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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 Hong Kong The Hong Kong University of Science and Technology Division of Integrative Systems and Design Hong Kong 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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DETECTION OF PULMONARY EMBOLISM WITH IMPROVED XCEPTION ARCHITECTURE 10

DETECTION OF PULMONARY EMBOLISM WITH IMPROVED XCEPTION ARCHI...

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10th International Conference on AI and the Digital Economy, CADE 2024

作者： Paulraj, Getzi Jeba Leelipushpam Princess, P. Joyce Beryl Jebadurai, Immanuel Johnraja Celshia, S. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences India

ISBN: (纸本)9781837241880

The blocking of arteries in the lungs through blood clots is known as pulmonary embolism. This blood vessel block usually originates in the legs and travels to the lungs causing fatal damage. An imaging test called Computed Tomography (CT) pulmonary angiography scans the lungs for blood clots. The preferred method for identifying pulmonary embolism is CT pulmonary angiography. Manual examination by radiologists increases the workload, consumes more time and also requires experience for correct examination. The objective of this paper is to propose an improved Xception architecture based on Convolutional Neural Networks. The existing architecture is improvised by replacing the Relu activation function with the Swish activation function. This improves the feature extraction of the algorithm thereby improving efficiency in terms of loss and accuracy. The algorithm has been trained and tested using the standard RSNA-STR Pulmonary Embolism CT Dataset. It is evident from the experimental results that our model achieves an improvement of 3.4% and 1.4% in terms of accuracy and loss respectively. © The Institution of engineering & technology 2024.

关键词： Blood vessels

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Nemeth Braille Mathematical Code Recognition using Deep Learning Techniques

Nemeth Braille Mathematical Code Recognition using Deep Lear...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Malar, P. Jeba Raimond, Kumudha Kanmani, S. Deepa Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Sri Krishna College Of Engineering And Technology Department Of Information Technology Coimbatore India

ISBN: (纸本)9798350371567

The Nemeth Braille Mathematical Code (NBMC) is an essential resource for individuals with visual impairments to comprehend scientific and mathematical symbols. It enables them to understand intricate mathematical equations, symbols, and declarations. Still, there are many challenges in accurately identifying NBMC. This makes it more difficult for those with vision problems to learn information and be self-driven. In this work, a deep learning (DL) based approach is proposed for recognizing NBMC. Comparisons are made between different deep learning models such as convolutional neural network (CNN), InceptionV3, DenseNet201, CNN combined with ResNet50, ResNet50, and VGG16;where CNN combined with ResNet50 model provides the best accuracy among them with a value of 91.20%. Then by using the layers of CNN model combined with ResNet50 works as backend for the web application. The NBMC are then translated into text-to-speech by the web application, to make it easily accessible to visually challenged people. © 2024 IEEE.

关键词： Convolutional neural networks

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Bayes and Biased Estimators Without Hyper-Parameter Estimation: Comparable Performance to the Empirical-Bayes-Based Regularized Estimator

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IEEE Control Systems Letters 2025年 9卷 294-299页

作者： Ju, Yue Wahlberg, Bo Hjalmarsson, Håkan KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm 10044 Sweden

Regularized system identification has become a significant complement to more classical system identification. It has been numerically shown that kernel-based regularized estimators often perform better than the maximum likelihood estimator in terms of minimizing mean squared error (MSE). However, regularized estimators often require hyper-parameter estimation. This paper focuses on ridge regression and the regularized estimator by employing the empirical Bayes hyper-parameter estimator. We utilize the excess MSE to quantify the MSE difference between the empirical-Bayes-based regularized estimator and the maximum likelihood estimator for large sample sizes. We then exploit the excess MSE expressions to develop both a family of generalized Bayes estimators and a family of closed-form biased estimators. They have the same excess MSE as the empirical-Bayes-based regularized estimator but eliminate the need for hyper-parameter estimation. Moreover, we conduct numerical simulations to show that the performance of these new estimators is comparable to the empirical-Bayes-based regularized estimator, while computationally, they are more efficient. © 2017 IEEE.

关键词： asymptotic theory Bayes estimator biased estimator empirical Bayes hyper-parameter estimator ridge regression

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An Optimal Real-Time Routing and Clustering Protocol for UAV Networks Based on Security 4

An Optimal Real-Time Routing and Clustering Protocol for UAV...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sophia, S. Leelipushpam Paulraj, Getzi Jeba Yuvarajan, D. Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Unmanned Aerial Vehicle (UAV) networks play an important role in different application areas such as military surveillance, emergency services, and infrastructure. However, these networks face significant challenges such as data leakage, unauthorized access, limited processing capacity, and energy constraints. Available techniques such as Elliptic Curve Cryptography (ECC), hash-based techniques, and dynamic clustering algorithms have been proposed to enhance security and operational performance;however, they often encounter challenges concerning scalability, adaptability to real-time changes within the network, and resource-related constraints. This research presents a streamlined authentication framework with the intent to facilitate secure and effective functionalities in turbulent and fluctuating settings. The proposed protocol uses Dijkstra's algorithm for optimal real-time routing, Adaptive Optimization Clustering (AOC) for dynamic resource distribution, and K-means clustering for noise classification. The proposed methodology achieves substantial improvements in power efficiency, latency, and Packet Delivery Rate (PDR) due to reduced computational overhead as well as risks such as data interception and replay attacks. Simulation results conclude that the system suggested here would be secure, scalable, and energy-efficient to be used for applications in demanding scenarios, such as mission-critical scenarios. © 2025 IEEE.

关键词： Emergency services

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SCADBIN: A Blockchain-based Security Framework for Electronic Health Record Management 14

SCADBIN: A Blockchain-based Security Framework for Electroni...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Fasila, K.A. Mathew, Sheena Division of Computer Engineering School of Engineering Cochin University of Science and Technology Kerala India Computer Science and Engineering Muthoot Institute of Technology and Science Kerala India

Tremendous increase in the use of distributed Internet of Things (IoT) based applications leads to the evolution of huge amount of data every second. As the volume of data and correspondingly the number of users increase, the need for protecting information also increases. Among various distributed IoT applications, healthcare industry is in the dominating position. The management of Electronic Health Record (EHR) is a serious matter of concern since this includes the patient’s personal as well as health related information. This work proposes the use of an efficient security framework for EHR management. SCADBIN stands for Security using Cellular Automata, Dynamic key and Blockchain for IoT Networks. This architecture provides multilevel protection to EHRs starting from the data acquisition phase till the data access phase by a requesting node. Thus, SCADBIN assures end to end protection to EHRs. The paper explains the methodology and implementation details. The comparison of this architecture with existing methods is provided at the end with valid experimental results. © Grenze Scientific Society, 2023.

关键词： Internet of things

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Effect of Graphite Addition on Microstructure, Mechanical Properties and Thermal Properties of Injection Molded AZ91D Alloy+1

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Materials Transactions 2024年第4期65卷 374-380页

作者： Hideshima, Yasutoshi Maeda, Fumiya Fukuta, Tadao Ozaki, Koichi Course of Advanced Systems Engineering Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan Process Technology Development Department Technology Development Division Seiko Epson Corp. Suwa399-0211 Japan Department of Systems Engineering Faculty of Computer Science and Systems Engineering Okayama Prefectural University Soja719-1197 Japan

Magnesium chips were coated with a high concentration of graphite using a binder and were used as the raw material for injection molding. The microstructure of the magnesium injection-molded product with added graphite exhibited a dispersion of needle-like graphite particles. No significant voids were observed at the interfaces between the graphite and the matrix. The addition of more than 0.5 mass% graphite decreased the proof stress and tensile strength of the injection-molded products. The Young’s modulus of the graphite-added products tended to decrease with an increase in the graphite content, which is consistent with the lower limit of the rule of mixtures. The thermal conductivity of the 6.9 mass% graphite-added product increased compared with that of the AZ91D magnesium alloy and the coefficient of linear thermal expansion decreased. Both values are within a range that satisfied the rules of mixtures. © 2024 The Japan Institute of Metals and Materials.

关键词： Thermal expansion

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Weed detection in precision agriculture: leveraging encoder-decoder models for semantic segmentation

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3547-3561页

作者： Thiagarajan, Shreya Vijayalakshmi, A. Grace, G. Hannah Shell India Markets Private Limited Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Division of Mathematics School of Advanced Sciences Vellore Institute of Technology Chennai India

Precision agriculture uses data gathered from various sources to improve agricultural yields and the effectiveness of crop management techniques like fertiliser application, irrigation management, and pesticide application. Reduced usage of agrochemicals is a key step towards more sustainable agriculture. Weed management robots which can perform tasks like selective sprinkling or mechanical weed elimination, contribute to this objective. A trustworthy crop/weed classification system that can accurately recognise and classify crops and weeds is required for these robots to function. In this paper, we explore various deep learning models for achieving reliable segmentation results in less training time. We classify every pixel of the images into different categories using semantic segmentation models. The models are based on an encoder-decoder architecture, where feature maps are extracted during encoding and spatial information is recovered during decoding. We examine the segmentation output on a beans dataset containing different weeds, which were collected under highly distinct environmental conditions, including cloudy, rainy, dawn, evening, full sun, and shadow. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

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A Comparative Study of DenseNet121 and InceptionResNetV2 model for DeepFake Image Detection 3

A Comparative Study of DenseNet121 and InceptionResNetV2 mod...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Manoranjitham, R. Swaroop, Somu Santhi Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The rise of deep fake technology poses a significant challenge to societal integrity, necessitating robust detection methods. This research study investigates the effectiveness of DenseNet121 and InceptionResNetV2 deep learning models in detecting deep fake images. The study starts with the curation of the dataset and then applies different preprocessing methods to improve the quality of the dataset like scaling, normalization, and data augmentation. The DenseNet121 and InceptionResNetV2 models are trained to differentiate authentic and manipulated images based on distinctive features. DenseNet121's dense connectivity facilitates feature reuse, enabling effective extraction of discriminative features crucial for identifying manipulated content. In contrast, InceptionResNetV2's sophisticated architecture exploits both inception and residual connections, enhancing its ability to capture intricate patterns indicative of deep fake manipulation. The comparative analysis of performance metrics reveals InceptionResNetV2's superiority, achieving an impressive accuracy rate of 99.78% compared to DenseNet121. These findings underscore the efficacy of leveraging existing architectures for identifying deep fake images, thereby contributing to the ongoing efforts to combat misinformation and safeguard digital content integrity. © 2024 IEEE.

关键词： Deep learning

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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