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4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Kale, Chaitanya P. Gawali, Mahendra B. Sanjivani College of Engineering Department of Computer Engineering MH Kopargaon India Sanjivani College of Engineering Department of Information Technology MH Kopargaon India

ISBN: (纸本)9798350393248

This article presents a comprehensive review of the technical advancements that have been made in offshore wind turbines via the use of digital twins. It discusses the digital twin architecture, which can anticipate and enhance turbine performance by using real-time data and predictive analytics. A digital twin is essentially a digital replica of a turbine. The study highlights how digital twins may assist improve the accuracy of energy output forecasting, save money on maintenance expenses, and raise the reliability of offshore wind energy systems. This paper investigates the intricate interactions that take place between virtual models and physical systems, illustrating how the combination of the two might lead to smarter and more environmentally friendly energy management. © 2023 IEEE.

关键词： Predictive analytics

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Icon-to-Icon Translation using Pixel-Level Weight Adversarial Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 147-160页

作者： Pan, Yao-Ming Lin, Kai-Biao Chen, Chin-Ling School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Engineering Research Center of Big Data Application in Private Health Medicine Fujian Provincial University Putian351100 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

We investigate image-to-image translation as a solution to icon-to-icon translation problems. Icon-to-icon translation aims to translate the input icon to appear like the target domain’s icons and simultaneously preserve the original icon’s information (e.g., application logo, text and background color). We present a novel approach to trans-lating icons to the version with the target style intelligently. By observing many icons with various styles, we propose a pixel-level weight matrix to calculating the weighted losses. These losses guide the generator to focus on the critical regions of the icon. People can clearly get the original icon’s information from the proposed method’s results, while the existing state-of-the-art image-to-image translation methods may fail to do this. © 2022, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Pixels

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Enhancing Crop Yield and Growth Prediction Using IoT-Based Smart Irrigation Systems and Machine Learning Algorithms 2

Enhancing Crop Yield and Growth Prediction Using IoT-Based S...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Shilpa, M. Ravi, P. Sharmila, N. Mallikarjunaswamy, S. Deepak, B.L. Pavithra, G.S. Thazeen, Sadiya Komala, M. Mahendra, H.N. Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Mysore College of Engineering & Management Dept of Computer Science and Engineering Mysuru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Sjc Institute Of Technology Dept of Information Science and Engineering chickballapur India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350372892

This paper introduces 'Smart Irrigation Yield Optimization (SIYO),' a sophisticated IoT-based smart irrigation system aimed at significantly improving crop yield and growth predictions. Traditional irrigation methods such as furrow irrigation, overhead sprinkler systems, and manual watering are analyzed for their inherent inefficiencies, especially in precision and resource management. SIYO overcomes these drawbacks by employing a network of real-time sensors that monitor soil moisture, temperature, humidity, and nutrient levels, thus enabling precise and automated irrigation tailored to the specific needs of crops. This technology not only optimizes water and nutrient usage but also adapts to varying environmental conditions to maximize crop potential. Comparative studies reveal that SIYO enhances water efficiency by 0.30% over furrow irrigation, increases nutrient utilization by 0.25% compared to overhead systems, and boosts the accuracy of crop yield and growth predictions by 0.40% against manual methods. The implementation of SIYO demonstrates the substantial improvements in agricultural productivity and resource sustainability achievable with advanced IoT technologies. © 2024 IEEE.

关键词： Smart agriculture

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Sign Language Finger-Spelling Recognition System Using Deep Convolutional Neural Network 5

Sign Language Finger-Spelling Recognition System Using Deep ...

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5th Biennial International Conference on Nascent Technologies in engineering, ICNTE 2023

作者： Howal, Antara Golapkar, Atharva Khan, Yunus Bokade, Siddhantha Varma, Satishkumar Vyawahare, Madhura Vikram Pillai College of Engineering Dept. Information Technology New Panvel India Computer Engineering Pillai College of Engineering New Panvel India

ISBN: (纸本)9781665465045

Sign Language is used by deaf and hard hearing people as it is the efficient way to communicate among themselves as well as with other people. The demand for an automated system, which converts sign language into a normal message and vice versa, has been increased to provide a normal life to people with hearing difficulties. Each sign language has a distinct character with variations in the shape of the hand, the profile of movement, and the position of the hand, face, and body parts that contribute to each character. Pattern recognition and gesture recognition are majorly used technologies for resolving the problem. In this work, real-time American Sign Language is carried out for finger-spelling recognition using Deep Convolutional Neural Network for the classification of data to bridge the gap of communication between hearing disabled people and normal people. A new dataset of more than 50 thousand images is prepared for training purposes. Outputs are based on inputs given by the user, while the designed interface provides the end user with alphabets A to Z and Numerals from 0 to 9 prediction. The overall performance based on the proposed approach is being evaluated by the use of a dataset of real-depth images captured from users. Accuracy of the system is 99.70%. © 2023 IEEE.

关键词： Audition

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An energy-efficient resource allocation strategy in massive MIMO-enabled vehicular edge computing networks

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High-Confidence Computing 2023年第3期3卷 40-49页

作者： Yibin Xie Lei Shi Zhenchun Wei Juan Xu Yang Zhang School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of EducationHefei 230009China Hefei Origin IoT Technology Co. LtdHefei 230088China

The vehicular edge computing(VEC)is a new paradigm that allows vehicles to offload computational tasks to base stations(BSs)with edge servers for *** general,the VEC paradigm uses the 5G for wireless communications,where the massive multi-input multi-output(MIMO)technique will be ***,considering in the VEC environment with many vehicles,the energy consumption of BS may be very *** this paper,we study the energy optimization problem for the massive MIMO-based VEC *** at reducing the relevant BS energy consumption,we first propose a joint optimization problem of computation resource allocation,beam allocation and vehicle grouping *** the original problem is hard to be solved directly,we try to split the original problem into two subproblems and then design a heuristic algorithm to solve *** results show that our proposed algorithm efficiently reduces the BS energy consumption compared to other schemes.

关键词： Vehicular edge computing Massive MIMO Resource allocation Energy-efficient

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P2P-assisted distributed-server content delivery of 8K media : a DLT-based approach

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Multimedia Tools and Applications 2024年 1-38页

作者： Barlas, Gerassimos El-Fakih, Khaled Hiromori, Akihito Yamaguchi, Hirozumi Department of Electrical and Computer Engineering American University in Dubai Dubai Dubai United Arab Emirates Computer Science & amp Engineering Dept American University of Sharjah Sharjah Sharjah26666 United Arab Emirates Graduate School of Information Science and Technology Osaka University Osaka Japan

In this paper we study the problem of multi-source, P2P-assisted, multimedia content delivery of 8K content. Such media constitute a big challenge for contemporary VoD services, as their media rate can surpass the clients’ upload/download speeds. Our proposed methods tackle this shortcoming while also allowing a reduction in the server resource consumption. Two methods are presented and evaluated, that include global and local multi-installment schemes, that are shown to surpass server-only or non-DLT-based content distribution by a fair margin, in all the significant metrics. Our methods can either offer a significant improvement in access time or a boost in the number of clients served, while at the same time reducing the consumed server resources. The performance of the proposed methods is evaluated with a rigorous simulation study based on the current state of the Internet on two geographic locations that represent best-case and average-case scenarios. Our findings suggest that the methodology used to assign the content parts to individual sources, has a big influence of the consumption of server resources, the system’s scalability and service quality. One of the proposed methods (LMI) achieves a maximum of 70.5% improvement on the average client access time, while saving up to 23% of server resources. Based on the system settings the reduced access-time/faster access to content can be balanced against an increase in the served clients by up to 25%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Multimedia systems

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EEG-Based Emotion Recognition Using Quantum Machine Learning

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SN computer Science 2023年第5期4卷 1-13页

作者： Garg, Divya Verma, Gyanendra Kumar Singh, Awadhesh Kumar Department of Computer Engineering NIT Kurukshetra Haryana Kurukshetra India Department of Information Technology NIT Raipur Chhattisgarh Raipur India

Recognizing emotions is crucial for the development of artificial intelligence in various fields. This study explores the application of quantum support vector machines (SVMs) on emotion recognition from electroencephalogram (EEG) signals and compares its performance to traditional SVMs. SVMs are a popular machine-learning algorithm for this task due to their ability to handle high-dimensional data and non-linear relationships between input features. This study uses a quantum SVM to generate distinct solutions based on quantum principles. We applied this method to the DEAP benchmark dataset for binary class classification and gained new insights into the quantum nature of emotions. The algorithm has trained on D-Wave quantum annealer using various samples, achieving accuracies of 65.6% and 75.0% for valence and arousal dimensions, respectively, with 22 × 40 × 32 (subjects × trials × channels) data points, demonstrating the potential of quantum machine learning for EEG-based emotion recognition. However, there are methodological challenges due to the quantum arbitrariness of current annealers and the sensitivity of quantum-based machines to initial values. To address this, we conducted multiple investigations under similar circumstances and successfully recognized emotions using our proposed method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Emotion recognition Quantum machine learning QUBO SVM

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Exploring the Benefits of Leveraging AI for Enhancing Big Data Retrieval Performance 1

Exploring the Benefits of Leveraging AI for Enhancing Big Da...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Sharma, Neeraj Kaur, Ashmeet Murali, M.J. Sakhare, Sachin R. Singh, Vaishali Haroon, Syed Vivekananda Global University Department of Electrical Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350359756

AI algorithms were used to improve massive information retrieval overall performance as they lessen the quantity of manual work involved in data processing and retrieval. These algorithms use numerous device studying (ML) strategies along with supervised and unsupervised learning, as well as deep gaining knowledge of, to procedure, analyze, and interpret big datasets. Device learning (ML) models can be used to manner the records and choose out the maximum essential functions, that are then used to create predictive models to improve large facts retrieval performance. The predictive fashions can discover styles and anomalies within the facts that may be used to form better seek queries and enhance results. This allows lessen the time taken for statistics retrieval and might cause better accuracy and performance. additionally, AI algorithms may be used to lessen storage necessities due to the fact they can organization redundant statistics, thereby enhancing seek *** fashions can also be used to become aware of styles which might be predictive but which may not be obvious from a records set. This may similarly improve search overall performance, as the styles may be used to create tailored seek requests and tailor search results in order that they may be more relevant to the user. © 2023 IEEE.

关键词： Big data

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Maximum Bandwidth Allocation in Underwater Fibre Optic Networks 1

Maximum Bandwidth Allocation in Underwater Fibre Optic Netwo...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Meshram, Vishal A. Yadav, Dhananjay Vinitha, A. Murari, Krishn Kalra, Hitesh Srisathirapathy, S. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttarpradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Civil Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350359756

This paper offers an overview of the most Bandwidth Allocation hassle in underwater fiber optic networks. The bandwidth in optical fiber networks is traditionally allotted according to the users' needs for the guaranteed satisfaction of the carrier. However, because of the specific characteristics of underwater optical fibred networks and the constrained capability of the hyperlink, it is miles necessary to layout exceptional bandwidth allocation algorithms for allocating frequencies reusing the subsea optical fiber network efficiently and looking forward to the wishes of a couple of users. While traditional techniques treat all users equally, the maximum Bandwidth Allocation algorithm does not forget traits like a priority, call for size, occupancy element, and others to allocate the available bandwidth for this reason. These paper opinions the distinctive methods of the most Bandwidth Allocation trouble and their application for Underwater Optical Fibre Networks. Furthermore, it offers numerous scenarios to acquire the premier wide variety of slots for allocating customers based on their priority and demand size. The paper also presents specific techniques for allocating and adapting the to-be-had network slots to meet customers' changing needs. This paper demonstrates the promising performance of the most Bandwidth Allocation set of rules compared to conventional algorithms, showing the desire to customize those procedures for precise packages, including underwater optical fibre networks. © 2023 IEEE.

关键词： Bandwidth

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Joint Pilot Design and Beamforming Optimization in Massive MIMO Surveillance Systems

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China Communications 2022年第4期19卷 83-97页

作者： Caihong Kai Xiangru Zhang Xinyue Hu Wei Huang School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of TechnologyHefei 230601China School of Electronics and Information Engineering Anhui UniversityHefei 230601China

This paper proposes a novel joint channel estimation and beamforming scheme for the massive multiple-input-multiple-output(MIMO)frequency-division duplexing(FDD) wireless legitimate surveillance system. With the proposed scheme,the monitor with the full duplex capability realizes the proactive eavesdropping of the suspicious link by leveraging the pilot attack approach. Specifically, exploiting the effective eavesdropping rate and the mean square error as performance metrics and setting a total power budget at the training and transmission phases,while guaranteeing the information from suspicious source can be successfully decode, joint pilot design,power allocation and beamforming strategy are formulated as optimization problems for the two objective functions: MSE and effective eavesdropping rate. A closed-form expression of the optimal pilot with the limited length can be obtained via the channel correlation. The optimal power problem at the training phase can be solved by a simple bisection method. Then,based on the obtained imperfect estimated channel,the jamming beamforming at monitor optimization algorithm is proposed by utilizing the convex Semidefinite Programming approach to maximize the effective eavesdropping rate. Numerical results show that the proposed joint pilot design, power allocation and beamforming optimization scheme can improve the surveillance performance of the legitimate monitor as compared to the existing passive eavesdropping and jamming-assisted eavesdropping.

关键词： proactive eavesdropping massive MIMO channel estimation pilot contamination beamforming

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