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Diketopyrrolopyrrole based organic semiconductors: pioneering advances in organic electronic devices

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Discover Electronics

Discover Electronics 2025年第1期2卷 1-28页

作者： He, Zhengran Asare-Yeboah, Kyeiwaa Bi, Sheng Department of Electrical and Computer Engineering Pennsylvania State University at Erie Erie USA Key Laboratory for Precision and Non-Traditional Machining Technology of the Ministry of Education Dalian University of Technology Dalian China

Recent advancements in the field of organic electronics have attracted great attention, with a focus on implementing various benchmark organic semiconductors in high-performance electronic device applications. Among them, diketopyrrolopyrrole (DPP)-based semiconductors stand out for their unique molecular structure, and are known for their excellent semiconducting properties and broad optical absorption range. In this paper, we first discuss how the recent advances in the charge carrier mobilities of organic semiconductors have enhanced organic electronic device applications, and also outline the challenges associated with mobility variation, crystallinity, and environmental stability. The paper then examines controlled crystallization methods, and focuses on the intrinsic properties of DPP-based materials, including external alignment and thermal annealing techniques, which significantly improve molecular ordering and device performance. Following this, the application of DPP-based semiconductors in various organic electronic devices is covered, with particular emphasis on their role in optoelectronics, organic solar cells, and sensors. Each of these applications benefits from the unique electronic properties of DPP materials, and has been shown to enable advances in device efficiency and sensitivity. By synthesizing recent research and developments, this review highlights the critical role of DPP-based semiconductors in the advancement of high-performance organic electronics, and offers insights into their future potential and applications.

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Android-Based Visualization of Vehicle Trip Data for Accident Reconstruction

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Android-Based Visualization of Vehicle Trip Data for Acciden...

International Conference on electrical engineering and Informatics (ICELTICs)

作者： Ahmad Mufadhdhal Alkifani Maya Fitria Muhammad Daffa Adrian Sitorus T. Muhammad Caesar Maulana Humaira Rahmad Dawood Department of Electrical and Computer Engineering Universitas Syiah Kuala Interactive Intelligent Systems Research Group Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798350366822

ISBN: (纸本)9798350366839

Vehicle traffic accident investigations often depend on eyewitness testimony, which can be unreliable, especially in remote areas where no closed circuit television (CCTV) footage is available. A mobile data logger was developed, equipped with a range of sensors, including an accelerometer and gyroscope, to collect and store data related to vehicle movements. An additional Flutter-driven program was developed to extract and visualize data from the mobile logger, and thus improve the accuracy of accident detection and allow incidents to be rebuilt. These programs were subjected to black-box testing, which produced results consistent with the anticipated outcomes for both the recording and data visualization tests. The data visualization application was subjected to testing using the system usability scale (SUS) technique and obtained a score of 89.5, corresponding to an A grade, the highest possible score of the 5 grade levels.

关键词： Visualization Data visualization Closed box Recording Sensors Gyroscopes Usability Vehicle dynamics Accidents Testing

Hybrid Single Image Super-Resolution Algorithm for Medical Images

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computers, Materials & Continua 2022年第9期72卷 4879-4896页

作者： Walid El-Shafai Ehab Mahmoud Mohamed Medien Zeghid Anas MAli Moustafa H.Aly Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Engineering Department College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Addwasir11991Saudi Arabia Electrical Engineering Department Aswan UniversityAswan81542Egypt Electronics and Micro-Electronics Laboratory(E.μ.E.L) Faculty of SciencesUniversity of MonastirMonastir5000Tunisia Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

High-quality medical microscopic images used for diseases detection are expensive and difficult to ***,low-resolution images are favorable due to their low storage space and ease of sharing,where the images can be enlarged when needed using Super-Resolution(SR)***,it is important to maintain the shape and size of the medical images while enlarging *** of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image ***,this paper suggests a multi-SR and classification framework based on Generative Adversarial Network(GAN)to generate high-resolution images with higher quality and finer details to reduce *** proposed framework comprises five GAN models:Enhanced SR Generative Adversarial Networks(ESRGAN),Enhanced deep SR GAN(EDSRGAN),Sub-Pixel-GAN,SRGAN,and Efficient Wider Activation-B GAN(WDSR-b-GAN).To train the proposed models,we have employed images from the famous BreakHis dataset and enlarged them by 4×and 16×upscale factors with the ground truth of the size of 256×256×***,several evaluation metrics like Peak Signal-to-Noise Ratio(PSNR),Mean Squared Error(MSE),Structural Similarity Index(SSIM),Multiscale Structural Similarity Index(MS-SSIM),and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency,training time,and storage *** obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.

关键词： GAN medical images SSIM MS-SSIM PSNR SISR

Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

Z-STACK SCANNING CAN IMPROVE AI DETECTION OF MITOSIS: A CASE STUDY OF MENINGIOMAS

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arXiv 2025年

作者： Gu, Hongyan Onstott, Ellie Yan, Wenzhong Xu, Tengyou Wang, Ruolin Wu, Zida Chen, Xiang Haeri, Mohammad Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Pathology and Laboratory Medicine University of Kansas Medical Center United States

Z-stack scanning is an emerging whole slide imaging technology that captures multiple focal planes alongside the z-axis of a glass slide. Because z-stacking can offer enhanced depth information compared to the single-layer whole slide imaging, this technology can be particularly useful in analyzing small-scaled histopathological patterns. However, its actual clinical impact remains debated with mixed results. To clarify this, we investigate the effect of z-stack scanning on artificial intelligence (AI) mitosis detection of meningiomas. With the same set of 22 Hematoxylin and Eosin meningioma glass slides scanned by three different digital pathology scanners, we tested the performance of three AI pipelines on both single-layer and z-stacked whole slide images (WSIs). Results showed that in all scanner-AI combinations, z-stacked WSIs significantly increased AI’s sensitivity (+17.14%) on the mitosis detection with only a marginal impact on precision. Our findings provide quantitative evidence that highlights z-stack scanning as a promising technique for AI mitosis detection, paving the way for more reliable AI-assisted pathology work-flows, which can ultimately benefit patient management. © 2025, CC BY.

关键词： Flow visualization

Version Innovation Age and Age of Incorrect Version for Monitoring Markovian Sources

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arXiv 2024年

作者： Salimnejad, Mehrdad Kountouris, Marios Ephremides, Anthony Pappas, Nikolaos Department of Computer and Information Science Linköping University Linköping Sweden Communication Systems Department EURECOM Sophia-Antipolis France ‡ Electrical and Computer Engineering University of Maryland College ParkMD United States

In this paper, we propose two new performance metrics, coined the Version Innovation Age (VIA) and the Age of Incorrect Version (AoIV) for real-time monitoring of a two-state Markov process over an unreliable channel. We analyze their performance under the change-aware, semantics-aware, and randomized stationary sampling and transmission policies. We derive closed-form expressions for the distribution and the average of VIA, AoIV, and AoII for these policies. We then formulate and solve an optimization problem to minimize the average VIA, subject to constraints on the time-averaged sampling cost and time-averaged reconstruction error. Finally, we compare the performance of various sampling and transmission policies and identify the conditions under which each policy outperforms the others in optimizing the proposed metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov processes

Incremental Composition of Learned Control Barrier Functions in Unknown Environments

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arXiv 2024年

作者： Lutkus, Paul Anantharaman, Deepika Tu, Stephen Lindemann, Lars Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States

We consider the problem of safely exploring a static and unknown environment while learning valid control barrier functions (CBFs) from sensor data. Existing works either assume known environments, target specific dynamics models, or use a-priori valid CBFs, and thus provide limited safety guarantees for general control-affine systems during exploration. We present a method for safely exploring by incrementally composing a global CBF from local CBFs. The challenge here is that local CBFs may not have well-defined end behavior outside their training domain, i.e. local CBFs may be positive (indicating safety) in regions where no training data is available. We show that well-defined end behavior can be obtained when local CBFs are parameterized by compactly-supported radial basis functions. For learning local CBFs, we collect sensor data, e.g. LiDAR capturing obstacles in the environment, and augment it with simulated data from a safe oracle controller. Our work complements recent efforts to learn CBFs from safe demonstrations, where learned safe sets are limited to their training domains, by demonstrating how to grow the safe set over time as more data becomes available. We evaluate our approach on two simulated systems, where our method successfully explores an unknown environment while maintaining safety throughout the entire execution. Copyright © 2024, The Authors. All rights reserved.

关键词： Radial basis function networks

Quad-Port Dual Circularly Polarized Multi-Mode Patch Antenna with Mutual Coupling Reduction by Using Characteristic Modes Analysis

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Liu, Neng-Wu Lin, Zi-Jie Liu, Zhong-Xun Zhu, Lei Liu, Ying Xidian University National Key Laboratory of Radar Detection and Sensing Xi'an710071 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology SAR China

In this letter, a novel approach to reduce mutual coupling (MC) of quad-port circularly polarized (CP) microstrip patch antenna (MPA) is proposed using a multi-mode concept. Firstly, the dual-port MPA with decoupling branch under mode1 and mode2 is theoretically studied. It proves that these two modes could be combined together to shift its electric-field null. As such, the decoupling branches are loaded on the radiator of the quad-port MPA so as to combine these dual modes. After that, the quadrature hybrid coupler (QHC) and LC network are successively connected to the feeding probes to widen its impedance matching and generate the dual-CP beams at quad-port. In final, our proposed MPA is fabricated and measured. The results that the |S11| © 2002-2011 IEEE.

关键词： Radiators

When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment

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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Kim, Dong In Letaief, Khaled B. The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Hong Kong

AI agents based on multimodal large language models (LLMs) are expected to revolutionize human-computer interaction and offer more personalized assistant services across various domains like healthcare, education, manufacturing, and entertainment. Deploying LLM agents in 6G networks enables users to access previously expensive AI assistant services via mobile devices democratically, thereby reducing interaction latency and better preserving user privacy. Nevertheless, the limited capacity of mobile devices constrains the effectiveness of deploying and executing local LLMs, which necessitates offloading complex tasks to global LLMs running on edge servers during long-horizon interactions. In this article, we propose a split learning system for LLM agents in 6G networks leveraging the collaboration between mobile devices and edge servers, where multiple LLMs with different roles are distributed across mobile devices and edge servers to perform user-agent interactive tasks collaboratively. In the proposed system, LLM agents are split into perception, grounding, and alignment modules, facilitating inter-module communications to meet extended user requirements on 6G network functions, including integrated sensing and communication, digital twins, and task-oriented communications. Furthermore, we introduce a novel model caching algorithm for LLMs within the proposed system to improve model utilization in context, thus reducing network costs of the collaborative mobile and edge LLM agents. © 2024, CC BY.

关键词： Learning systems