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In-band full-duplex MIMO PLC systems for relaying networks

Digital communications and Networks 2025年第1期11卷 145-159页

作者： Francisco J.Canete Gautham Prasad Lutz Lampe Communications and Signal Processing Lab Telecommunication Research Institute(TELMA)ETS Ingenieria TelecomunicacionUniversidad de MalagaSpain Ofinno RestonVAUSA Department of Electrical and Computer Engineering The University of British ColumbiaCanada

In Power Line communications(PLC),there are regulatory masks that restrict the transmit power spectral density for electromagnetic compatibility reasons,which creates coverage issues despite the not too long ***,PLC networks often employ repeaters/relays,especially in smart grid neighborhood area *** in broadband indoor PLC systems that offer a notable data rate,relaying may pave the way to new applications like being the backbone for wireless technologies in a cost-effective manner to support the Internet-of-things *** this paper,we study Multiple-Input Multiple-Output(MIMO)PLC systems that incorporate inband full-duplex functionality in relaying *** present several MIMO configurations that allow end-to-end half-duplex or full-duplex operations and analyze the achievable performance with state-of-the-art PLC *** reach this analysis,we get channel realizations from random network layouts for indoor and outdoor *** adopt realistic MIMO channel and noise models and consider transmission techniques according to PLC *** concepts discussed in this work can be useful in the design of future PLC relay-aided networks for different applications that look for a coverage extension and/or throughput:smart grids with enhanced communications in outdoor scenarios,and“last meter”systems for high-speed connections everywhere in indoor ones.

关键词： PLC MIMO Relaying networks OFDM Spatial correlation Performance analysis

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Toward an accurate mobility trajectory recovery using contrastive learning

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Frontiers of Information Technology & Electronic engineering 2024年第11期25卷 1479-1496页

作者： Yushan LIU Yang CHEN Jiayun ZHANG Yu XIAO Xin WANG Shanghai Key Lab of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Department of Information and Communications Engineering Aalto UniversityEspoo FI-02150Finland

Human mobility trajectories are fundamental resources for analyzing mobile behaviors in urban computing ***,these trajectories,typically collected from location-based services,often suffer from sparsity and irregularity in *** support the development of mobile applications,there is a need to recover or estimate missing locations of unobserved time slots in these trajectories at a fine-grained spatial–temporal *** methods for trajectory recovery rely on either individual user trajectories or collective mobility patterns from all *** potential to combine individual and collective patterns for precise trajectory recovery remains ***,current methods are sensitive to the heterogeneous temporal distributions of the observable trajectory *** this paper,we propose CLMove(where CL stands for contrastive learning),a novel model designed to capture multilevel mobility patterns and enhance robustness in trajectory *** features a two-stage location encoder that captures collective and individual mobility *** graph neural network based networks in CLMove explore location transition patterns within a single trajectory and across various user *** also design a trajectory-level contrastive learning task to improve the robustness of the *** experimental results on three representative real-world datasets demonstrate that our CLMove model consistently outperforms state-of-the-art methods in terms of trajectory recovery accuracy.

关键词： Human mobility Mobility trajectory recovery Contrastive learning

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CGAPoseNet+GCAN: A Geometric Clifford Algebra Network for Geometry-aware Camera Pose Regression

CGAPoseNet+GCAN: A Geometric Clifford Algebra Network for Ge...

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2024 IEEE Winter Conference on Applications of computer Vision, WACV 2024

作者： Pepe, Alberto Lasenby, Joan Buchholz, Sven University of Cambridge Signal Processing and Communications Lab United Kingdom Technical University Department of Computer Science and Media Brandenburg Germany

ISBN: (纸本)9798350318920

We introduce CGAPoseNet+GCAN, which enhances CGAPoseNet, an architecture for camera pose regression, with a Geometric Clifford Algebra Network (GCAN). With the addition of the GCAN we obtain a geometry-aware pipeline for camera pose regression from RGB images only. CGAPoseNet employs Clifford Geometric Algebra to unify quaternions and translation vectors into a single mathematical object, the motor, which can be used to uniquely describe camera poses. CGAPoseNet can obtain comparable results to other approaches without the need of expensive tuning of the loss function or additional information about the scene, such as 3D point clouds, which might not always be available. CGAPoseNet, however, like several approaches in the literature, only learns to predict motor coefficients, and it is unaware of the mathematical space in which predictions sit in and of their geometrical meaning. By leveraging recent advances in Geometric Deep Learning, we modify CGAPoseNet with a GCAN: proposals of possible motor coefficients associated with a camera frame are obtained from the InceptionV3 backbone, and the GCAN downsamples them to a single motor through a sequence of layers that work in G4,00. The network is hence geometry-aware, has multivector-valued inputs, weights and biases and preserves the grade of the objects that it receives in input. CGAPoseNet+GCAN has almost 4 million fewer trainable parameters, it reduces the average rotation error by 41% and the average translation error by 8.8% compared to CGAPoseNet. Similarly, it reduces rotation and translation errors by 32.6% and 19.9%, respectively, compared to the best performing PoseNet strategy. CGAPoseNet+GCAN reaches the state-of-the-art results on 13 commonly employed datasets. To the best of our knowledge, it is the first experiment in GCANs applied to the problem of camera pose regression. © 2024 IEEE.

关键词： Cameras

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Time series numerical association rule mining variants in smart agriculture

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16853-16866页

作者： Fister, Iztok Fister, Dušan Fister, Iztok Podgorelec, Vili Salcedo-Sanz, Sancho Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor2000 Slovenia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Madrid28805 Spain

Numerical association rule mining offers a very efficient way of mining association rules, where algorithms can operate directly with categorical and numerical attributes. These methods are suitable for mining different transaction databases, where data are entered sequentially. However, little attention has been paid to the time series numerical association rule mining, which offers a new technique for extracting association rules from time series data. This paper presents a new algorithmic method for time series numerical association rule mining and its application in smart agriculture. We offer a concept of a hardware environment for monitoring plant parameters and a novel data mining method with practical experiments. The practical experiments showed the method’s potential and opened the door for further extension. © 2023, The Author(s).

关键词： Internet of things

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Image Segmentation for Improved Lossless Screen Content Compression 48

Image Segmentation for Improved Lossless Screen Content Comp...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Uddehal, Shabhrish Reddy Strutz, Tilo Och, Hannah Kaup, André Coburg University Department of Electrical Engineering and Computer Science Coburg Germany Friedrich-Alexander University Multimedia Communications and Signal Processing Erlangen Germany

ISBN: (纸本)9781728163277

In recent years, it has been found that screen content images (SCI) can be effectively compressed based on appropriate probability modelling and suitable entropy coding methods such as arithmetic coding. The key objective is determining the best probability distribution for each pixel position. This strategy works particularly well for images with synthetic (textual) content. However, usually screen content images not only consist of synthetic but also pictorial (natural) regions. These images require diverse models of probability distributions to be optimally compressed. One way to achieve this goal is to separate synthetic and natural regions. This paper proposes a segmentation method that identifies natural regions enabling better adaptive treatment. It supplements a compression method known as Soft Context Formation (SCF) and operates as a pre-processing step. If at least one natural segment is found within the SCI, it is split into two subimages (natural and synthetic parts) and the process of modelling and coding is performed separately for both. For SCIs with natural regions, the proposed method achieves a bit-rate reduction of up to 11.6% and 1.52% with respect to HEVC and the previous version of the SCF. © 2023 IEEE.

关键词： lossless coding probability distribution screen content images segmentation soft context formation

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Effect of Pectoral Muscles on CNN based Mammographic Cancer Detection 18

Effect of Pectoral Muscles on CNN based Mammographic Cancer ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Dhimann, Santoresh Kumari Sawhney, Tinny Yadav, Rakesh Kumar Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India University of Jammu Digital Signal Processing Lab Department of Electronics Jammu India

ISBN: (纸本)9789380544519

Cancer is attributed to abnormal growth of tissue cells in any part of the living body. The number of cancer patients is increasing worldwide. The reason of the development of abnormal cells within the body is still unknown to the researchers. The most common is breast cancer and it is affective affecting one out of every ten women. The reason of breast lesions formation may be calcification or mass deposit and digital mammography is generally used for verifying it. It is a cheap, non-toxic, and user-friendly test, but needs expert opinion for ascertaining the presence of cancer. The accuracy of mammographic detection varies between 80% and 90%. Recently, Convolutional neural networks (CNNs) have shown their application in automatic detection of the cancer, but inclusion of pectoral muscle in the mammogram reduces the efficiency of the CNN system as the texture of the muscle resembles with cancerous tissues. Further, CNN based detection requires high resolution images which in turn requires state of art technology-based machines, generally, not available in rural area-based clinics. The focus of the proposed research is to investigate effect of low-quality blurred input mammograms on the detection of pectoral muscle boundary, so that the pectoral muscle removed mammograms can be applied to CNN system for automatic prediction of cancerous images. The investigations carried out have shown that low quality blurred images can also be employed for detecting cancerous tissues provided the pectoral muscle is removed from the mammograms before using CNN based classifier. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： cancer detection CNN Hough lines mammogram pectoral muscle

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Evaluation of LoRaWAN for Remote Health Monitoring: A Preliminary Study

Evaluation of LoRaWAN for Remote Health Monitoring: A Prelim...

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2024 IEEE Conference on Standards for communications and Networking, CSCN 2024

作者： Katzis, Konstantinos Evetovic, Nina Cloteaux, Brian Sayrafian, Kamran Bajic, Dragana European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus University of Novi Sad Department of Communications & Signal Processing Novi Sad Serbia Information Technology Laboratory National Institute of Standards & Technology MD United States

ISBN: (纸本)9798331507428

Lack of sufficient patient monitoring data patterns and scenarios are among the challenges for accurate traffic modeling and network performance optimization when considering IoT medical devices. This paper examines how health data-traffic models can be used to investigate the capability and proper configuration of Long-Range Wide Area Networks (LoRaWAN) for remotely monitoring patients in a hospital environment or at home. For the hospital environment, it is assumed that there are two types of patients at any given time i.e., patients in the intensive care unit (ICU) or surgical wards and those located in the general medical wards. The transmission times of the health data from patients in the ICU/Surgical wards and those in general wards are randomly distributed within one and four-hour periods, respectively. For personal health monitoring devices that people use at their homes, a two-state Markov chain is used to model the generated traffic pattern. Preliminary results indicate that LoRaWAN may be used to support a significant number of nodes with sufficient reliability in both scenarios. © 2024 IEEE.

关键词： Electronic health record

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EEG based stress classification by using difference plots of variational modes and machine learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16347-16360页

作者： Salankar, Nilima Qaisar, Saeed Mian School of Computer Science University of Petroleum and Energy Studies Dehradun India Persistent System Limited IT Park Maharashtra Nagpur India Department of Electrical and Computer Engineering EFfat University Jeddah22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah22332 Saudi Arabia

The recent trend in healthcare is to use the automated biomedical signals processing for an augmented and precise diagnosis. In this context, an original approach is presented for categorization of stress and non-stress classes by processing the multichannel Electroencephalogram (EEG) signals. The EEG signals are decomposed by using the "Empirical Mode Decomposition" (EMD) and "Variational Mode Decomposition" (VMD), respectively render the "Intrinsic Mode Functions" (IMFs) and Modes. The Second Order Difference Plots (SODPs) are traced for each IMF and Mode. Shapes of SODPs are used to discriminate the two intended categories. The feature space includes areas, Central Tendency Measures (CTMs) and means of SODPs. It is derived from first 7 IMFs and Modes. The identification of stress and non-stress categories is performed by using the robust machine learning algorithms namely, the Support Vector Machine (SVM), Boosting with Random Forest (RF) and Multilayer Perceptron Neural Network (MLPNN). The applicability is tested by using a publicly available EEG signals dataset, recorded during mental arithmetic test. A detailed evaluation is carried out at the subset, individual channel and lobes wise. Noteworthy highest accuracies of subset wise 99.89%, channel wise 98.89% and lobe wise 99.99% are obtained respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Empirical mode decomposition

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Enhancing Early Detection of Lung Cancer through Advanced Image processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Blockchain-based color medical image cryptosystem for industrial Internet of Healthcare Things (IoHT)

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Multimedia Tools and Applications 2024年 1-55页

作者： Khallaf, Fatma El-Shafai, Walid El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Ahram Canadian University 6th October City Giza Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In recent years, the proliferation of smart devices and associated technologies, such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Medical Things (IoMT), has witnessed a substantial growth. However, the limited processing power and storage capacity of smart devices make them vulnerable to cyberattacks, rendering traditional security and cryptography techniques inadequate. To address these challenges, blockchain (BC) technology has emerged as a promising solution. This study introduces an efficient framework for the Internet of Healthcare Things (IoHT), presenting a novel cryptosystem for color medical images using BC technology in conjunction with the IoT, Secure Hash Algorithm 256-bit (SHA256), shuffling, and bitwise XOR operations. The encryption scheme is specifically designed for an IIoT grid network computing system, relying on diffusion and confusion principles. In this paper, the proposed cryptosystem strength is evaluated against differential attacks with several comprehensive metrics. Simulation results and theoretical analysis demonstrate the cryptosystem effectiveness, showcasing its ability to provide high levels of security and immunity to data leakage. The proposed cryptosystem offers a versatile range of technical solutions and strategies that are adaptable to various scenarios. The evaluation metrics, with approximate values of 99.61% for Number of Pixels Change Rate (NPCR), 33.46% for Unified Average Changed Intensity (UACI), and 8 for information entropy, closely align with the desired ideal outcomes. Consequently, this paper contributes to the advancement of secure and private systems for medical image encryption based on BC technology, potentially mitigating the risks associated with cyberattacks on smart medical devices. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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