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arXiv 2022年

作者： Xia, Huiyun Mao, Yijie Zhou, Xiaokang Clerckx, bruno Han, shuai Li, Cheng School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University St. John’sA1B 3X5 Canada

As physical layer security evolves to multi-user systems, multi-user interference (MUI) becomes an unavoidable issue. Recently, rate-splitting multiple access (RsMA) emerges as a powerful non-orthogonal transmission framework and interference management strategy with high spectral efficiency. Unlike most works fully treating MUI as noise, we take all users’ secrecy rate requirements into consideration and propose an RsMA-based secure beamforming approach to maximize the weighted sum-rate (WsR), where MUI is partially decoded and partially treated as noise. User messages are split and encoded into common and private streams. Each user not only decodes the common stream and the intended private stream, but also tries to eavesdrop other users’ private streams. A successive convex approximation (sCA)-based approach is proposed to maximize the instantaneous WsR under perfect channel state information at the transmitter (CsIT). We then propose a joint weighted minimum mean square error and sCA-based alternating optimization algorithm to maximize the weighted ergodic sum-rate under imperfect CsIT. Numerical results demonstrate RsMA achiev.s.better WsR and is more robust to channel errors than conventional multi-user linear precoding technique while ensuring all users’ security requirements. besides, RsMA can satisfy all users’ secrecy rate requirements without introducing WsR loss thanks to its powerful interference management capability. Copyright © 2022, The Authors. All rights reserved.

关键词： beamforming

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DRbM-ClustNet: A Deep Restricted boltzmann-Kohonen Architecture for Data Clustering

arXiv

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arXiv 2022年

作者： senthilnath, Jayavelu Nagaraj, G. sumanth simha, C. Kulkarni, sushant Thapa, Meenakumari Indiramma, M. benediktsson, Jón Atli Singapore138632 Singapore The B.M.S. College of Engineering Bengaluru560019 India The Indian Institute of Technology Chennai600036 India The Birla Institute of Technology and Science Pilani333031 India The Faculty of Electrical and Computer Engineering University of Iceland Reykjavik107 Iceland

A bayesian Deep Restricted boltzmann-Kohonen architecture for data clustering termed as DRbM-ClustNet is proposed. This core-clustering engine consists of a Deep Restricted boltzmann Machine (DRbM) for processing unlabeled data by creating new features that are uncorrelated and have large variance with each other. Next, the number of clusters are predicted using the bayesian Information Criterion (bIC), followed by a Kohonen Network-based clustering layer. The processing of unlabeled data is done in three stages for efficient clustering of the non-linearly separable datasets. In the first stage, DRbM performs non-linear feature extraction by capturing the highly complex data representation by projecting the feature vectors of d dimensions into n dimensions. Most clustering algorithms require the number of clusters to be decided a priori, hence here to automate the number of clusters in the second stage we use bIC. In the third stage, the number of clusters derived from bIC forms the input for the Kohonen network, which performs clustering of the feature-extracted data obtained from the DRbM. This method overcomes the general disadvantages of clustering algorithms like the prior specification of the number of clusters, convergence to local optima and poor clustering accuracy on non-linear datasets. In this research we use two synthetic datasets, fifteen benchmark datasets from the UCI Machine Learning repository, and four image datasets to analyze the DRbM-ClustNet. The proposed framework is evaluated based on clustering accuracy and ranked against other state-of-the-art clustering methods. The obtained results demonstrate that the DRbM-ClustNet outperforms state-of-the-art clustering algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Clustering algorithms

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Mitigating machining damage: Advanced modelling of pumice loading effects in Kevlar fibre-pumice sandwich polymer composites at the AWJM zone

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Journal of Materials Research and Technology 2025年 37卷 1019-1028页

作者： venkatesh Chenrayan A. saravanakumar Kiran shahapurkar s. vaishnavi s. selvam Chandru Manivannan vineet Tirth Ali Algahtani Jana Petrů Muhammad Nasir bashir Manzoore Elahi M. soudagar Krishna Prakash Arunachalam H.M. vidhya Centre of Excellence-Advanced Materials Synthesis (CoE-AMS) Department of Mechanical Engineering Alliance School of Applied Engineering Alliance University Bengaluru 562106 India AU-Sophisticated Testing and Instrumentation Centre (AU-STIC) Alliance University Bengaluru 562106 India Department of Computer Science Engineering PSG Institute of Technology and Applied Research Coimbatore India Department of Mechanical Engineering Coimbatore Institute of Technology Coimbatore India Department of Mechanical Engineering Dhirajlal Gandhi College of Technology Salem 63840: India Mechanical Engineering Department College of Engineering King Khalid University Abha 61421 Asir Kingdom of Saudi Arabia Centre for Engineering and Technology Innovations King Khalid University Abha 61421 Asir Kingdom of Saudi Arabia Research Center for Advanced Materials Science (RCAMS) King Khalid University Guraiger PO Box 9004 Abha 61413 Asir Kingdom of Saudi Arabia Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engi-neering VŠB Technical University of Ostrava Ostrava Czech Republic Department of Mechanical Engineering University of Malaya 50603 Kuala Lumpur Malaysia Multi-Scale Fluid Dynamics Lab Department of Mechanical Engineering Yonsei University Seoul 120-749 Republic of Korea Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Centre for Promotion of Research Graphic Era (Deemed to be University) Clement town Dehradun India Departamento de Ciencias de la Construcción Facultad de Ciencias de la Construcción Ordenamiento Territorial Universidad Tecnológica Metropolitana Santiago Chile Centre of Molecular Medicine and Diagnostics (COMManD) Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 600 077 India

The development of varieties of fibre-reinforced polymer composite (FRP) has become a part of day-to-day life and is the core agenda of the manufacturing industries in new product development. Despite the wider applications, the machining of FRPs is encountering a greater challenge in overcoming the machining damage. It is the right proposal to deplete the prominent machining damages like kerf taper, delamination and surface roughness by introducing novel hybrid statistical and soft computing models. The novel Kevlar fiber woven mat-pumice particles reinforced sandwich is fabricated using open moulding and machined with Abrasive Water Jet Machining by following L9 orthogonal guidelines. The three different pumice compositions, standoff distance, abrasive flow rate and hydraulic pressure were considered the prime variables to influence the damages. The hybrid analysis results declare that trial No.3 is an optimal parameter, which comprises 100 (MPa) of hydraulic pressure, 2 mm of standoff distance, 300 (gm/min) of abrasive flow rate and 10 % of pumice particles to scale down the damages. A noticeable 1.69 %, 25.12 % and 40.71 % decrement in machining damages, along with the finding of a higher contribution of pumice content in minimizing the damages, would benefit the FRP industries to march towards the implementation of lean manufacturing and new product development (NPD).

关键词： Kevlar fibre Pumice Delamination Kerf taper surface roughness Grey relational analysis (GRA) Machine learning (ML)

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Effect of temperature reaction on chemically synthesized ZnO nanoparticles change in particle size

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Materials Today: Proceedings 2021年 45卷 3997-4001页

作者： P. Rajarajeswari R.s. shaikh L.s. Ravangave Department of Computer science and Engineering Kingston engineering college Vellore Tamilnadu India Department of Physics S.G.B. College Purna(Jn) Dist: Parbhani M.S. India Department of Physics S.S.G.M. College Loha Dist: Nanded M.S. India

Previously ZnO nanostructures production such as Hydrothermal, Co-operation and Anodization were described. In this paper FTIR, XRD, scanning electron microscope processes are used for determining various characteristics of ZnO particles. It shows morphological changes of ZnO particles. We have presented the study of ZnO nanoparticles properties by using Uv–v.s.ble spectrophotometer, photoluminescence spectroscopy devices. It determines the properties of ZnO nanoparticles. We have described the effect of temperature reaction on chemically synthesized ZnO Nano particles based on change in Particle size.

关键词： ZnO nanoparticles XRD scanning electron microscope Fourier-transform infrared spectroscopy Ultraviolet photo detectors Photoluminescence

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Reduced plaintext for ciphers (RPC) algorithm

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AIP Conference Proceedings 2024年第1期2802卷

作者： U. Thirupalu A. Rajasekhar Reddy P. Chandrakanth Dept. of Computer Science and Engineering Audisankara College of Engineering & Technology Gudur Andhra Pradesh India Dept. of Computer Science and Engineering N.B.K.R.I.S.T Vidyanagar Andhra Pradesh India

In Cryptosystem, the cipher text plays an important role for protecting, storing and transmitting through different medium. still today most of the algorithms like DEs, AEs, RC family, RsA etc. produced cipher text is equal size or greater than the actual size of plaintext. so that these cipher text occupies more memory space, took more time by transmitting over the internet and this is a prone to break the cipher text. If we reduce the size of the plaintext, relatively the size of cipher text is reduced and also the storage and transmitting rate is minimized. To reduce the size, storage and transmitting rate, I introduce a new algorithm that is Reduced Plaintext Cipher (RPC) Algorithm. Through this algorithm, the plaintext is reduced relatively the cipher text is also reduced. so that the cipher text size is small, occupies less memory, and transmitting rate is high and also it difficult to break by the cryptanalysts because the reduced text is as an intermediate code between actual plaintext and generated cipher text.

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Robust security Energy Efficiency Optimization for RIs-Aided Cell-Free Networks with Multiple Eavesdroppers

arXiv

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arXiv 2022年

作者： Hao, Wanming Li, Junjie sun, Gangcan Huang, Chongwen Zeng, Ming Dobre, Octavia A. Yuen, Chau The School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Department of Electrical Engineering and Computer Engineering Universite Laval QCG1V 0A6 Canada The Faculty of Engineering and Applied Science Memorial University St. JohnsNLA1B 3X5 Canada The Singapore University of Technology and Design 487372 Singapore

In this paper, we investigate the energy efficiency (EE) problem under reconfigurable intelligent surface (RIs)aided secure cell-free networks, where multiple legitimate users and eavesdroppers (Eves) exist. We formulate a max-min secure EE optimization problem by jointly designing the distributed active beamforming and artificial noise at base stations as well as the passive beamforming at RIss under practical constraints. To deal with it, we first divide the original optimization problem into two sub-ones, and then propose an iterative optimization algorithm to solve each sub-problem based on the fractional programming, constrained convex-convex procedure (CCCP) and semi-definite programming (sDP) techniques. After that, these two sub-problems are alternatively solved until convergence, and the final solutions are obtained. Next, we extend to the imperfect channel state information of the Eves' links, and investigate the robust sEE beamforming optimization problem by bringing the outage probability constraints. based on this, we first transform the uncertain outage probability constraints into the certain ones by the bernstein-type inequality and sphere boundary techniques, and then propose an alternatively iterative algorithm to obtain the solutions of the original problem based on the s-procedure, successive convex approximation, CCCP and sDP techniques. Finally, the simulation results are conducted to show the effectiveness of the proposed schemes. © 2022, CC bY.

关键词： beamforming

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Efficient DNA sequence Identification: A Classification Approach Integrating Gaussian RbM

Efficient DNA Sequence Identification: A Classification Appr...

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Electronics, Computing, Communication and Control Technology (ICECCC), International Conference on

作者： Chahat Gulati biswajit barman Govind Hanmantrao balde Arul Mary Rexy v Meena sachdeva Harshal Patil Department of Electronics and Communication Birla Institute of Technology and Science BITS Pilani Rajasthan India Department of Biotechnology Asian International University Imphal West Manipur India Department of Zoology N.T.V.S'S G.T.Patil Arts Commerce and Science College Nandurbar India Department of Commerce Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Department of Computer Science JIMS Engineering Management and Technical Campus Greater Noida India Department of Data Science Balaji Institute of Technology & Management (BITM) Sri Balaji University Pune Maharashtra India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

Using machine learning techniques, this article examines cancer patients' DNA sequences. Cancer is certainly one of the most common and devastating diseases in the contemporary era, with a large number of new cases and deaths every year. Many people lose their lives each year to this terrible illness. Methods to combat this illness are the focus of intense investigation by scientists from a wide range of fields. Recently, machine learning has improved the analysis of cancer patients' DNA sequences. sequencing is crucial for preprocessing, feature extraction, and model training. Among the preprocessing stages is cleaning up the sequence, which entails eliminating PCR repetitions, impurities, and vectors. Feature extraction has been carried out on each DNA sequence in order to retrieve statistical characteristics. It is of utmost importance to regulate features accurately when training a Gaussian RbM. A grade of 96.44% demonstrated a considerable improvement in accuracy, according to the results.

关键词： Training sequential analysis Accuracy sequences Reviews DNA Machine learning

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High resolution spatial modelling of soil parent material: An initial investigation study using random forest machine learning techniques

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Journal of south American Earth sciences 2025年 162卷

作者： vijayakumar, A. Alshardan, Amal Alghamdi, Abdulaziz G. Durai, s. Department of Civil Engineering V.S.B. Engineering College Tamilnadu Karur 639111 India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Soil Sciences College of Food and Agricultural Sciences King Saud University P.O. Box 2460 Riyadh 11451 Saudi Arabia Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai 600062 India

This study uses Random Forest machine learning to explore the distribution of soil parent material in Morelos, Mexico. Elemental composition was determined using a portable X-ray fluorescence (pXRF) spectrometer, and soil variability was examined using magnetic susceptibility (Ms) measurements over the A and b horizons. The goal was to improve soil categorisation and mapping accuracy to promote sustainable land management and agricultural operations. The statistical study indicated substantial differences between soil horizons: Magnesium content ranged from 7.1 % to 15.7 % (mean: 11.4 %) in the A horizon and from 8.0 % to 20.9 % (mean: 10.4 %) in the b horizon;iron concentrations exhibited lower variability, with mean values of 8.9 % (A horizon) and 7.4 % (b horizon);calcium displayed the most significant variation, with a maximum of 28.5 % in the b horizon;silicon remained relatively stable, with mean values of 3.8 % (A horizon) and 4.0 % (b horizon);and magnetic susceptibility showed substantial dif The Random Forest model accurately reflected geographic variations in soil parent material, emphasizing important geochemical and mineralogical distinctions. The findings highlight machine learning's potential for high-resolution soil mapping and categorisation. This approach lays the groundwork for enhanced soil fertility assessments, erosion management, and land-use planning. Future study should include additional geographic and environmental factors to improve model accuracy and prediction capabilities. © 2025 Elsevier Ltd

关键词： Geochemical analysis Machine learning Magnetic susceptibility pXRF spectrometry Random forest soil classification soil parent material spatial modelling

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RustsEG - Automated segmentation of corrosion using deep learning

arXiv

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arXiv 2022年

作者： burton, b. Nash, W.T. birbilis, Nick College of Engineering and Computer Science Australian National University ActonACT2601 Australia WSP Canada Inc. 1935 Bollinger Road NanaimoBCV9S 5W9 Canada

The inspection of infrastructure for corrosion remains a task that is typically performed manually by qualified engineers or inspectors. This task of inspection is laborious, slow, and often requires complex access. Recently, deep learning based algorithms have revealed promise and performance in the automatic detection of corrosion. However, to date, research regarding the segmentation of images for automated corrosion detection has been limited;due to the lack of availability of per-pixel labelled data sets which are required for model training. Herein, a novel deep learning approach (termed RustsEG) is presented, that can accurately segment images for automated corrosion detection - without the requirement of per-pixel labelled data sets for training. The RustsEG method will first, using deep learning techniques, determine if corrosion is present in an image (i.e. a classification task), and then if corrosion is present, the model will examine what pixels in the original image contributed to that classification decision. Finally, the method can refine its predictions into a pixel-level segmentation mask. In ideal cases, the method is able to generate precise masks of corrosion in images, demonstrating that the automated segmentation of corrosion without per-pixel training data is possible - addressing a significant hurdle in automated infrastructure inspection. © 2022, CC bY-NC-ND.

关键词： semantic segmentation

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IoT Edge and Fog Computing Architecture for Educational systems in Universities

IoT Edge and Fog Computing Architecture for Educational Syst...

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Current Development in engineering and Technology (CCET), IEEE International Conference on

作者： shams Tabrez siddiqui Md Oqail Ahmad Alighazi siddiqui Haneef Khan Mohammad Rafeek Khan Ashjan Hamad Alsabhan Department of Computer Science College of CS & IT Jazan University Jazan Saudi Arabia Department of Computer Applications B.S Abdur Rahman Crescent Institute of Science and Technology Chennai Tamilnadu India Department of Computer and Network Engineering College of CS & IT Jazan University Jazan Saudi Arabia CST Department Applied College University of Hafr Al Batin Hafar al-Batin Saudi Arabia

smart lights, ovens, and industrial data-gathering equipment are all instances of internet-connected gadgets. With 41.6 billion IoT devices being used, 79.4 Zb of data will be generated by 2025, according to research firm IDC. The earliest IoT gadgets sent information to the cloud for analysis. When sending many billions of gigabytes to the cloud, the data pipeline clogs up. With computing at the edge, IoT gadgets can analyze data without transferring it to a remote server. Data is handled locally, at the “edge” of your network, rather than being transmitted elsewhere. Technology's malleability makes it a valuable tool in the field of education. The specifications for edge computing in IoT networks vary from those of other networks. At the edge, well-suited setting to handle the massive volumes of devices and data generated by the Internet of Things. several tasks can be moved to the device's edge, which can help keep costs down. The importance of IoT edge computing architecture in education has grown as a result of its deployment in real-time applications. The research simplifies the process of analyzing the structural layout of educational systems by proposing a framework for Internet of Things (IoT) Edge computing in the field of education. There will be a focus on concerns and issues with the IoT that will be brought out by the framework. In this research, potential and pitfalls of using IoT edge computing in education institutions is explored. In addition to this, the research article analyzes the effectiveness of edge computing for Internet of Things applications in an educational environment.

关键词： Productivity Cloud computing Education computer architecture Real-time systems Internet of Things servers

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