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Performance Comparison of Centralized and Distributed Antenna system in 5G

Performance Comparison of Centralized and Distributed Antenn...

2019 Imam Al-Kadhum International Conference for Modern Applications of Information and Communication technology, MAICT 2019

作者： Hameed, Saif Saad Mohammed Saleh, Hadeel Department of Computer Networking System Faculty of Computer Science and Information Technology University of Anbar Iraq Faculty Pharmacy University of Anbar Anbar-Iraq Iraq

The usage range of nowadays wireless communications become wide, all of the develop applications uses wireless communication which improve the mobility and improve the mobility of the network subscribers. As known that the antenna diversity scheme developed from "SISO" to "MIMO" that has a maximum capacity and ability to improve the communication quality with a good shields among the fading effects and other impairments. In this research work study and analysis to the antenna systems and antenna schemes was done taking into consideration the central antenna system and the distributed antenna system "CAS" and "DAS". A Matlab simulation was developed to test antenna configuration system in term of effeteness of fading channel while using different modulation order. © Published under licence by IOP Publishing Ltd.

关键词： Fading channels

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On-road vehicle detection using support vector machine and decision tree classifications 20

On-road vehicle detection using support vector machine and d...

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6th International Conference on Engineering and MIS, ICEMIS 2020

作者： Ali, Asma Mustafa Eltarhouni, Wafa I. Bozed, Kenz A. Computer Science Department Faculty of Information Technology Benghazi University Jalo Libya Computer Science Department Faculty of Information Technology Benghazi University Benghazi Libya Computer System Design Department Faculty of Information Technology Benghazi University Benghazi Libya

ISBN: (纸本)9781450377362

On-road vehicle detection is a major part of various applications, such as driver assistance systems and auto-driving, and these systems need to detect vehicles robustly and accurately. This paper proposes a robust vehicle detection system to detect the front vehicles by using a single camera mounted on the car. The proposed system consists of two main steps, which are, hypotheses generation (HG) and hypotheses verification (HV). The first step is to find the candidate regions to the vehicles in the image. The guide to these regions is the shadow underneath the vehicle because it is always darker than the road surface. The system fits the generated regions with the width of the vehicle and reduces the number of hypotheses by calculating the entropy in two different ways. The second step is to verify whether the generated hypotheses contain a vehicle or not, and this done by Histogram of Oriented Gradients (HOG) to extract the features. In designing the proposed system, the Support Vector Machine (SVM) and Decision Tree (DT) classifiers are used for classification. Experiments were conducted using the challenging GTI DATA database to ascertain the usefulness of the approaches. The methodology was evaluated against the state-of the-art and it was found that the proposed approaches produce outstanding results. © 2020 ACM.

关键词： Entropy

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Virtual Horizons: Pioneering the Metaverse in Education and Industry Across ASEAN and Beyond

Virtual Horizons: Pioneering the Metaverse in Education and ...

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International Conference on Electrical Engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Ittipon Yamyuan Sushank Chaudhary Muhammad Saadi Piya Kovintavewat Juthatip Wisanmongkol Mitsunori Makino La-or Kovavisaruch Kamol Kaemarungsi Taweesak Sanpechuda Lunchakorn Wuttisittikulkij Nitthita Chirdchoo Petchpilin Tontiwinyoopong Pisit Vanichchanunt Pruk Sasithong Suebphong Noisri Avishi Waidyaratne Panithan La-aiddee Rosa Delima Department of Electrical Engineering Chulalongkorn University Thailand Department of Electrical Engineering University of Central Punjab Pakistan Faculty of Science and Technology Nakhon Pathom Rajabhat University Nakhon Pathom Thailand National Electronics and Computer Technology Center Pathumthani Thailand Department of Information and System Engineering Chuo University Japan Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Informatics Department Universitas Kristen Duta Wacana Yogyakarta Indonesia

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

The integration of the Metaverse into the educational sector heralds a transformative era in teaching and learning paradigms, with the ASEAN region at its forefront. This manuscript examines the Metaverse's potential to transcend traditional educational barriers such as resource limitations, geographic isolation, and the lack of personalized learning. Active case studies from Nakhon Pathom Rajabhat University (NPRU), Thailand, the University of Central Punjab (UCP), Pakistan, the National Electronics and computer technology Center, Thailand, and Chuo University, Japan, are presented to offer a comprehensive overview of the Metaverse's current applications. These case studies showcase the Metaverse's role in creating inclusive, immersive, and engaging educational experiences. The paper identifies critical research gaps in technological feasibility, pedagogical adaptation, and equitable access that need bridging to harness the Metaverse's full potential. It underscores the necessity for robust technological infrastructure, innovative teaching methodologies for 3D learning environments, and inclusivity strategies for diverse socioeconomic and physical ability spectra. The inclusion of Chuo University's advancements extends the discourse beyond ASEAN, indicating the global momentum of Metaverse adoption in education. The paper posits that ASEAN's venture into this digital frontier, alongside international collaborations, prepares the ground for a more interconnected and technologically advanced future. This investigation into the ASEAN region and its collaboration with Japanese innovation underscores a unique global position, suggesting that the convergence of technology and education is key to resolving deep-rooted disparities in educational access and quality.

关键词： Industries Technological innovation Three-dimensional displays Metaverse Education Collaboration International collaboration

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DeepCell: Multiview Representation Learning for Post-Mapping Netlists

arXiv

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arXiv 2025年

作者： Shi, Zhengyuan Ma, Chengyu Zheng, Ziyang Zhou, Lingfeng Pan, Hongyang Jiang, Wentao Yang, Fan Yang, Xiaoyan Chu, Zhufei Xu, Qiang Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China School of Computer Science Hangzhou Dianzi University Hangzhou China School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University Shanghai China National Center of Technology Innovation for EDA Nanjing China

Representation learning for post-mapping (PM) netlists is a critical challenge in Electronic Design Automation (EDA), driven by the diverse and complex nature of modern circuit designs. Existing approaches focus on intermediate representations like And-Inverter Graphs (AIGs), limiting their applicability to post-synthesis stages. We introduce DeepCell, a multiview representation learning framework that integrates structural and functional insights from both PM netlists and AIGs to learn rich, generalizable embeddings. At its core, DeepCell employs the novel Mask Circuit Modeling (MCM) mechanism, which refines PM netlist representations in a self-supervised manner using pretrained AIG encoders. DeepCell sets a new benchmark in PM netlist representation, outperforming existing methods in predictive accuracy and reconstruction fidelity. To validate its efficacy, we apply DeepCell to functional Engineering Change Orders (ECO), achieving significant reductions in patch generation costs and runtime while improving patch quality. Copyright © 2025, The Authors. All rights reserved.

关键词： Mapping

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Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Liang, Hanzhe Zhou, Jie Chen, Xuanxin Dai, Tao Wang, Jinbao Gao, Can College of Computer Science and Software Engineering Shenzhen University China Shenzhen Audencia Financial Technology Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Faculty of Education Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preprocessing as a dual-objective semantic isolator: (1) mitigating cross-semantic interference to the greatest extent feasible and (2) confining anomaly judgements to aligned semantic spaces wherever viable, thereby establishing intra-semantic comparability. Any preprocessing approach achieves this goal through a two-stage process of Semantic-Division and Spatial-Constraints stage. Through systematic deconstruction, we theoretically and experimentally subsume existing preprocessing methods under this theorem via tripartite evidence: qualitative analyses, quantitative studies, and mathematical proofs. Guided by the Fence Theorem, we implement Patch3D, consisting of Patch-Cutting and Patch-Matching modules, to segment semantic spaces and consolidate similar ones while independently modeling normal features within each space. Experiments on Anomaly-ShapeNet and Real3D-AD with different settings demonstrate that progressively finer-grained semantic alignment in preprocessing directly enhances point-level AD accuracy, providing inverse validation of the theorem’s causal logic. Copyright © 2025, The Authors. All rights reserved.

关键词： Fences

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Collaborative Learning in Bilateral Academic Training on Robotics Design and Development

Collaborative Learning in Bilateral Academic Training on Rob...

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STEM Education Conference (iSTEM-Ed), International

作者： Patinya Sang-Aroon Sethakarn Prongnuch Tadchanon Chuman Aphirak Thitinaruemit Kalaphath Kounlaxay Chanida Niyomtum Dept. of Industrial Design and Packaging Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Robotics Engineering Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Electrical Technology Faculty of Engineering and Industrial Technology Suan Sunandha Rajabhat University Dusit Bangkok Thailand Dept. of Computer Engineering Faculty of Engineering Souphanouvong University Luangprabang City Lao PDR Education Automation System Co. Ltd. Bangplee Samutprakran Thailand

ISBN: (数字)9798350365979

ISBN: (纸本)9798350365986

This paper presents how collaborative learning can facilitate academic training in robotics design and development, with the objective of establishing a foundation in robotics structure design and microcontroller programming for controlling robots, particularly for bilateral undergraduate students. The students comprise 20 engineering students and 20 architecture students, who are divided into groups of two, consisting of an engineering students and an architecture students each. Group activities focusing on robot structure design and robot Development are provided for the students. The results demonstrate that collaborative learning can effectively facilitate the training activities.

关键词： Training Educational robots Federated learning Microcontrollers Focusing Engineering students Programming profession

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Preliminary Research Design on Sensor Data Gathering for Air Quality Text Generation 4

Preliminary Research Design on Sensor Data Gathering for Air...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Aulia, Indra Hizriadi, Ainul Seniman Muhibuddin Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia Data Science and Intelligent System Research Group Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia Computer System Research Group Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia

Air pollution is one of the environmental problems faced by many countries (i.n. the developing countries). It often harms the health of people, plants, and animals as well as our environment. To decrease the risks, e ective and e cient technology for air quality monitoring is indispensable. In Indonesia, it is still limited in the case of the number of devices and the dissemination of air quality information. This paper describes the preliminary research design of air quality monitoring technology based on the Internet of Things (IoT) and Natural Language Generation (NLG) for solving these limitations. It de nes air quality knowledge in the form of a set of rules and presents the architecture design for data gathering and text generation. Data gathering uses the related sensors which are able to collect the values of parameters as air quality indicators. Then, the collected values are identi ed to be used for generating the textual information in Indonesian language to social media, such as twitter. So, in this research, the proposed solutions are able to solve the problems properly. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

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Social Media Multi-Emotion Detection

Social Media Multi-Emotion Detection

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Soha S. Ibrahiem Bahy Ibrahim El-Sayed Mohammed Mohammed Ahmed El Sayed Ahmed Khaled ElBahnasy Computality R&D System Analyst & NLP Researcher Faculty of science Menofia university Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Computer Science and Information Technology Department Jeddah international College Jeddah KSA

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Natural language in social media is often ambiguous, carrying diverse perspectives that are critical for opinion analysis. With vast amounts of unstructured data, particularly on platforms like Twitter, it is impractical to manually analyse relevant insights. This rapid data growth has highlighted the need for intelligent filtering and mining techniques in decision support systems to help organizations build brand reputation, foster online communities, and enhance profitability. Multi-Emotion detection is key for capturing implicit user opinions and specific emotional states; however, challenges such as text bipolarity, limited representative feature vectors, and inadequate annotated lexicons often hinder accuracy. This research aims to improve multi-emotion detection accuracy by extracting emotion implied tokens and converting them into feature vectors that capture semantic relevance. Using annotated lexicons, word embeddings, unigrams, and term frequency, these vectors are trained across five machine learning models, which are Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K- nearest neighbor (KNN), and Multi- layer Percepton (MLP). Finally, two ensemble techniques are applied to refine model outputs, achieving an improvement accuracy over previous work worked. The proposed system, evaluated on a real dataset, achieves a hamming score of 0.55 and an average F1 score of 0.68, demonstrating its potential for effective multi-label emotion detection.

关键词： Support vector machines Technological innovation Accuracy Social networking (online) Smart cities Profitability Semantics Lexicon Feature extraction Vectors

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Comparison of Unsupervised Learning Algorithms for K-Means Clustering Efficiency in IoE Sensor Detection systems for Smart Agriculture

Comparison of Unsupervised Learning Algorithms for K-Means C...

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International Electrical Engineering Congress (iEECON)

作者： Wallop Tipwong Adisorn Sirikham Jessada Konpang Putthiphong Kirdpipat Yutana Chongjarearn Oradee Preutisrunyanont Department of Electrical and Telecommunication Engineering Rajamangala University of Technology Krungthep Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Thammasat School of Engineering Thammasat University Pathum Thani Thailand Department of Robotic Engineering and Automation System Dhurakij Pundit University Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This study investigates the application of clustering algorithms to data collected from color sensors providing frequency signals during the cultivation process. Continuous sampling allowed for real-time data collection, and variations in temperature and humidity were observed to influence signal reception. The dataset's large volume and varying sample sizes necessitated the evaluation of several clustering models for efficient data processing. The experiment tested K-Means, K-Means++, Mini-Batch K-Means, and Bisecting K-Means algorithms. The results reveal that the computation time for each model increased with the number of groups. Specifically, the K-Means, K-Means++, and Mini-Batch K-Means models exhibited shorter computation times, ranging from 0.02 to 0.03 seconds for two-group scenarios, while the Bisecting K-Means algorithm took significantly longer, approximately 0.10 seconds. Despite the variation in processing times, all models performed similarly in terms of clustering quality, with an average Silhouette Score of 0.82. This score indicates that the algorithms effectively separated data points into well-defined groups while maintaining high accuracy in clustering. Additionally, the analysis highlighted the importance of balancing computation time and clustering quality, particularly as the number of clusters increases. These findings suggest that while computational efficiency may vary across models, the clustering quality remains robust, providing valuable insights for data analysis in agricultural sensor networks. The study's results demonstrate that each clustering model is capable of handling large datasets with high accuracy in grouping, making them applicable to real-world agricultural data processing scenarios. In conclusion, the experiment confirms that all tested models, despite differing in computation time, successfully partitioned the data with high-quality results, offering promising applications for data analysis and optimization in agricult

关键词： Temperature sensors Analytical models Image color analysis Computational modeling Clustering algorithms Approximation algorithms Data models Sensor systems Computational efficiency Optical sensors

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Comparison of Unsupervised Learning Algorithms for K-Means Clustering Efficiency in IoE Sensor Detection systems for Smart Agriculture

Comparison of Unsupervised Learning Algorithms for K-Means C...

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13th International Electrical Engineering Congress, iEECON 2025

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University of Technology Krungthep Department of Electrical and Telecommunication Engineering Bangkok Thailand Thammasat University Faculty of Engineering Thammasat School of Engineering Department of Electrical and Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department of Robotic Engineering and Automation System Bangkok Thailand

ISBN: (纸本)9798331543952

关键词： K-means clustering

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