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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

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Constructing Semantical Structure by Segmentation Integrated Video Embedding for Temporal Action Detection

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5980-5992页

作者： Zhao, Zixuan Liu, Shuming Zhao, Chengze Zhao, Xu Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China King Abdullah University of Science and Technology Computer Science Program Thuwal23599 Saudi Arabia

Video embedding is the pivot in Temporal Action Detection (TAD). Once the video embedding can robustly capture the essence of actions and perceive activities in complex scenes, the TAD model can more accurately localize action boundaries. Currently, video embedding is typically based on rule-based pixel convolution or cube-based transformer, wherein structured semantic information is intertwined, leading to the submergence of crucial spatial semantic information, such as the intrinsic motion of key semantic objects and interactions among semantic objects. To address these limitations, it is imperative to explore alternative approaches. With the remarkable performance of general semantic segmentation models in visual representation, we introduce the general segmentation model SEEM into the video embedding paradigm, constructing a semantically structured representation from perceptual semantics to cognitive semantics. To more effectively utilize SEEM for structured video representation, we designed the Semantic Adapter (Sem-Adapter) as a bridge to connect the two models. Firstly, we design a Self-Motion Module (SMM) to pay attention to the self-motion of key semantic regions. Secondly, we propose a Mutual Relation Module (MRM) to construct the interactions between semantic regions. Extensive experiments on ActivityNet-1.3, THUMOS-14 and EPIC-Kitchens-100 reveal that our method significantly outperforms state-of-the-art methods under the same input modality, and our method improves the average mAP from 60.6% to 64.2% on THUMOS-14 with the same backbone. The code is available on https://***/shouxiaozixuan/semtad. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

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Information Processing in Agriculture 2023年第3期10卷 289-300页

作者： Inna Novianty Ringga Gilang Baskoro Muhammad Iqbal Nurulhaq Muhammad Achirul Nanda Computer Engineering Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Informatics Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Technology and Plantation Production Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial TechnologyUniversitas PadjadjaranJatinangor 45363Indonesia

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and *** content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product *** on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm ***,350 palm fruits with Tenera varieties(Elaeis guineensis ***),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB University,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content ***,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil ***,a robust EMD-ANN model is generated by optimizing the lowest possible *** on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±*** results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally ***,the proposed technique has a promising potential to be applied in the oil palm *** like this will lead to the effective and efficient management of oil palm production.

关键词： Artificial neural network Empirical mode decomposition Oil palm Oil content prediction

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Application of a Voting-Based Ensemble Method for Recognizing Seven Basic Emotions in Real-Time Webcam Video Images 9

Application of a Voting-Based Ensemble Method for Recognizin...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Sanli, Ahmet Tunahan Saran, Murat Cankaya University Information Technology Program Ankara Turkey Cankaya University Computer Engineering Department Ankara Turkey

ISBN: (纸本)9798350394474

This study examines the automatic recognition of human emotions in real-time through facial expressions from webcams. Real-time emotion recognition is a crucial element in human-computer interaction and emotional computing. The study evaluates the effectiveness of various techniques in real-time facial emotion recognition using a custom CNN model, creating an ensemble with a voting mechanism, and integrating the system for real-time emotion recognition. The CNN model was trained on the FER2013 dataset, which consists of facial images labeled with different emotional states. It achieved a remarkable accuracy of 95%. In this study, we developed a dataset named ATS-FER2024, which consists of 184 images depicting seven distinct emotions. The tests conducted on this dataset yielded an accuracy rate of 89%. Despite its small size, the dataset's accuracy is noteworthy. The findings contribute to academic knowledge on developing emotion recognition systems, enhancing empathy, and creating context-sensitive interactions in real-world applications. © 2024 IEEE.

关键词： Emotion Recognition

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Asynchronous I/O Persistence for In-Memory Database Servers: Leveraging io_uring to Optimize Redis Persistence 17th

Asynchronous I/O Persistence for In-Memory Database Servers:...

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17th International Conference on Cloud Computing, CLOUD 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Le-Gao Li, Yanzhi Laohakangvalvit, Tipporn Sugaya, Midori Innovative Global Program College of Engineering Shibaura Institute of Technology Tokyo Koto City Japan Department of Computer Science and Engineering College of Engineering Shibaura Institute of Technology Tokyo Koto City Japan

ISBN: (纸本)9783031771521

With constant hardware improvements allowing for increasingly large memory sizes, in-memory database servers have become an attractive option for various cloud applications. Even though in-memory database servers store all data in memory, there is still a need to persist data onto an I/O device for maintenance, updates, or protection against server crashes. In high throughput environments, persistence happens often, significantly hindering server throughput performance. Although popular in-memory database servers such as Redis or Dragonfly DB have adopted asynchronous I/O for socket operations, none have extended this approach for persistence. This results in significantly longer time taken for persistence, leading to prolonged reduced throughput. Given the recent introduction of io_uring, which has demonstrated significant performance improvements over past asynchronous I/O APIs, we have decided to implement it into Redis to evaluate its effectiveness in reducing these performance overheads. Our findings indicate that io_uring can significantly enhance performance in use cases with medium to large payload sizes but may hinder performances in use cases involving small payload sizes due to higher complexity and higher operation cost per datapoint. This method can likely be applied to other servers, such as Dragonfly DB and Memcached, to achieve similar benefits in appropriate scenarios. Additionally, in-memory database server persistence can serve as valuable benchmarks for further io_uring development and optimization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Benchmarking

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Physician Scheduling using Constraint Satisfaction programming 11

Physician Scheduling using Constraint Satisfaction Programmi...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Bakr, Hend A. Salama, Ahmed M. Fares, Ahmed Zaky, Ahmed B. Cairo University Biomedical Engineering Program Faculty of Engineering Giza Egypt Benha University Computer Systems Engineering Program Faculty of Engineering at Shoubra Banha Egypt Computer science and information technology Programs Alexandria Egypt Benha University On leave from Computer Systems Engineering Program Faculty of Engineering at Shoubra Egypt

ISBN: (纸本)9798350369328

Physician scheduling is a critical task that impacts the quality of patient care, staff satisfaction, and operational efficiency in healthcare institutions. The traditional approach to physician scheduling is manual and time-consuming, which can result in errors, staff burnout, and suboptimal schedules. To address these challenges, researchers have turned to optimization techniques like CSP, which has shown promise in solving physician scheduling problems. This paper reviews the existing literature on CSP for physician scheduling and highlights the benefits and limitations of this approach. CSP's benefits include generating schedules quickly and efficiently, incorporating complex constraints and preferences, and handling changes and disruptions in real time. However, CSP also has some limitations, such as the need for a formalized model and the fact that it may not always generate the most intuitive schedules. Overall, the findings suggest that CSP is a promising approach to physician scheduling that can produce high-quality schedules while minimizing staff burnout and improving operational efficiency. © 2023 IEEE.

关键词： Constraint satisfaction problems

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High-Throughput Independent Component Analysis Processor for Full Duplex Systems

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IEEE Journal on Selected Areas in Communications 2023年第9期41卷 2821-2832页

作者： Cheng, Jen-Hao Chang, Tien-Min Shen, Chung-An Fouda, Mohammed E. Eltawil, Ahmed M. National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan University of California at Irvine Electrical Engineering and Computer Science Department IrvineCA92697 United States King Abdullah University of Science and Technology Electrical and Computer Engineering Program Thuwal23955 Saudi Arabia

This paper presents the algorithm and very-large-scale integration (VLSI) architecture of a high-throughput and highly efficient independent component analysis (ICA) processor for self-interference cancellation (SIC) in in-band full-duplex (IBFD) systems. This is the first VLSI architecture reported in the literature based on the state-of-the-art entropy bound minimization (EBM) approach. A novel ICA algorithm is presented in this paper with momentum gradient descent optimization. Simulation results show that the number of iterations for the proposed algorithm is significantly reduced compared to the conventional ICA algorithms. Furthermore, a novel early-distribution estimation scheme is proposed in the designed ICA processor to compute multiple distribution functions with low latency and low complexity. The processing flow and the efficiency for the hardware utilization are specifically designed so that the processing speed is maximized with minimum employment of hardware components. The proposed ICA processor is designed and implemented based on the application-specific-integrated circuit (ASIC) flow. The post-layout estimations show that compared with the conventional EBM-based scheme, the proposed design improves the throughput and efficiency by 30x. In addition, compared to prior designs shown in the literature, the proposed ICA processor also demonstrates a significant enhancement in terms of throughput and efficiency. © 1983-2012 IEEE.

关键词： VLSI circuits

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IoT-Integrated Machine Learning for Real-Time Soil Nutrient Monitoring and Horticultural Crop Recommendations 10

IoT-Integrated Machine Learning for Real-Time Soil Nutrient ...

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10th IEEE Information technology International Seminar, ITIS 2024

作者： Wahid, Abdul Parenreng, Jumadi Mabe Aswandi Alwi, Ananta Dwi Prayoga Zain, Satria Gunawan Setialeksana, Wirawan Universitas Negeri Makassar Informatics and Computer Engineering Department Makassar Indonesia Politeknik Negeri Lhokseumawe Network Engineering Technology Study Program Lhokseumawe Indonesia

ISBN: (纸本)9798331521295

Farmers and agricultural newcomers often rely on intuition for plant nutrient management, leading to inaccuracies. This study presents a real-time soil nutrient monitoring and plant recommendation system using IoT and machine learning. The prototype integrates an ESP32 microcontroller, NPK sensor, soil pH sensor, and DHT11 with an application and a machine learning model. The model employs a hard voting method combining random forest, LightGBM, and support vector machine algorithms, deployed via Flask API and hosted on Vercel. Testing showed the system effectively monitors soil nutrients in real-time, with a pH sensor error margin of ±0.51 and NPK sensor readings displaying a non-linear increase in levels. The hard voting method achieved 98.5% accuracy, with recommendation processing taking less than one second (371.17ms) and CPU usage at 7%. System performance depends on internet speed, while recommendation accuracy is influenced by temperature and humidity. This system offers a practical solution for precision agriculture by optimizing nutrient application and plant recommendations. © 2024 IEEE.

关键词： pH sensors

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Boost DC-DC Converter with Non-dissipative Snubber

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IEEJ Transactions on Electrical and Electronic engineering 2024年第11期19卷 1858-1878页

作者： Yau, Yeu-Torng Department of Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung No.57 Sec. 2 Zhongshan Rd. Taiping Dist Taichung41170 Taiwan

To provide a hold-up time function in DC-DC supplies for cell site stations or data centers, using a boost converter with a bulk output capacitor as a front-end converter stage is a simple and highly cost-effective solution. However, conventional boost converters with a hard-switching operation result in low conversion efficiency and increased electromagnetic interference emissions. In this paper, a boost converter with a new non-dissipative snubber is proposed that only requires two additional diodes, an inductor, and a multilayer ceramic capacitor. The main feature of the proposed snubber is that the components are not located on the main power processing path. This means they only require low ratings, improving cost-effectiveness. A prototype converter is analyzed, realized, and applied to a 300 W redundant DC-DC power supply module for a 5G cell base station. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： Boost converter

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