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Color-conversion displays:current status and future outlook

Light(science & Applications) 2024年第12期13卷 2941-2961页

作者： Guijun Li Man-Chun Tseng Yu Chen Fion Sze-Yan Yeung Hangyu He Yuechu Cheng Junhu Cai Enguo Chen Hoi-Sing Kwok Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong KongChina National and Local United Engineering Laboratory of Flat Panel Display Technology College of Physics and Information EngineeringFuzhou UniversityCollege of Physics and Information Engineering 350108FuzhouChina Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou 350108China

The growing focus on enhancing color quality in liquid crystal displays(LCDs)and organic light-emitting diodes(OLEDs)has spurred significant advancements in color-conversion ***,color conversion is also important for the development and commercialization of *** article provides a comprehensive review of different types of color conversion methods as well as different types of color conversion *** summarize the current status of patterning process,and discuss key strategies to enhance display ***,we speculate on the future prospects and roles that color conversion will play in ultra-high-definition micro-and projection displays.

关键词： materials projection outlook

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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Tomato Leaf Diseases Prediction Using Deep Learning Algorithms

Tomato Leaf Diseases Prediction Using Deep Learning Algorith...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Ananthi, P. Devi, K. Nirmala Gopinath, D. Shanmugapriya, P. Gowtham, S. Kongu Engineering College Department Of Computer Technology Tamilnadu Perundurai India Bangaluru India Kristu Jayanti College Department Of Computer Science Bangaluru India Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798350364828

In the realm of agriculture, infections on tomato leaves pose a worldwide danger to established tomato production, impacting a large number of farmers worldwide. To ensure healthy tomato plant growth and food security for the world's expanding population, early disease detection, treatment, and solution identification are essential. The use of computer-assisted technologies for disease detection in plant leaf is prevalent in modern agriculture. The goal was to develop an effective deep learning model for the detection of diseases in tomato plant leaf. To address memory and computational resource constraints, the proposed model incorporates a lightweight 'Region Convolutional Neural Network (R-CNN)' head and conducted experiments with various neural network architectures, including Densenet-169, optimized Mobilenet-V2 and Resnet-50, to enhance detection accuracy and computational efficiency. The research extends the capabilities of these neural network architectures by optimizing anchor proportions in the Conventional Neural Network (CNN) and modifying feature extraction topologies. These optimizations resulted in improved detection accuracy and computational efficiency and compared proposed technique against cutting-edge models to evaluate its viability and robustness. The outcomes of the research were promising. Impressive outcomes were obtained by the proposed approach regarding accuracy, F1-score, recall, and precision. © 2024 IEEE.

关键词： Computational efficiency

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Self-Triggered Consensus Filtering over Asynchronous Communication Sensor Networks

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computer Modeling in engineering & sciences 2023年第2期134卷 857-871页

作者： Huiwen Xue Jiwei Wen Akshya Kumar Swain Xiaoli Luan Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan UniversityWuxi214000China Department of Electrical Computer and Software EngineeringUniversity of AucklandAuckland0632New Zealand

In this paper,a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering *** from existing event-triggered filtering,the self-triggered one does not require to continuously judge the trigger condition at each sampling instant and can save computational burden while achieving good state *** triggering policy is presented for pre-computing the next execution time for measurements according to the filter’s own data and the latest released data of its neighbors at the current ***,a challenging problem is that data will be asynchronously transmitted within the filtering network because each node self-triggers ***,a co-design of the self-triggered policy and asynchronous distributed filter is developed to ensure consensus of the state ***,a numerical example is given to illustrate the effectiveness of the consensus filtering approach.

关键词： Self-triggered policy sensor networks distributed consensus filtering

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Data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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New tunable phase oscillator structure

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International Journal of Electronics Letters 2024年第1期12卷 41-48页

作者： Elwakil, A.S. Psychalinos, Costas Maundy, Brent Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical and Software Engineering University of Calgary Calgary AB Canada Electronics Laboratory Department of Physics University of Patras Rio Patras Greece

In this Letter, we introduce and experimentally verify a very simple tunable-phase oscillator structure based on cascaded standard filter sections. In particular, a standard inverting second-order low-pass filter is cascaded with a first-order bi-linear filter in a closed loop resulting in a third-order structure that can be made marginally stable. The phase difference between the outputs of the two filter sections is a design parameter that is tunable independent of the oscillation frequency or start-up gain. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： all-pass filters low-pass filters multi-phase oscillators Sinusoidal oscillators tunable phase

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Improved MPPT algorithm: Artificial neural network trained by an enhanced Gauss-Newton method

AIMS Electronics and Electrical Engineering

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AIMS Electronics and Electrical engineering 2023年第4期7卷 380-405页

作者： Dkhichi, Fayrouz Laboratory of Networks Computer Science Telecommunications & Multimedia Electrical Engineering Department Superior School of Technology Hassan II University of Casablanca Morocco

A novel approach defined by the artificial neural network (ANN) model trained by the improved Gauss-Newton in conjunction with a simulated annealing technique is used to control a step-up converter. To elucidate the superiority of this innovative method and to show its high precision and speed in achieving the right value of the Maximum Power Point (MPP), a set of three comparative Maximum Power Point Tracker (MPPT) methods (Perturbation and observation, ANN and ANN associated with perturbation and observation) are exanimated judiciously. The behavior of these methods is observed and tested for a fixed temperature and irradiance. As a result, the proposed approach quickly tracks the right MPP = 18.59 W in just 0.04382 s. On the other hand, the outstanding ability of the suggested method is demonstrated by varying the irradiance values (200 W/m , 300 W/m , 700 W/m , 1000 W/m , 800 W/m and 400 W/m ) and by varying the temperature values (15°C, 35°C, 45°C and 5°C). Therefore, the ANN trained by Gauss-Newton in conjunction with simulated annealing shows a high robustness and achieves the correct value of MPP for each value of irradiance with an efficiency 99.54% and for each value of temperature with an efficiency 99.98%;the three other methods sometimes struggle to achieve the right MPP for certain irradiance values and often remains stuck in its surroundings. © 2023 The Author(s).

关键词： Maximum power point trackers

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A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Iu, Herbert Ho-Ching Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Dalian University of Technology School of Mathematical Sciences Dalian116024 China Karamanoglu Mehmetbey University Faculty of Engineering Department of Computer Engineering Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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Breaking the Cycle: Exploring the Advantages of Novel Evolutionary Cycles

Breaking the Cycle: Exploring the Advantages of Novel Evolut...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Tisdale, Braden N. Tauritz, Daniel R. Auburn University Bonsai Lab Department of Computer Science and Software Engineering AuburnAL United States

ISBN: (纸本)9781665430654

There have been many different forms of evolution-ary algorithms (EAs) designed by humans over the past 50 years, with many variants optimized for specific classes of problems. As computational resources grow, the automated design of EAs has become an increasingly viable method for improving performance. However, many components of EAs have been treated as largely immutable, for both human and automated designers, dramatically constraining design space. In particular, the evolutionary cycle (the repeating pattern of reproduction and survival) has little or no differences between most popular forms of EA. In a previous paper, we proposed a technique for automatically designing evolutionary cycles using directed graphs, greatly increasing the explorable design space. In this paper, we showcase an improved representation and evolutionary process, provide preliminary experiments demonstrating that EAs produced by this process can outperform those with a traditional cycle, and explore the phenotype landscape to show that the new space explored by our technique may contain better EAs than traditional cycles allow. © 2023 IEEE.

关键词： automated design evolutionary algorithms hyperheuristics

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Effectiveness of Deep Learning Technologies to Assist Educational Inclusion for Children with Down Syndrome 5

Effectiveness of Deep Learning Technologies to Assist Educat...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Silva, Shehan Jayasinghe, Anjula Dilshan, Rishitha Dissanayake, Chandur Vidhanaarachchi, Samitha De Silva, Dilshan Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Down Syndrome (DS) is a genetic disorder causing intellectual disability and developmental delays. Despite instances of discrimination, several individuals with DS have achieved success through proper education and community support. This research addresses the challenges faced by children with DS in terms of motor skill development, cognitive impairments, and speech difficulties with the use of Deep Learning Technologies such as LSTM, CNN machine learning models. To bridge the educational gap and improve speech intelligibility, this study focuses on oral motor exercises that enhance articulation skills. Cognitive development in individuals with DS is also a concern, prompting the proposal of a self-learning system to foster cognitive growth. Fine motor skills improvement is explored through yoga exercises, and interventions like interactive technologies are considered to enhance learning experiences. This paper discusses the development of a user-friendly mobile application for early intervention in DS, integrating interactive activities using advanced technologies and a parent therapy dashboard. The system's potential for improving developmental skills and future expansion is emphasized. © 2023 IEEE.

关键词： Speech intelligibility

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