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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Goyal, Dinesh Shreenidhi, H.S. Aher, Vijaya N. Amudha, A. Parmar, Yuvraj Ashok, V. Quantum University Research Center Quantum University India School of Engineering and Technology Computer Science and Engineering Karnataka Bangalore India Vishwakarma Institute of Technolog E&TC Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India

ISBN: (纸本)9798350370249

Advanced modulation and coding schemes (AMCS) are techniques used to enhance the records charge and throughput in satellite to for pc communications links with the aid of taking benefit of multiplexing, errors correction, and forward error-correction techniques. AMCS are designed to beautify records transmission from the satellite to the receiving station, as well as from the ground station to the satellite. When a signal is sent through a satellite hyperlink, a top restriction to its statistics charge is reached due to elements which include sign-to-noise ratio and the quantity of interference detected. With a proper combination of modulation and coding strategies, but, this upper restrict can be prolonged, accordingly improving throughput and reliability. AMCS use modulation techniques to encode records whilst coding techniques are used to manipulate and verify information integrity between the source and the vacation spot. Modulation strategies permit records signals to skip thru a transmission channel, together with a satellite to for pc link, through converting the traits of the signal consisting of the amplitude, frequency, or section. Coding techniques make sure that records may be reliably transmitted over the hyperlink by using adding redundant information to the authentic message. Such redundant statistics is used to detect and accurate any errors inside the facts that could occur during the transmission technique. © 2024 IEEE.

关键词： Satellite links

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Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study

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BMC Medical Imaging 2024年第1期24卷 92页

作者： Chang, Joseph Lin, Bo-Ru Wang, Ti-Hao Chen, Chung-Ming Department of Biomedical Engineering College of Medicine and College of Engineering National Taiwan University No. 1 Sec. 1 Jen-Ai Road Taipei 100 Taipei 100 Taiwan EverFortune.AI Co. Ltd Taichung Taiwan The Data Science Degree Program College of Electrical Engineering and Computer Science National Taiwan University and Academia Sinica Taipei Taiwan Department of Radiation Oncology China Medical University Hospital Taichung Taiwan Department of Medicine China Medical University Taichung Taiwan

Background: The study aimed to develop and validate a deep learning-based computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States. Methods: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists. Results: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong’s CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings. Conclusion: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings. © The Author(s) 2024.

关键词： Active learning Chest radiographs Deep learning Pleural effusion X-rays

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Subwavelength-engineered Antislot Photonic Crystals in a Silicon Photonics Foundry for On-chip Communications

Subwavelength-engineered Antislot Photonic Crystals in a Sil...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Arnold, Kellen P. Allen, Joshua A. Halimi, Sami I. Ryder, Landen D. Afzal, Francis O. Bian, Yusheng Aboketaf, Abdelsalam Hirokawa, Takako Dezfulian, Kevin Rakowski, Michal Augur, Rod Nummy, Karen Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 West End Ave NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 West End Ave NashvilleTN37235 United States NASA Goddard Space Flight Center Code 561 GreenbeltMD20771 United States GlobalFoundries 400 Stone Break Rd. Ext. MaltaNY12020 United States

We report scalable foundry fabrication and characterization of photonic crystal nanobeam waveguides incorporating subwavelength-scale dielectric antislot unit cells. This work enables enhanced light-matter interaction and three-fold improvement in VπL when incorporated in Mach-Zehnder modulators. © Optica Publishing Group 2024

关键词： Silicon photonics

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Text2Scene: Text-driven Indoor Scene Stylization with Part-aware Details

arXiv

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arXiv 2023年

作者： Hwang, Inwoo Kim, Hyeonwoo Kim, Young Min Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University Korea Republic of

We propose Text2Scene, a method to automatically create realistic textures for virtual scenes composed of multiple objects. Guided by a reference image and text descriptions, our pipeline adds detailed texture on labeled 3D geometries in the room such that the generated colors respect the hierarchical structure or semantic parts that are often composed of similar materials. Instead of applying flat stylization on the entire scene at a single step, we obtain weak semantic cues from geometric segmentation, which are further clarified by assigning initial colors to segmented parts. Then we add texture details for individual objects such that their projections on image space exhibit feature embedding aligned with the embedding of the input. The decomposition makes the entire pipeline tractable to a moderate amount of computation resources and memory. As our framework utilizes the existing resources of image and text embedding, it does not require dedicated datasets with high-quality textures designed by skillful artists. To the best of our knowledge, it is the first practical and scalable approach that can create detailed and realistic textures of the desired style that maintain structural context for scenes with multiple objects. © 2023, CC BY.

关键词： Textures

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Compliance Design Options for Offline CBDCs: Balancing Privacy and AML/CFT

Compliance Design Options for Offline CBDCs: Balancing Priva...

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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Panagiotis Michalopoulos Odunayo Olowookere Nadia Pocher Johannes Sedlmeir Andreas Veneris Poonam Puri Department of Electrical and Computer Engineering University of Toronto Osgoode Hall Law School York University Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Department of Computer Science University of Toronto

ISBN: (数字)9798350316742

ISBN: (纸本)9798350316759

Many central banks are researching and piloting digital versions of fiat money, specifically retail Central Bank Digital Currencies (CBDCs). Core to these systems’ design is the ability to perform transactions even without network connectivity. Due to the lack of direct involvement of third parties in these offline transfers, various regulatory requirements that are key in the financial space need to be accommodated. This paper deploys a compliance-by-design approach to evaluate technologies that can balance privacy with anti-money laundering and counterterrorism financing (AML/CFT) measures. It classifies privacy design options and corresponding technical building blocks for offline CBDCs, along with their impact on AML/CFT measures, and outlines commonalities and differences between offline and online solutions. As such, it provides a conceptual framework for further techno-legal assessments and implementations.

关键词： Privacy Data privacy Online banking Hardware Software Cryptocurrency Blockchains

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Unveiling Multivariate EEG Features: A Novel Approach to Enhancing ADHD Diagnosis Through Visual and Auditory Attention Tests

Unveiling Multivariate EEG Features: A Novel Approach to Enh...

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2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023

作者： Fan, Zuo-Cian Lin, Ro-Wei Tsai, Ching-Shu Chou, Wen-Jiun Li, Chia-Jung Huang, Zih-Jun Shih, Bin-Yu Wang, Liang-Jen Ko, Li-Wei Department of Biological Science & Technology Hsinchu300 Taiwan National Yang Ming Chiao Tung University International Ph.D. Program in Interdisciplinary Neuroscience Hsinchu300 Taiwan Kaohsiung Chang Gung Memorial Hospit Department of Child and Adolescent Psychiatry National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering Hsinchu300 Taiwan

ISBN: (纸本)9798350305791

We sought to revolutionize the classification of Attention-Deficit/Hyperactivity Disorder (ADHD) by pioneering an innovative approach that seamlessly integrated auditory and visual tests with electroencephalogram (EEG) coherence analysis, forming the foundation for our Support Vector Machine (SVM) models. Our primary objective was to achieve precise differentiation between individuals with ADHD and those without, aiming to reshape the landscape of ADHD diagnosis and early intervention. We constructed an SVM model based on CATA coherence, incorporating a carefully selected set of 180 features, and another grounded in CPT coherence with 70 features. Astonishingly, our exploration uncovered that a model incorporating CATA's coherence and Power Spectral Density (PSD) as features achieved an astounding accuracy rate of 97.7%, using only half the number of features. This revelation hints at untapped potential for multivariate advancements in the realm of ADHD classification. The landscape of ADHD diagnosis and intervention is perpetually evolving, most notably with the FDA's approval of game therapy for ADHD. Leveraging the transformative power of our findings, we envision the development of user-friendly game therapies for in-home use, empowering both clinicians and parents alike. This marks a paradigm shift in the realm of ADHD intervention, offering objective, data-driven tools for the early management of ADHD. Gamified therapy not only enhances accessibility but also fosters engagement and compliance, all of which are pivotal in the long-term care of individuals with ADHD. In summary, our study represents a significant advancement in our comprehension and classification of ADHD. By fusing auditory and visual tests with EEG coherence analysis, fortified by state-of-the-art machine learning techniques, we have achieved an unprecedented level of accuracy in identifying individuals with ADHD. Our findings, aligned with the FDA-approved game therapy, offer a glimpse into a

关键词： Electroencephalography

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Subwavelength-engineered Antislot Photonic Crystals in a Silicon Photonics Foundry for On-chip Communications

Subwavelength-engineered Antislot Photonic Crystals in a Sil...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Mach-Zehnder interferometers

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A Multi-UAV Charging Station Enabling Free Landing by Grid Pattern Transmitter

A Multi-UAV Charging Station Enabling Free Landing by Grid P...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Jungho Kim Hyunkyeong Jo Seoktae Seo Bonyoung Lee Hyungki Min Franklin Bien School of Electrical and Computer Engineering Ulsan National Institute of Science and Technology Ulsan Korea Program in Information & Communication Technology Convergence Ulsan National Institute of Science and Technology Ulsan Korea

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

This paper proposed a novel landing platform with wireless power transfer (WPT) to charge multiple unmanned aerial vehicles (UAVs) without restrictions on landing locations or alignment. A transmitter is designed using wires to charge a wide area. To overcome the disadvantages of system made by wires, a grid pattern transmitter and double power line are proposed. These structures provide multiple charging with free positioning of receivers and excite the transmitter lines in wide area properly. The optimized system is composed of 0.15m square cells, providing a 0.9 m by 0.9 m charging area. By experiment, it can efficiently charge four 0.1 m square receivers located anywhere in transmitter. The overall efficiency of four receivers is 37.5%. Also, the experimental results verify that it is unnecessary to align the receiver within the cell, as it has a 2.62% error due to rotation and a 2.94 % error due to position within the cell.

关键词： Coils Magnetic flux leakage Transmitters Wires Windings Receivers Wireless power transfer Autonomous aerial vehicles Current distribution Power electronics

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Signal Quality Prediction for Cognitive Radio Networks using Deep Learning Techniques 15

Signal Quality Prediction for Cognitive Radio Networks using...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dev, Sudhanshu Santhoshkumar, M.P. Goyal, Udita Raja Praveen, K.N. Pathak, Kishor R. Ravivarman, G. Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Computer Science and Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Information Technology Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India

ISBN: (纸本)9798350370249

Cognitive radio networks (CRNs) are getting increasingly famous due to their potential to deliver excessive-bandwidth offerings to numerous users. Despite this, the signal satisfactory of those networks can be fantastically variable and hard to predict, leading to degraded communication. Currently, deep gaining knowledge of strategies had been proposed as a method to improve the capability of CRNs to correctly expect signal first-rate, allowing multiplied device reliability. On this paper, we gift an assessment of present deep gaining knowledge of tactics to be expecting signal excellent in CRNs, focusing at the structure and application of convolutional neural networks (CNNs), recurrent neural networks (RNNs) and deep reinforcement studying (DRL). The overview additionally examines the overall performance of those processes in terms of accuracy, scalability and generalization to new environments. Subsequently, we provide a survey of capability future guidelines for signal first-rate prediction in CRNs using deep getting to know. © 2024 IEEE.

关键词： Deep reinforcement learning

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