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Periodica polytechnica electrical engineering and computer science 2024年第3期68卷 223-231页

作者： Baba, Tamana Javed, Yasir Khan, Zafar Iqbal Ali, Turab Hasbullah, Nurul Fadzlin Department of Electrical and Computer Engineering Kulliyyah of Engineering International Islamic University Malaysia Selangor P.O.B. 10 50728 Malaysia College of Computer Science and Information Sciences Prince Sultan University P.O.B. 66833 Riyadh11586 Saudi Arabia National Centre for Physics P.O.B. 2141 Shahdra valley road Islamabad Pakistan

This paper investigates the effects of proton radiation on the electrical and optical properties of InGaN light-emitting diodes (LEDs). InGaN LEDs are known for their high brightness and efficiency, making them useful in various applications. However, they are vulnerable to radiation damage, which can degrade their performance over time. In this study, InGaN LEDs were exposed to proton radiation with the fluences of 1 × 1013 cm-2, 3 × 1013 cm-2 and 3 × 1014 cm-2 and their electrical and optical properties were measured before and after irradiation. Results show that proton radiation causes asignificant increase in the reverse leakage current. The light intensity also increases due to radiation. These changes are attributed to radiation-induced defects created in the LED material. The findings of this study provide important insights into the reliability and durability of InGaN LEDs in space and other radiation environments. © 2024 Budapest University of Technology and Economics. All rights reserved.

关键词： Light emitting diodes

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Using Stable Diffusion Model for Text-Driven Generation in Augmented Reality Applications 33rd

Using Stable Diffusion Model for Text-Driven Generation in A...

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33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Rücker, Kristian Livada, Časlav Galba, Tomislav Baumgartner, Alfonzo Faculty of Electrical Engineering Josip Juraj Strossmayer University in Osijek Computer Science and Information Technology Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031805967

Rapid advances in science and technology have led to the integration of artificial intelligence (AI) in many areas, including augmented reality (AR). This study explores the application of the Stable Diffusion (SD) model to develop an AR application that generates images based on text descriptions. Using Unity, a versatile game development platform, we implemented this AR application, scripting in C# with Visual Studio 2022. The user interface and functionality of the SD model were managed via the UI Stable Diffusion Automatic 1111 tool, allowing seamless integration and execution of SD functions. This work demonstrates the potential of AI-driven models in enhancing AR experiences and introduces a novel approach to dynamic visual content creation and editing. Furthermore, this technology has significant implications for the maintenance of municipal infrastructure and maintenance organizations. By converting text instructions into visual AR overlays, maintenance teams can improve the accuracy and efficiency of tasks such as inspections, repairs, and infrastructure management, streamlining operations and reducing errors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Augmented reality

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Empowering Healthcare with AI: Brain Tumor Detection Using MRI and Multiple Algorithms

Empowering Healthcare with AI: Brain Tumor Detection Using M...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Arowolo, Micheal Olaolu Oluwatobiloba Ajayi, Williams Olawoye, Prisca Babajide, Oluwatomilola Aigbogun, Happiness Eric Damola Salawu, Mukhtar Adebiyi, Marion Olubunmi Adebiyi, Ayodele University of Missouri Department of Electrical Engineering and Computer Science Columbia65201 United States Landmark University Department of Computer Science Omu-Aran Nigeria Western Illinois University Department of Public Health 61455 United States Stowers Institute for Medical Research Kansas CityMO United States Kwara State University Department of Computer Science Malete Nigeria

ISBN: (纸本)9798350358155

Artificial intelligence (AI) has revolutionized neuroimaging, especially in the identification of brain cancers, by giving patients greater control and providing highly accurate diagnostic imaging. utilizing MRI data, this study investigates the feasibility of AI-driven brain tumor diagnosis utilizing methods including K-nearest neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB). Careful data preprocessing allowed us to access Kaggle's brain MRI datasets labeled with cancer information, which allowed us to obtain accurate results. While Naive Bayes achieved 60% accuracy and 84% sensitivity, SVM achieved 82% sensitivity and 62% accuracy. Enhanced sensitivity and precision, in addition to 63% accuracy, were shared by Decision Tree and KNN. These findings suggest that AI could be helpful in the multi-method detection strategy that is essential for the correct diagnosis of brain tumors. This study provides clinicians with flexible diagnostic tools powered by artificial intelligence, opening a new chapter in the fight against neurological disorders and for better patient outcomes. The combination of artificial intelligence with magnetic resonance imaging (MRI) holds great promise for the better early detection and treatment planning of brain cancers. As AI keeps changing the healthcare landscape, these studies show how AI can revolutionize diagnostics and the fight against neurological illnesses. © 2024 IEEE.

关键词： Support vector machines

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Far-field super-resolution chemical microscopy

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Light(science & Applications) 2023年第7期12卷 1246-1267页

作者： Mingwei Tang Yubing Han Danchen Jia Qing Yang Ji-Xin Cheng State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China Intelligent Perception Research Institute Zhejiang LabHangzhou 311100China Department of Biomedical Engineering Department of Electrical and Computer EngineeringPhotonics CenterBoston UniversityBostonMA 02459USA

Far-feld chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological,material,and chemical *** microscopy provides a nondestructive way of chemical identification without exterior ***,the diffraction limit of optics hindered it from discovering more details under the resolution *** development of super-resolution techniques gives enlightenment to open this door behind far-field chemical ***,we review recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial *** further highlight applications in biomedical research,material characterization,environmental study,cultural heritage conservation,and integrated chip inspection.

关键词： chemical details optics

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Cyberbullying Recognition on social media for an Afro-Asiatic language Content Using Attention Mechanism and Bi-RNN Classifier

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Procedia computer science 2025年 258卷 2232-2243页

作者： Rajesh Sharma R Muluken Ayele Alemu Nune Sreenivas Akey Sungheetha Sheila Mahapatra Department of Computer Science and Engineering Alliance University Bangalore-India Department of Computer Science College of Business and Economics Arsi University Asella Ethiopia Department of Computer Science and Engineering Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronic Engineering Alliance University Bangalore-India

In the present era, cyberbullying on social media has developed into a complicated issue. In Ethiopia, cyberbullying involving sexual content material has become a prevalent issue in recent times. Because the content material on social media is unstructured, cyberbullying that is just motivated by sexual texts on the platform can be a laborious and complicated process. The difficulty of finding sexual texts on public media has raised in recent years, leading some experts to focus their attention on the detection of cyberbullying. Because deep learning algorithms do greatly on tasks involving natural language processing, several academics have suggested using these models to detect cyberbullying. Additionally, the majority of research on this challenge count number has focused on socio-political context, handicap, religion, and ethnicity. As a remedy, this observer suggested a thorough investigation of cyberbullying detection for Amharic sexual writing on social media. To create models, a binary Amharic sexual dataset is paired with a sexual dataset trained from collected Amharic content from the Facebook platform. "Bullying" and "Non-bullying" are the binary classes that make up the prepared dataset. Because the W2Vec model works well for describing non-unusual place key phrases in constrained period datasets, it is mostly based on a Skip-gram model. Furthermore, GRU and LSTM networks are used for model comparison, while the Bi-RNN and Attention Mechanism models are employed for classification. Trends in the application of N-fold cross-validation are understood. N-fold cross-validation on our dataset yields very good universal overall performance, according to the results.

关键词： Cyberbullying detection Deep Learning Natural language processing Amharic language sexual content Bidirectional RNN

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Piccolo: Large-Scale Graph Processing with Fine-Grained in-Memory Scatter-Gather 31

Piccolo: Large-Scale Graph Processing with Fine-Grained in-M...

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Shin, Changmin Song, Jaeyong Jang, Hongsun Kim, Dogeun Sung, Jun Kwon, Taehee Ju, Jae Hyung Liu, Frank Choi, Yeonkyu Lee, Jinho Seoul National University Department of Electrical and Computer Engineering Korea Republic of Old Dominion Univeristy School of Data Science United States Samsung Electronics Korea Republic of

ISBN: (纸本)9798331506476

Graph processing requires irregular, fine-grained random access patterns incompatible with contemporary off-chip memory architecture, leading to inefficient data access. This inefficiency makes graph processing an extremely memory-bound application. Because of this, existing graph processing accelerators typically employ a graph tiling-based or processing-in-memory (PIM) approach to relieve the memory bottleneck. In the tiling-based approach, a graph is split into chunks that fit within the on-chip cache to maximize data reuse. In the PIM approach, arithmetic units are placed within memory to perform operations such as reduction or atomic addition. However, both approaches have several limitations, especially when implemented on current memory standards (i.e., DDR). Because the access granularity provided by DDR is much larger than that of the graph vertex property data, much of the bandwidth and cache capacity are wasted. PIM is meant to alleviate such issues, but it is difficult to use in conjunction with the tiling-based approach, resulting in a significant disadvantage. Furthermore, placing arithmetic units inside a memory chip is expensive, thereby supporting multiple types of operation is thought to be impractical. To address the above limitations, we present Piccolo, an end-to-end efficient graph processing accelerator with fine-grained in-memory random scatter-gather. Instead of placing expensive arithmetic units in off-chip memory, Piccolo focuses on reducing the off-chip traffic with non-arithmetic function-in-memory of random scatter-gather. To fully benefit from in-memory scatter-gather, Piccolo redesigns the cache and miss-handling architecture (MHA) of the accelerator such that it can enjoy both the advantage of tiling and in-memory operations. Piccolo achieves a maximum speedup of 3.28 × and a geometric mean speedup of 1.62 ×, along with up to 59.7% reduction in energy consumption across various and extensive benchmarks. © 2025 IEEE.

关键词： Memory architecture

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Afra: An Eclipse-Based Tool with Extensible Architecture for Modeling and Model Checking of Rebeca Family Models 1

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10th IPM International Conference on Fundamentals of Software engineering, FSEN 2023

作者： Khamespanah, Ehsan Sirjani, Marjan Khosravi, Ramtin School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Computer Science Reykjavik University Reykjavik Iceland School of Innovation Design and Engineering Mälardalen University Västerås Sweden

ISBN: (数字)9783031424410

ISBN: (纸本)9783031424403

Afra is an Eclipse-based tool for the modeling and model checking of Rebeca family models. Together with the standard enriched editor, easy to trace counter-example viewer, modular temporal property definition, exporting a model and its transition system to some other formats facilities are features of Afra. Rebeca family provides actor-based modeling languages which are designed to bridge the gap between formal methods and software engineering. Faithfulness to the system being modeled, and the usability of Rebeca family languages help in ease of modeling and analysis of the model, together with the synthesis of the system based on the model. In this paper, architectural decisions and design strategies we made in the development of Afra are presented. This makes Afra an extensible and reusable application for the modeling and analysis of Rebeca family models. Here, we show how different compilers can be developed for the family of languages which are the same in general language constructs but have some minor differences. Then we show how the model checking engine for these different languages is designed. Despite the fact that Afra has a layered object-oriented design and is developed in Java technology, we use C++ codes for developing its model checking for the performance purposes. This decision made the design of the application even harder. © 2023, IFIP International Federation for Information Processing.

关键词： Model checking

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Multilevel Chaotic Maps with Differential Encoding-Based Encryption Method for DICOM Images 5th

Multilevel Chaotic Maps with Differential Encoding-Based En...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India

ISBN: (纸本)9789819773701

This paper proposes an encryption method based on multiple chaotic maps and differential encoding method to secure the medical images. The proposed method uses a permutation-based image scrambling method in the confusion phase. In the diffusion phase, Piecewise Linear Chaotic Map (PWLCM), Piecewise Chaotic Map (PWCM), and tent map with differential encoding method have been employed at different levels of the phase to secure the medical images. The model has been tested on Digital Imaging and Communications in Medicine (DICOM) images. The proposed encryption method demonstrates robust security against statistical and entropy attacks, with encrypted images exhibiting a correlation close to zero and achieving a maximum entropy value of 7.9993. The low SSIM value between the original and encrypted images confirms their dissimilarity. Higher Mean Square Error (MSE) values and lower Peak Signal-to-Noise Ratio (PSNR) values for encrypted images prove a negligible presence of original pixel values. Additionally, the encryption approach showcases resilience against differential attacks, with a Number of Pixel Change Rate (NPCR) value of 99.6% and a Unified Averaged Changed Intensity (UACI) value of 33.4%, both within acceptable standard ranges. The effectiveness of the proposed method demonstrates that these encrypted DICOM images are safe against various forms of attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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Enhancing Cloud-Based Video Streaming Efficiency using Neural Networks

Enhancing Cloud-Based Video Streaming Efficiency using Neura...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Darwich, Mahmoud Khalil, Kasem Ismail, Yasser Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi Department of Electrical and Computer Engineering UniversityMS38677 United States Southern University and AandM College Electrical Engineering Department Baton RougeLA70807 United States University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA70504 United States

ISBN: (纸本)9798350346473

The growing demand for high-quality video streaming services has increased the need for efficient utilization of cloud resources. This research proposes a novel solution to this challenge by utilizing neural networks to enhance video streaming performance in the cloud. Specifically, the research introduces a neural network architecture capable of dynamically adjusting video quality based on network conditions and user preferences. By combining convolutional and recurrent neural networks, the proposed model learns patterns in video streams and predicts the optimal bit rate for each frame. The architecture also considers network conditions such as bandwidth, latency, and packet loss to adapt video quality in real-Time. The proposed approach aims to improve quality of service while reducing cloud resource utilization, leading to cost-effective and scalable video streaming services. Extensive simulations and experiments were conducted to evaluate the feasibility and effectiveness of the approach. The proposed neural network architecture achieved up to 30% and 40% higher PSNR and SSIM scores, respectively, than the baseline approach. These results demonstrate the effectiveness of the proposed approach in enhancing video streaming efficiency in the cloud. Overall, this research provides valuable insights into designing efficient video streaming systems and highlights the potential benefits of neural networks in cloud-based video streaming services. © 2023 IEEE.

关键词： Quality of service

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Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 2085-2128页

作者： Wang, Xiaofei Zhao, Yunfeng Qiu, Chao Hu, Qinghua Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen518000 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system's adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research, with the hope of arousing the research community's interest in this emerging and exciting interdisciplinary field. © 1

关键词： Resource allocation

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