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International Conference on Wireless and Telematics (ICWT)

作者： I Made Aditya Putra J.D Lisdatul Alifa Muhammad Hilmi Fawwaz Nissha Kinaya Mauren Sibayak Ratna Mayasari Hasan Nur Arifin Syaiful Ahdan Ridha Muldina Negara Nana Rachmana Syambas Dept. of Telecommunication Engineering Bandung Institute of Technology Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Lampung Indonesia

Named data networks are a new concept in network architecture that can process data transmission by name. This concept can be utilized in the case of video streaming. TIPHON is a standard used to support streaming multimedia activities. According to TIPHON, three factors-delay, packet loss, and throughput-can influence how a stream behaves. This study's goal is to evaluate the quality of service (QoS) of video streaming services using an NDN network that adheres to the TIPHON NDN standard. Several things can affect throughput, such as bottlenecks on the computer or network device used.

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Carotid Plaque Motion Analysis in Ultrasound Videos to Discover Rupture-Prone Plaque Areas

Carotid Plaque Motion Analysis in Ultrasound Videos to Disco...

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IEEE International Symposium on Biomedical Imaging

作者： Georgia D. Liapi Christos P. Loizou Andrew Nicolaides Maura Griffin Dimitrios Kardoulas Constantinos Pattichis Efthyvoulos Kyriacou Dept. of Electrical Engineering Computer Eng. and Informatics Cyprus University of Technology Limassol Cyprus Vascular Screening and Diagnostic Center Nicosia Cyprus Hemodynamic Unit Vascular Surgery Clinic National and Kapodistrian University of Athens Attikon General University Hospital Athens Greece Dept. of Computer Science University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Identification of carotid plaque motion alterations, throughout the cardiac cycle (CC), may reveal rupture-prone plaque components and assist doctors in ischemic stroke risk stratification. In this study, we investigated Ultrasound (U/S) carotid plaque motion, focusing on middle-systole (MS), the interval between early systole (ES) and peak systole (PS) expecting that the dynamic forces applied on the plaque at the beginning of systole are more profound from cardiac ES to MS, compared to PS. We deployed a computer tool, previously developed by researchers of the e-health laboratory at Cyprus University of technology, for carotid plaque motion estimation in B-mode U/S videos. We included 16 carotid U/S videos (8 Asymptomatic, AS; 8 Symptomatic, SY patients) and performed video frame (VF) resolution standardization, manual region of interest (ROI) selection, identification of 5 CCs (ES and MS pairs), and plaque motion spread and magnitude measurement, based on dense optical flow. The Maximum Angular Spread (MAXFW 20 , first 20% of the plaque’s overall MAXFW; units: 0° to 360°), a measure we created to determine angle differences between motion vectors, in all plaque areas, can assist in classifying the plaque as concordant (low-strain), moderate(m)-discordant (mid-strain) or discordant (highstrain). We identified 1 concordant, 8 m-discordant, and 7 discordant plaques, with MAXFW 20 ranging from 58±7.45°, 91±43.9°, 156±49.2° (mean±standard deviation), respectively. As our primary findings support our hypothesis, in the future, we will also derive the stress applied on the carotid plaque, in ES and MS.

关键词： Ultrasonic imaging Standardization Stroke (medical condition) Vectors Real-time systems Motion measurement Motion analysis

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Breast Cancer Detection Using Deep Learning Technique

Breast Cancer Detection Using Deep Learning Technique

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sachin Urabinahatti Devappa Jayadevappa Dept. of Computer Science & Engineering Tatayasaheb Kore Inst of Engg & Technology Warananagar India Dept of Electronics & Instrumentation Engineering JSS Academy of Technical Education Bengaluru India

One of the harmful types of cancer that can affect females is breast cancer. With the aid of images of the microscopic structure, breast cancer can be identified. This study uses mammography images to categorize various types of breast cancer. Image processing techniques can be used successfully in the classification of mammography images. Deep learning provides wonderful performance for the classification of images in many applications among various image processing algorithms. Convolutional neural network (CNN) designs like VGG19, Inception-Net, ResNet50, and others are used in effective classification.

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Hierarchical Context Transformer for Multi-level Semantic Scene Understanding

arXiv

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arXiv 2025年

作者： Hao, Luoying Hu, Yan Yue, Yang Wu, Li Fu, Huazhu Duan, Jinming Liu, Jiang Research Institute of Trustworthy Autonomous Systems Dept. of Computer Science and Engineering Southern University of Science and Technology China School of Computer Science University of Birmingham United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences University of Manchester United Kingdom MGI Tech Co. Ltd. China Singapore

A comprehensive and explicit understanding of surgical scenes plays a vital role in developing context-aware computer-assisted systems in the operating theatre. However, few works provide systematical analysis to enable hierarchical surgical scene understanding. In this work, we propose to represent the tasks set [phase recognition → step recognition → action and instrument detection] as multi-level semantic scene understanding (MSSU). For this target, we propose a novel hierarchical context transformer (HCT) network and thoroughly explore the relations across the different level tasks. Specifically, a hierarchical relation aggregation module (HRAM) is designed to concurrently relate entries inside multi-level interaction information and then augment task-specific features. To further boost the representation learning of the different tasks, inter-task contrastive learning (ICL) is presented to guide the model to learn task-wise features via absorbing complementary information from other tasks. Furthermore, considering the computational costs of the transformer, we propose HCT+ to integrate the spatial and temporal adapter to access competitive performance on substantially fewer tunable parameters. Extensive experiments on our cataract dataset and a publicly available endoscopic PSI-AVA dataset demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin. The code is available at https://***/Aurorahao/HCT. Copyright © 2025, The Authors. All rights reserved.

关键词： Transplantation (surgical)

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Classifying Emotions in Roman Urdu Posts using Machine Learning 1

Classifying Emotions in Roman Urdu Posts using Machine Learn...

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Nabeel, Zahra Mehmood, Maseeha Baqir, Anees Amjad, Anam Dept. of Software Engineering Sialkot Pakistan Ca'Foscari University of Venice Dept. of Environmental Sciences Informatics and Statistics Venice Italy National University of Sciences and Technology Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665424134

Emotion, a state of human mind has a significant impact on human behavior, social interactions and decision making. Few emotions from Carroll E. Izard model such as long-term fear, anger and sadness cause mental disorders which can lead to deterioration of mental and physical health. For the well-being of mental health, different psychological parameters such as voice, facial expressions and body movements were used to extract these emotions in earlier times. This paper focuses on verbal language for emotion classification. With the huge popularity of Web 3.0, people feel more comfortable in sharing their emotions over social media due to the anonymity of the web. Recently, researchers around the globe are interested to classify the emotions from social media posts (i.e., tweets, comments or micro-blogs etc.) using resource-rich languages e.g., English. However, less attention is paid towards the resource-poor language such as Urdu in Roman script. According to the Gallup survey in Pakistan, Urdu in Roman script is widely used to transmit text. To detect emotions in Roman Urdu text is a big challenge due to its scripting and morphology. In this paper, the following contributions are made;1) A corpus of Roman Urdu is developed for emotion classification. 2) We aim to identify user's mental states using textual posts on social media plat-forms;instagram, Facebook, Twitter and YouTube. Particularly, Natural Language Processing methods incorporated with feature extraction methods (TF-IDF) and four supervised classifiers such as Support Vector Machine (SVM),Naïve Bayes, Random Forest and K-nearest Neighbor are used. By utilizing 80% training and 20% testing data, Naive Bayes secures 70.57% accuracy. 3) Moreover, suitability analysis is performed to identify the most suitable classifier in terms of accuracy, training and testing time. Results show that Naive Bayes is the most suitable classifier for emotion classification. © 2021 IEEE.

关键词： Deterioration

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Design and Simulation of an Affordable Vehicle Speed Detection System

Design and Simulation of an Affordable Vehicle Speed Detecti...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Mohammed G. S. Al-safi Thabit H. Thabit Firas M. Mahmood Dept. of Computer Technology Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Accounting Al-Esraa University College Baghdad Iraq Dept. of Computer Engineering and Information Ninevah University Mosul Iraq Dept. of Informatics and Software Engineering Cihan University-Erbil Kurdistan Region Iraq

The objective of this paper is to detect the over speed of vehicles on the roads. Although maximum speed limit signs exist on the roads, many accidents keep on because of high-speed driving. It is necessary to solve these problems through automation techniques. This paper describes a speed detection system for vehicles, the traffic police can easily check the speed of vehicles by using this system. This system consists of a PLC (Programmable Logic Controller) LOGO! V8.3, two Laser sensors, an HMI (Human Machine Interface), a speed camera, and a LOGO! CMR (Communication Module Radio). The detected speed is displayed on the HMI display, moreover, if a vehicle crosses the limited speed, this system displays the over-speed on HMI, and the speed camera is activated to capture an image of that vehicle. Also, an SMS (Short Messages Service) will be sent by the CMR to the traffic authority informing them about the violation to take the necessary legal actions. The system can also store the collected data for documentation purposes. The authors describe a simple, affordable technique for monitoring vehicles' speeds in this paper.

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Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization

arXiv

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arXiv 2023年

作者： Balaskas, Konstantinos Karatzas, Andreas Sad, Christos Siozios, Kostas Anagnostopoulos, Iraklis Zervakis, Georgios Henkel, Jörg The Department of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece Karlsruhe Institute of Technology Karlsruhe76131 Germany The School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale CarbondaleIL62901 United States The Dept. of Computer Engineering & Informatics University of Patras Patras26504 Greece

Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of domains. However, DNNs are becoming computationally intensive and energy hungry at an exponential pace, while at the same time, there is a vast demand for running sophisticated DNN-based services on resource constrained embedded devices. In this paper, we target energy-efficient inference on embedded DNN accelerators. To that end, we propose an automated framework to compress DNNs in a hardware-aware manner by jointly employing pruning and quantization. We explore, for the first time, per-layer fine- and coarse-grained pruning, in the same DNN architecture, in addition to low bit-width mixed-precision quantization for weights and activations. Reinforcement Learning (RL) is used to explore the associated design space and identify the pruning-quantization configuration so that the energy consumption is minimized whilst the prediction accuracy loss is retained at acceptable levels. Using our novel composite RL agent we are able to extract energy-efficient solutions without requiring retraining and/or fine tuning. Our extensive experimental evaluation over widely used DNNs and the CIFAR-10/100 and ImageNet datasets demonstrates that our framework achieves 39% average energy reduction for 1.7% average accuracy loss and outperforms significantly the state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Assessing the Impact of Primary Frequency Support from IBRs in Low Inertia Isolated Power Systems

Assessing the Impact of Primary Frequency Support from IBRs ...

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IEEE General Meeting Power& Energy Society

作者： Phivos Therapontos Rogiros Tapakis Petros Aristidou Distribution System Operator Electricity Authority of Cyprus Nicosia Cyprus Transmission System Operator of Cyprus Nicosia Cyprus Dept. of Electrical & Computer Engineering & Informatics Cyprus University of Technology Limassol Cyprus

The anticipated massive penetration of Renewable Energy Sources (RES) will significantly impact the operation of electric power systems. The effect will be more prominent on low-inertia, isolated, power systems. Modern grid code requirements can be used to ensure that RES will remain connected to the system during abnormal or transient operating conditions and provide support. In this paper, the Cyprus transmission system is used to evaluate the impact of primary frequency support (PFS) from Inverter-Based Resources (IBRs), both during low- and high-loading conditions. A sensitivity analysis of the major parameters of PFS has been performed to evaluate their impact on the frequency response in low inertia conditions. It is demonstrated that PFS provided by IBRs during low loading conditions positively affects the power system frequency stability without any additional curtailments. During high-loading conditions, PFS from RES also improves the system performance, however, significant RES curtailments from the IBGs participating in PFS are required which may raise fairness concerns.

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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition Research

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Jamiu R. Olasina Obiseye Obiyemi Dept. of Electrical and Information Engineering Covenant Applied Informatics and Communication Africa Center of Excellence Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Dept. of Computer Engineering Federal Polytechnic Ilaro Ilaro Nigeria Dept. of Electrical Power Engineering Space Science Centre Durban University of Technology Durban South Africa

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain.

关键词： Wireless communication Technological innovation Transfer learning Stochastic processes Rician channels Market research Natural language processing Optimization Residual neural networks Radio spectrum management

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Development of a Calibration-Free Brain-computer Interface Utilizing Common Spatial Patterns and Artificial Neural Networks for EEG Signal Analysis

Development of a Calibration-Free Brain-Computer Interface U...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Korhan Cengiz Shreyas J Udayaprasad P K Gururaj H L Dilip Kumar S M College of Information Technology University of Fujairah UAE Faculty of Organization and Informatics University of Zagreb Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology.

关键词： Training Machine learning algorithms Artificial neural networks Electroencephalography Brain-computer interfaces Calibration Reliability

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