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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Tapolcai, Janos Babarczi, Peter Ho, Pin-Han Ronyai, Lajos Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics BudapestH-1111 Hungary University of Waterloo Deptartment of Electrical and Computer Engineering Canada Institute for Computer Science and Control Eötvös Loránd Research Networkand Department of Algebra Hungary Institute of Mathematics Budapest University of Technology and Economics BudapestH-1111 Hungary

ISBN: (纸本)9798350334142

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high, while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences. © 2023 IEEE.

关键词： Graph theory

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Design oligoporous-core based multimode fiber for mode division multiplexing applications

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Optical and Quantum Electronics 2025年第5期57卷 1-24页

作者： Mitu, Sumaiya Akhtar Abdulrazak, Lway Faisal Ibrahim, Sobhy M. Tahhan, Shaymaa R. Hossain, Md Bellal Ahmed, Kawsar Bui, Francis M. Chen, Li Department of Computer Science and Engineering University of Asia Pacific 74/A Green Rd Dhaka1205 Bangladesh Department of Information and Communication Technology Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh11451 Saudi Arabia Department of Laser and Optoelectronics Engineering College of Engineering Al-Nahrain University Jadriya Baghdad64074 Iraq School of Electrical and Information Engineering The University of Sydney SydneyNSW Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh

A polarization-maintaining oligoporous-core-based multi-mode fiber is proposed. By tuning the air hole, as well as the core number, shape, size, and position up to 28 distinct linearly polarized (LP) modes are obtained. The Finite Element Method (FEM) is used to perform the numerical investigations. In addition, various materials combinations are used as a doping with silica which is highly helpful to increase or decrease the refractive index of the core material. The multimode fiber is identified by the normalized frequency or V parameter. Besides, the high birefringence value, low loss value, minimum crosstalk with high sensitivity response of 1.46×10-2, 2×10-11dB/m, 41.80 dB and 88,280.46 nm/RIU, respectively, are achieved from the numerical investigations over the wavelength range from 1.55 µm to1.65 µm for the different LP modes. Moreover, good responses also obtain for the numerical aperture, V number, coupling length and other parameters. In the end, every value reveals both an easy-to-fabricate structure design as well as adequate performance analysis. The suggested fiber structure can support many modes and might be applicable in the field of optical communications and spatial multiplexing based on the user demand. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fiber to the x

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Defect Recognition based on Radon Transform in Pentacene organic thin-film Archimedean interdigitated spirals transistor

Defect Recognition based on Radon Transform in Pentacene org...

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2023 International Conference on Clean electrical Power, ICCEP 2023

作者： Sciuto, Grazia Lo Linde, Sivan Shikler, Rafi Kowol, Pawel Coco, Salvo Capizzi, Giacomo Silesian University of Technology Department of Mechatronics Gliwice Poland Ben-Gurion University of the Negev School of Electrical and Computer Engineering Optoelectronic Organic Semiconductor Devices Laboratory Department of Electrical and Computer Engineering Beer-Sheva Israel Ben-Gurion University of the Negev Ilse Katz Institute for Nanoscale Science and Technology Beer-Sheva Israel University of Catania Department of Electrical Electronics and Informatics Engineering Catania Italy

ISBN: (纸本)9798350348378

In this paper the problem of automatic defects detection in thin film transistor is considered in order to increase the efficiency of electronic devices. An effective approach based on defect pattern recognition in organic transistor is developed using the Radon transform. The adopted design and technology in Pentacene organic thin-film transistor exhibits two interdigitated Archimedean spirals pattern. In the conducted experiments, the images acquisition is performed with a microscope camera to capture the various defects of surface morphology on the top of the examined transistors. The defects detection is obtained by using the Radon transform on each channel (R,G,B) of these images. © 2023 IEEE.

关键词： Thin film transistors

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Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio 48

Dynamic Independent Component Extraction with Blending Mixin...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cmejla, Jaroslav Koldovsky, Zbynek Kautsky, Vaclav Adali, Tulay Technical University of Liberec Faculty of Mechatronics Informatics and Interdisciplinary Studies Czech Republic University of Maryland Department of Electrical and Computer Engineering BaltimoreMD21250 United States

ISBN: (纸本)9781728163277

This paper deals with dynamic Blind Source Extraction (BSE) from where the mixing parameters characterizing the position of a source of interest (SOI) are allowed to vary over time. We present a new source extraction model called CvxCSV which is a parameter-reduced modification of the recent Constant Separation Vector (CSV) mixing model. In CvxCSV, the mixing vector evolves as a convex combination of its initial and final values. We derive a lower bound on the achievable mean interference-to-signal ratio (ISR) based on the Cramér-Rao theory. The bound reveals advantageous properties of CvxCSV compared with CSV and compared with a sequential BSE based on independent component extraction (ICE). In particular, the achievable ISR by CvxCSV is lower than that by the previous approaches. Moreover, the model requires significantly weaker conditions for identifiability, even when the SOI is Gaussian. © 2023 IEEE.

关键词： Blind source separation

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Optimizing Gabor texture features for materials recognition by convolutional neural networks

Optimizing Gabor texture features for materials recognition ...

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London Imaging Meeting, LIM 2023

作者： Bianconi, Francesco Cusano, Claudio Napoletano, Paolo Schettini, Raimondo Department of Engineering Universitá degli Studi di Perugia Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy Department of Informatics Systems and Communication University of Milano Bicocca Italy

ISBN: (纸本)9780892083633

In this paper, we present a novel technique that allows for customized Gabor texture features by leveraging deep learning neural networks. Our method involves using a Convolutional Neural Network to refactor traditional, hand-designed filters on specific datasets. The refactored filters can be used in an off-the shelf manner with the same computational cost but significantly improved accuracy for material recognition. We demonstrate the effectiveness of our approach by reporting a gain in discrimina tion accuracy on different material datasets. Our technique is particularly appealing in situations where the use of the entire CNN would be inadequate, such as analyzing non-square im ages or performing segmentation tasks. Overall, our approach provides a powerful tool for improving the accuracy of material recognition tasks while retaining the advantages of handcrafted filters. © 2023 Society for Imaging Science and Technology.

关键词： Convolutional neural networks

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Volumetric Hippocampus Segmentation Using 3D U-Net Based On Transfer Learning

Volumetric Hippocampus Segmentation Using 3D U-Net Based On ...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Ramadhan Sanyoto Sugiharso Widodo I Ketut Eddy Purnama Reza Fuad Rachmadi Department of Electrical Engineering Department of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

The hippocampus, a crucial component of the human brain, is involved in fundamental cognitive processes such as learning, memory, and spatial navigation. However, it is susceptible to several neuropsychiatric disorders, including epilepsy, Alzheimer’s disease, and depression. Utilizing Magnetic Resonance Imaging (MRI) techniques with efficient spatial navigation capabilities is crucial for assessing the physiological condition of the hippocampus. Labeling the hippocampus on MRI images primarily depends on manual methods, which are time-consuming and prone to errors between observers. The issue with MRI image processing lies in its demanding computational requirements and lengthy duration. Furthermore, there is a need for more three-dimensional hippocampal datasets for training deep-learning models, in which 3D labeled medical datasets are often scarce in medical imaging. This paper introduces a 3D U-Net architecture that utilizes a transfer learning model to segment the hippocampus from different pre-trained model scenarios. The results of all test scenarios indicate that the suggested model exhibits an average Dice Score, Intersection over Union (IoU) Score, and Sensitivity exceeding 0.85, 0.75, and 0.80, respectively. The proposed methodology enhances the model’s ability to generalize within a shorter timeframe, even when dealing with limited volumetric datasets. These results are achieved through transfer learning, which decreases computational complexity by utilizing pre-learned characteristics from previous tasks.

关键词： Solid modeling Three-dimensional displays Navigation Magnetic resonance imaging Computational modeling Volume measurement Transfer learning

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Blockchain-based Security for Cloud Data Storage 9

Blockchain-based Security for Cloud Data Storage

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9th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2023

作者： Bin Idrus, Mohammad Ariff Zakwan Rahman, Farah Diyana Abdul Khalifa, Othman Omran Yusoff, Nelidya Md Islamic University Malaysia Department of Electrical and Computer Engineering International Kuala Lumpur Malaysia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia

ISBN: (纸本)9798350343380

Data Cloud Storage is a service that allows data to save data on offsite storage system and managed by third-party and is made accessible by a web services user. It has become one of the most convenient and efficient methods to store data online. However, like other technologies and services, cloud storage has its advantages and disadvantages, but the challenge of security is the big issue. In this project, an investigation of the use of blockchain technology to improve the security of cloud data storage. The focus to address in this project is the vulnerability of cloud data storage systems to cyber-attacks and data breaches. The design and implementation of a proof-of-concept blockchain-based data storage system, and an evaluation of the system's security and performance will be presented. A proposal system to secure cloud storage could be an alternative for traditional methods. It is proposed to use blockchain as a data protection system. © 2023 IEEE.

关键词： Cloud storage

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Comparative Evaluation between Accelerated RISC- V and ARM AI Inference Machines 6

Comparative Evaluation between Accelerated RISC- V and ARM A...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Christofas, Vasileios Amanatidis, Petros Karampatzakis, Dimitris Lagkas, Thomas Goudos, Sotirios K. Psannis, Konstantinos E. Sarigiannidis, Panagiotis International Hellenic University Department of Computer Science Kavala Greece Aristotle University of Thessaloniki Department of Physics Thessaloniki Greece University of Macedonia Department of Applied Informatics Thessaloniki Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350339505

Embedded AI development has been rapidly im-proving for the past few years and has had a great impact on edge AI networks. However, as neural networks become deeper and deeper it becomes more difficult to execute complicated tasks without sacrificing a good amount of power and performance. In this paper, we make a comparative evaluation between two AI acceleration devices. The first one features a RISC- V 64-bit processor while the other one is ARM powered. These devices are combined with AI co-processors, or ASICs, with computer vision capabilities. Our benchmark consists of a simple classification task split into multiple versions. The results showed that the RISC- V inference machine had 4 times lower consumption while the ARM machine was up to 15 times faster in our largest network. We discuss the results in great detail while keeping our focus on all aspects equally. Finally, we make recommendations based on their usage and application. © 2023 IEEE.

关键词： computer vision

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Past, Present, and Future of CASS Educational Programs and Initiatives [Feature]

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IEEE Circuits and Systems Magazine 2024年第2期24卷 7-15页

作者： Rokhani, Fakhrul Zaman Zhang, Xinmiao Joshi, Rajiv V. Reis, Ricardo Grimblatt, Victor Tang, Kea-Tiong Li, Yongfu Amara, Amara Delgado-Restituto, Manuel Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Engineering Serdang43400 Malaysia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre90010-150 Brazil Synopsys California CA92037 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Shanghai Jiao Tong University Department of Micro and Nano Electronics Engineering Shanghai200240 China Paris75006 France Institute of Microelectronics of Sevilla Sevilla41092 Spain

The Circuits and Systems Society (CASS) places a strong emphasis on advancing education within its domain. Various educational programs and resources have contributed to the professional development of CASS members for more than 20 years now. A major transformation within the Society was started in 2021 with a clear vision to become a premier educational resource within circuits and systems areas of interest. This article provides a comprehensive review of flagship educational programs and past initiatives. Insights from the lessons learned and future trends in the education ecosystem guided society's strategies and frameworks for improving and developing new educational products and services. The benefits of learning with the CASS program are presented, highlighting the Society's deep commitment to serving the community at large in educational pursuit for years to come. © 2001-2012 IEEE.

关键词： engineering education

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Developing an Anti -Phishing Large Language Model: A Focus Group Study on Human, Technological, and Legal Factors

Developing an Anti -Phishing Large Language Model: A Focus G...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： George A. Thomopoulos Panagiota Kiortsi Damianos Dumi Sigalas Dimitris Kosmopoulos Ioannis Igglezakis Christos Fidas Department of Electrical and Computer Engineering University of Patras Faculty of Law Aristotle University of Thessaloniki Department of Computer Engineering and Informatics University of Patras

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

The phishing problem poses a significant threat in modern information systems, putting both individuals and businesses at risk of financial and professional harm. Owing to social media's rapid development and widespread appeal, deception of this sort is hurting millions of people and growing more dangerous. The current work, as part of the AILA (Artificial Intelligence-driven Framework and Legal Advice Tools for Phishing Prevention and Mitigation in Information Systems) project, aims to specify and validate an AI-driven multifactor (human, technology and legal) anti-phishing data model, with the implementation of focus group studies. The findings assist to provide human, technology, and legislative user model endpoints that will be identified and discussed for explicit and implicit user modeling, which will guide the development of the corresponding AI-driven user modeling and profiling mechanisms. To this end a Large Language System is planned to be employed.

关键词： Analytical models Data privacy Law Social networking (online) Phishing Prevention and mitigation Data models User experience Security Information systems

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