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International Conference on Communications and electronics, ICCE

作者： Andrew C.S. Liao Bryan M.H. Pong H.J. Chiu Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9798350379792

ISBN: (纸本)9798350379808

Output ripple of a power converter is an important parameter and it is desired to be reduced for high quality applications. Traditional filters are bulky and their parasitic elements degrade the performance. Here an Active Module is proposed for parallel connection to a power converter. It can effectively amplify the effective capacitance and suppress the parasitic elements. In this paper analysis is presented and effective result is demonstrated by practical experiment.

关键词： Analytical models Filters Power supplies Capacitors Voltage Capacitance Active circuits

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Preliminary Study on the Online Teaching Mode of "Technical Fundamentals of Web Front-end" Based on SPOC and WeChat 22

Preliminary Study on the Online Teaching Mode of "Technical ...

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7th International Conference on Distance Education and Learning, ICDEL 2022

作者： Qiu, Xunchao Zhang, Chunyue Zhao, Libo College of Electronic Information Engineering Liuzhou Vocational and Technical College Liuzhou545006 China Department of Computer Engineering Harbin Finance University Harbin150040 China

ISBN: (纸本)9781450396417

During the period of COVID-19, the national universities support the policy of "continuing teaching and learning while stopping the class", and fully apply the teaching advantage of "Internet +"to conduct the online teaching activity. "Technical Fundamentals of Web Front-end", as the main course of the major of financial science and technology, plays a significant role in cultivating professionals and improving the core competitiveness of students. During the epidemic period, the online teaching mode based on SPOC and WeChat, which coordinates the pre-class guidance, in-class research, after-class supervision and development, has been applied to complete the online teaching of 2019 and 2020 students. This paper not only shares the online teaching experience of the course, but also summarizes and discusses the advantages and disadvantages of online education, in order to provide reference for improving the quality of online teaching of financial technology courses. © 2022 ACM.

关键词： Teaching

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An Iterative Frequency Estimation Algorithm Using Generalized Fourier Interpolation

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Chinese Journal of electronics 2023年第3期18卷 564-568页

作者： Yanhui Liu Zaiping Nie Zhiqin Zhao School of Electronic Engineering University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering Duke University Durham NC USA

Motivated by the iterative estimation approach based on Fourier interpolation in a recent literature, this paper proposes a generalized interpolation on Fourier coefficients and an iterative frequency estimation algorithm based on the generalized interpolation. Both theoretical analysis and simulation tests show that the iterative generalized Fourier interpolation algorithm converges in two iterations with the estimation variance only marginally above the Asymptotical Cramer-Rao bound (ACRB) over the entire frequency estimation range. Moreover, the proposed algorithm allows setting different values for its initial parameter. An approach on how to choose the initial parameter is also presented. By using a suggested initial value, the proposed algorithm is much more efficient than the original algorithm in the literature while maintains totally the same estimation accuracy.

关键词： Interpolation Analytical models Cramer-Rao bounds Scholarships Discrete Fourier transforms Estimation Iterative algorithms

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Space Shift Keying with Reconfigurable Intelligent Surfaces: Phase Configuration Designs and Performance Analysis

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2021年 2卷 322-333页

作者： Li, Qiang Wen, Miaowen Wang, Shuai Alexandropoulos, George C. Wu, Yik-Chung Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou510632 China School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Sifakis Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong Hong Kong

Reconfigurable intelligent surface (RIS)-assisted transmission and space shift keying (SSK) appear as promising candidates for future energy-efficient wireless systems. In this article, two RIS-based SSK schemes are proposed to efficiently improve the error and throughput performance of conventional SSK systems, respectively. The first one, termed RIS-SSK with passive beamforming (RIS-SSK-PB), employs an RIS for beamforming and targets the maximization of the minimum squared Euclidean distance between any two decision points. The second one, termed RIS-SSK with Alamouti space-time block coding (RIS-SSK-ASTBC), employs an RIS for ASTBC and enables the RIS to transmit its own Alamouti-coded information while reflecting the incident SSK signals to the destination. A low-complexity beamformer and an efficient maximum-likelihood (ML) detector are designed for RIS-SSK-PB and RIS-SSK-ASTBC, respectively. Approximate expressions for the average bit error probabilities of the source and/or the RIS are derived in closed-form assuming ML detection. Extensive computer simulations are conducted to verify the performance analysis. Results show that RIS-SSK-PB significantly outperforms the existing RIS-free and RIS-based SSK schemes, and RIS-SSK-ASTBC enables highly reliable transmission with throughput improvement. © 2020 IEEE.

关键词： Beamforming

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Multivariable Extremum Seeking Controllers for Multi-Beam Steering using Reconfigurable Metasurfaces

Multivariable Extremum Seeking Controllers for Multi-Beam St...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Masood, Abdullah Bin Vassiliou, Vasos Pitsillides, Andreas Liaskos, Christos Lestas, Marios University of Cyprus Department of Computer Science Nicosia Cyprus CYENS-Centre of Excellence Nicosia Cyprus University of Johannesburg Department of Electrical and Electronic Engineering Science South Africa University of Ioannina Department of Computer Science and Engineering Ioannina Greece Frederick University Department of Electrical Engineering Nicosia Cyprus

ISBN: (纸本)9781665459754

Intelligent Reflecting Metasurfaces constitute a revolutionary technology that can alleviate the blockage problem in mm-Wave communications. In this paper, we consider a metasurface coding that can realize beam splitting of an incident beam in multiple directions and we address the problem of feedback-based adaptive reconfiguration of the metasurface controller states so that the reflected beams are guided towards two intended receivers. As the receiver locations are unknown and to counter system uncertainties, we employ feedback to maximize the received power at both receivers, aiming for standalone operation of the metasurface. Changes in both the elevation and azimuth angles of the transmitted beams are considered, thus necessitating a design beyond standard extremum-seeking controllers, which have proved to be ineffective. Our approach involves online parameter identification techniques for gradient estimation, coupled with the method of steepest ascent. The effectiveness of the method in guiding the beams towards the reference values is shown through simulations. © 2022 IEEE.

关键词： Controllers

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Utilization of GIS Machine Learning for Mapping of Building Shapes in Tibang Village, Banda Aceh City, Aceh, Indonesia

Utilization of GIS Machine Learning for Mapping of Building ...

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2022 International Conference on Electrical engineering and informatics, ICELTICs 2022

作者： Nizamuddin, Nizamuddin Wirandha, Freddy Sapta Ardiansyah, Ardiansyah Affan, Muzailin Syiah Kuala University Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia Syiah Kuala University Faculty of Engineering Department of Geophysics Engineering Banda Aceh Indonesia Syiah Kuala University Faculty of Engineering Department of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (数字)9781665487665

ISBN: (纸本)9781665487665

Tibang is one of the villages in Banda Aceh which is located in a high tsunami-hazard zone. Today, many initial aid houses in the area have grown larger than the original, and new housing complex has begun to emerge. Therefore, it is necessary to find an efficient method to map the buildings in the village, so that an up-to-date buildings map of the village can be prepared quickly. Machine learning GIS is the use of machine learning algorithms in processing and analyzing spatially-based GIS data. This study aimed at applying the Support Vector Machines (SVM) method of GIS Machine learning for classifying buildings in a very-high-resolution image of Tibang Village, SVM has been used with Object-Based Image analysis (OBIA) to classify UAV-based images for various Land-Use / Land Cover (LULC) applications. The stages in classifying buildings using SVM are starting with processing high-resolution data from UAV photos to create an ortho-mosaic image. The image was then segmented using OBIA techniques and then was classified using the SVM method. The result of the SVM classification was 230 house objects identified in the village of Tibang, Syiah Kuala District with the number of training data samples used for the building class being 29 with various variations of the shape of the building objects identified in the UAV images. © 2022 IEEE.

关键词： Support vector machines

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Practical aberration correction using deep transfer learning with limited experimental data

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Optics Express 2025年第6期33卷 14431-14444页

作者： Kok, Yong En Bentley, Alexander Parkes, Andrew J. Somekh, Michael G. Wright, Amanda J. Pound, Michael P. University of Nottingham School of Computer Science NottinghamNG8 1BB United Kingdom Optics and Photonics Group Department of Electrical and Electronic Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Research Center for Humanoid Sensing Zhejiang Laboratory Hangzhou3111100 China

Adaptive optics is a technique for correcting aberrations and improving image quality. When adaptive optics was first used in microscopy, it was common to rely on iterative approaches to determine the aberrations present. It is advantageous to avoid iteration, and therefore there has been a shift to deep learning for aberration prediction. However, issues remain regarding the practicalities of machine learning for adaptive optics, an important one being the requirement for a large training dataset. Here, we explore transfer learning to overcome this need for data by pre-training a network on a large simulated dataset and fine-tuning it with reduced experimental data for application in an experimental setting. We demonstrate that the pre-trained network can make noticeable improvements with fine-tuning on just 24 experimental samples. To further enhance practicality, we significantly extend the range of aberrations present, predicting up to 25 Zernike modes with each coefficient ranging from −1 to 1, and perform a thorough analysis of the type and magnitude of phase-diversity required in the input data for a successful network. Our approach demonstrates substantial aberration reduction on experimental data for 10 Zernike modes, with an average 73% decrease in RMS wavefront error from 1.81 to 0.48 rad when correction is applied. This method achieves complete experimental image capture and aberration inference at rates comparable to the image acquisition time of a typical laser scanning microscope. Additionally, we consider the benefits of further improvements via an iterative step. As such, this work addresses some of the key practical hurdles that remain in the use of deep learning for aberration prediction and correction. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Adaptive optics

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Point Cloud Resampling With Learnable Heat Diffusion

Point Cloud Resampling With Learnable Heat Diffusion

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wenqiang Xu Wenrui Dai Duoduo Xue Ziyang Zheng Chenglin Li Junni Zou Hongkai Xiong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generative diffusion models have shown empirical successes in point cloud resampling, generating a denser and more uniform distribution of points from sparse or noisy 3D point clouds by progressively refining noise into structure. However, existing diffusion models employ manually predefined schemes, which often fail to recover the underlying point cloud structure due to the rigid and disruptive nature of the geometric degradation. To address this issue, we propose a novel learnable heat diffusion framework for point cloud resampling, which directly parameterizes the marginal distribution for the forward process by learning the adaptive heat diffusion schedules and local filtering scales of the time-varying heat kernel, and consequently, generates an adaptive conditional prior for the reverse process. Unlike previous diffusion models with a fixed prior, the adaptive conditional prior selectively preserves geometric features of the point cloud by minimizing a refined variational lower bound, guiding the points to evolve towards the underlying surface during the reverse process. Extensive experimental results demonstrate that the proposed point cloud resampling achieves state-of-the-art performance in representative reconstruction tasks including point cloud denoising and upsampling.

关键词： Point cloud compression Heating systems Lower bound Surface reconstruction Schedules Three-dimensional displays Smoothing methods Diffusion models Speech processing Surface treatment

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Workload Prediction of Virtual Machines Using Integrated Deep Learning Approaches Over Cloud Data Centers 1st

Workload Prediction of Virtual Machines Using Integrated Dee...

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Leka, Habte Lejebo Fengli, Zhang Kenea, Ayantu Tesfaye Sharma, Durga P. Tegene, Abebe Tamrat School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama1888 Ethiopia AMUIT MOEFDRE Under UNDP & MAISM RTU Kota Kota India

ISBN: (纸本)9789811954023

Exponential growth in the use of cloud computing services makes it difficult to forecast loads of virtual machines (VMs). Accurate virtual machine (VM) workload forecasting is the most critical task in appropriately managing cloud resources such as memory and central processing units while minimizing energy usage. To address this problem, an integrated deep learning model is proposed in this research paper. The model employs two popular neural networks: a bidirectional Long Short-Term Memory Network (BiLSTM) with a convolutional neural networks (CNN). The CNN component pulls high-level attributes from all VM workload data, whereas the BiLSTM component forecasts future VM workload. The experimental results reveal that the suggested model outperforms commonly used workload prediction methods in terms of forecasting accuracy of VMs workloads in cloud computing environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Scheduling with a Limited Testing Budget 31

Scheduling with a Limited Testing Budget

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31st Annual European Symposium on Algorithms, ESA 2023

作者： Damerius, Christoph Kling, Peter Li, Minming Xu, Chenyang Zhang, Ruilong Department of Informatics Universität Hamburg Germany Department of Computer Science City University of Hong Kong Hong Kong Shanghai Key Laboratory of Trustworthy Computing East China Normal University China Department of Computer Science and Engineering University at Buffalo NY United States

ISBN: (纸本)9783959772952

Scheduling with testing falls under the umbrella of the research on optimization with explorable uncertainty. In this model, each job has an upper limit on its processing time that can be decreased to a lower limit (possibly unknown) by some preliminary action (testing). Recently, Dürr et al. [10] has studied a setting where testing a job takes a unit time, and the goal is to minimize total completion time or makespan on a single machine. In this paper, we extend their problem to the budget setting in which each test consumes a job-specific cost, and we require that the total testing cost cannot exceed a given budget. We consider the offline variant (the lower processing time is known) and the oblivious variant (the lower processing time is unknown) and aim to minimize the total completion time or makespan on a single machine. For the total completion time objective, we show NP-hardness and derive a PTAS for the offline variant based on a novel LP rounding scheme. We give a (4 + ϵ)-competitive algorithm for the oblivious variant based on a framework inspired by the worst-case lower-bound instance. For the makespan objective, we give an FPTAS for the offline variant and a (2 + ϵ)-competitive algorithm for the oblivious variant. Our algorithms for the oblivious variants under both objectives run in time O(poly(n/ϵ)). Lastly, we show that our results are essentially optimal by providing matching lower bounds. © Christoph Damerius, Peter Kling, Minming Li, Chenyang Xu, and Ruilong Zhang.

关键词： Approximation algorithms

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