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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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Application of Adaptive UKF Algorithm in Multi-target Tracking and Positioning System 2

Application of Adaptive UKF Algorithm in Multi-target Tracki...

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2021 2nd International Conference on Artificial Intelligence, computer Networks and Communications, AICNC 2021

作者： Liu, Guofang Wang, Xiong School of Computer Science Sichuan University Jinjiang College China Experimental Teaching Center Sichuan University Jinjiang College China

Adaptive filtering algorithm (FIR) is a design method of adaptive variable target tracking system based on probability density distribution model. The algorithm realizes the target movement in the global range by estimating the parameters of different regions in the image, which improves the real-time performance and effectiveness. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Probability distributions

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A Carbon-Neutral, Community-Based, Reactive and Scalable Ride-Sharing Service 12

A Carbon-Neutral, Community-Based, Reactive and Scalable Rid...

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12th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2023

作者： Nagarajan, Avinash McGibney, Alan Fenton, Pio Castiñeiras, Ignacio Department of Computer Science Munster Technological University Cork Ireland NIMBUS Centre Munster Technological University Cork Ireland Teaching & Learning Unit Munster Technological University Cork Ireland

ISBN: (纸本)9789897586514

This paper presents the design, implementation and evaluation of a community-based, reactive ride-sharing service that promotes carbon-neutral management of city commutes using autonomous vehicles. The problem is formulated as a variant of the classical Dynamic Vehicle Routing problem with Time Windows, considering both dynamic resources and requests over a simulated time horizon. A solution approach is provided, based on an algorithm following a reactive-based simulation on top of a greedy decision-making process. A parameterised instance generator is developed to align existing benchmarks (i.e. Google HashCode) and public datasets (i.e. NYC taxis) to the proposed problem formulation and to enable testing of the solution under various configurations. The ride-sharing service is proven to scale well, when applied to very large instances, it provides fast and competitive results, both in terms of trip petitions satisfied and overall distance traversed. Copyright © 2023 by SCITEPRESS – science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Vehicle routing

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A Consensus-Based Multi-Criteria Group Decision-Making Method Based on an Aggregated Operator Customised by Experts 9

A Consensus-Based Multi-Criteria Group Decision-Making Metho...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Trillo, José Ramón Cabrerizo, Francisco Javier Del Moral, María José Morente-Molinera, Juan Antonio Tapia, Juan Miguel Herrera-Viedma, Enrique University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain University of Granada Granada18071 Spain University of Granada Department of Quantitative Methods for Business and Economics Granada18071 Spain

ISBN: (纸本)9798350311402

Group decision-making is a daily process where a group of experts has to choose from a set of alternatives. To help this group of experts, methods were developed that allowed them to recommend an alternative or a group of alternatives from the information generated by the experts, these methods were called Group Decision-Making methods. Nonetheless, some Group Decision-Making methods have some problems, such as evaluating alternatives in a general way. This problem leads to a loss of information because it is not possible to evaluate each alternative in detail and it may be the case that one alternative is better than another on one criterion but not on another. Another problem is that experts have priority for each criterion and for one expert one criterion may be more important than another and vice versa. To solve these two problems, in this novel method, we propose a consensual multi-criteria Group Decision-Making system, where experts can customise the order of the criteria. With the order of each expert, the method creates an aggregation operator for each criterion. Moreover, this method performs a consensus analysis for each criterion, so that there is a minimum consensus on each criterion. In this way, a solution is obtained that allows the set of alternatives to be evaluated in a detailed way and each criterion has the correct importance because the experts are the ones who create the order of importance. © 2023 IEEE.

关键词： Decision making

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LightGCN-LSC: Lightweight Graph Convolution Network for Learning Style Classification in Online Education

LightGCN-LSC: Lightweight Graph Convolution Network for Lear...

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International Conference on Networking and Network Applications (NaNA)

作者： Wanying Suo Bello Ahmad Muhammad Bo Liu Ministry of Education Key Laboratory of Modern Teaching Technology Xi’an China School of Computer Science Shaanxi Normal University Xi’an China School of Computer Science and Technology Henan Institute of Science and Technology Henan China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

How to understand learners have always been the core goal of educational information science and technology, and will greatly lead the practical process of future education. Learning style recognition can largely reflect the learning status and internal cognitive structure of learners, then achieve personalized guidance and precise recommendations. However, the existing collaborative or automatic methods are limited by a large amount of data and have poor portability due to being limited to specific platforms. In this paper, we propose lightGCN-LSC, a novel learning style classfication approach using lightweight graph neural network. First, we build different embedding technique to extract hidden features of S-M bipartite graph, then we employ the K-means clustering algorithm to cluster learners and obtain labels utilizing the FSLSM model. Finally, lightweight graph neural network suitable for S-M graph data size and structure is trained to classify student’s learning style. The superiority of our method was demonstrated through experiments on dataset KDD CUP 2015. Our proposed lightweight models achieved outstanding results (about $1.18-8.24 \%$ improvement) over Graph Convolutional Networks (GCN), and the best result with improvment values of $2.05 \%$ accuracy, $8.24 \%$ precision, $2.05 \%$ recall, and $4.05 \%$ F1 score.

关键词： Representation learning Information science Graph convolutional networks Education Clustering algorithms Lead Feature extraction

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Optimal Control in the Problem With Two Gas Fields

Optimal Control in the Problem With Two Gas Fields

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Alexander Skiba Department of Mathematical Methods of Regional Programming Federal Research Center “Computer Science and Control” of RAS Moscow Russia

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

We explore a continuous aggregated dynamic model for developing two gas fields. The new borehole commissioning rates are the control parameters. Changes in the average flow rate of producing boreholes and current natural gas production are proportional. It is necessary to solve the problem of maximizing discounted accumulated income for two gas fields. We analyze the optimal control problem with an unrestricted right end and a fixed time. The Pontryagin maximum principle is a main tool for solving the problem. The special optimal control mode is of particular interest. We highlight the critical aspects of all possible optimal controls.

关键词： Drilling Road transportation Limiting Optimal control Switches Production Trajectory Planning Large-scale systems Natural gas

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Use of digital platforms for enhancing academic performance. Systematic Review 7

Use of digital platforms for enhancing academic performance....

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7th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2024

作者： Costescu, Cristina Anamaria Şogor, Mǎlina Pǎlimariu, Sabina Katona, Jozsef Tufar, Ioana Avram, Carla Maria Babe-Bolyai University Special Education Department Cluj-Napoca Romania Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

The purpose of this study was to determine the effect of technology-based interventions on academic performance. To achieve this purpose a systematic review was conducted. Within the scope of this study EBSCO, Springer, ProQuest, science Direct, Wiley Online Library, Taylor and Francis Online, databases were scanned, and 18 studies were included in the review. Our results show that the iPad is the most preferred digital device for enhancing math and reading skills and significant results can be seen when used. Similar positive effects were observed with other technological devices, reinforcing the effectiveness of technology as a tool for supporting academic performance. This evidence underscores the value of incorporating digital devices in educational strategies, particularly for improving key academic skills. © 2024 IEEE.

关键词： academic performance children digital devices

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Current Trends in Artificial Intelligence for Educational Advancements

Current Trends in Artificial Intelligence for Educational Ad...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Abdallah M. A. Al-Tarawneh Reem AbdElkareem AlOmoush Tanveer ul Islam Jamshaid Iqbal Janjua Tahir Abbas Anaum Ihsan Clinical Psychology Al-Ahliyya Amman University Amman Jordan Department of Curricula and Methods of Teaching Faculty of Educational Sciences Yarmouk University Jordan Department of Computer Science TIMES Institute Multan Pakistan Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The focus of this paper is the integration of artificial intelligence (AI) within the educational field including but not limited to, adaptive learning, teaching evaluation, online learning environments, and individualized instruction. The authors investigate the positive role of AI in enhancing teaching performance and the quality of students' learning experiences. The study aims to demonstrate what role AI may play in the course of education development and practice improvement. It also sheds light on the limitations AI will have in the realm of education, such as accessibility, data ownership, and ethical issues. The paper does not contain only the limitations but the opportunities for this technology as well and describes how AI could be effectively incorporated into education, subsequently providing insights into the evolution of AI-based educational transformation.

关键词： Ethics Adaptive learning Education Learning (artificial intelligence) Market research Artificial intelligence

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A Modular Deep Denoising Autoencoder for speech enhancement 3

A Modular Deep Denoising Autoencoder for speech enhancement

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3rd International Conference for Emerging Technology, INCET 2022

作者： Al-Taai, Raghad Yaseen Lazim Wu, Xiaojun School of Computer Science Ministry of Education Shaanxi Normal University Snnu Key Laboratory of Modern Teaching Technology Xi'an China

ISBN: (纸本)9781665494991

Recent researches have proven that deep denoising autoencoder is an effective method for noise reduction and speech enhancement, and can provide better performance than several existing methods. However, training deep denoising autoencoder has proven to be difficult computationally. The goal of this study is to develop a modular approach for training deep denoising autoencoders as a set of classifiers based on collaborative learning. Each classifier is a multilayer deep denoising autoencoder network and specialized for a particular enhancement task and handles a subset of the complete training dataset. The approach performance was assessed using a perceptual evaluation of speech quality and the segmental signal-to-noise ratio. We have trained two individual DDAEs with three and five hidden layers respectively for comparison purposes. We have also compared our proposed model with the traditional spectral subtraction and, log MMSE methods. The results showed that DDAE with three and five hidden layers was sufficient (deeper is better), while the proposed approach provided higher intelligibility results and was more suitable for high-quality cases. © 2022 IEEE.

关键词： Speech enhancement

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Verification of Genuineness of Educational Internet Resources Using Machine Learning methods

Verification of Genuineness of Educational Internet Resource...

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Technology Enhanced Learning in Higher Education (TELE), International Conference on

作者： Aleksandr N. Privalov Vadim A. Smirnov Institute of Advanced Information Technologies Tula State Lev Tolstoy Pedagogical University Tula Russia Department of mathematics computer science and teaching methods Ivanovo State University Shuya Russia

ISBN: (数字)9798350353532

ISBN: (纸本)9798350353549

The analysis of various sources has been carried out, confirming the demand for protective solutions against information threats from fake websites of organizations. The relevance of the research topic is confirmed by the presence of addresses similar in spelling to popular educational Internet resources in the list of domain names of the RU zone registered over the past year. When developing security solutions, it is proposed to use a site verifying the genuineness method based on the use of machine learning algorithms. A review of research by various authors has been conducted in order to determine the most frequently used features for verifying the genuineness of Internet resources in the machine learning models. The authors propose their own verifying the genuineness features that can be used in a situation where data about an Internet resource is extracted from the domain name registry. The proposed features differ from the known ones in that they contain signs of micro-markup on the site and storage of site settings files for search engines on servers. The developed features were tested using a naive Bayesian classifier as a machine learning algorithm. The proposed features can be used in the implementation of a software tool to counteraction threats from fake educational sites.

关键词： Machine learning algorithms Reviews Machine learning Organizations Search engines Feature extraction Internet

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