geographically distributed control elements,and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based *** recent years,there has been a lot of focus on the security of industrial control *** to the advancement in information technologies,the risk of cyberattacks on industrial control system has been drastically *** they are so inextricably tied to human life,any damage to them might have devastating *** provide an efficient solution to such problems,this paper proposes a new approach to intrusion ***,the important features in the dataset are determined by the difference between the distribution of unlabeled and positive data which is deployed for the learning ***,a prior estimation of the class is proposed based on a support vector *** results show that the proposed approach has better anomaly detection performance than existing algorithms.

关键词： Industrial control system anomaly detection intrusion detection system protection

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Real Time Conversion of American Sign Language to text with Emotion using Machine Learning 6

Real Time Conversion of American Sign Language to text with ...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Jamwal, Aryan Vasukidevi, G. Malleswari, Tyj Naga Vijayakumar, T. Chandra Sekhar Reddy, L. Gupta, Amara S A L G Gopala Srm Institute of Science and Technology Department of Networking and Communications Tamilnadu Chennai India Rmk College of Engineering and Technology Department of Science and Humanities Tamilnadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore India Cmr College of Engineering & Technology Department of Computer Science and Engineering Telangana Kandlakoya India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Auditory impairment or hearing loss is a major problem in today's human population. Sign language helps hearing-impaired people to live their social life without much difficulty. The latest technology used in detecting sign language connects them to the rest of the world. Sign language recognition and conversion to the text based on the movement of hands and the shape formed by the fingers is a complex system. The solutions using machine learning give significant success for this complex system. This paper mainly focuses on developing a system for recognizing the different hand signs in American Sign Language and their emotions simultaneously in real-time and converting them into text. The system resolves the need for a translator to bridge the gap between a sign language based user and a non-sign language-based user. Most state-of-the-art technology involves CNN models with image pixels by identifying specific key point coordinates on the face/hand obtained. Using the latest technologies like MediaPipe, an improved CNN model is developed based on the distances between each unique identified vital point. The customized model able to achieve 80 percentage of accuracy for the live image. © 2022 IEEE.

关键词： computer vision

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Human Activity Recognition Using Feedforward Backpropagation Neural Network

Human Activity Recognition Using Feedforward Backpropagation...

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International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Fadi Al Debs Makram El Jurdy Elias Fares Gaby Abou Haidar Michel Owayjan Roger Achkar Department of Computer and Communications Engineering American University of Science and Technology Beirut Lebanon Department of Mechatronics Engineering American University of Science and Technology Beirut Lebanon

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

This paper presents a feedforward backpropagation neural network designed to recognize human activities using loT sensor data. Human activity recognition based on mobile phones and wearable sensor data has garnered significant attention due to its wide range of applications in healthcare., smart environments., and beyond. Previous studies in this field have utilized various sensors., such as accelerometers., gyroscopes., and orientation sensors., among others., to categorize human activities using modern wearable technologies. The proposed system employs a Human Activity Recognition framework., which collects real-time sensor data and analyzes human movements using deep learning techniques. The model achieves an accuracy of approximately 96% in identifying human motions based on the sensor data.

关键词： Backpropagation Technological innovation Medical services Real-time systems Mobile handsets Feedforward neural networks Gyroscopes Human activity recognition Informatics Wearable sensors

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