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4th Information Communication Technologies Conference, ICTC 2023

作者： Zhang, Yongjian Wang, Dongyu Zhang, Zhiqiang Wang, Shiguo University of International Relations School of Cyber Science and Engineering Beijing China North China University of Technology Department of Electronic Engineering Beijing China Shanxi Police College Department of Cyber Security Taiyuan China University of Science and Technology Computer and Communication Engineering Institute Changsha Changsha China

ISBN: (纸本)9781665462587

This work presents a radical approach for space-time codings, which could realize the goal of unequal error protection (UEP) transmitting for scalable video. This analysis leads us to develop a hybrid coding framework that combines vertical bell-labs layered space-time (V-BLAST) to ensure high-data-rate for the less crucial enhancement layer (EL) and Alamouti coding to ensure high reliability for the base layer (BL). According to those simulation findings, the suggested hybrid delivery method performs substantially better based on peak signal-to-noise ratio than either a pure special multiplexing or a pure spatial diversity delivery scheme. © 2023 IEEE.

关键词： MIMO systems

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Enhancing WMMSE With EVM Feedback Through Bayesian Optimization

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhao, Zhuobing Guan, Xin Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai201804 China Tongji University College of Electronic and Information Engineering Shanghai201804 China Shenzhen Research Institute of Big Data Shenzhen518172 China

This paper presents an enhancement to the classical weighted minimum mean square error (WMMSE) approach which assumes ideal Gaussian-distributed transmitted symbols, addressing its performance decline for multi-user precoding in practice. By incorporating scaling coefficients that bridge the gap between theoretical communication rates and actual spectral efficiency based on the modulation and coding schemes (MCS) of each user, the proposed method maximizes real communication rates by optimizing precoding vectors and estimating scaling coefficients jointly. With fixed scaling coefficients, we first design an efficient algorithm for optimal precoders, which is surprisingly almost the same as the pure WMMSE. Due to the implicitness of the scaling coefficients, we then estimate these coefficients by minimizing the error vector magnitude (EVM) of all users, which is solved by Bayesian optimization due to the black box nature of the mapping between these coefficients and EVM. Simulation results demonstrate the superiority of the proposed method over existing benchmarks. © 1967-2012 IEEE.

关键词： Mean square error

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Apache Kafka on Big data Event Streaming for Enhanced data Flows 8

Apache Kafka on Big Data Event Streaming for Enhanced Data F...

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8th International Conference on I-SMAC (IoT in Social, Mobile, analytics and Cloud), I-SMAC 2024

作者： Padmanaban, K. Babu, T.R. Ganesh Karthika, K. Pattanaik, Balachandra Dhanabhavithra, K. Srinivasan, C. Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Kcg College of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Wollega University Department of Electrical and Computer Engineering Nekemte Ethiopia Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

Apache Kafka's distributed architecture and message queuing capabilities offer significant improvements in real-time and batch data processing efficiency and reliability. This research aims to optimize Kafka setups, data partitioning, and Kafka Connect integration to create a robust and scalable data streaming infrastructure. The study focuses on enhancing data input, processing, and dissemination across systems and applications. By optimizing Kafka configurations and leveraging its capabilities, the research seeks to achieve significant improvements in data processing speed, real-time analytics, and scalable data pipelines. The evaluation of Kafka Event Stream Throughput Over Time and Latency Distribution Across Brokers demonstrates the system's performance and efficiency. The results indicate a throughput of 750-1340 events per hour and a latency distribution of 6-15 milliseconds. Additionally, Consumer Lag Over Time analysis reveals consistent performance with values ranging from 70-140. This research contributes to the advancement of big data processing by demonstrating the effectiveness of Apache Kafka in creating a robust and efficient data streaming infrastructure. The findings provide valuable insights for organizations seeking to optimize their data pipelines and leverage the power of real-time analytics. © 2024 IEEE.

关键词： data streams

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Train++: An Incremental ML Model Training Algorithm to Create Self-Learning IoT Devices

Train++: An Incremental ML Model Training Algorithm to Creat...

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2021 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People, and Smart City Innovations, SmartWorld/ScalCom/UIC/ATC/IoP/SCI 2021

作者： Sudharsan, Bharath Yadav, Piyush Breslin, John G. Intizar Ali, Muhammad Data Science Institute Confirm SFI Research Centre For Smart Manufacturing NUI Galway Ireland Data Science Institute Insight SFI Centre For Data Analytics NUI Galway Ireland Dublin City University School of Electronic Engineering Ireland

ISBN: (纸本)9781665412360

The majority of Internet of Things (IoT) devices are tiny embedded systems with a micro-controller unit (MCU) as its brain. The memory footprint (SRAM, Flash, and EEPROM) of such MCU-based devices is often very limited, restricting onboard Machine Learning (ML) model training for large trainsets with high feature dimensions. To cope with memory issues, the current edge analytics approaches train high-quality ML models on the cloud GPUs (uses large volume historical data), then deploy the deep optimized version of the resultant models on edge devices for inference. Such approaches are inefficient in concept drift situations where the data generated at the device level vary frequently, and trained models are clueless on how to behave if previously unseen data arrives. In this paper, we present Train++, an incremental training algorithm that trains ML models locally at the device level (e.g., on MCUs and small CPUs) using the full n-samples of high-dimensional data. Train++ transforms even the most resource-constrained MCU-based IoT edge devices into intelligent devices that can locally build their own knowledge base on-the-fly using the live data, thus creating smart self-learning and autonomous problem-solving devices. Train++ algorithm is extensively evaluated on 5 popular MCU-boards, using 7 datasets of varying sizes and feature dimensions. A few exciting findings when analyzing the evaluation results are: (i) The proposed method reduces the onboard binary classifier training time by ≈ 10 - 226 sec across various commodity MCUs;(ii) Train++ can infer on MCUs for the entire test set in real-time of 1 ms;(iii) The accuracy improved by 5.15 - 7.3% since the incremental characteristic of Train++ enabled the loading of full n-samples of the high-dimensional datasets even on MCUs with only a few hundred kBs of memory. © 2021 IEEE.

关键词： Microcontrollers

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Fusing Local and Global Mobility Patterns for Trajectory Recovery 28th

Fusing Local and Global Mobility Patterns for Trajectory R...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Deng, Liwei Zhao, Yan Sun, Hao Yang, Changjie Xie, Jiandong Zheng, Kai University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China Aalborg University Aalborg Denmark Peking University Beijing China Cloud Database Innovation Lab of Cloud BU Huawei Technologies Co. Ltd. Chengdu China

ISBN: (纸本)9783031306365

The prevalence of various mobile devices translates into a proliferation of trajectory data that enables a broad range of applications such as POI recommendation, tour recommendation, urban transportation planning, and the next location prediction. However, trajectory data in real-world is often sparse, noisy and incomplete. To make the subsequent analysis more reliable, trajectory recovery is introduced as a pre-processing step and attracts increasing attention recently. In this paper, we propose a neural attention model based on graph convolutional networks, called TRILL, to enhance the accuracy of trajectory recovery. In particular, to capture global mobility patterns that reflect inherent spatio-temporal regularity in human mobilities, we construct a directed global location transition graph and model the mobility patterns of all the trajectories at point level using graph convolutional networks. Then, a self-attention layer and a window-based cross-attention layer are sequentially adopted to refine the representations of missing locations by considering intra-trajectory and inter-trajectory information, respectively. Meanwhile, an information aggregation layer is designed to leverage all the historical information. We conduct extensive experiments using real trajectory data, which verifies the superior performance of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

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computers, Materials & Continua 2021年第2期66卷 2041-2059页

作者： Gopi Krishna Durbhaka Barani Selvaraj Mamta Mittal Tanzila Saba Amjad Rehman Lalit Mohan Goyal School of Electrical and Electronics Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering G.B.Pant Government Engineering CollegeNew Delhi110020India Artificial Intelligence and Data Analytics(AIDA)Laboratory CCISPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering J.C.Bose University of Science and TechnologyFaridabad121006India

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling *** of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult *** monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last *** mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the *** paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the *** helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher *** results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

关键词： Gearbox long short term memory fault classification swarm intelligence optimization condition monitoring

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Movable Antenna Enabled Near-Field Communications: Channel Modeling and Performance Optimization

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IEEE Transactions on Communications 2025年

作者： Zhu, Lipeng Ma, Wenyan Xiao, Zhenyu Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Beihang University School of Electronic and Information Engineering Beijing100191 China The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China

Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, where a base station (BS) with multiple movable subarrays serves multiple users, each equipped with a fixed-position antenna (FPA). First, we extend the field response channel model for MA systems to the near-field propagation scenario. Next, we examine MA-aided multiuser communication systems under both digital and analog beamforming architectures. For digital beamforming, spatial division multiple access (SDMA) is utilized, where an upper bound on the minimum achievable rate across users is derived in closed form. A low-complexity algorithm based on zero-forcing (ZF) is then proposed to jointly optimize the antenna position vector (APV) and digital beamforming matrix (DBFM) to approach this bound. For analog beamforming, orthogonal frequency division multiple access (OFDMA) is employed, and an upper bound on the minimum achievable rate among users is also derived. An alternating optimization (AO) algorithm is proposed to iteratively optimize the APV, analog beamforming vector (ABFV), and power allocation until convergence. For both architectures, we further explore MA design strategies based on statistical channels, with the APV updated less frequently to reduce the antenna movement overhead. Simulation results demonstrate that our proposed algorithms achieve performance close to the derived bounds and also outperform the benchmark schemes using dense or sparse arrays with FPAs. 1972-2012 IEEE.

关键词： Near field communication

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Word Sense Disambiguation: Adaptive Word Embedding with Adaptive-Lexical Resource

Word Sense Disambiguation: Adaptive Word Embedding with Adap...

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International Conference on data analytics and Insights, ICDAI 2023

作者： Kokane, Chandrakant D. Babar, Sachin D. Mahalle, Parikshit N. Patil, Shivprasad P. Department of Computer Engineering Smt. Kashibai Navale College of Engineering Maharashtra Pune India Department of Computer Engineering Sihngad Institute of Technology Maharashtra Lonavala India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Maharashtra Pune India NBN Sinhgad School of Engineering Maharashtra Pune India

ISBN: (纸本)9789819938773

Word sense disambiguation (WSD) is a subdomain of natural language processing (NLP). WSD mainly deals with identifying the correct sense of ambiguous words. The discussed disambiguation model is tested for homonymous and polysemous words, and the same is tested with SemCor, OMSTI, and the newly generated Adaptive-Lex dataset. The Adaptive-Lex dataset is generated by considering the scope of SemCor and OMSTI. The discussed dataset is constructed by considering the dynamic features of ambiguous words. The main objective of this dataset is to represent ambiguous words with dynamic features. While dealing with ambiguous words, disambiguation is expected by considering available context information. The word vectors are constructed dynamically, and the dataset is also created by considering the countable features of ambiguous words. If the word vector is prepared concerning context information, it generates more fine results for WSD. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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Real-time behavior recognition of animal: an IoT-based system design using acceleration data

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Multimedia Tools and Applications 2024年 1-26页

作者： Tran, Duc-Nghia Manh, Do Viet Van Thanh, Pham Shankar, A. Achyut Joshi, Kireet Tran, Duc-Tan Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam University of Fire Prevention and Fighting Hanoi100000 Viet Nam Department of Cyber Systems Engineering WMG University of Warwick CoventryCV74AL United Kingdom Department of CSE University Centre for Research & ampDevelopment Chandigarh University Punjab Mohali140413 India School of Computer Science Engineering Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Center of Research Impact and Outcome Chitkara University Punjab Chandigarh India Department of Computer Science & amp Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam

By analyzing large amounts of animal behavior data, humans can assess cattle’s condition. It leads to research and development a field using accelerometers and machine learning algorithms to ‘study’ behavior from acceleration data. It is challenging to analyze animal behavior due to the activity complexity. For example, acceleration data of some cow behaviors (e.g., feeding and standing) are similar if these behaviors contain similar gestures. In practical, real-time applications even bring more challenging since it is necessary to minimize a data window for behavior consideration. Using short data window often reduces the performance of classification algorithms. This study proposes a new solution for the mentioned challenges by combining neck-mounted and leg-mounted accelerometers, then using the vector of the dynamic body acceleration (VeDBA) and Mean features of synchronized acceleration data for the Decision Tree algorithm. The proposed method is demonstrated that performs well (0.94 accuracy, 0.91 sensitivity, 0.90 positive predictive value) within a low computational complexity algorithm and short data window (3-second data window, at the rate of one sample per second). It is particularly suited for the case of a real-time IoT-based system. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Accelerometers

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A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor data and Periodic Sampling

A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zheng, Jianbo Yang, Chao Zheng, Fangrong Jiang, Bin College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China School of Computational Science and Electronics Hunan Institute of Engineering China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In recent years, bearing fault diagnosis based on deep learning has gradually become the mainstream. However, the existing studies still have some defects, such as unreasonable sampling and incomplete utilization of bearing data, limiting the further improvement of the performance of the fault diagnosis model. This paper proposes a fault diagnosis method using multi-sensor data and periodic sampling to solve the problems above. First, the vibration data of different bearing positions are fused into multi-channel fusion data to improve the defect of insufficient data utilization. Second, based on the sampling length and sampling stride, periodic sampling is carried out for the fusion data to solve the problem of unreasonable sampling. Third, the traditional convolutional neural network is adjusted to extract more detailed fault features and obtain the best recognition effect. Finally, the experimental results verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Failure analysis

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