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检索条件"机构=Dep. of Computer Science and Communication Engineering"

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MPI Impact in C+L+S Multiband Transmission Reach

MPI Impact in C+L+S Multiband Transmission Reach

引用

2024 Photonic Networks and Devices, NETWORKS 2024

作者： Cancela, Luís G. Pires, João O. Instituto de Telecomunicações Dep. Information Science and Technology Iscte Instituto Universitário de Lisboa Portugal Instituto de Telecomunicações Dep. Electrical and Computer Engineering Instituto Superior Técnico Portugal

Multipath interference (MPI) impact is assessed in a C+L+S multiband transmission scenario. For a typical -34 dB/span MPI, transmission reach in the L-band suffers a 20% reach penalty considering the QPSK modulation f... 详细信息

关键词： C++ (programming language)

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Maximize Leader Election Algorithm Performance in Bi-Directional Ring Networks With Both Links Down Between Two Nodes 25

Maximize Leader Election Algorithm Performance in Bi-Directi...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Al Refai, Mohammed N. Fawareh, Hamed Aljawawdeh, Hamzeh Alshraiedeh, Fuad Sameh Al-Quraan, Mohammad Al-Odat, Hisham Ali Otoom, Ahmed Jamhawi, Zeyad M. Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Zarqa Jordan Applied Science Private University Department of Cyber Security Amman Jordan Applied Science Private University Department of Computer Science Amman Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The process of selecting a leader to avoid instability in distributed system network after leader failure is called Leader Election Algorithm (LEA). This process becomes more complicated when it faces intermittent or complete links disconnected. the network becomes a one-way behavior if both lines of communication between two points are disrupted and thus the network becomes very slow, especially in systems where time is a critical issue, and the process of communication between points becomes much slower if the network leader is next to the fault or close to it, so the criteria will be established to ensures that the system leader in the Bidirectional ring communication network is in a place that allows next leader to communicate with all points as quickly as possible, and this will be during the election *** research presents a solution for leader failure problem in bidirectional ring network. This research takes into account the existence of intermittent or disconnection links between nodes. It will produce three algorithms to determine a new leader in the network with maximum network performance. It uses the least probability of time steps and the number of messages needed to elect the new leader in bidirectional ring network with N communicated nodes. so that the system remains able to work and communicate with its best capacity during this problem with the number of message complexity O(N) in the worst case. The third scenario minimizes the value of proper leader ID IDCurrent/IDCurrent-idStepCount', and maximizes the value of proper leader place N - (N - nStepCount). © 2024 IEEE.

关键词： Artificial intelligence

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Enhanced Patch Antenna Characteristics Analysis and Implementation with DGS and SMS

Enhanced Patch Antenna Characteristics Analysis and Implemen...

引用

2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Miligy, Ahmed F. Elfeky, Assem H. Amar, Ahmed S.I Abdel-Aleim M., M. Eid, Alla M. Abo Sree, Mohamed Fathy Air Defence College Alexandria Univerisity Electronics and Communication Dep. Alexandria Egypt Ahram Candian University Faculty of Engineering Department of Electronics and Communications Engineering Cairo Egypt Air Defence College Ain Shams University Electronics and Communication Dep. Cairo Egypt Arab Academy for Science Technology and Maritime Transport Department of Electronics and Communications Engineering Cairo Egypt

ISBN: (纸本)9798350351408

The development of Defected Ground Structures (DGS) and Slab microstrip Structures (SMS) techniques improved the properties of numerous microwave devices. While the DGS and SMS are advantageous in the design of microwave filters and microwave oscillators, they are also utilised in the design of microstrip antennas for various purposes for enhanced patch antenna characteristics, including reducing antenna size, suppressing harmonics, and improving matching. In this paper, the combination of the two techniques helped in enhancing patch antenna characteristics such as return loss, bandwidth, gain, and radiation efficiency. The proposed structures are analyzed, simulated, and implemented using a Roger substrate with ϵr = 3.4 and tan = 0.004 using HFSS. The proposed structure introduces improvement of the antenna parameters such as RL = -58 dB, BW = 290 MHz, G = 6 dB, and 90% radiation efficiency at 9 GHz for wireless applications in the x-band. © 2024 IEEE.

关键词： Microwave oscillators

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Enhancing Freezing of Gait Detection in Parkinson’s Through Fine-Tuned Deep Learning Models

Enhancing Freezing of Gait Detection in Parkinson’s Through...

引用

Digital Health (ICDH), IEEE International Conference on

作者： Michele Tebaldi Graziano Pravadelli Florenc Demrozi Rosalba Giugno Cristian Turetta Dep. of Computer Science University of Verona Italy Dep. of Engineering for Innovation Medicine University of Verona Italy Dep. of Electrical Engineering and Computer Science University of Stavanger Norway

ISBN: (数字)9798350368574

ISBN: (纸本)9798350368581

Freezing of Gait (FoG) is a common and disabling symptom in Parkinson’s Disease (PD), characterized by a sudden and temporary inability to initiate or continue walking. FoG arises from various factors such as environmental triggers, or physiological status of people with Parkinson’s. Traditional methods for preventing or alleviating FoG have limitations, prompting exploration into new technologies, such as the combination of sensing technologies and Deep Learning (DL) and Machine Learning (ML) algorithms. However, recognizing FoG with sensors and ML/DL poses challenges, such as the generalizability of the FoG recognition models over different individuals. Moreover, current approaches often require extensive time and effort to personalize the FoG recognition models. To mitigate these challenges, we propose a system that reduces the workload for creating personalized models through a fine-tuning approach. Our methodology has undergone rigorous testing in a subject-indep.ndent setup on a self-collected dataset of 22 subjects. Through the fine-tuning phase, we observed a remarkable average increase of up to $20.9 \%$ in F1-score performance compared to the training and testing approach without fine-tuning.

关键词： Training Deep learning Radio frequency Measurement Machine learning algorithms Refining Physiology

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Parallel and hierarchically-distributed Shoreline Alert Model (SAM) 31

Parallel and hierarchically-distributed Shoreline Alert Mode...

引用

31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023

作者： De Vita, Ciro Giuseppe Mellone, Gennaro Florio, Aniello Charles, Catherine Alessandra Torres Di Luccio, Diana Lapegna, Marco Benassai, Guido Budillon, Giorgio Montella, Raffaele University of Naples 'Parthenope' Dep. of Science and Technologies Naples Italy University of Madrid Carlos Iii Dep. of Computer Science and Engineering Madrid Spain University of Naples 'Federico II' Dept. of Mathematics and Applications Naples Italy

ISBN: (纸本)9798350337631

In this paper, the Shoreline Alert Model (SAM) is presented as a component of a computation platform based on workflows dedicated to extreme weather/marine event simulation. The model aims to mitigate the effects of global change by providing decision-makers, scientists, and engineers with a novel, next-generation tool set for facing extreme weather events and implementing related management or emergency responses. SAM uses a parallelization schema, allowing users to run it on heterogeneous parallel architectures. As a result, SAM produces approximately 24 times faster results than the baseline when using shared memory with distributed memory and dealing with about 20,000 transects along the Campania coastline. The system is based on the algorithms of the open-source numerical models WRF (Weather Research and Forecasting) and WW3 (Wave-watch III) implemented with refraction and shoaling routines together with run-up equations to form the modeling chain used for coastal flooding assessment. © 2023 IEEE.

关键词： Parallel architectures

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Exploring Genetic Programming Models in computer-Aided Diagnosis of Skin Cancer Images 13

Exploring Genetic Programming Models in Computer-Aided Diagn...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ain, Qurrat Ui Al-Sahaf, Harith Xue, Bing Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand College of Engineering and Info. Technology AlShaab University Dep. of Cybersecurity Engineering Baghdad Iraq

ISBN: (纸本)9798350308365

Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-dep.h analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of utilizing feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyzes the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learned during the evolutionary process. © 2024 IEEE.

关键词： Genetic programming

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Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams 11

Deep Learning Technique for Real-Time Forest Fire Detection ...

引用

11th International Conference on Intelligent Systems and Embedded Design, ISED 2023

作者： Gupta, Nidhi Chakravarty, Sujata Dash, Satyabrata Centurion University of Technology & Management Dept. of Computer Science & Engineering Odisha India Gandhi Institute of Technology & Management Gitam Deemed to Be University Dep. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350318159

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities. © 2023 IEEE.

关键词： Fires

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Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers

Planing It by Ear: Convolutional Neural Networks for Acousti...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Deschênes, Anthony Georges, Rémi Subakan, Cem Ugulino, Bruna Henry, Antoine Morin, Michael Dep. of Wood and Forest Sciences Université Laval QC Canada Dep. of Computer Science and Software Engineering Université Laval QC Canada Dep. of Operations and Decision Systems Université Laval QC Canada FORAC Research Consortium Université Laval QC Canada Mila-Quebec AI Institute QC Canada FPInnovations QC Canada Concordia University QC Canada

ISBN: (纸本)9798350368741

In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities. © 2025 IEEE.

关键词： Acoustic Anomaly detection Convolutional neural networks Transformer Wood planer

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Comparative Analysis of ML Algorithms for Fraud Detection

Comparative Analysis of ML Algorithms for Fraud Detection

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Gulnara Abitova Miras Abalkanov Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University Binghamton NY USA Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University Astana Kazakhstan Department of Computer Engineeering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

The challenge of credit card fraud presents a significant threat to both consumers and financial institutions alike, leading to substantial economic detriment. In response, this research applies various machine learning strategies to sift through transactional data for the identification of fraudulent activities. Specifically, we employ logistic regression, decision trees, and random forest algorithms to analyze a compiled dataset of genuine and deceptive transactions. This dataset undergoes segmentation into training and testing phases, allowing for a comprehensive assessment of each algorithm's effectiveness. The determination of accuracy scores across these phases serves as a metric for model evaluation. The findings lay down an essential groundwork for ongoing exploration into leveraging machine learning for the mitigation of credit card fraud. The integration of these analytical models promises to bolster the fraud detection capabilities of both financial entities and individual cardholders, significantly reducing the incidence of financial loss.

关键词： Training Logistic regression Machine learning algorithms Accuracy Sensitivity Credit cards Fraud

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Prediction of Electrical Characteristics of a-IGZO TFT Based on Transfer Learning-Based Variational Autoencoder

Prediction of Electrical Characteristics of a-IGZO TFT Based...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bea, Khean-Thye Wei-Hsuan Lin, Shih-Shin Hu Lin, Da-Zheng Hu, Hsin-Hui Chen, Xiu-Zhi Lin, Ting-Ru Chen, Yen-Lin Chen, Kun-Ming Cheng, Wai Khuen National Taipei University of Technology Dep. of Electronic Engineering Taipei106 Taiwan National Taipei University of Technology Dep. of Computer Science and Information Engineering Taipei106 Taiwan Taiwan Semiconductor Research Institute Hsinchu30078 Taiwan Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Kampar31900 Malaysia

ISBN: (纸本)9798350337020

In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a high R2 score of 0.9704 with a low-computing-power hardware-friendly method that reduced time consumption significantly compared to prior approaches. The findings have practical implications for mitigating the time-consuming nature of TCAD simulations, and the method can expand to various types of input data while ensuring high performance and generalization. We demonstrated significant improvement in generalization and accuracy through a k-fold validation. © 2023 IEEE.

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