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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Nagpal, Manish Pattnaik, Sampa Chau Baxi, Paramjit Hannah Jessie Rani, R. Canessane, R. Aroul Chaudhary, Pavan Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Applications Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

One of the most important issues for secure communication in the physical layer is developing physical layer security techniques to provide confidentiality and data integrity in wireless communication systems. The properties of the physical channel are used in these methodologies to safeguard communication from eavesdropping and some other security threats. This article presents physical layer security methods that can be implemented in wireless communication systems. Artificial Noise (AN) is one of the most popular techniques. The original signal is combined with a generated by the transmitter in such a way that eavesdroppers cannot easily decode the data. Cooperative Anti-jamming: Yet another strategy for jammer localization is to employ cooperative jamming, where multiple friendly users can add noise signals over the signal in an effort to confuse potential eavesdroppers. In addition, additional technologies such as multi-input multi-output beamforming and transmit power control can further improve the security of wireless communication systems. In this regard, strategies based on game theory have been proposed as a viable alternative to enhancing physical layer security. Viewing the interaction between the transmitter, receiver, and eavesdropper as a game allows us to reveal their optimal strategies and security levels. © 2025 IEEE.

关键词： Signaling

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Cross-Domain Multicarrier Waveform Design for Integrated Sensing and Communication

Cross-Domain Multicarrier Waveform Design for Integrated Sen...

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IEEE Conference on Wireless Communications and Networking

作者： Fan Zhang Tianqi Mao Ruiqi Liu Zhu Han Octavia A. Dobre Sheng Chen Zhaocheng Wang Department of Electronic Engineering Tsinghua University Beijing China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing China Wireless and Computing Research Institute ZTE Corporation Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA Faculty of Engineering and Applied Science Memorial University St. John's NL Canada School of Electronics and Computer Science University of Southampton Southampton U.K.

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Integrated sensing and communication (ISAC) is expected to be a promising technology in the sixth-generation (6G) wireless networks for its ability to alleviate resources shortage and excessive hardware expenses. One typical representative for ISAC waveforms is the orthogonal frequency division multiplexing (OFDM) waveform, which divides the time-frequency resources into orthogonal resource elements (REs). In order to satisfy their diverse design requirements and mitigate mutual interference, the communication and sensing subsystems can be assigned with different REs, which necessitates effective allocation strategies of different resources across time and frequency domains. In this article, a cross-domain multicarrier waveform design method-ology is proposed, which optimizes the RE assignment and power allocation strategies for the OFDM-based ISAC system. Specifically, for sensing performance enhancement, the unit cells of the ambiguity function (AF) of the sensing components are spe-cially shaped to achieve a “locally” perfect auto-correlation (AC) property within a predefined region of interest (RoI) in the Delay-Doppler domain. Afterwards, the irrelevant cells outside the RoI, which can determine the sensing power allocation strategy, are optimized alternatively with the communication power allocation strategy to maximize the throughput for the communication purpose. Numerical results demonstrate the superiority of the cross-domain multicarrier waveform design, which also provides useful guidelines for parameter settings of the proposed OFDM-based ISAC system.

关键词： Time-frequency analysis OFDM Design methodology Wireless networks Radar Interference Throughput

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GUEST EDITORIAL

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China Communications 2023年第2期 3-6页

作者： Yuan Wu Xu Chen Feng Lyu Xianfu Chen Xumin Huang Jie Gao Yueyue Dai Tony Q.S.Quek Yan Zhang the State Key Laboratory of Internet of Things for Smart City University of Macau the Department of Computer and Information Science University of Macau Sun Yat-sen University Sun Yat-sen University Institute of Advanced Networking and Computing Systems Sun Yat-sen University National and Local Joint Engineering Laboratory the School of Computer Science and Engineering Central South University the VTT Technical Research Centre of Finland the School of Automation Guangdong University of Technology the School of Information Technology Carleton University Huazhong University of Science and Technology Singapore University of Technology and Design(SUTD) the IEEE Communications Society Technical Committee on Green Communications and Computing(TCGCC)

With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and l...

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Dynamic Optimization for Trade-off in Hyperledger Fabric towards Latency-Sensitive IoT Services

Dynamic Optimization for Trade-off in Hyperledger Fabric tow...

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IEEE International Conference on Web Services (ICWS)

作者： Hao Ding Xuefeng Piao Yuxi Hong Yizhou Liu Huihui Song Meng Liu Song Gao School of Computer Science and Engineering Harbin Institute of Technology Weihai China School of Economics and Management Harbin Institute of Technology Weihai China School of New Energy Harbin Institute of Technology Weihai China Electric Power Research Institute State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Hyperledger Fabric is currently the most widely used open-source enterprise blockchain platform in IoT services. However, Hyperledger Fabric still suffers from high latency in practical applications, hindering its further adoption in latency-sensitive IoT services. To address this issue, this paper selects Hyperledger Fabric v2.0 as the research target. The focus is on the latency of the ordering phase, which accounts for the highest proportion of latency in the EOV architecture. The ordering phase is primarily responsible for block packaging and generation, requiring careful parameter configuration to ensure the efficient operation of the blockchain system. We discuss the impact of BatchSize and BatchTimeout on block generation under two different consensus algorithms, Kafka and Raft. Additionally, based on the M/M/1 queue, we propose a latency analysis model for the ordering phase and analyze the causes of latency. Subsequently, we frame the optimization of blockchain experimentation as a Markov decision process, introducing a dynamic parameter configuration method grounded in deep reinforcement learning. This approach aims to strike a balance between BatchSize and BatchTimeout in response to network loads. The experimental results indicate that compared to existing methods, the proposed dynamic optimization strategy can reduce the average transaction latency of the blockchain system by 11.37%. Additionally, this strategy demonstrates good fault tolerance capability, with a reduction in the latency of node failures by approximately 6.35%. Therefore, the proposed dynamic optimization strategy meets the QoS requirements for IoT services.

关键词： Analytical models Distributed ledger Web services Fabrics Blockchains Complexity theory Internet of Things Optimization Queueing analysis Load modeling

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Hemochromatosis Pathogenesis and Its Association with Liver Disease: An Analysis Through Machine Learning

Hemochromatosis Pathogenesis and Its Association with Liver ...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Isra Imtiaz Attique Ur Rehman Sabeen Javaid Tahir Muhammad Ali Azka Mir Mehedi Masud Deepak Kumar Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science Gulf University for Science and Technology Kuwait Department of Computer Science Taif University Saudia Arabia Division of Research and Development Lovely Professional University Phagwara India KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

A significant risk factor for the development of liver disease is hemochromatosis, an inherited iron overload illness, but the complex pathophysiology behind this link is still unknown. To understand the intricate interactions between hemochromatosis and liver disease, this study uses a cutting-edge strategy that incorporates machine learning techniques. Promising prediction skills are demonstrated by machine learning models, which open up new possibilities for early detection and individualized intervention approaches. The results shed important light on the relationship between hemochromatosis and liver disease and improve our understanding of the pathogenesis of the disease. This study clarifies the pathophysiology of hemochromatosis and its subsequent connection to liver disease, highlighting the revolutionary power of machine learning in understanding intricate biological processes. This selected dataset contains 15 parameters and has 202 dated records with a target value of ‘Disease Name’ which has 5 different categories Type 1, 2, 2A, 2B,2C, 3, 4, and Type5. The experiment is performed using two classifiers including KNN and Naïve Bayes. The k-fold cross-validation technique is also used to improve the performance. After the comparative analysis of the resulting accuracy percentage of different classifiers, we have proposed the model with the classifier with the highest accuracy. Our proposed model has achieved an accuracy rate of 98.00% using a K-NN classifier. Rapid miner platform is used for the application of the machine learning tools and techniques for this research These discoveries have consequences that go beyond clarifying the mechanisms behind disease; they also open the door to better clinical care and tailored treatment therapies for high-risk people.

关键词： Accuracy Liver diseases Pathogenesis Medical treatment Machine learning Predictive models Nearest neighbor methods Market research Iron Diseases

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Performance Analysis of Support Vector Machine Implementations on the D-Wave Quantum Annealer 1

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21st International Conference on Computational science, ICCS 2021

作者： Bhatia, Harshil Singh Phillipson, Frank Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India TNO The Netherlands Organisation for Applied Scientific Research The Hague Netherlands

ISBN: (数字)9783030779801

ISBN: (纸本)9783030779795

In this paper a classical classification model, Kernel-Support Vector machine, is implemented as a Quadratic Unconstrained Binary Optimisation problem. Here, data points are classified by a separating hyperplane while maximizing the function margin. The problem is solved for a public Banknote Authentication dataset and the well-known Iris Dataset using a classical approach, simulated annealing, direct embedding on the Quantum Processing Unit and a hybrid solver. The hybrid solver and Simulated Annealing algorithm outperform the classical implementation on various occasions but show high sensitivity to a small variation in training data. © 2021, Springer Nature Switzerland AG.

关键词： Support vector machines

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Advanced Bandwidth Management Techniques Using Wireless Mesh Networks for High-Speed Optical Fiber Communications

Advanced Bandwidth Management Techniques Using Wireless Mesh...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Rakesh Kumar Yadav Sunil Sharma Kanika Seth Jeyalaxmi M Yashwant S. Ingle Vivek V Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Faculty of Engineering and Technology Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Karnataka India

This abstract encompasses superior bandwidth management techniques the usage of Wi-Fi mesh networks to improve the performance of high-velocity optical fiber communications. the point of interest lies at the access-, relay-and center-mesh architectures with their diverse, discrete techniques consisting of dynamic frequency allocation, load balancing and node optimization, as well as on highlights from latest research. Within the experiments, the effects exhibit giant enhancement in signal first-rate and effectiveness of bandwidth resources when the use of the proposed version, based totally at the convergence speed of the network fee. Furthermore, it's also evident that the optimized wireless mesh configuration brings about enormous fee financial savings. All the findings obtained at some stage in this research have eminently greater the talents of the proposed structure for growing green communications networks.

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Exploring Reinforcement Learning in Large-Scale Data Processing

Exploring Reinforcement Learning in Large-Scale Data Process...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Pawan Bhambu Ritesh Kumar P. Sharmila Vivek D. Patil Shivam Khurana Vivek V Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

Reinforcement mastering (RL) is vital for big-scale fast processing. RL utilizes the availability of large datasets and dealers skilled in this information to remedy a wide variety of obligations, from automated diagnostics and self-reliant using structures to clinical information analysis. Using exploiting the traits of the modern-day significant information era, RL allows efficient and correct decision-making in massive-scale information processing and answers complicated troubles. This paper explores the use of reinforcement learning (RL) in large-scale data processing. RL is a subfield of machine learning that focuses on decision making in dynamic environments. With the increasing demand for processing large amounts of data, traditional techniques such as rule-based systems and batch processing are becoming inadequate. This has led to the emergence of RL as a potential solution due to its ability to learn and adapt to changing environments. Through a comprehensive review of existing literature, this paper discusses the applications of RL in large-scale data processing and its potential benefits. Furthermore, it examines various challenges and their potential solutions in implementing RL for data processing tasks. The paper concludes by highlighting the future research directions in this domain and the potential impact of RL on improving data processing efficiency and accuracy. It similarly examines some of the demanding situations and possibilities related to using RL for huge-scale records processing and the capability programs of RL in the future.

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The Effect of Cobalt Ions on Boron Glass: A Review

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Nonlinear Optics Quantum Optics 2021年第3-4期54卷 311-339页

作者： ElbasHar, Y.H. Rayan, Diaa Elgabaly, Shimaa G. Mohamed, A.A. Cairo University Giza Egypt Department of Basic Science ELGazeera High institute for Engineering and Technology Cairo Egypt Central Metallurgical Research & Development Institute P.O. Box: 87 Helwan 11421 Cairo Egypt Cairo higher institute for engineering computer science and management Cairo Egypt Chemistry Department Faculty of Science Helwan University Helwan Cairo Egypt

In this review we will show the borate glass has a good glass former, and finds many applications. B2O3 glass is formed from BO3 triangles that composed of three – membered (boroxol) rings. Addition of some oxides to the glasses led to a lot of new applications and improve the glass optical, electrical and physical properties. The modifier oxide changes a part of the BO3 triangles to BO4 tetrahedral thus cracking bridging oxygen bonds to create non-bridging oxygen (NBOs). The influence of Zinc oxide doped borate matrix had dual role in it that acted as former or modifier glass had been illustrated by different tools of characterization. When it acted as a network modifier, it converted the bridging oxygen to a non-bridging one in glass specimens. While when it acted as a glass former, it entered the network with ZnO4 structural units. ©2021 Old City Publishing, Inc.

关键词： Cobalt compounds

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Social Media Event Summarization using Neural Networks

Social Media Event Summarization using Neural Networks

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Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Sucharitha Yadala Anitha Gurram Yamunadevi S Veeresh U S Bhargavi Latha TN Chitti Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad India Computer Science and Engineering CMR Institute of Technology Hyderabad India Computer Science and Engineering Panimalar Engineering College Tamilnadu India School of Computer Science and Engineering REVA University Bengaluru Karnataka India Kalasalingam Academy of Research and Education Krishnankoil India

Micro blogging sites have become extremely popular, with a huge number of tweets being posted daily on a broad assortment of subjects. Recent studies have exposed that a significant part of these tweets are regarding “events”, and the discovery of new events in the tweet-stream has pulled in a lot of attention. In the Natural Language Processing (NLP) field, automatic text summarization is viewed as an extremely tricky problem. The content substance on the internet, specifically, is rising at an outstanding level. The capacity to decode through such immense volumes of data, to facilitate mine the require knowledge, is a foremost undertaking and needs an automatic system to serve with the present warehouse of data. A decent text summarization framework should comprehend the entire content, reorganize information, and create coherent, useful and amazingly tiny summaries to pass on the significant information of the innovative content. In this article, an inventive content summarization framework has been built, which combines RNN with LSTM and encoder-decoder structure. Our framework is assessed on the LCSTS dataset, which is an excellent corpus of Chinese short content summarization dataset created from “SinaWeibo”. Empirical outcomes exhibit the adequacy of the proposed framework, which outperforms all the existing approaches. Particularly, the proposed framework enhances the semantic consistency by 5% in terms of human assessment.

关键词： Social networking (online) Semantics Neural networks Blogs Natural language processing Data mining

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