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Novel Design of UWB Jeans Based Textile Antenna for Body-Centric Communications

computer Systems science & engineering 2022年第9期42卷 1079-1093页

作者： Mohammad Monirujjaman Khan Bright Yeboah-Akowuah Kaisarul Islam Eric Tutu Tchao Sumanta Bhattacharyya Rajesh Dey Mehedi Masud Fahad Alraddady Department of Electrical and Computer Engineering North South UniversityDhaka1229Bangladesh Department of Computer Engineering Kwame Nkrumah University of Science and TechnologyKumasiGhana Electronics and Communication Engineering Cambridge Institute of TechnologyRanchiIndia Department of Electronics and Communication Engineering Brainware Group of Institutions-SDETKolkataIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

This research presents an ultra-wideband (UWB) textile antenna designfor body-centric applications. The antenna is printed on a 1 mm thick denim substrate with a 1.7 relative permittivity. The jeans substrate is sandwiched between apartial ground plane and a radiating patch with a Q-shaped slot. The slotted radiating patch is placed above the substrate and measures 27.8 mm × 23.8 mm. In freespace, the antenna covers the ultra-wideband spectrum designated by the FederalCommunication Commission (FCC). Various parameters of the antenna designwere changed for further performance evaluation. Depending on the operating frequency, the antenna's realized gain varied from 2.7 to 5 dB. The antenna achievedhigh radiation efficiency with an omnidirectional radiation pattern. A parametricstudy was performed in research on varying antenna substrates and other components of the antenna. The three outermost layers of the human body are used tomodel a human phantom for on-body simulation. After that, the antenna wasplaced at five different distances from the phantom. The findings demonstrate thatat close distances to the phantom, the antenna's gain and efficiency at lower frequencies are reduced. The antenna's radiation efficiency and gain were muchhigher at higher frequencies for distances greater than 6 mm. Compared to freespace, the antenna's radiation pattern was more omnidirectional, especially athigher frequencies. This antenna is novel, compact and has an ultra wide bandwidth, a maximum of 94.60% radiation efficiency and a 5 dBi gain that will makeit a good candidate for body-centric communications.

关键词： Ultra-wideband body-centric communication on-body textile jeans slotted-patch WBAN

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DICNet: A Novel CNN Model based on DenseNet with Interleaved Convolutional Block Attention Module for the Classification of Breast Cancer Histopathology Images

DICNet: A Novel CNN Model based on DenseNet with Interleaved...

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2024 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2024

作者： Kumar, Arvind Singh, Chandan Sachan, Manoj Kumar Sant longowal Institute of Engineering and Technology Department of Computer Science and Engineering Sangrur India Punjabi University Department of Computer Science Patiala India

ISBN: (纸本)9798350364040

Histopathology image analysis is crucial for the accurate diagnosis of breast cancer (BC), which is the most prevalent cancer among women. The prompt secondary opinions based on automated analysis can assist pathologists, reduce workload, and minimize interobserver variability. This paper introduces the DICNet, a novel convolutional neural network (CNN) architecture based on DenseNet with interleaved convolutional block attention module (CBAM) to classify the BC histopathology images. Specifically, the interleaved attention mechanism utilizes inter-channel and inter-spatial relationships among convolutional features to accentuate more informative and discriminative features. Furthermore, the pooled responses from multiple convolutional layers with varying spatial resolutions are concatenated to obtain a multi-scale representation. The classification performance of the proposed model is evaluated on the publicly available benchmark BreaKHis dataset with four magnification levels. The proposed DICNet model achieves an overall best classification accuracy of 94.02 % at the 40× magnification level. Additionally, DICNet demonstrates enhanced classification performance at every magnification level, and significantly outperforms existing state-of-the-art approaches. © 2024 IEEE.

关键词： Image classification

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Enhancing Image Segmentation in the Marine Atmosphere for Time-Sensitive Contextualisation using Convolutional Long Short-Term Memory 2

Enhancing Image Segmentation in the Marine Atmosphere for Ti...

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2nd International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2024

作者： Reethika, A. Rajeshwari, R. Vijayalakshmi, S. Malathy, S. Gnanasaravanan, S. Suresh Kumar, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Information Technolog KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Information Technology Sri Ranganathar Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

The work investigates autonomous surface vehicle obstacle avoidance and detection using computer vision techniques. It recognizes boundaries in marine environments by using the temporal context from video feeds from unmanned surface vehicles. The study shows how successful LSTM cells are in the CNN topologies that are currently in use, emphasizing how they might perform better. Nevertheless, datasets and network training techniques are not perfect. The work also uses a benchmarking approach to compare new network configurations. © 2024 IEEE.

关键词： Image segmentation

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WSETO: wild stock exchange trading optimization algorithm enabled routing for NB-IoT tracking system

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International Journal of information technology (Singapore) 2024年第8期16卷 4895-4905页

作者： Sivadevuni, Sreeparnesh Sharma Naveen, J. Department of Computer Science and Engineering School of Engineering and Technology Christ University Karnataka Bangalore 560001 India

The Narrowband Internet of Things (NB-IoT) communication plays a significant role in the IoT due to the capability of generating broad exploration with the usage of limited power. Over the past few years, the Low Power Wide Area Networks (LPWAN) have been efficient in the data acquisition and remote monitoring area however they failed to generate high data rates, low latency, and the consumption of low power. To solve these problems, NB-IoT technology has developed in long-term asset tracking and it replaces the Global Positioning System (GPS) with its ubiquitous coverage. In this research, the Wild Stock Exchange Trading Optimization technique (WSETO) is proposed for a routing-based NB-IoT tracking system. The WSETO is the combination of the Wild Geese Algorithm (WGA) and SETO. By employing WSETO, the routing to the relevant target location is established effectively. The existing techniques like Low Power Asset Tracking of NB-IoT (LoPATraN), Monitoring system based on NB-IoT and BeiDou System/GPS (BDS/GPS), and Narrowband Physical Uplink Shared Channel (NPUSCH) are used to compare the WSETO approach. In rounds with a value of 2000, the WSETO demonstrates a superior location error of 0.001 in comparison to existing methods such as LoPATraN, a monitoring system utilizing NB-IoT and BDS/GPS, as well as NPUSCH. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Global positioning system Low power wide area networks Narrowband internet of things Stock exchange trading optimization Wild geese algorithm

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Spatial data intelligence and city metaverse: A review

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Fundamental Research 2025年第3期5卷 1169-1193页

作者： Meng, Xiaofeng Li, Yong Liu, Ke Liu, Yu Yang, Bin Song, Xuan Liao, Guoqiong Wang, Senzhang Yu, Ziqiang Chen, Longbiao Pan, Xiao Lin, Yuming School of Information Renmin University of China Beijing 100872 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Information Sciences National Natural Science Foundation of China Beijing 100085 China Institute of Remote Sensing and Geographic Information Systems School of Earth and Space Sciences Peking University Beijing 100871 China School of Data Science and Engineering East China Normal University Shanghai 200062 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China School of Information Management Jiangxi University of Finance and Economics Nanchang 330013 China School of Computer Science and Engineering Central South University Changsha 410017 China School of Computer and Control Engineering Yantai University Yantai 264005 China Department of Computer Science Xiamen University Xiamen 361000 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang 050043 China

Spatial Data Intelligence (SDI) encompasses acquiring, storing, analyzing, mining, and visualizing spatial data to gain insights into the physical world and uncover valuable knowledge. These understandings and knowledge play a crucial role in connecting physical and virtual realms, such as in developing a City Metaverse (CM) aimed at enhancing and optimizing modern urban environments. The advancement of CM holds immense potential to benefit urban dwellers, making research on SDI an increasingly prominent area of focus. This paper contributes significantly by organizing the relevant research and technologies within a coherent framework. Firstly, we identify SDI technologies capable of collecting real-world information to construct a virtual CM. Subsequently, we delve into the technologies that can be compositely integrated with SDI to facilitate interaction with and management of actual cities from the virtual perspective. Additionally, we emphasize the effectiveness and potential of these methods in practical applications. Lastly, we conclude our survey by discussing emerging challenges associated with technological progress, the industrial chain, legal and regulatory aspects, and ethical and moral considerations. © 2024

关键词： Application prospect City metaverse Smart city Spatial data intelligence Virtual-real interaction

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Deep-Based Hybrid Model For Enhancing Land Region Prediction Outcomes 2

Deep-Based Hybrid Model For Enhancing Land Region Prediction...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Selvapandian, D. Raja, Laxmi Tharageswari, K. Vm, Senthilkumar Jc, Nikitha Hemalatha, M. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Faculty of Engineering Department of Cyber Security Coimbatore India Mahendra Institute of Technology Department of Information Technology Namakkal India Nandha Arts and Science College Department of Information Technology Erode India

ISBN: (纸本)9798331534950

Although satellite data is not a novel approach to mapping wetlands, its accuracy depends on data quality and categorization algorithms, making it a continuous challenge. The increasing accessibility of satellite data and the development of data modelling technologies, such as big data modelling using machine learning (ML) algorithms, have improved the accuracy of this type of mapping. Our work demonstrated a simple, scalable, and reliable wetland classification using linear Support Vector Machine (l-SVM) and unsupervised k-Nearest Neighbor (k-NN) machine learning approaches. Normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), the two primary inputs were modelled using Landsat optical data. Subsequently, l-SVM was used to supervise k-NN data using training data collected from outside observations. The accuracy test results indicate that there is a considerable reduction in error for both optical indices, and all prediction models have an accuracy of about 99%. © 2024 IEEE.

关键词： Data accuracy

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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

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Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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Envisioning Educational Success Through Advanced Analytics and Intelligent Performance Prediction 10

Envisioning Educational Success Through Advanced Analytics a...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Sureshkumar, T. Charanya, J. Kumaresan, T. Rajeshkumar, G. Kavin Kumar, P. Anuj, B. Nehru Institute of Technology Department of Information Technology Coimbatore641 105 India Department of Computer Science and Engineering Erode638 401 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam638 401 India Bannari Amman Institute of Technology Dept of Cse Sathyamangalam India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India

ISBN: (纸本)9798350353068

The accurate prediction of students academic success holds significance in enhancing educational outcomes. The study focuses on overcoming the challenge of assessing whether a student will successfully pass the final exam, taking into account a range of socio-economic and academic factors. Every student's journey is different, and it is influenced by various factors such as their socioeconomic background, academic abilities, and personal experiences. To evaluate their performance, we use various machine learning algorithms such as KNN, Naive Bayes, SVM, AdaBoost, and Naive Bayes - and assess their performance using key metrics such as ROC curve, confusion matrix, precision, recall, and F1 score. The F1 score offers a balanced assessment of precision and recall, offering insights into overall model accuracy. Precision and recall focus specifically on the model's ability to correctly identify positive instances and capture all actual positive instances, respectively. ROC curves demonstrate the balance between the rates of correctly identifying true positives and incorrectly identifying false positives, allowing for a nuanced assessment of classifier performance. The confusion matrix further breaks down true positives, true negatives, false positives, and false negatives, enhancing our understanding of algorithmic effectiveness. Through a comprehensive comparison of these classification methods, our goal is to identify the most accurate approach for predicting student outcomes, thus contributing valuable insights to educational institutions and policymakers aiming to implement effective strategies for student success and academic improvement. © 2024 IEEE.

关键词： Students

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Food Recognition and Calorie Measurement Using Machine Learning 1

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Vasudha, Maganti Rashmi, D. Mahalakshmi Jain, B.A. Department of Information Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

This study is one of many that investigate the relationship between determining the nutritional ingredients in food and calculating the calories using data analysis utilizing machine learning techniques. Due to the availability of multifood photos, which must be cropped before processing, the Indian food recipes database is used for the research. The study uses a large dataset of various food photos to train state-of-the-art deep convolutional neural networks (CNNs) to recognize and categorize distinct food items with an amazing 99.89% accuracy. This study’s applicability spans several sectors in addition to food recognition, including calorie measurement, meal planning services, and nutritional monitoring systems. The solution is widely available to a wide range of users thanks to a user-friendly web interface. The system’s 99.89% accuracy in food detection and calorie measurement demonstrates its dependability and distinguishes it from competing options. Its ability to improve individual health, fight obesity, and encourage healthy eating habits makes it a vital tool in today’s health-conscious culture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Large datasets

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Pseudo-label Data Construction Method and Syntax-enhanced Model for Chinese Semantic Error Recognition 31

Pseudo-label Data Construction Method and Syntax-enhanced Mo...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wu, Hongyan Lin, Nankai Jiang, Shengyi Wang, Lianxi Yang, Aimin College of Computer National University of Defense Technology China School of Information Science and Technology Guangdong University of Foreign Studies China School of Information Technology and Engineering Guangzhou College of Commerce China School of Computer Science and Technology Guangdong University of Technology China School of Computer Science and Intelligence Education Lingnan Normal University China

ISBN: (纸本)9798891761964

Chinese Semantic Error Recognition (CSER) has always been a weak link in Chinese language processing due to the complexity and obscureness of Chinese semantics. Existing research has gradually focused on leveraging pre-trained models to perform CSER. Although some researchers have attempted to integrate syntax information into the pre-trained language model, it requires training the models from scratch, which is time-consuming and laborious. Furthermore, despite the existence of datasets for CSER, the constrained size of these datasets impairs the performance of the models. Thus, in order to address the difficulty posed by a limited sample set and the need of annotating samples with semantic-level errors, we propose a Pseudo-label Data Construction method for CSER (PDC-CSER), generating pseudo-labels for augmented samples based on perplexity and model respectively, which overcomes the difficulty of constructing pseudo-label data containing semantic-level errors and ensures the quality of pseudo-labels. Moreover, we propose a CSER method with the Dependency Syntactic Attention mechanism (CSER-DSA) to explicitly infuse dependency syntactic information only in the fine-tuning stage, achieving robust performance, and simultaneously reducing substantial computing power and time cost. Results demonstrate that the pseudo-label technology PDC-CSER and the semantic error recognition method CSER-DSA surpass the existing models. © 2025 Association for Computational Linguistics.

关键词： Syntactics

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