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Conference on Lasers and Electro-Optics (CLEO)

作者： Haoqi Zhao Yichi Zhang Zihe Gao Jieun Yim Shuang Wu Natalia M. Litchinitser Li Ge Liang Feng Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA Department of Materials Science and Engineering University of Pennsylvania Philadelphia PA USA Department of Electrical and Computer Engineering and Department of Physics Duke University Durham NC USA Department of Engineering Science and Physics College of Staten Island CUNY Staten Island NY USA The Graduate Center CUNY New York NY USA

ISBN: (纸本)9798350369311

Preparing and manipulating N-dimensional flying qudits as well as subsequently establishing their entanglement are still challenging tasks. Here, using an integrated approach, we explore the synergy from two degrees of freedom of light, spatial mode and polarization, to generate, encode, and manipulate flying photon qudits in a four-dimensional Hilbert space with high quantum fidelity, intrinsically enabling enhanced noise resilience and higher quantum data rates.

关键词： Integrated optics Polarization Optical polarization Laser noise Laser modes Hilbert space Optical noise Electro-optical waveguides Photonics Resilience

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An Evolutionary Algorithm Based on Fully Connected Weight Networks for Mixed-Variable Multiobjective Optimization

SSRN

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SSRN 2023年

作者： Dong, Nanjiang Zhang, Tao Wang, Rui Liao, Xiangke Wang, Ling College of Systems Engineering National University of Defense Technology Changsha410073 China Xiangjiang Laboratory Changsha410205 China College of Computer Science National University of Defense Technology Changsha410073 China Department of Automation Tsinghua University Beijing100084 China

Multiobjective optimization research has mostly focused on continuous-variable problems. However, real-world optimization problems often involve multiple types of variables (real, integer, and discrete) and multiple conflicting optimization objectives, called mixed-variable multiobjective optimization problems (MVMOPs). Discrete variables make the decision space of the problem discrete, while different types of variables need different treatments by the evolutionary algorithm, which poses a challenge to the efficient search of the evolutionary algorithm. To address MVMOPs, we propose a fully connected weight network-based evolutionary algorithm (FCWNEA) in this paper. The fully connected network structure characterizes the entire decision space, the node access count records the frequency of visits to the node, and the weights of connections and the activity of variables estimate the distribution of the decision space. This information assists in generating offspring solutions. Furthermore, since the fully connected network discretizes the continuous variables, we propose a strategy to dynamically adjust the node values to perform a fine-grained global search for continuous variables. The results show that the proposed algorithm has a significant advantage in mixed-variable multiobjective problems. Moreover, the proposed algorithm is also quite competitive in continuous problems and can better handle the correlation between variables in optimization problems. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Collecting Child Psychiatry Documents of Clinical Trials from PubMed by the SVM Text Classification Method with the MATF Weighting Scheme 15th

Collecting Child Psychiatry Documents of Clinical Trials fro...

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15th International Conference on Computing and Information Technology, IC2IT 2019

作者： Polpinij, Jantima Kachai, Tontrakant Nasomboon, Kanyarat Bheganan, Poramin Intellect Laboratory Department of Computer Science Faculty of Informatics Mahasarakham University Mahasarakham Province Thailand Computer Science Program Mahidol University International College Mahidol University Nakhonpathom Province Thailand

ISBN: (纸本)9783030198602

Child psychiatry is a branch of psychiatry focused on the diagnosis, treatment, and prevention of mental health issues in children and their families. In many countries, the study of disorders such as ADHD (Attention-Deficit/Hyperactivity Disorder) by child and adolescent psychiatry is still in its infancy, with the result that children’s mental health issues can be the source of embarrassment for the family and of shame for many children. Misunderstanding, denying, and ignoring children’s mental health issues by parents are the main problem encountered in diagnosis and treatment of mental health issues in children. To help parents and extended families understand this problem better, and thus help them to better care for children with mental health issues, starting with seeking help from a psychiatrist without embarrassment, an easily accessible and reliable source of information is urgently needed. To develop such a single source of information, relevant documents need to be gathered together. This study presents a method of gathering reports of clinical trials from PubMed which describe diagnosis and treatment of child mental health issues. The main mechanism of the proposed method is a Support Vector Machine with a Multi Aspect TF (MATF) weighting scheme. After testing by recall, precision, and F1, it can return satisfactory results of 0.82, 0.79, and 0.80 respectively. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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Applications of Knowledge Distillation in Remote Sensing: A Survey

arXiv

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arXiv 2024年

作者： Himeur, Yassine Aburaed, Nour Elharrouss, Omar Varlamis, Iraklis Atalla, Shadi Mansoor, Wathiq Al Ahmad, Hussain College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates MBRSC Lab University of Dubai Dubai2713 United Arab Emirates Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates Department of Informatics and Telematics Harokopio University of Athens AthensGR-17778 Greece

With the ever-growing complexity of models in the field of remote sensing (RS), there is an increasing demand for solutions that balance model accuracy with computational efficiency. Knowledge distillation (KD) has emerged as a powerful tool to meet this need, enabling the transfer of knowledge from large, complex models to smaller, more efficient ones without significant loss in performance. This review article provides an extensive examination of KD and its innovative applications in RS. KD, a technique developed to transfer knowledge from a complex, often cumbersome model (teacher) to a more compact and efficient model (student), has seen significant evolution and application across various domains. Initially, we introduce the fundamental concepts and historical progression of KD methods. The advantages of employing KD are highlighted, particularly in terms of model compression, enhanced computational efficiency, and improved performance, which are pivotal for practical deployments in RS scenarios. The article provides a comprehensive taxonomy of KD techniques, where each category is critically analyzed to demonstrate the breadth and depth of the alternative options, and illustrates specific case studies that showcase the practical implementation of KD methods in RS tasks, such as instance segmentation and object detection. Further, the review discusses the challenges and limitations of KD in RS, including practical constraints and prospective future directions, providing a comprehensive overview for researchers and practitioners in the field of RS. Through this organization, the paper not only elucidates the current state of research in KD but also sets the stage for future research opportunities, thereby contributing significantly to both academic research and real-world applications. © 2024, CC BY.

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Trash to Treasure: Using text-to-image models to inform the design of physical artefacts

arXiv

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arXiv 2023年

作者： Smith, Amy Schroeder, Hope Epstein, Ziv Cook, Michael Colton, Simon Lippman, Andrew School of Electronic Engineering and Computer Science Queen Mary University London United Kingdom MIT Media Lab United States Department of Informatics King's College London United Kingdom SensiLab Faculty of Information Technology Monash University Australia

Text-to-image generative models have recently exploded in popularity and accessibility. Yet so far, use of these models in creative tasks that bridge the 2D digital world and the creation of physical artefacts has been understudied. We conduct a pilot study to investigate if and how text-to-image models can be used to assist in upstream tasks within the creative process, such as ideation and visualization, prior to a sculpture-making activity. Thirty participants selected sculpture-making materials and generated three images using the Stable Diffusion text-to-image generator, each with text prompts of their choice, with the aim of informing and then creating a physical sculpture. The majority of participants (23/30) reported that the generated images informed their sculptures, and 28/30 reported interest in using text-to-image models to help them in a creative task in the future. We identify several prompt engineering strategies and find that a participant's prompting strategy relates to their stage in the creative process. We discuss how our findings can inform support for users at different stages of the design process and for using text-to-image models for physical artefact design. © 2023, CC BY.

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Spatial Computing: Concept, Applications, Challenges and Future Directions

arXiv

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arXiv 2024年

作者： Yenduri, Gokul Ramalingam, M. Maddikunta, Praveen Kumar Reddy Gadekallu, Thippa Reddy Jhaveri, Rutvij H. Bandi, Ajay Chen, Junxin Wang, Wei Shirawalmath, Adarsh Arunkumar Ravishankar, Raghav Wang, Weizheng School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology TamilNadu 632014 India Zhongda Group Haiyan County Zhejiang Province Jiaxing City314312 China College of Information Science and Engineering Jiaxing University Jiaxing314001 China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India School of Computer Science and Information Systems Northwest Missouri State University 800 University Dr MaryvilleMO64468 United States School of Software Dalian University of Technology Dalian116621 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Computer Science and Engineering Vellore Institute of Technology TamilNadu 632014 India Department of Computer Science City University of Hong Kong Hong Kong

Spatial computing is a technological advancement that facilitates the seamless integration of devices into the physical environment, resulting in a more natural and intuitive digital world user experience. Spatial computing has the potential to become a significant advancement in the field of computing. From GPS and location-based services to healthcare, spatial computing technologies have influenced and improved our interactions with the digital world. The use of spatial computing in creating interactive digital environments has become increasingly popular and effective. This is explained by its increasing significance among researchers and industrial organisations, which motivated us to conduct this review. This review provides a detailed overview of spatial computing, including its enabling technologies and its impact on various applications. Projects related to spatial computing are also discussed. In this review, we also explored the potential challenges and limitations of spatial computing. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of spatial computing, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions. © 2024, CC BY.

关键词： Augmented reality

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Reinforcement Learning Infused MAC for Adaptive Connectivity

Reinforcement Learning Infused MAC for Adaptive Connectivity

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IEEE Conference on Wireless Communications and Networking

作者： Dinesh Kumar Sah Ali Nauman Muhammad Ali Jamshed Korhan Cengiz Nikola Ivković Vedran Uroš Division of Networked and Embedded Systems Research Group Data Communication Mälardalens University Västerås Sweden Department of Information and Communication Engineering Yeungnam University Gyeongbuk Republic of Korea College of Science and Engineering University of Glasgow United Kingdom Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Faculty of Organization and Informatics University of Zagreb Varaždin Croatia Polytechnic “Marko Marulić” in Knin Knin Croatia

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.

关键词： 6G mobile communication Adaptive systems Redundancy Reinforcement learning Media Access Protocol Throughput Resource management

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Neural NILM Learning Paradigms: From Centralised to Decentralised Learning

Neural NILM Learning Paradigms: From Centralised to Decentra...

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International Conference on Signal Processing and Information Security (ICSPIS)

作者： Hafsa Bousbiat Christoph Klemenjak Yassine Himeur Wilfried Elmenreich Abbes Amira Wathiq Mansoor Shadi Atalla Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria College of Engineering and IT University of Dubai Dubai UAE Department of Computer Science University of Sharjah UAE

ISBN: (纸本)9781665492669

Non-intrusive Load Monitoring (NILM) is becoming a paramount in both industrial and residential sectors to achieve efficient energy consumption. Thus, research on this matter flourished in recent years, where deep neural networks gained the highest interest from the research community, commonly referred to as neural NILM. As a predominant practice, neural NILM models follow a centralised based learning scheme where the energy data is assumed to be available in a central node for training. However, this practice and the enormous amount of data required by these algorithms raise privacy and security concerns from the consumer’s side since energy data can reveal in-home activities and occupancy records if intercepted. Federated Learning (FL), also referred to as collaborative learning, is seen as viable solution to address these issues. Nonetheless, its application in neural NILM is still in its infancy and many challenges are yet to be *** current paper presents an overview of neural NILM models following both a centralised and a federated learning paradigm. Furthermore, it identifies the main challenges with regard to both learning paradigms along with potential future research directions for more robust, secure and privacy-preserving models in the neural NILM industry.

关键词： Deep learning Training Industries Load monitoring Federated learning Neural networks Signal processing algorithms

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Model performance evaluation for color and grayscale images in malaria classification using deep CNN

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AIP Conference Proceedings 2024年第1期3219卷

作者： Mohammed Abdulameer Jabbar Al-Mohana Suhad Faisal Behadili Ali A. Khalaf Abdulkareem Merhej Radhi University of Information Technology and Communications Medical Informatics Baghdad-Iraq University of Baghdad College of Science Computer Science Department Baghdad-Iraq Al-Nahrain University College of Science Department: Computer Science Baghdad-Iraq

Malaria is a contagious disease that infects many people each year. It is diagnosed in the laboratory using an expert and thorough examination of healthy and sick red blood cells (RBCs). This study refers to classifying the RBCs that are infected and non-infected with Malaria using a deep convolutional neural network (CNN). Also, it referred to evaluate the model performance using color and grayscale cell images. Indeed, the concluded results have shown that color image cells have accurate classification results over the gray ones. The proposed model achieved classification accuracy at 98.25 over 40 epochs using CNN designed from scratch.

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Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge Control

SSRN

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SSRN 2023年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. Instrument Maintenance support Engineer DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

The incidence of surge within axial compressors profoundly influences the efficacy and reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on the precise and efficient forecasting of critical characteristics during such occurrences. This paper presents a novel approach for predicting the surge phenomenon in axial compressors. Surge is a major issue in the operation of axial compressors, causing reduced performance, increased wear and tear, and in some cases, complete machine failure. The proposed approach utilizes a neural network model based on the compressor's design data to forecast the behavior of the compressor's working map under off-design conditions. The model is verified using real data collected over five years during compressor operation, including the opening angle of the Inlet Guided Van (IGV), an important parameter often ignored or assumed to be fixed in previous studies. The results show that the neural network-based approach effectively predicts the compressor's behavior under different operating conditions, providing valuable insights into the onset of surge and offering a real-time surge prediction and control tool. The ability to predict the compressor's working map enables optimizing its performance and controlling instability. This model was validated by comparing its predictions with real operating conditions observed over five years across various compressor operating modes. As a result, this predictive model enables the safe operation of axial compressors by mitigating instability risks during operation. This study's findings highlight the model's effectiveness, as evidenced by the minimal error observed between the predicted and actual data collected across various compressor operating modes. The model exhibits a strong performance, characterized by a high regression factor of 0.92667 and a low mean squared error of 3.04465 x 10^-3. These numerical values underscore the reliability and pr

关键词： Forecasting

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