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Fundamental Limits of Two-Hop MIMO Channels: An Asymptotic Approach

IEEE Transactions on Information Theory 2025年第6期71卷 4069-4111页

作者： Zhuang, Zeyan Zhang, Xin Xu, Dongfang Song, Shenghui The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong The Hong Kong University of Science and Technology Division of Integrative Systems and Design Hong Kong

Multi-antenna relays and intelligent reflecting surfaces (IRSs) have been utilized to construct favorable channels to improve the performance of wireless systems. A common feature between relay systems and IRS-aided systems is the two-hop multiple-input multiple-output (MIMO) channel. As a result, the mutual information (MI) of two-hop MIMO channels has been widely investigated with very engaging results. However, a rigorous investigation on the fundamental limits of two-hop MIMO channels, i.e., the first and second-order analysis, is not yet available in the literature, due to the difficulties caused by the two-hop (product) channel and the noise introduced by the relay (active IRS). In this paper, we employ large random matrix theory, specifically Gaussian tools, to derive the closed-form deterministic approximation for the mean and variance of the MI. Additionally, we determine the convergence rate for the mean, variance and the characteristic function of the MI, and prove the asymptotic Gaussianity. Furthermore, we also investigate the analytical properties of the fundamental equations that describe the closed-form approximation and prove the existence and uniqueness of the solution. An iterative algorithm is then proposed to obtain the solutions for the fundamental equations. Numerical results validate the accuracy of the theoretical analysis. © 1963-2012 IEEE.

关键词： Iterative methods

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A Comparative Study on Deep Learning based Segmentation Techniques for Tamil Inscriptions 8

A Comparative Study on Deep Learning based Segmentation Tech...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Poornimathi, K. Muralibhaskaran, V. Priya, L. Rajalakshmi Engineering College Department of Computer Science and Business Systems India Rajalakshmi Engineering College Department of Computer Science and Engineering India Rajalakshmi Engineering College Department of Information Technology India

ISBN: (纸本)9798350376425

The digitization and preservation of Tamil inscriptions are crucial for safeguarding the rich cultural heritage they represent. This study presents an in-depth evaluation of deep learning-based segmentation methods specifically designed for the complex and ancient Tamil script. Evaluate several cutting-edge deep learning models, including Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, and attention-based mechanisms, using a dataset of Tamil inscriptions. The evaluation criteria encompass segmentation accuracy, computational efficiency, and the ability to handle the noise and degradation common in historical artifacts. Our findings indicate that traditional methods often falter when faced with the complexities of Tamil script, whereas advanced deep learning techniques markedly enhance segmentation quality. This improvement facilitates more precise transcription and analysis. The comparison emphasizes the benefits and challenges of each method, delivering important insights into their effectiveness for cultural heritage preservation and paving the way for future research in the digitization of ancient scripts. © 2024 IEEE.

关键词： Convolutional neural networks

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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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Countering Deepfakes using an Improved Advanced CNN and its Ensemble with Pretrained Models 3

Countering Deepfakes using an Improved Advanced CNN and its ...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Mathur, Abeer Tejpal, Moulik Bhargava, Kshitiz Natarajan, Krishnaraj Singh, Manvendra Vellore Institute of Technology School of Computer Science & Engineering Department of Software Systems Vellore India Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India

ISBN: (纸本)9798350369083

The extensive spread of DeepFake images on the internet has emerged as a significant challenge, with applications ranging from harmless entertainment to harmful acts like blackmail, misinformation, and spreading false propaganda. To tackle this issue, this paper introduces a sophisticated DeepFake detection model designed to identify and mitigate the increase of these deceptive images. The model architecture integrates an ensemble approach, combining the strengths of two pre-trained Convolutional Neural Network (CNN) models - MobileNet and Xception - with a novel CNN architecture, the Advanced CNN (ACNN). This rigorous validation process enabled the model to achieve a high accuracy rate of 97.89% in detecting DeepFakes. The successful implementation of this ensemble CNN approach demonstrates its effectiveness in distinguishing between real and fabricated imagery with high precision. This research makes a substantial contribution to the field of digital image forensics, offering a reliable tool for stakeholders across various sectors to identify and counteract the spread of DeepFake images online. © 2024 IEEE.

关键词： Generative adversarial networks

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GHA: a Gated Hierarchical Attention Mechanism for the Detection of Abusive Language in Social Media

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IEEE Transactions on Affective Computing 2024年第02期16卷 946-959页

作者： Jarquin-Vasquez, Horacio Escalante, Hugo Jair Montes-Y-Gomez, Manuel Gonzalez, Fabio A. Department of Computer Science Instituto Nacional de Astrofisica Óptica y Electrónica Puebla Mexico Department of Computer Systems and Industrial Engineering Universidad Nacional de Colombia Bogotá Colombia

The use of attention mechanisms in deep learning solutions has become popular within natural language processing tasks. The use of these mechanisms allows managing the relevance of the elements of a sequence in accordance with their context, however, this relevance has been observed independently between the pairs of elements of a sequence (self-attention) or between the application domain of a sequence (contextual attention), leading to the loss of relevant information and limiting the representation of the sequences. To tackle these particular issues, we propose a dual attention mechanism, which trades off the previous limitations, by considering the internal and contextual relationships between the elements of the sequence. Additionally, we propose the extension of the dual attention mechanism into a multi-layer perspective, through the weighted fusion of the different encoding layers of deep architectures. As the interpretation of abusive language is highly contextdependent, its identification is an ideal task to evaluate the proposed attention mechanism. Accordingly, we considered six standard collections for the abusive language identification task. The obtained results are encouraging;the proposed hierarchical attention mechanism outperformed the current self-attention and contextual attention mechanisms coupled with recurrent neural networks and Transformers, as well as, state-of-the-art approaches in detecting abusive language. © 2010-2012 IEEE.

关键词： Recurrent neural networks

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Tech-Enabled Altruism: Harnessing Technology in Social Media for Food Waste Reduction 3

Tech-Enabled Altruism: Harnessing Technology in Social Media...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sujatha, E. Jayanthi, M. Sandeepa, N. Raju, D. Naveen Nagaraj, S. Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Business Systems Chennai India R. M. K. Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518592

Altruistic Connections is a pioneering social media platform that amalgamates the functionalities of popular networking applications with a unique emphasis on fostering altruistic endeavors. Drawing inspiration from established platforms like Instagram, this project introduces a distinct dimension by enabling users not only to share their stories and experiences but also to engage in philanthropic activities seamlessly. The platform facilitates direct communication through messaging, creating a communitydriven space where users can connect over shared values and altruistic initiatives. Users can post, like, save, and comment on content while also exploring and participating in a marketplace specifically designed for charitablecontributions. The technical architecture of 'Altruistic Connections' ensures a secure and user-friendly experience. Robust messaging features, seamless posting mechanisms, and an integrated altruistic marketplace form the core of the platform. Emphasis is placed on privacy and data security, aligning with the trust users place in the community. Strategies for user acquisition involve collaborations with philanthropic influencers, engagement with altruistic communities, and partnerships to expand the platform's reach. The project's financial sustainability is achieved through potential collaborations with philanthropic organizations and businesses that align with the spirit of altruism. Addressing potential challenges related to the verification of charitable activities, transaction security, and content moderation, the platform implements rigorous measures to create a trustworthy and fulfilling environment. The abstract concludes by emphasizing the commitment of 'Altruistic Connections' to redefine social media engagement through technology, altruism, and the power of community. An altruistic marketplace forms the core of the platform. Emphasis is placed on privacy and data security, aligning with the trust users place in the community. © 2024

关键词： Marketplaces

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Traffic and Pollution Control Using IoT-Enabled Smart Routing Algorithm 2

Traffic and Pollution Control Using IoT-Enabled Smart Routin...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Hammed, S Shahul Preethi, C. Haripriya, K. Pavalarajan, S. Gowtham, M.R. Krithick, N. Akshaay PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798331505745

Urbanization has led to increased traffic congestion and air pollution, primarily from vehicle emissions, posing risks to public health and the environment. Existing traffic management systems are inefficient in integrating real-time pollution data, leading to reactive control measures. This project proposes an IoT-based smart traffic management system that integrates real-time air quality monitoring with dynamic traffic control. IoT sensors collect pollution and vehicle data, which is processed using cloud computing and machine learning to optimize traffic flow and reduce emissions. AI techniques provide transparency, helping city authorities understand and trust the system's decisions. The system also offers predictive analytics for proactive pollution management and a user-friendly dashboard for real-time visualization. The solution aims to reduce urban emissions, and enhance traffic efficiency, offering a sustainable approach for modern cities. © 2025 IEEE.

关键词： Traffic congestion

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Suicide risk assessment using word-level model with dictionary-based risky posts selection

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Multimedia Tools and Applications 2024年第7期83卷 21435-21454页

作者： Tsai, Yun Sheng Chen, Arbee L. P. Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

Suicide is a serious issue around the world and is a leading cause of death in US. In the past 20 years, the suicide rate has seen a significant increase of 35%. With the rapid development of information technology, more and more people begin to use social media to share their inner feelings. It enables social media data to be widely used for research on suicide risk assessment. However, not all social media posts are suicide related. Previous research addressed this problem with post-level attention mechanism. However, post-level attention mechanism may not find relevant suicide posts. This problem becomes more serious in the feature-based post embeddings since each post is converted into a single vector to serve as the input of the model, resulting in the loss of word-level information during training. In this paper, we addressed this problem by introducing a novel word-level model including a post-selectin layer as a solution. Firstly, we utilize a suicide keyword dictionary to identify risky posts that may be missed by the post-level attention mechanism. We then convert the words in the risky posts into word embeddings and use self-attention to generate the post embeddings for the risky posts. Finally, we pass the post embeddings to a multilayer perceptron to classify the suicide risk. We also demonstrate that the FScore used in previous studies can be reduced to a function of accuracy, which does not reflect the model performance in predicting imbalanced datasets. Therefore, we additionally adopt macro F1 score as the evaluation function. Experiment results show that our model not only outperforms previous studies in FScore performance, but also achieves macro F1 Score a nearly 4% improvement compared to previous studies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Embeddings

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Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

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computers, Materials & Continua 2023年第1期74卷 1819-1835页

作者： Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil Radzi Ambar Ahmed Abdu Alattab Anwar Ali Yahya Yousef Asiri Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit RajaBatu Pahat86400JohorMalaysia Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran61441Saudi Arabia Department of Computer Science Faculty of Computer Science and Info.SystemsThamar UniversityDhamar87246Yemen Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or ***,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different ***,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and *** was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable *** experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,*** model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification ***,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

关键词： Cardiac short-axis MRI images automatic left ventricle localization deep learning models faster R-CNN

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IoT based Leakage Detection and Emergency Shutdown 6

IoT based Leakage Detection and Emergency Shutdown

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Abarna, P.S. Priya, S. Padma Pavalarajan, S. Sivatharani, S. Manshi, M. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331522667

The risk of gas leaks has grown significantly as a life threatening issue in industrial activities, cooking, and heating. This system integrates automatic reaction mechanisms, real-time monitoring capabilities, and advanced detection technologies in a multifaceted manner to address various dangers. The comprehensive gas leak detection and automatic cut off system is intended to improve safety in a variety of situations, including industrial and residential ones. To precisely identify the existence of dangerous gases like methane and propane, the proposed system combines infrared, electrochemical, and semiconductor types of gas sensors integrated with a mobile application, users may remotely administer and monitor the system in real-time. The system initiates a multi-tiered response upon detecting a gas leak;it sounds an alarm, notifies user-specified The mobile devices about the leak, and turns on an exhaust fan to release concentrated gas. A solenoid valve is used by the system to quickly shut off the gas supply and electricity from any location. The need of user-friendly controls and prompt notifications in emergency situations is highlighted in this paper's discussion of software architecture, sensor integration, and user interface design. Experimental results show that the system is reliable and capable of responding quickly under different circumstances. In future the application of machine learning algorithms for predictive maintenance and the possibility of integrating IoT to further strengthen security protocols. The results emphasize importance to deploy these kinds of extensive safety systems widely in order to reduce the risks associated with gas exposure. © 2025 IEEE.

关键词： Solenoid valves

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