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International Electrical Engineering Congress (iEECON)

作者： Jiran Sithiyopasakul Tuanjai Archevapanich Saran Sithiyopasakul Attasit Lasakul Boonchana Purahong Chawalit Benjangkaprasert School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

The integration of cloud computing and the Internet of Things (IoT) holds transformative potential across diverse industries. Performance assessment is essential to gauge the quality and efficiency of cloud computing and IoT systems. This paper presents a comprehensive performance evaluation of cloud computing and IoT systems, focusing on three major platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Experimental results encompass various scenarios, including normal operation, heavy load conditions, IoT applications, and scalability testing. The outcomes reveal distinct performance metrics such as response time, throughput, latency, and reliability for each cloud platform.

关键词： Performance evaluation Cloud computing Web services System performance Throughput Internet of Things Time factors

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Influence of 3D laparoscopic surgery on surgeon’s visual pattern and mental workload

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Journal of Medical Engineering and technology 2021年第5期45卷 375-379页

作者： Zhang, Jian-Yang Shen, Zhi-Hao Wang, Bao-Ping Liu, Feng Li, Juan School of Computer and Information Technology Nanyang Normal University Nanyang China Department of Medical Engineering Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China School of Civil Engineering and Architecture Nanyang Normal University Nanyang China

Previous studies have found that surgeons perform better in three-dimensional (3D) surgery than in two-dimensional (2D) surgery. However, no comparative studies have revealed the impact of 3D laparoscopic surgery on the surgeon’s vision. To explore the effect of laparoscopic surgeons’ depth perception during 3D laparoscopic surgery, 10 participants were recruited and performed 4 sets comparative simulated laparoscopic procedures in a virtual simulator, and eye movement signals were acquired, which were used to characteristics the visual differences. Fixation rate and saccade speed were used to characterise the influence of moderating variables for visual characteristics. The results from the data showed significant differences in eye movement behaviour. Compared with 2D laparoscopic surgery, surgeons have more average fixation rate (p-values = 0.001, 0.000, 0.003 and 0.015, respectively) and faster saccade speed (p-values = 0.037, 0.003, 0.073 and 0.105, respectively) in 3D laparoscopic surgery. The results of this study showed that surgeons had more efficient visual search in 3D laparoscopic surgery. At the same time, the results also indicated that surgeon’s mental workload in 3D laparoscopic surgery was low. The relevant conclusions of this paper revealed the advantages of 3D laparoscopic surgery through visual efficiency. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Eye movements

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Safety Guaranteed Power-Delivered-to-Load Maximization for Magnetic Wireless Power Transfer

Safety Guaranteed Power-Delivered-to-Load Maximization for M...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods System performance Inductive charging Prototypes Wireless power transfer Parallel processing

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Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

arXiv

引用

arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation

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An Efficient Timing System for IFMIF-DONES Facility Based on Ethernet Time Transfer Protocols

An Efficient Timing System for IFMIF-DONES Facility Based on...

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2022 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium (EFTF/IFCS)

作者： Carlos Megí as Ví ctor Vá zquez Eduardo Ros Javier Dí az Mauro Cappelli Department of Computer Architecture and Technology Universidad de Granada Granada Spain Department of Fusion and Nuclear Safety ENEA Rome Italy

ISBN: (数字)9781665497183

ISBN: (纸本)9781665497190

This work analyses the synchronization requirements of the IFMIF-DONES infrastructure systems and proposes a cost-effective solution based on standard protocols: Network Time Protocol (NTP) and IEEE-1588 profiles, which are compliant with Ethernet networks. This approach simplifies the deployment of the timing system and its integration in already existing networks, being able to provide high scalability in the number of devices and limiting the number of time-aware devices or independent timing networks in the solution. The proposal takes full advantage of the capabilities of White-Rabbit technology to disseminate low phase noise frequency over the network without requiring wired radiofrequency solutions. Final considerations are made to understand the simplicity and viability of this solution.

关键词： Radio frequency Phase noise Time-frequency analysis Protocols Scalability Ethernet Synchronization

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Enhancement of the Color Image Compression Using a New Algorithm based on Discrete Hermite Wavelet Transform

arXiv

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arXiv 2023年

作者： Muhi-Aldeen, Hassan Mohamed Abdulrahman, Asma A. Eleiwy, Jabbar Abed Tahir, Fouad S. Khlaponin, Yurii Department of Computer Engineering Aliraqia University 22 Sabaabkar Adamia Baghdad Iraq Department of Applied Sciences University of Technology 52Alsena street Baghdad Iraq Department of Cybersecurity and Computer Engineering Kyiv National University of Construction and Architecture 31 Povitroflotskyi ave Kyiv03037 Ukraine

The Internet has turned the entire world into a small village;this is because it has made it possible to share millions of images and videos. However, sending and receiving a huge amount of data is considered to be a main challenge. To address this issue, a new algorithm is required to reduce image bits and represent the data in a compressed form. Nevertheless, image compression is an important application for transferring large files and images. This requires appropriate and efficient transfers in this field to achieve the task and reach the best results. In this work, we propose a new algorithm based on discrete Hermite wavelets transformation (DHWT) that shows the efficiency and quality of the color images. By compressing the color image, this method analyzes it and divides it into approximate coefficients and detail coefficients after adding the wavelets into MATLAB. With Multi-Resolution Analyses (MRA), the appropriate filter is derived, and the mathematical aspects prove to be validated by testing a new filter and performing its operation. After the decomposition of the rows and upon the process of the reconstruction, taking the inverse of the filter and dealing with the columns of the matrix, the original matrix is improved by measuring the parameters of the image to achieve the best quality of the resulting image, such as the peak signal-to-noise ratio (PSNR), compression ratio (CR), bits per pixel (BPP), and mean square error (MSE). © 2023, CC BY.

关键词： Image compression

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Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data

Cox-ResNet: A Survival Analysis Model Based on Residual Neur...

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IEEE International Conference on Networking, Sensing and Control

作者： Qingyan Yin Wangwang Chen Ruiping Wu Zhi Wei School of Science Xi'an University of Architecture and Technology Xi'an China Department of Computer Science New Jersey Institute of Technology Newark USA

ISBN: (纸本)9781665472449

Survival analysis with genomics data provides a deep understanding of biological processes related to prognosis and disease progression at the molecular level. However, high-dimensional small sample genome data causes computational challenges in survival analysis. To address this problem of overfitting and poor interpretation of existing models, we applied the deep learning technology to genome data and proposed a survival analysis model based on an image-based residual neural network model, called Cox-ResNet. High-dimensional gene expression data was embedded into 2D images according to gene positions on chromosomes, and then a residual network model based on Cox proportional hazards was introduced to perform survival analysis. We demonstrated the performance of Cox-ResNet on five datasets of different cancer types from TCGA, comparing it with the cutting-edge survival analysis methods. The Cox-ResNet model not only shows better performance in prediction accuracy, but also biologically interpretable, by generating heat-maps and prognostic genes for high-risk groups with the guided Grad-Cam visualization method. By performing protein-protein interaction network analysis, we examined hub genes and their biological functions for the bladder cancer. These findings confirm that Cox-ResNet model provides a new solution for discovering the driver genes of poor cancer prognosis.

关键词： Analytical models Biological system modeling Computational modeling Genomics Data models Gene expression Bioinformatics

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Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Robust Global Localization in Autonomous Mobile Robotics

Redundant and Loosely Coupled LiDAR-Wi-Fi Integration for Ro...

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International Conference on Advanced Robotics (ICAR)

作者： Nikolaos Stathoulopoulos Emanuele Pagliari Luca Davoli George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden Department of Engineering and Architecture Internet of Things (IoT) Lab University of Parma Parma Italy

This paper presents a framework addressing the challenge of global localization in autonomous mobile robotics by integrating LiDAR-based descriptors and Wi-Fi finger-printing in a pre-mapped environment. This is motivated by the increasing demand for reliable localization in complex scenarios, such as urban areas or underground mines, requiring robust systems able to overcome limitations faced by traditional Global Navigation Satellite System (GNSS)-based localization methods. By leveraging the complementary strengths of LiDAR and Wi-Fi sensors used to generate predictions and evaluate the confidence of each prediction as an indicator of potential degradation, we propose a redundancy-based approach that enhances the system's overall robustness and accuracy. The proposed framework allows independent operation of the LiDAR and Wi-Fi sensors, ensuring system redundancy. By combining the predictions while considering their confidence levels, we achieve enhanced and consistent performance in localization tasks.

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IALF-YOLO: Insulator defect detection method combining improved attention mechanism and lightweight feature fusion network

引用

Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Mei, Zhiyu Xu, Hongzhen Yan, Liyue Wang, Kafeng School of Software East China University of Technology Nanchang330013 China School of Architecture and Urban Planning Shenzhen University Shenzhen518061 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Effect and efficient insulator defect detection is critical for advancing smart grid technologies. Current deep learning-based methods face limitations in small-object recognition accuracy, insufficient key feature extraction, and high computational complexity, which restrict their application in grid inspections. We propose IALF-YOLO, an improved YOLOv5s-based model, to address these problems. Firstly, by fusing the shallow feature map of Backbone and the deep feature map of Neck, a detection layer dedicated to small objects is created in Head, significantly improving small objects’ detection accuracy. Secondly, a S-CBAM attention mechanism is proposed, which addresses the issue of feature information loss in conventional CBAM by synchronizing the extraction channel with spatial attention. Finally, the lightweight GSConv module replaces the convolutional layer in the Neck network to construct a lightweight feature fusion network, which improves detection accuracy while reducing model complexity and the number of parameters. Our method improves mAP by 2.8% and 2.5% on both datasets, respectively. The detection speed is 2× faster than other methods. © 2025 Elsevier Ltd

关键词： Leak detection

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COVID-19 Biomarkers Detection Using ‘KnowSeq’ R Package 1st

COVID-19 Biomarkers Detection Using ‘KnowSeq’ R Package

引用

1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Bajo-Morales, Javier Castillo-Secilla, Daniel Herrera, Luis Javier Rojas, Ignacio Department of Computer Architecture and Technology University of Granada C.I.T.I.C. Periodista Rafael Gomez Montero 2 Granada18014 Spain

ISBN: (纸本)9783030881627

‘KnowSeq’ R Package includes all the essential tools to carry out transcriptomic analysis, providing intuitive functions to build efficient and robust pipelines. In this paper, its capacities are demonstrated in a practical COVID-19 biomarkers detection problem using RNA-Sequencing data. Through Machine Learning techniques such as feature selection and supervised classification models, a clinical decision system for COVID-19 was developed using four genes proposed as COVID-19 signature: OAS3, CXCL9, IFITM1 and IFIT3. These four genes are highly related to different processes that affect the immune system behaviour and its response when facing viruses such as SARS-CoV-2. The final model reaches an accuracy over 97% when predicting over unseen samples. © 2021, Springer Nature Switzerland AG.

关键词： COVID-19

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