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3rd International Conference on Applied sciences Mathematics and Informatics, ICASMI 2020

作者： Aldino, A.A. Darwis, D. Prastowo, A.T. Sujana, C. Department of Informatics Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia Department of Accounting Information System Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia Department of Computer Engineering Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia

South Lampung is a regency with the capital of Kalianda which has an area of 2,007.01 km2 that dominates the agricultural area. Based on the data of corn crops in the South Lampung Regency Agriculture Office through BPS (Central Bureau of Statistics), showing several areas with corn crops that vary in number. Therefore, a grouping of potential corn-producing regions is required to know which areas produce large or small amounts of corn. The distribution of crops is usually done based on the name of the corn-producing sub-district. The K-Means clustering method is one of the data mining methods that is non-hierarchical clustering that groups data in the form of one or more clusters. Data that have the same characteristics are grouped in one cluster and the remaining is grouped into another cluster so that the data that is in one cluster has a small degree of variation. So the authors tried to apply the K-Means clustering method from the corn crop data of the last 2 years to produce feasibility information from each sub-district. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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Deep learning-based channel estimation for RIS-assisted UAV-enabled wireless communications system

Deep learning-based channel estimation for RIS-assisted UAV-...

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International Conference on Innovation, Communication and engineering (ICICE 2023)

作者： Belayneh A Tesfaw Hsin-piao Lin Getaneh B Tarekegn Rong-Terng Juang Shiann-Shiun Jeng Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Institute of Aerospace and System Engineering National Taipei University of Technology Taipei 10608 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu 30010 Taiwan Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

ISBN: (数字)9781839539954

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. This problem can be effectively addressed by employing reconfigurable intelligent surfaces (RIS). To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communications system. Simulation results showed that LSTM can effectively improve the channel estimation performance of RIS-assisted UAV-enabled wireless communications.

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Satellite-pose estimation using IMU sensor data and Kalman filter with RF-433 Mhz powered communication and helical antenna design for ground station

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Measurement: Sensors 2024年 35卷

作者： Paneru, Biplov Adhikari, Ankit Paneru, Bishwash Shah, Krishna Bikram Chhetri Sapkota, Sanjog Poudyal, Ramhari Poudyal, Khem Narayan Department of Electronics and Communication Engineering Nepal Engineering College Affiliated to Pokhara University Bhaktapur Nepal Department of Mechanical and Aerospace Engineering Institute of Engineering Pulchowk Campus Affiliated to Tribhuvan University Lalitpur Nepal Department of Applied Sciences and Chemical Engineering Institute of Engineering Pulchowk Campus Affiliated to Tribhuvan University Lalitpur Nepal Department of Computer Science Engineering Nepal Engineering College Affiliated to Pokhara University Nepal Nepal Research and Collaboration Center Nepal Department of Information System Engineering HIST Engineering College Affiliated to Purbanchal University Nepal

Space research relies heavily on satellites, and technological developments are critical to the success of these missions. In order to estimate satellite pose, a simulation of an IMU sensor using Matplotlib is presented in this study. A roll, pitch, and yaw value analysis is performed on data obtained from the BNO-055 IMU sensor. A prototype ground station's helical antenna is used to investigate these parameters. For effective communication between the satellite body and ground station setup, a low-cost transceiver module RF-433Mhz module is connected to the ground station. IMUsensors are essential for inertial measurements in spacecraft, which support ground station monitoring. This study makes use of a four-turn helical antenna that is built for deployment and designed using the 4nec2 simulation program for RF-based satellite-ground station communication. The antenna's VSWR is 1.432, and its directivity is 18.581. Matplotlib is utilized to model variations in roll, pitch, and yaw values. Roll, pitch, and yaw ranges that are observed are −0.375 to 0.25, −0.375 to 0.5625, and −0.3125 to 0.1875, in that order. These ranges are useful in effectively visualizing the body pose of the CubeSat when connecting via PySerial to a Matplotlib-developed program. © 2024 The Authors

关键词： Kalman filters

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Advancements in Wireless Networks: Transition from 5G to Beyond

Advancements in Wireless Networks: Transition from 5G to Bey...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Gunasekaran Thangavel C. Malarvizhi M. Ameena Banu Arun Anthonisamy M. Hemalatha Raja Manikandan Department of Electronics and Communication Engineering University of Technology and Applied Sciences Muscat Sultanate of Oman Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chembarambakkam India Department of Electronics and Communication Engineering NPR College of Engineering and Technology Natham India Department of Computer Science and Business System Panimalar Engineering College Chennai Tamil Nadu India Department of Information Technology Nandha Arts and Science College Erode Tamil Nadu India Department of ECE K. Ramakrishnan College of Technology Trichy Tamil Nadu India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Earlier generations of mobile technology experienced exponential growth in mobile customers, disruptive ecosystems like Internet of Things applications, and massive channel capacity needed to connect trillions of devices. 5G networks are a vital solution. 5G aims to provide 10 Gbps to a single user, as well as quality of service, increased spectrum efficiency, ultra-reliable communication, extended device battery life, massive machine-type communication (mMTC), and a 1000-fold capacity gain. Due to the heavy traffic volume and high resource consumption of 5G applications like augmented reality (AR), virtual reality (VR), and wireless body area networks, there are major barriers to channel capacity and potential service disruptions for other users. New strategies are needed to increase spectral efficiency and channel capacity. This paper explores many major new wireless technologies that aim to increase spectral efficiency and channel capacity to meet growing user demands and improve network performance. This paper also examines these technologies’ benefits and research challenges. Massive Multi-Input Multi-Output (mMIMO) technology’s effectiveness and potential distinguish it for 5G and Beyond 5G (B5G) networks, offering a variety of benefits and characteristics. This article can also assist researchers who are conducting in-depth studies, as well as wireless network operators who aim to streamline the development of 5G and B5G networks.

关键词： Technological innovation 5G mobile communication Channel capacity Spectral efficiency Array signal processing Wireless networks Signal processing algorithms Throughput Millimeter wave communication Ultra-dense networks

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Online News Extraction and Multiclass Classification Using Linear Support Vector Machines

Online News Extraction and Multiclass Classification Using L...

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International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Apoorva Gupta Smriti Arora Niyati Baliyan Data Science Team Barclays UK Knutsford England Computer Science and Infromation System Birla Institute of Technology and Science Pilani India Computer Engineering Department National Institute of Technology Kurukshetra Kurukshetra India

Online news articles, blogs, sites are a rich source of diverse text-based data. However, the data contained in these sources cannot be manually extricated, recorded, and listed because it comes in colossal size. Accurate mapping of precise news into their corresponding category is challenging in these times. Several methods have been proposed over time for news classification when training documents for each predefined class are present readily, however such methods were tried and tested upon a small dataset. With the underlying research, the aim is to propose a method that can be used when lakhs and lakhs of instances are present. This research analysis involves the task of news classification using multiclass classifiers - OneVsRest and OneVsOne classifiers over the Linear Support Vector Classification to learn the performance of multiclass news categorization. The proposed methodology “Keyword Based Classification Technique (KBCT)” in this study was executed and concluded using Python and deployed using Google Colaboratory. The result was expressed using four distinguished news classes over a multivariate dataset of 422419 instances from the uci-news-aggregator dataset. The OneVsRestClassifier's accuracy was computed to be 95.76% that was 0.09% more than the OneVsOneClassifier's accuracy of 95.67%. The proposed prototype was compared with some of the related studies and algorithms, and the outcomes produced by the OneVsRest model were the most optimum in terms of accuracy.

关键词： Training Prototypes Support vector machine classification Organizations Search engines Real-time systems Internet

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big Data Analytics

A Comparative Analysis of Artificial Intelligence and Busine...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Bhagath Chandra Chowdari Marella Gopi Chand Vegineni Sireesha Addanki Ehsan Ellahi Arun Kumar K Rishabh Mandal Department of Financial Services Insights & Data Capgemini India Enrollment and Eligibility Team India System soft Technologies llc Information Technology India Department of Information Systems Cleveland State University Ohio United States Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system ( $\mathbf{92.6 \%}$ ) and the neural network (NNs) models ( ${92}.7 \%$ ). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans.

关键词： Radio frequency Machine learning algorithms Accuracy Artificial neural networks Big Data Market research Classification algorithms Business intelligence Random forests Recruitment

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Transformer‐based time series prediction of the maximum power point for solar photovoltaic cells

arXiv

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arXiv 2024年

作者： Agrawal, Palaash Bansal, Hari Om Gautam, Aditya R. Mahela, Om Prakash Khan, Baseem Birla Institute of Technology and Science Rajasthan Pilani India Power System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Ltd. Rajasthan Jaipur India Department of Electrical and Computer Engineering Hawassa University Hawassa Ethiopia

This paper proposes an improved deep learning‐based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series-based environmental inputs. Generally, artificial neural network‐based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time‐based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer‐based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological‐year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non‐zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours;resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real‐time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions. © 2024, CC BY.

关键词： Photovoltaics

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A novel semi supervised approach for text classification

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International Journal of Information Technology (Singapore) 2020年第4期12卷 1147-1157页

作者： Barman, Debaditya Chowdhury, Nirmalya Department of Computer and System Sciences Visva-Bharati Santiniketan 731235 India Department of Computer Science and Engineering Jadavpur University Kolkata 700032 India

Text categorization, also known as text classification is a supervised classification problem. It aims to assign a predefined class label or group to a new or unknown text document. Most of the time we need a collection of large data from each class to train the classifier. It may be noted that, it is very hard or expensive to collect labelled text data. In most cases we assign the label manually which is neither cost effective nor efficient. In this paper, we have introduced a semi-supervised classification approach where the learner needs very small amount of labelled data with a large amount of unlabeled data to assign a class label to a new or unknown text document. The proposed method uses Kohonen self organizing map (SOM) for labelling the unlabeled data and three classifiers namely support vector machine (SVM), Naïve Bayes (NB), and decision tree (DT): classification and regression tree (CART) for observing the accuracy of classification. The experimental results obtained show the effectiveness of our proposed method. © 2018, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Decision tree Kohonen self organizing map Naïve Bayes Semi supervised learning Support vector machine Text categorization

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Joint Secure and Covert Communications for Active STAR-RIS Assisted ISAC systems

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Liang Jia, Jie Mu, Xidong Liu, Yuanwei Chen, Jian Wang, Xingwei Northeastern University School of Computer Science and Engineering China Northeastern University Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China BelfastBT3 9DT United Kingdom Department of Electrical and Electronic Engineering China

This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR-RIS) assisted integrated sensing and communication (ISAC) system. Due to the unified waveform design of ISAC signals, we consider a challenging scenario with two targets being suspicious attackers, where one warden target potentially detects the confidential transmission behavior of covert users and another eavesdropper target attempts to intercept the broadcasted confidential information of security users. We investigate the joint beamforming design at the base station (BS) and the a-STAR-RIS to achieve a high-quality sensing beampattern while meeting covertness and security communication requirements. (1) For the ideal scenario with perfect channel state information (CSI) and precise target locations, we propose an alternative optimization (AO) method to address the optimization problem involving highly coupled variables. Specifically, the optimal beamforming design at the BS is handled using the semi-definite relaxation (SDR) technique, while the beamforming design at the a-STAR-RIS is addressed through a penalty-based iterative algorithm. (2) A more practical case with uncertain target locations and imperfect CSI is considered to achieve a robust beamforming design, where the non-deterministic outage probability constraints are effectively transformed by employing the Bernstein-type inequality. Numerical results demonstrate the superiority of the a-STAR-RIS over the baseline cases and certify that the proposed algorithms can effectively balance the tradeoff among the sensing quality, covert and secure communication requirements. Besides, results also show that the proposed robust beamforming scheme can construct adequate sensing beampattern, even with imperfect CSI and uncertain target locations. © 2002-2012 IEEE.

关键词： Beamforming

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Distributed Optimization of Clique-Wise Coupled Problems via Three-Operator Splitting

arXiv

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arXiv 2023年

作者： Watanabe, Yuto Sakurama, Kazunori Department of Electrical and Computer Engineering University of California San Diego San DiegoCA92093 United States Department of System Innovation Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan

This study explores distributed optimization problems with clique-wise coupling via operator splitting and how we can utilize this framework for performance analysis and enhancement. This framework extends beyond conventional pairwise coupled problems (e.g., consensus optimization) and is applicable to broader examples. To this end, we first introduce a new distributed optimization algorithm by leveraging a clique-based matrix and the Davis-Yin splitting (DYS), a versatile three-operator splitting method. We then demonstrate that this approach sheds new light on conventional algorithms in the following way: (i) Existing algorithms (NIDS, Exact diffusion, diffusion, and our previous work) can be derived from our proposed method;(ii) We present a new mixing matrix based on clique-wise coupling, which surfaces when deriving the NIDS. We prove its preferable distribution of eigenvalues, enabling fast consensus;(iii) These observations yield a new linear convergence rate for the NIDS with non-smooth objective functions. Remarkably our linear rate is first established for the general DYS with a projection for a subspace. This case is not covered by any prior results, to our knowledge. Finally, numerical examples showcase the efficacy of our proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization algorithms

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