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Robust Transmission Design in Multiobjective RIS-Aided SWIPT IoT Communications

IEEE Internet of Things Journal 2024年第10期11卷 18605-18618页

作者： Sharma, Vaibhav Allu, Raviteja Singh, Sandeep Kumar Singh, Keshav Duong, Trung Q. Tsiftsis, Theodoros A. Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Motilal Nehru National Institute of Technology Allahabad Electronics and Communication Engineering Department Prayagraj211004 India Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided Internet of Things (IoT) communications under imperfect channel state information (CSI). We formulate a multiobjective optimization problem (MOOP) to design a transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that jointly maximizes energy efficiency (EE) and harvested power (HP) under the norm-bounded CSI error model. Due to the conflicting objective functions and nonconvex nature of the above optimization problem, the MOOP is simplified using the ϵ-constraint method and subsequently adopting advanced optimization tools, such as the Dinkelbach method, S-procedure, general sign-definiteness, semidefinite programming and convex-concave procedure. Thereafter, we propose an alternating optimization-based algorithm which determines optimal TPV and PSM iteratively that jointly maximizes the EE and HP of the considered system. Through numerical simulations, we validate the robustness, optimality, convergence, accuracy, and effectiveness of our proposed algorithm. Furthermore, we assess the impact of several key parameters, such as the number of RIS elements, available transmit power at BS, and the minimum HP on the performance of the considered system. © 2014 IEEE.

关键词： Energy efficiency

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Implementing Long Short-Term Memory Network for Enhancing Hyper Spectral Time Series Modeling

Implementing Long Short-Term Memory Network for Enhancing Hy...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Eti, Muktha Shekhawat, Jyoti Nagpal, Manish Srisainath, R. Chavhan, Pranali G. Yadav, Rakesh Kumar Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350329773

This take a look at proposes using lengthy quick-time period reminiscence (LSTM) networks as a tool for enhancing the accuracy of Hyper Spectral Time series (HSTS) fashions. via the use of this recurrent neural community approach, the model can account for the temporal dating among the hyper spectral spectral time collection variables. additionally, with this utility, the version can research from the spectral time series over a longer time period. This study examined the efficacy of the carried out LSTM network with the aid of comparing the results to the Hyper Spectral Time collection model performance performed prior to using this method. The effects recommend that the addition of the LSTM network accelerated the accuracy of the version through about 25%. This generation has the potential to substantially beautify the accuracy of temporal records inside the discipline of faraway sensing records analysis. In conclusion, this examine indicates that LSTM networks are a promising tool for boosting the predictive energy of HSTS models. long quick-term memory Networks (LSTM) are a type of Recurrent Neural Networks (RNNs) that have been applied to many duties in synthetic Intelligence including language information, herbal language processing, and image processing. more recently, they had been used to enhance hyper spectral time series modeling. The shape of LSTM permits it cope with and keep lengthy-term sequences of statistics, making it particularly suitable for modeling hyper-spectral time series. At whenever step, an LSTM community takes as input a vector representing the hyper-spectral values, and outputs a vector which represents the intermediate representation of the time series. these intermediate representations are then used to generate the output at every next time step. LSTM networks are for his or her ability to capture lengthy-time period dependencies, and because of their structure, they are additionally nicely-appropriate to hyper-spectral time collecti

关键词： Time series

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & technology 2023.

关键词： Support vector machines

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Metaheuristic Approach for Optimizing Neural Networks Parameters in Battery State of Charge Estimation 13

Metaheuristic Approach for Optimizing Neural Networks Parame...

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13th IEEE Symposium on computer Applications and Industrial electronics, ISCAIE 2023

作者： Mustaffa, Zuriani Sulaiman, Mohd Herwan Aziz, Azlan Abdul Universiti Malaysia Pahang Faculty of Computing Pahang Pekan26600 Malaysia Universiti Malaysia Pahang Faculty of Electrical & Electronics Engineering Technology Pahang Pekan26600 Malaysia Universiti Teknologi MARA Faculty of Computer Science and Mathematics Perlis Arau02600 Malaysia

ISBN: (纸本)9798350347319

To accurately estimate the battery state of charge (SOC), it is vital to improve the performance of a battery-powered system. This paper employs the recent proposed Evolutionary Mating Algorithm (EMA) for optimizing the weights and biases of Feed-Forward Neural Network (FNN) in estimating the state of charge (SOC) of Lithium-ion batteries. SOC estimation is the critical aspect in battery management system (BMS) to ensure the reliable operation of electric vehicles (EV) since there are no direct way to measure it. In addition, it is very nonlinear due to variation of charge/discharge currents and temperature. EMA is the recent evolutionary algorithm based on mating theory and environmental factor will be used in this paper to optimize the weights and biases of FNN on a common Li-ion battery, multiple data measurements, drive cycles and training repetitions. The performance of EMA will be compared with other algorithms to show the effectiveness of EMA in solving the SOC estimation problem. Findings of the study demonstrate the superiority of EMA in estimating the SOC of the batteries in terms of Root Mean Square Error (RMSE), mean Absolute Error (MAE) and Standard Deviation. © 2023 IEEE.

关键词： Feedforward neural networks

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Utilizing Machine Learning Algorithms to Optimize Real-Time Data Analysis 3

Utilizing Machine Learning Algorithms to Optimize Real-Time ...

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

作者： Singh, Vaishali Gupta, Eesha Jana, D. Little Femilin Deshmukh, Gopal B. Singla, Abhishek Krishna, K. Gopala Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

This paper offers a way for optimizing actual-time records analysis the usage of a variety of machine gaining knowledge of algorithms. The algorithms paintings by creating predictive fashions which extract useful information from datasets, follow appropriate preprocessing techniques to make sure the high-quality of the records, and model the information to deduce meaningful capabilities from the data. The predictive models are then used to discover applicable developments, find patterns, and summarize the data for clean analysis. specifically, this paper outlines the technique of using supervised and unsupervised learning algorithms, consisting of Random wooded area, Naïve Bayes, and k-approach clustering, to extract meaningful styles and relationships from facts points in real-time. Additionally, the paper discusses wonderful capacity packages of the proposed technique, including fraud detection and prediction of stock costs. Such capability programs demonstrate the fee of actual-time records analysis for organizations and organizations. © 2023 IEEE.

关键词： Clustering algorithms

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Investigations on Cyber Security Vulnerability using Distribution Analysis

Investigations on Cyber Security Vulnerability using Distrib...

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2024 IEEE International Students' Conference on Electrical, electronics and computer Science, SCEECS 2024

作者： Andrews, Leo John Baptist Alagappan, Annamalai Sarathkumar, D. Fathima, M. Venkatachary, Sampath Kumar Rajeshkanna, R. Raj, Raymon Antony Botho University Faculty of Engineering and Technology Department of Information Technology Gaborone Botswana Botho University Faculty of Engineering and Technology Department of Network and Infrastructure Management Gaborone Botswana Kongu Engineering College Department of Electrical and Electronics Engineering Tamilnadu Erode District638 060 India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Grant Thornton Botswana Plot 50370 Acument Park Fair Grounds Gaborone Botswana Thamirabarani Engineering College Department of Eee Tamil Nadu Tirunelveli627 358 India

ISBN: (纸本)9798350348460

Internet communication is used as a mode of engagement by businesses, organizations, and even nations in a variety of contexts. As a result, both the quantity of data produced and the importance of the information that can be derived from it are growing. Multiple safeguards are required in order to ensure that the accessibility, integrity, and confidentiality of company information and assets are maintained. When it comes to protecting the privacy of a company and its reputation, adopting countermeasures and minimizing the damage caused by irreversible cyberattacks are both very crucial. It is possible to utilize both active and passive attack tactics, as well as social engineering tests, to evaluate the weaknesses of a corporation's cyber security. The identification and ranking of vulnerabilities both play an active part in the process of raising the level of corporate awareness, as well as closing shortfalls and enhancing measures. In this investigation, ethics committee reports and confidentiality agreements were followed in order to do a cyber security vulnerability assessment on seven separate organizations. These organizations represented the governmental sector, the corporate sector, and the civil society. In order to evaluate the effectiveness of the firms' cyber security infrastructures, security scans such as port, web, application-based system and network penetration tests, distributed denial of service attacks, and social engineering were carried out. The Common Vulnerability Scoring System (CVSS) was used in order to change the base score of each institution and to identify the vulnerabilities that were present. Enterprise Networks, Cyber Security Analysis, Cyber Attacks, Penetration, and Vulnerability are some of the Keywords that can be found in this article. © 2024 IEEE.

关键词： cyber analysis Introduction cyber security cyber-attacks vulnerabilities

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A Novel Bridgeless Interleaved-Based Dual-Output Boost-Type PFC Rectifier 17

A Novel Bridgeless Interleaved-Based Dual-Output Boost-Type ...

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17th IEEE International Conference on Compatibility, Power electronics and Power engineering, CPE-POWERENG 2023

作者： Monteiro, Vitor Martins, Joao Afonso, Joao L. University of Minho Campus de Azurem ALGORITMI Research Centre / LASI Department of Industrial Electronics Portugal NOVA University of Lisbon Faculty of Sciences and Technology Electrical and Computer Engineering Department Portugal

ISBN: (纸本)9798350300048

Power factor correction (PFC) rectifiers is an old topic of research, which is assuming extra applicability to alleviate power quality complications in modern power grids. In this background, this paper proposes a novel PFC rectifier, which has as core differentiating characteristics the bridgeless structure, the interleaved current control, the operation with three voltage levels, and the dual output voltage. The proposed PFC rectifier works as a boost voltage topology, but offering an asymmetrical split dc-link, which can be advantageous in many applications as an AC-DC front-end converter. For all power range, the proposed PFC rectifier ensures a sinusoidal AC current in phase with the voltage, and controlled dc-link asymmetrical voltages. The paper outlines a broad clarification of the proposed topology, the operation principle, the employment of the control theory, and a set of computational results, attained with an AC voltage of 230 V-50 Hz and a split DC-link voltage stabilized at 400 V. The proof of the topology and control of the proposed PFC rectifier is performed in steady-state, and also for sudden variations of the operation power. © 2023 IEEE.

关键词： Topology

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Desertification Detection in Makkah Region based on Aerial Images Classification

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computer Systems Science & engineering 2022年第2期40卷 607-618页

作者： Yahia Said Mohammad Barr Taoufik Saidani Mohamed Atri Electrical Engineering Department College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) Faculty of Sciences of MonastirUniversity of MonastirTunisia Department of Computer Sciences Faculty of Computing&Information TechnologyNorthern Border UniversityRafhaSaudi Arabia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia

Desertification has become a global threat and caused a crisis,especially in Middle Eastern countries,such as Saudi *** is one of the most important cities in Saudi Arabia that needs to be protected from *** vegetation area in Makkah has been damaged because of desertification through wind,floods,overgrazing,and global climate *** damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation *** this paper,we propose an automatic desertification detection system based on Deep Learning *** images are classified using Convolutional Neural Networks(CNN)to detect land state variation in *** have been widely used for computer vision applications,such as image classification,image segmentation,and quality *** proposed CNN model was trained and evaluated on the Arial Image Dataset(AID).Compared to state-of-the-art methods,the proposed model has better performance while being suitable for embedded *** has achieved high efficiency with 96.47% *** light of the current research,we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.

关键词： Desertification detection deep learning convolutional neural networks(CNN) aerial images classification Makkah region

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Experimental Fire Detection and Alarm System Based on the Integration of IoT and Image Processing

Experimental Fire Detection and Alarm System Based on the In...

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IEEE International Conference on Consumer electronics-Asia (ICCE-Asia)

作者： Dat Vuong Van Vu Tran Van Thanh Tran Huynh Anh Nhat Tran Pham Hoang Hao Le Tran The Son Faculty of Computer Engineering and Electronics Vietnam-Korea University of Information and Communication Technology Da Nang Vietnam

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

Fires are considered a risk that causes human death and property loss. To reduce fire damage, fires must be detected and warned promptly. This paper aims to design and experimentally implement a fully-function fire detection and warning system. The developed system is based on a Wireless Sensor Network (WSN) that includes various nodes distributed across the city. Each node has two main blocks which are the image processing and communication blocks. The image processing block applies the EfficientN et B0 model to detect the fire and smoke, whereas the communication block uses the Module 4G to contact the house owner over a mobile phone network and LoRa gateway to send the signal warning to the fire center over the a LoRaWAN. The Module 4G is responsible for sending captured images of suspected fire if any to the house owner's device for getting his/her acknowledgement. Then an urgent alarm message consisting of fire position and risk level are sent to the fire center if the fire is acknowledged by the house owner, otherwise, the alarm is ceased. Besides, the proposed system is installed the gas sensor, smoke sensor, water spray system and light-sound alarm to augment the reliability of fire detection. The proposed system is experimentally demonstratedand achieved positive results: Delay time sending from the proposed fire detection system to the user is recorded as 5 seconds and from the user to the fire center is recorded as 10 seconds, within a LoRaWAN coverage of1.5km.

关键词： Wireless sensor networks Image processing Urban areas LoRaWAN Optimization methods Alarm systems Logic gates Mobile handsets Internet Reliability

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A Dual Broadband Antenna for V2X, LTE, and 5G Vehicular Communication Systems 7

A Dual Broadband Antenna for V2X, LTE, and 5G Vehicular Comm...

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7th IEEE International Conference on Electronic Information and Communication technology, ICEICT 2024

作者： Hu, Yiting Feng, Botao Chung, Kwok L. Ding, Xiao Gu, Wenzhe Deng, Li Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Huizhou University School of Computer Science and Engineering Huizhou China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China

In this paper, we present a novel dual broadband antenna tailored for vehicular applications. The antenna design incorporates circular and square patches, metal cylinders, and conductive vias. Our simulated results de... 详细信息

ISBN: (纸本)9798350384437

关键词： Advanced driver assistance systems

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