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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Raymond Gyaang Ahmed Abdul-Rahman Dennis Agyemanh Nana Gookyi Sung-Joon Jang Sang-Seol Lee Department of Electrical and Electronics Engineering Bolgatanga Technical University Bolgatanga Ghana Department of Telecommunication Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana Institute for Scientific and Technological Information Council for Scientific and Industrial Research Accra Ghana Intelligent Image Processing Research Center Korea Electronics Technology Institute Seongnam-si Republic of Korea

This paper presents an accurate analysis and design of a Schiffman phase shifter with optimally positioned compensation capacitances for improved performances. The approach synthesized the microstrip coupler's even and odd mode characteristic impedance and its coupling effect to enhance the phase shift with minimal phase deviation. This was achieved by positioning artificial capacitances along the microstrip directional coupler. The new design method is valid for all substrate thicknesses and permittivity thereby overcoming the bandwidth limitation presented by the minimum spacing between the coupled lines in a conventional microstrip directional coupler. The simulation result demonstrated a maximum phase deviation of ± 3° and a maximum return loss of -0.613 dB. The source and load reflections were well below - 10 dB. The obtained dataset is highly accurate and can be used to train a deep neural network for improved phase error and return loss of a Schiffman phase shifter.

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Skin Cancer Classification using Machine Learning Techniques

Skin Cancer Classification using Machine Learning Techniques

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BIT Conference (BITCON), International

作者： I. Sapthami Ch. Srilakshmi Batta Saranya N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology VNR VJIET Bachupally Hyderabad India AIDS Department CBITGandipet Hyderabad India Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The death rate from skin cancer has radically grown due to a lack of understanding about the indications and prevention measures. It is one of the most lethal types of cancers, and the mortality rate due to skin cancer has risen sharply. Cancer must be diagnosed as early as possible to halt its growth and prevent metastasis. Even though there are numerous varieties of skin cancer, melanoma, basal cell carcinoma, and squamous cell carcinoma are considered the three most dangerous types of skin cancer. This research, using the CNN image-processing technology and machine learning, tries to detect and classify into the many kinds of skin cancer. Artificial intelligence (AI) based algorithms for automated skin cancer classification have consistently outperformed human professionals in several clinical studies, laying the groundwork for a future in which AI will be widely used.

关键词： Squamous cell carcinoma Prevention and mitigation Mortality Melanoma Machine learning Data augmentation Skin Classification algorithms Convolutional neural networks Skin cancer

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On the Secrecy Performance of Reconfigurable Intelligent Surfaces-Assisted Satellite Networks Under Shadow-Rician Channels

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6794-6808页

作者： Le, Si-Phu Nguyen, Tan N. Le-Tien, Thuong Duy, Tran Trung Nguyen, Tien-Tung Ng, Derrick Wing Kwan Tu, Lam-Thanh Voznak, Miroslav Ton Duc Thang University Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ho Chi Minh72900 Viet Nam VSB-Technical University of Ostrava Department of Telecommunications Faculty of Electrical Engineering and Computer Science Ostrava70800 Czech Republic University of Technology Vietnam National University Ho Chi Minh72900 Viet Nam Posts and Telecommunications Institute of Technology Faculty of Telecommunications 2 Ho Chi Minh72900 Viet Nam Industrial University of Ho Chi Minh City Faculty of Electronics Technology Ho Chi Minh70000 Viet Nam University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia

In this study, we investigate the secrecy performance of reconfigurable intelligent surfaces-assisted satellite (RISAS) networks. Specifically, we analytically derive closed-form expressions of the secrecy outage probability (SOP) and the tight lower bound secrecy rate (SR) exploiting the moment matching method (MMM). Furthermore, we explore the asymptotic behavior of the SOP as the satellite transmit power is sufficiently large, offering valuable insights into network performance. Numerical simulations are conducted to validate the established framework and elucidate the influence of critical parameters such as transmission distance, transmit power, and the number of reconfigurable intelligent surfaces (RIS) elements on SOP and SR. Our findings highlight the efficacy of increasing the number of phase shifters of the RIS to mitigate the adverse effects of fading, shadowing, and transmission environments. Finally, we unveil the adaptability of the proposed networks, illustrating their suitability for various satellite systems, including low earth orbit (LEO) and geostationary earth orbit (GEO). Among these, LEO offers superior performance due to its shorter transmission distance compared to medium earth orbit (MEO) and GEO. © 1965-2011 IEEE.

关键词： Satellite communication systems

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Low-Rank Reduced Biquaternion Tensor Ring Decomposition and Tensor Completion

arXiv

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arXiv 2025年

作者： Luo, Hui Liu, Xin Liu, Wei Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Artificial Intelligence Sun Yat-sen University The Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Mathematics University of Manitoba MB Canada

We define the reduced biquaternion tensor ring (RBTR) decomposition and provide a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completion algorithm RBTR-TV integrating RBTR ranks with total variation (TV) regularization to optimize the process. Numerical experiments on color image and video completion tasks indicate the advantages of our method. © 2025, CC BY.

关键词： Tensors

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Convolutional Neural Network for Pleural Effusion Classification

Convolutional Neural Network for Pleural Effusion Classifica...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Brunna C. R. S. Furriel Artur J. M. Paulo Guilherme A. S. Ribeiro Paulo V. Santos Eduardo P. Reis Giovanna S. Mendes Joselisa Q. Paiva Rafael M. Loureiro Letícia Rittner Márcio R. C. Reis Image Department Albert Einstein Israelite Hospital (HIAE) Sao Paulo Brazil Studies and Researches in Science and Technology Group (GCITE) Federal Institute of Goias (IFG) Goias Brazil Electrical and Computer Engineering Department University of Campinas (UNICAMP) Sao Paulo Brazil

This paper presents an approach for classifying pleural effusion using convolutional neural networks. The study utilizes a dataset of chest X-rays from the Chest X-Ray 14 database, comprising both normal and pleural effusion images. To enhance the model’s robustness and generalization, the Data Augmentation technique is employed for each class. The obtained results are highly promising. In Experiment I, the model achieves remarkable training and test success rates of 98.21% and 97.70%, respectively. In Experiment II, where an extensive data augmentation technique is applied, the model yields training, validation, and test rates of 78.66%, 87%, and 91%, respectively. These outcomes indicate the potential of the proposed classification model in facilitating the automated detection of pleural effusion and other lung diseases. This research makes a significant contribution to the advancement of computer-aided medical diagnosis, particularly by leveraging convolutional neural networks for chest X-ray image analysis.

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Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition

arXiv

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arXiv 2022年

作者： Luo, Cheng Song, Siyang Xie, Weicheng Shen, Linlin Gunes, Hatice Computer Vision Institute Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing China Department of Computer Science and Technology University of Cambridge United Kingdom

The activations of Facial Action Units (AUs) mutually influence one another. While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display. This paper proposes an AU relationship modelling approach that deep learns a unique graph to explicitly describe the relationship between each pair of AUs of the target facial display. Our approach first encodes each AU’s activation status and its association with other AUs into a node feature. Then, it learns a pair of multi-dimensional edge features to describe multiple task-specific relationship cues between each pair of AUs. During both node and edge feature learning, our approach also considers the influence of the unique facial display on AUs’ relationship by taking the full face representation as an input. Experimental results on BP4D and DISFA datasets show that both node and edge feature learning modules provide large performance improvements for CNN and transformer-based backbones, with our best systems achieving the state-of-the-art AU recognition results. Our approach not only has a strong capability in modelling relationship cues for AU recognition but also can be easily incorporated into various backbones. Our PyTorch code is made available at https://***/CVI-SZU/ME-GraphAU. Copyright © 2022, The Authors. All rights reserved.

关键词： Chemical activation

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Optimizing the Size of Peritumoral Region for Assessing Non-Small Cell Lung Cancer Heterogeneity Using Radiomics 12th

Optimizing the Size of Peritumoral Region for Assessing No...

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12th International Conference on Health Information science, HIS 2023

作者： Zhang, Xingping Zhang, Guijuan Qiu, Xingting Yin, Jiao Tan, Wenjun Yin, Xiaoxia Yang, Hong Wang, Kun Zhang, Yanchun Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China School of Computer Science and Technology Zhejiang Normal University Jinhua321000 China Department of Respiratory and Critical Care First Affiliated Hospital of Gannan Medical University Ganzhou341000 China Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne3011 Australia Department of New Networks Peng Cheng Laboratory Shenzhen518000 China Department of Radiology First Affiliated Hospital of Gannan Medical University Ganzhou341000 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110189 China

ISBN: (纸本)9789819971077

Objectives: Radiomics has a novel value in accurately and noninvasively characterizing non-small cell lung cancer (NSCLC), but the role of peritumoral features has not been discussed in depth. This work aims to systematically assess the additional value of peritumoral features by exploring the impact of peritumoral region size. Materials and methods: A total of 370 NSCLC patients who underwent preoperative contrast-enhanced CT scans between October 2017 and September 2021 were retrospectively analyzed. The study was carefully designed with a radiomics pipeline to predict lymphovascular invasion, pleural invasion, and T4 staging. To assess the impact of peritumoral features, tumor regions of interest (ROIs) annotated by two medical experts were automatically expanded to produce peritumoral ROIs of different regional sizes, with edge thicknesses of 1 mm, 3 mm, 5 mm, and 7 mm. In a custom pipeline, prediction models were constructed using peritumoral features with different margin thicknesses and intratumoral features of the primary tumor. Results: Radiomics features combining intratumoral and peritumoral regions were created based on the best features of each ROI. Models incorporating peritumoral features yielded varying degrees of improvement in AUCs compared to models using only intratumoral features. The choice of peritumoral size may impact the degree of improvement in radiomics analysis. Conclusions: The integration of peritumoral features has shown potential for improving the predictive value of radiomics. However, selecting an appropriate peritumoral region size is constrained by various factors such as clinical issues, imaging modalities, and ROI annotations. Therefore, future radiomics studies should consider these factors and optimize peritumoral features to cater to specific applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Tumors

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A Class of Fast and Accurate Multi-layer Block Summation and Dot Product Algorithms 18th

A Class of Fast and Accurate Multi-layer Block Summation a...

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18th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2021

作者： He, Kang Barrio, Roberto Chen, Lin Jiang, Hao Liu, Jie Gu, Tongxiang Qi, Jin Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Department of Applied Mathematics University of Zaragoza ZaragozaE50009 Spain College of Computer National University of Defense Technology Changsha410073 China Institute of Applied Physics and Computational Mathematics Beijing100000 China

ISBN: (纸本)9783030935702

Basic recursive summation and common dot product algorithm have a backward error bound that grows linearly with the vector dimension. Blanchard [1] proposed a class of fast and accurate summation and dot product algorithms respectively called FABsum and FABdot, which trades off the calculation accuracy and speed by the block size. Castaldo [2] proposed a multi-layer block summation and dot product algorithm called SuperBlocksum and SuperBlockdot that can increase the accuracy while adding almost no additional calculations. We combine the idea of [1] with the multi-layer block structure to propose SuperFABsum (for "super fast and accurate block summation") and SuperFABdot (for "super fast and accurate block dot product"). Our algorithms have two variants, one is SuperFAB(within), the other is SuperFAB(outside). Our algorithms further improve accuracy and speed compared with FAB and SuperBlock. We conducted accuracy and speed tests on the high-performance FT2000+ processor. Experimental results show that SuperFABdot(within) algorithm is more accurate than FABdot and SuperBlockdot. Compared with FABdot, SuperFABdot(outside) algorithm can achieve up to 1.2 × performance speedup while ensuring similar accuracy. © 2022, IFIP International Federation for Information processing.

关键词： Artificial intelligence

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IndianFood-7: Detecting Indian Food Items Using Deep Learning-Based computer Vision 1

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Agarwal, Ritu Bansal, Nikunj Choudhury, Tanupriya Sarkar, Tanmay Ahuja, Neelu Jyothi Uttarakhand Dehradun248007 India CSE Dept. Symbiosis Institute of Technology Symbiosis International University Maharashtra Lavale Mulshi Pune412115 India Uttarakhand Dehradun248007 India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Department of Food Processing Technology Government of West Bengal Malda Polytechnic Bengal State Council of Technical Education West Bengal Malda732102 India

ISBN: (数字)9789819959747

ISBN: (纸本)9789819959730

Indian Food Dishes are famous around the globe. Object detection in an image is a well-known task in computer vision. Indian Food Dishes detection using deep learning-based models will maximize the impact of computer vision-based models in the food domain. Deep learning-based model usage is still limited in recognizing and detecting Indian Food Items due to the lack of datasets on Indian Food. We introduce the IndianFood-7 Dataset, which contains images of more than 800 and having 1700 + annotations spreading beyond seven special Indian food items. We report the comparative study of numerous variants which are having current Avant-grade for object detection models, YOLOR and YOLOv5. Furthermore, we have inspected and evaluated the model performance by revising the predictions verified on the images of the test dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Object detection

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Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data of Using EM Algorithms and GMM Models

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data ...

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2022 Scholar's Yearly Symposium of Technology, Engineering and Mathematics, SYSTEM 2022

作者： Ponzi, Valerio Russo, Samuele Wajda, Agata Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Psychology Sapienza University of Rome Via dei Marsi 78 Roma00185 Italy Institute of Energy and Fuel Processing Technology Zabrze41-803 Poland Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Principal component analysis

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