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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Bhakar, Ruchika Singh, Ashuvendra Apoorrva Amudha, A. Gajendran, P. Kumar, Santosh K. R. Mangalam University Department of Computer Science & Engineering Gurugram India School of Civil Engineering Dev Bhoomi Uttarakhand University Uttarakhand India Invertis University Department of Computer Science & Engineering Uttar Pradesh Bareilly India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350304480

Due of its outstanding pro capability, composites insulation has been utilized extensively by electricity generation companies globally. Nevertheless, at the same power threshold, the electrical efficiency of entirely cold composites barriers is less than those of the ceramic and glassy insulators due to more condensed sheds designs. Recent investigations have demonstrated that shelter design affects the electronic properties of icy composites separators. Five distinct kinds of 00330-kV composites separators with varied numbers of huge enclosures were utilized to assess their V50 resist power capabilities in order to increase the electrical efficiency of compound separators during freezing situations. In Sichuan College's multipurpose temperature room, the trials were conducted. The test findings demonstrate that by including large shelters, the V50 resist power may be raised. garden shed separation needs to be sufficient to prevent frost arching in order to achieve the greatest performance. The test findings also demonstrate there is no assurance that the presence of corona rings under severe icing conditions would result in an improvement in the electrical efficiency of composite separators. When coronal rings are required, it is important to carefully plan both their size and the layout of the roof. © 2023 IEEE.

关键词： efficiency Electric Performance Insulation Material multipurpose temperature room V50

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A Computationally Effective SDP Solution for Target Localization Using DTD and DFS Measurements in Multistatic Systems With Unknown Signal Propagation Speed

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IEEE Sensors Journal 2025年第12期25卷 22264-22278页

作者： Qi, Qinke Li, Youming Wu, Yonghong Guo, Qiang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China China Research Institute of Radiowave Propagation Qingdao266107 China Harbin Engineering University College of Information and Communication Engineering Harbin150000 China

This paper investigates the moving target localization in multistatic systems for unknown signal propagation speed environments and presents a semi-definite programming (SDP) solution with reduced computational complexity by differential time delay (DTD) and differential frequency shift (DFS) measurements. Existing research on this problem defines numerous auxiliary variables to pseudo-linearize measurement models, proposing high requirement related to the number of transmitters/receivers for effective localization performance of the solution or causing the computationally demanding solution. To address the aforementioned shortcomings, the auxiliary variables introduced when constructing pseudo-linear equations from the measurement models are minimized as much as possible. After that, based on the weighted least squares (WLS) criterion, a constrained WLS formulation of reducing the number of constraints is proposed, as the constraints included are formulated according to the relationships among auxiliary variables. Considering the non-convexity of the formulation, semi-definite relaxation (SDR) is applied to transform the constrained WLS problem into its SDP form for the global optimum. Besides, the mean square error (MSE) and complexity analyses are conducted to validate the performance of the proposed method from two perspectives. Both analyses and simulation results demonstrate that the method has the ability to balance the localization accuracy and computational load. © 2001-2012 IEEE.

关键词： NP-hard

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AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification

AI inspired EEG-based spatial feature selection method using...

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作者： Asghar, Muhammad Adeel Khan, Muhammad Jamil Rizwan, Muhammad Shorfuzzaman, Mohammad Mehmood, Raja Majid Telecommunication Engineering Department University of Engineering and Technology Taxila Pakistan Computer Engineering Department University of Engineering and Technology Taxila Pakistan Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Information and Communication Technology Department School of Electrical and Computer Engineering Xiamen University Malaysia Sepang43900 Malaysia

Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition can play an important role in understanding a patient’s emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand emotions and can generate large feature vectors. In this article, we have proposed an efficient spatial feature extraction and feature selection method with a short processing time. The raw EEG signal is first divided into a smaller set of eigenmode functions called (IMF) using the empirical model-based decomposition proposed in our work, known as intensive multivariate empirical mode decomposition (iMEMD). The Spatio-temporal analysis is performed with Complex Continuous Wavelet Transform (CCWT) to collect all the information in the time and frequency domains. The multiple model extraction method uses three deep neural networks (DNNs) to extract features and dissect them together to have a combined feature vector. To overcome the computational curse, we propose a method of differential entropy and mutual information, which further reduces feature size by selecting high-quality features and pooling the k-means results to produce less dimensional qualitative feature vectors. The system seems complex, but once the network is trained with this model, real-time application testing and validation with good classification performance is fast. The proposed method for selecting attributes for benchmarking is validated with two publicly available data sets, SEED, and DEAP. This method is less expensive to calculate than more modern sentiment recognition methods, provides real-time sentiment analysis, and offers good classification accuracy. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Electroencephalography

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Pattern Transformation of Low-Profile Multi-Mode Antenna for A Phased Array with Simultaneous Wide-Angle Scanning and High-Gain Properties

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IEEE Transactions on Antennas and Propagation 2025年

作者： Yan, Qi-Long Liu, Neng-Wu Zhu, Lei Yun, Yu Liu, Ying Xidian University National Key Laboratory of Radar Detection and Sensing Xi'an710071 China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China Shaanxi University of Science & Technology School of electronic information and artificial intelligence Xi'an710021 China

In this article, a novel multi-mode design approach for pattern transformation of low-profile multi-mode microstrip patch antenna (MPA) has been proposed and developed, aiming to simultaneously achieve the wide-angle scanning and high-gain properties for a phased array. Herein, the TM00, TM01, TM20, and TM21 modes of the MPA are employed. Firstly, the working principle about how to move these four modes closely to each other in resonant frequency is deeply studied, the results prove that the shorting pins and slots could be loaded on the radiator for frequency reallocation of these four modes. Secondly, the radiation pattern transformation of the MPA at TM00, TM20, and TM21 modes are deeply investigated, which demonstrates that the non-broadside beam of its TM21 mode could be reshaped into the broadside one, and the omnidirectional pattern of its TM00 mode could be reshaped into the dual beam at yoz-plane. Most importantly, the radiation pattern of its TM20 mode could be rotated into the dual-beam with co-polarized Eθ component at yoz-plane. With these arrangements, the broadside-beam and dual-beam could be excited under same frequency at different modes. Thirdly, based on these two beams with different phases at two ports, the antenna element has successfully gained the improved beam scanning and gain performance simultaneously. Fourthly, the proposed multi-mode array is arranged in a triangular grid. By controlling the phase difference between these ports, the antenna could achieve both wide-beam scanning and high gain. Experimental results show that the antenna element has an impedance bandwidth (|S11| © 1963-2012 IEEE.

关键词： Antenna phased arrays

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Fuzzy Based Reliable Load Balanced Routing Approach for Ad hoc Sensor Networks

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computer Systems science & engineering 2022年第6期41卷 861-874页

作者： J.V.Anchitaalagammai Rajesh Verma M.Kavitha A.R.Revathi S.R.Preethi Kiranmai Bellam Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009TamilNaduIndia Department of Electrical Engineering King Khalid UniversityAbha62529Saudi Arabia Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112Tamil NaduIndia Department of Information Technology SRM Valliammai Engineering CollegeChennai603203Tamil NaduIndia Department of Electronics and Communication Engineering SRM Valliammai Engineering CollegeChennai603203Tamil NaduIndia Department of Computer Science Prairie View A&M UniversityPrairie ViewTXUnited State

Energy management and packet delivery rate are the important factors in ad hoc *** is the major network where nodes share the information without *** to the mobility of nodes,maximum energy is spent on transmission of *** energy is wasted on packet dropping and false route *** this research work,Fuzzy Based Reliable Load Balanced Routing Approach(RLRA)is proposed to provide high energy efficiency and more network lifetime using optimal multicast route discovery *** contains three *** first phase,optimal multicast route discovery is initiated to resolve the link *** second phase,the link quality is estimated and set to threshold value to meet the requirements of high energy *** third phase,energy model is shown to obtain total energy of network after transmission of packets.A multicast routing is established Based on path reliability and fault tolerant calculation is done and integrated with multicast *** routes can withstand the malicious *** decision model is integrated with propose protocol to decide the performance of network *** network simulation tool is used for evaluating the RLRA with existing schemes and performance of RLRA is good compared to others.

关键词： Fuzzy model link quality multicast route energy model cluster group node lifetime

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OntoBlock: a novel ontological-based and blockchain enabled spectrum sensing framework for detection of malicious users in cognitive radio internet of things (CR-IoT) networks

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International Journal of information technology (Singapore) 2024年第6期16卷 3913-3921页

作者： Marriwala, Nikhil Kumar Shukla, Vinod Kumar Raju, Ayalapogu Ratna Panda, Sunita S, Suma Purad, Hemavati C. Electronics and Communication Engineering Department University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India Information Technology Head of Academics–School of Engineering Architecture Interior Design Amity University Dubai Dubai International Academic City Dubai United Arab Emirates Department of Computer Science and Engineering Mahatma Gandhi Institute of Technology Hyderabad India Department of Electrical Electronics and Communication Engineering GITAM School of Technology Bengaluru Campus Karnataka Bengaluru India CMR Technical Campus Hyderabad India Department of AIML BITM Karnataka Ballari India

CR-IoT networks can benefit from the idea of blockchain technology to achieve accurate decisions in order to provide very high-accuracy spectrum sensing in the network. It allows for multi-user cooperation as the sensing readings are being exchanged in the spectrum. It leads to security issues in CR-IoT networks. Thus, in order to increase the quality of decision-making in spectrum sensing, the given paper introduces an ontological framework with a blockchain-enabled system to detect malicious users in networks. The proposed framework first captures network packets from available Wi-Fi, followed by the creation of blocks with private, public, and hash keys. The keys are used to encode information about the nodes, such as confidential information and their location, data relative to users, their position where packets are delivered, etc. This is subsequently followed by the generation of network ontology, which includes parsing, computation of similarity, and reduction in clustered packets. Finally, the performance of the suggested mechanism is validated using a set of evaluation measures such as authentication rate, intrusion rate, average network throughput time consumption, and energy efficiency. As seen from the findings, it is evident that the proposed framework performs better than existing recent studies, thereby improving the performance of CR-IoT networks. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blockchain Cognitive radio Internet of things (IoT) Ontology Spectrum sensing

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Monitoring Oceanographic and Cryosphere Changes: A Remote Sensing Approach to Climate-Induced Marine and Polar Dynamics

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 496-509页

作者： Shaik, Reddi Khasim Jeslet, D. Santhi Aroulanandam, Vijay Vasanth Siva, B.A. Painam, Ranjith Kumar Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India Department of Computer Science M.G.R. College Hosur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattakalathur India Department of ECE Chennai Institute of Tech Chennai India Department of Electronics and Communication Engineering Kallam Haranadhareddy Institute of Technology (Autonomous) Guntur Chowdavaram India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai-62 Avadi India

Climate change has accelerated the dynamics of cryospheric and oceanographic systems, leading to rising sea levels, increased ice melt, and significant shifts in polar regions. Monitoring these changes is crucial, yet traditional observational methods are often impractical in remote, harsh environments. This study presents a novel approach to monitoring oceanographic and cryospheric changes induced by climate dynamics, using a cascaded long short-term memory (LSTM) and recurrent neural network (RNN) model with ICESat and supplementary remote sensing data. The model leverages ICESat’s high-resolution laser altimetry for precise measurements of ice thickness and ice sheet elevation, combined with data from MODIS, Sentinel-1, and CryoSat-2 for comprehensive insights. Our results indicate that the cascaded LSTM-RNN model achieves high predictive accuracy, with root mean square error (RMSE) values as low as 0.08 m for ice thickness and a mean absolute error (MAE) of 0.05 m. Additionally, it attains a correlation coefficient of 0.96, significantly outperforming other models like standalone LSTM (RMSE 0.12 m, MAE 0.09 m) and CNN-LSTM hybrid (RMSE 0.10 m, MAE 0.08 m). This model captures both seasonal and long-term trends in ice dynamics, highlighting its utility for robust climate monitoring. The successful integration of ICESat and other remote sensing data demonstrates the potential for advanced machine learning techniques to enhance predictions of polar and oceanographic changes, supporting climate adaptation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change Cryosphere Ice thickness ICESat LSTM-RNN Machine learning Oceanographic changes Predictive modeling Remote sensing Sea ice extent

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DA 2 Net: a dual attention-aware network for robust crowd counting

DA 2 Net: a dual attention-aware network for robust crowd co...

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作者： Zhai, Wenzhe Li, Qilei Zhou, Ying Li, Xuesong Pan, Jinfeng Zou, Guofeng Gao, Mingliang School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Information Science and Engineering Shandong University Qingdao266237 China

Crowd counting in congested scenes is a crucial yet challenging task in video surveillance and urban security system. The performance of crowd counting has been greatly boosted with the rapid development of deep learning. However, robust crowd counting in high-density environment with scale variations remains under-explored. To address this problem, we propose a dual attention-aware network (DA 2Net) for robust crowd counting in dense crowd scene with scale variations. Specifically, the DA 2Net consists of two modules, namely Spatial Attention (SA) module and Channel Attention (CA) module. The SA module focuses on the spatial dependencies in the whole feature map to locate the heads accurately. The CA module attempts to handle the relations between channel maps and highlights the discriminative information in specific channels. Thus, it alleviates the mistaken estimation for background regions. The interactions between SA module and CA module provide the synergy which facilitates the learning of discriminative features with a focus on the essential head region. Experimental results on five benchmark datasets, i.e., ShanghaiTech, UCF_CC_50, UCF-QNRF, WorldExpo’10, and NWPU, demonstrate that the DA 2Net can achieve the state-of-the-art performance on both accuracy and robustness. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Security systems

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TOWARDS BIOMARKER DISCOVERY FOR EARLY CEREBRAL PALSY DETECTION: EVALUATING EXPLANATIONS THROUGH KINEMATIC PERTURBATIONS

arXiv

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arXiv 2025年

作者： Pellano, Kimji N. Strümke, Inga Groos, Daniel Adde, Lars Haugen, Pål Ihlen, Espen Alexander F. Department of Neuromedicine and Movement Science Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim7034 Norway Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7034 Norway Department of Clinical and Molecular Medicine Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim7034 Norway

Cerebral Palsy (CP) is a prevalent motor disability in children, for which early detection can significantly improve treatment outcomes. While skeleton-based Graph Convolutional Network (GCN) models have shown promise in automatically predicting CP risk from infant videos, their "black-box" nature raises concerns about clinical explainability. To address this, we introduce a perturbation framework tailored for infant movement features and use it to compare two explainable AI (XAI) methods: Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM). First, we identify significant and non-significant body keypoints in very low and very high risk infant video snippets based on the XAI attribution scores. We then conduct targeted velocity and angular perturbations, both individually and in combination, on these keypoints to assess how the GCN model’s risk predictions change. Our results indicate that velocity-driven features of the arms, hips, and legs have a dominant influence on CP risk predictions, while angular perturbations have a more modest impact. Furthermore, CAM and Grad-CAM show partial convergence in their explanations for both low and high CP risk groups. Our findings demonstrate the use of XAI-driven movement analysis for early CP prediction, and offer insights into potential movement-based biomarker discovery that warrant further clinical validation. © 2025, CC BY.

关键词： Risk assessment

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