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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Michael S. M. Pakpahan Lukito Edi Nugroho Widyawan Widyawan Ajie Kusuma Wardhana Muhammad Ardian R.A Rangga Satria Astagenta Dept. of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Dept. Informatics Faculty of Computer Science Universitas AMIKOM Yogyakarta Yogyakarta Indonesia

Addressing its growing number and vital role, decentralization of cloud computing becoming a necessity. Fog computing aims to bring application closer to the data source-typically at the network’s edge by leveraging local resources to provide faster data processing and decision-making. Fog computing then has to place application strategically to use its limited fog resource to improve application performance metrics. This problem known as Fog Application Placement Problem (FAPP) has been approached using previous methods that rely on rules and prior knowledge that may not be adaptive, but rather being overly specialized to specific problems. Deep learning with its learning mechanism, can offer more adaptable and dynamic solutions for a wide range of scenarios, especially in fog network that continuously evolve. This research investigate Seq2seq placement model inherent limitations, notably the impracticality of generating every possible pattern from all potential request configurations. We aim to address and answer the following critical questions: 1) How does the model’s performance vary when confronted with unseen requests or an augmented number of modules, especially considering the limitations in training data?; 2) Can the seq2seq model, even with its training limitations, adhere to the heuristic rules of the dataset when dealing with unfamiliar problems? This research shows that in similar availability, there is a reduction of almost half of the response time with 183.03 ms, 2.93 number of hops, and 0.87 megabyte of transmitted messages against the hop3 algorithm. Moreover, we highlight the ability of seq2seq model to follow heuristic rules in unseen scenarios.

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Machine Learning based Comparative Analysis of Cervical Cancer Risk Classifications Algorithms

Machine Learning based Comparative Analysis of Cervical Canc...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Tania Ganguly Peeta Basa Pati K. Deepa Tripty Singh Tolga Özer Department of Computer Science and Engineering Amrita School of Computing Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru India Technology Faculty Electric Electronic Engineering Afyon Kocatepe University Afyonkarahisar Turkey

Cervical cancer is a major health concern for women worldwide, and early detection is essential for successful treatment. Since symptoms often do not appear until later stages, early screening is necessary. Machine learning can help classify cervical cancer risk by analyzing patient datasets and identifying the important factors that predict the likelihood of emerging cervical cancer. This paper evaluates six different machine-learning approaches for analyzing risk factors associated with cervical cancer using a dataset of 838 instances with 36 features. Results show that the SVM classifier performs the best, with an accuracy of 99.60% which emphasize the possibility of utilizing machine learning to enhance the precision of cervical cancer risk assessment. This can result in the development of better screening and prevention techniques for cervical cancer, which can be more effective in identifying and managing this disease.

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Sensing in Bi-Static ISAC Systems with Clock Asynchronism: A Signal Processing Perspective

arXiv

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arXiv 2024年

作者： Wu, Kai Pegoraro, Jacopo Meneghello, Francesca Zhang, J. Andrew Lacruz, Jesus O. Widmer, Joerg Restuccia, Francesco Rossi, Michele Huang, Xiaojing Zhang, Daqing Caire, Giuseppe Guo, Y. Jay SydneyNSW2007 Australia The University of Padova Italy The University of Padova Italy The School of Electrical and Data Engineering University of Technology Sydney Australia IMDEA Networks Spain IMDEA Madrid Spain Electrical and Computer Engineering Northeastern University United States The Department of Mathematics The University of Padova Italy Australia Peking University China IP Paris France The Faculty of Electrical Engineering and Computer Science The Technical University of Berlin Germany Australia

Integrated Sensing And Communication (ISAC) has been identified as a pillar usage scenario for the impending 6G era. Bi-static sensing, a major type of sensing in ISAC, is promising to expedite ISAC in the near future, as it requires minimal changes to the existing network infrastructure. However, a critical challenge for bi-static sensing is clock asynchronism due to the use of different clocks at far-separated transmitters and receivers. This causes the received signal to be affected by time-varying random phase offsets, severely degrading, or even failing, direct sensing. Hence, to effectively enable ISAC, considerable research has been directed toward addressing the clock asynchronism issue in bi-static sensing. This paper provides an overview of the issue and existing techniques developed in an ISAC background. Based on the review and comparison, we also draw insights into the future research directions and open problems, aiming to nurture the maturation of bi-static sensing in ISAC. © 2024, CC BY-NC-ND.

关键词： Clocks

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Surface Acoustic Wave Sensor Interrogation Using Goubau Waves

Surface Acoustic Wave Sensor Interrogation Using Goubau Wave...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Greve, David W. Devkota, Jagannath Ohodnicki, Paul Wright, Ruishu Department of Electrical and Computer Engineering Carnegie Mellon U. PittsburghPA United States Orise Faculty National Energy Technology Laboratory PittsburghPA United States National Energy Technology Laboratory PittsburghPA United States Netl Support Contractor PittsburghPA United States Swanson School of Engineering University of Pittsburgh Department of Mechanical Engineering & Materials Science PittsburghPA15261 United States

Electromagnetic waves on single conductors (Goubau waves) are investigated for the interrogation of wireless sensors. A demonstration of interrogation of a surface acoustic wave (SAW) sensor is presented, followed by ... 详细信息

ISBN: (纸本)9781665442282

关键词： Electromagnetic waves

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Hyperbolic Pattern Detection in Ground Penetrating Radar Images Using Faster R-CNN

Hyperbolic Pattern Detection in Ground Penetrating Radar Ima...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Pachara Srimuk Akkarat Boonpoonga Kamol Kaemarungsi Krit Athikulwongse Danai Torrungrueng Nattawat Chantasen Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Department of Teacher Training in Electrical Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Computer Engineering Faculty of Engineering Rajamangala University of Technology Phra Nakhon Bangkok Thailand

This study presents the application of Faster RCNN, a popular Region Based Convolutional Neural Network, for detecting hyperbolic patterns in Ground Penetrating Radar (GPR) images. GPR is an important tool for subsurface imaging in various fields such as geology, archaeology, and engineering. However, the analysis of GPR images can be challenging due to noise, small objects, and variations in object sizes. To evaluate the performance of the proposed method, 369 simulated GPR B-scan images were generated using GprMax simulation software. These images included single, double, and triple hyperbolic patterns. The results showed that preprocessing improved the detection accuracy and led to higher Intersection over Union (IoU) scores. The experimental results demonstrate that Faster R-CNN is an effective tool for hyperbolic pattern detection in GPR images and provides a promising direction for future research in the field.

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Percussion Hero: A chest physical therapy game for people with cystic fibrosis and their caregivers 21

Percussion Hero: A chest physical therapy game for people wi...

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8th ACM SIGCHI Annual Symposium on computer-Human Interaction in Play, CHI PLAY 2021

作者： Sadprasid, Book Eddy, Ethan Tabor, Aaron Scheme, Erik Bateman, Scott Faculty of Computer Science University of New Brunswick Canada Biomedical Engineering University of New Brunswick Canada HCI Lab University of New Brunswick Canada Department of Electrical and Computer Engineering University of New Brunswick Canada

ISBN: (纸本)9781450383561

Chest physical therapy - including chest percussion, vibration, and postural drainage - is an important part of cystic fibrosis (CF) treatment. Chest percussion and vibration are exercises that require coordinated effort between patient and caregiver, during which the caregiver performs manual, rhythmic blows to the patient's chest and back. When practiced regularly alongside postural drainage techniques, percussion therapy mobilizes and removes fluid buildup in the lungs, reducing inflammation and risks of infection that could lead to hospitalization. Despite the importance of chest physical therapy for those with CF, adherence is often low. Low adherence to at-home therapies is common to the treatment of many diseases, yet one distinct challenge of chest physical therapy is the caregiver intervention - a role often assumed by a family member or loved one. The caregiver role is challenging for chest therapy because it is active, focused, and strenuous. While research has proposed many solutions for increasing patient engagement during similar airway clearance exercises, the critical role of the caregiver has often been overlooked. In this paper, we present Percussion Hero, a cooperative, rhythm-based game designed to improve chest physical therapy adherence by actively targeting both patients and caregivers during therapeutic exercises. © 2021 Owner/Author.

关键词： Serious games

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Backward Radiation Reduction and Bandwidth Enhancement of Metamaterial Antenna for UWB Applications

Backward Radiation Reduction and Bandwidth Enhancement of Me...

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2021 Emerging Technology in Computing, Communication and Electronics, ETCCE 2021

作者： Uddin, M. Jasim Ullah, M. Habib Bentley, Barry L. Shakib, M.N. Islam, Syed Zahurul Rahman, Md Arafatur Cardiff Metropolitan University Cardiff School of Technologies Department of Applied Computing and Engineering United Kingdom University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia Cardiff Metropolitan University Cardiff School of Technologies Department of Computer Science United Kingdom Green University of Bangladesh Faculty of Science and Engineering Dept. of Electrical and Electronics Engineering Dhaka1207 Bangladesh University Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Power Integration Systems Johor Malaysia Faculty of Science and Engineering University of Wolverhampton United Kingdom

ISBN: (纸本)9781665483643

A metamaterial inspired planar-patterned microstrip patch antenna, feeding on a Duroid substrate inset bottom ground soft-surface, is presented for ultra-wide band (UWB) applications. The present antenna is configured on a step-edge periodic unit-cell pattern, with a tiny air gap etched on the metal patch and finite ground plane. By employing these structures an ultra-wide band from 4 GHz ~ 16.2 GHz is achieved. The important advantage is retaining the suppression of back radiation while removing grounded edge metallic parts and replacing them with the finite cut-off square unit cell. The feeding ground plane and radiating patch are specially configured to develop artificial structures using series capacitance and shunt inductance. The efficiency of the proposed antenna is above 99.1%. To enhance gain and bandwidth, this antenna has etched Jerusalem crossed slots on the ground plane and star-slots on the patch, with the enhanced gain reaching a maximum of 8 dBi at 9.2 GHz. The antenna's overall dimensions exhibit 0.72λ × 0.83λ × 0.05λ at a 10.1 GHz cut-off frequency. The high absorption gain, directivity, and enhanced frequency bandwidth, verified by numerical modeling and experimentation, ensure the proposed antenna is well suited to any UWB application. © 2021 IEEE.

关键词： Metamaterial antennas

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Towards Intelligent Antenna Positioning: Leveraging DRL for FAS-Aided ISAC Systems

arXiv

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arXiv 2025年

作者： Yang, Shunxing Yao, Junteng Tang, Jie Wu, Tuo Elkashlan, Maged Yuen, Chau Debbah, Mérouane Shin, Hyundong Valenti, Matthew China Telecom Research Institute Guangzhou510000 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown United States

Fluid antenna systems (FAS) enable dynamic antenna positioning, offering new opportunities to enhance integrated sensing and communication (ISAC) performance. However, existing studies primarily focus on communication enhancement or single-target sensing, leaving multi-target scenarios underexplored. Additionally, the joint optimization of beamforming and antenna positions poses a highly non-convex problem, with traditional methods becoming impractical as the number of fluid antennas increases. To address these challenges, this letter proposes a block coordinate descent (BCD) framework integrated with a deep reinforcement learning (DRL)-based approach for intelligent antenna positioning. By leveraging the deep deterministic policy gradient (DDPG) algorithm, the proposed framework efficiently balances sensing and communication performance. Simulation results demonstrate the scalability and effectiveness of the proposed approach. © 2025, CC BY.

关键词： Deep reinforcement learning

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Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling

arXiv

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arXiv 2025年

作者： Fhima, Jonathan Van Eijgen, Jan Beeckmans, Lennert Jacobs, Thomas Freiman, Moti Nakayama, Luis Filipe Stalmans, Ingeborg Baskin, Chaim Behar, Joachim A. Faculty of Biomedical Engineering Technion-IIT Israel Faculty of Mathematics Technion-IIT Israel Department of Neurosciences KU Leuven Belgium Department of Ophthalmology UZ Leuven Belgium Department of Electrical Engineering KU Leuven Belgium Ophthalmology Department São Paulo Federal University Brazil Medical Engineering and Science Massachusetts Institute of Technology United States School of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel

Generalization in medical segmentation models is challenging due to limited annotated datasets and imaging variability. To address this, we propose Retinal Layout-Aware Diffusion (RLAD), a novel diffusion-based framework for generating controllable layout-aware images. RLAD conditions image generation on multiple key layout components extracted from real images, ensuring high structural fidelity while enabling diversity in other components. Applied to retinal fundus imaging, we augmented the training datasets by synthesizing paired retinal images and vessel segmentations conditioned on extracted blood vessels from real images, while varying other layout components such as lesions and the optic disc. Experiments demonstrated that RLAD-generated data improved generalization in retinal vessel segmentation by up to 8.1%. Furthermore, we present REYIA, a comprehensive dataset comprising 586 manually segmented retinal images. To foster reproducibility and drive innovation, both our code and dataset will be made publicly accessible (upon publication). © 2025, CC BY-NC-ND.

关键词： Ophthalmology

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Quantifying spatial, temporal, angular and spectral structure of effective daylight in perceptually meaningful ways

arXiv

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arXiv 2022年

作者： Yu, Cehao Wijntjes, Maarten Eisemann, Elmar Pont, Sylvia Faculty of Industrial Design Engineering Delft University of Technology Delft Netherlands Computer Graphics and Visualization Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

We present a method to capture the 7-dimensional light field structure, and translate it into perceptually-relevant information. Our spectral cubic illumination method quantifies objective correlates of perceptually relevant diffuse and directed light components, including their variations over time, space, in color and direction, and the environment’s response to sky and sunlight. We applied it "in the wild", capturing how light on a sunny day differs between light and shadow, and how light varies over sunny and cloudy days. We discuss the added value of our method for capturing nuanced lighting effects on scene and object appearance, such as chromatic gradients. © 2022, CC BY.

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