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arXiv 2022年

作者： Liu, Kaige Zhang, Hengkang Du, Shanshan Liu, Zeqi Zhang, Bin Fu, Xing Liu, Qiang Key Laboratory of Photonics Control Technology Tsinghua University Ministry of Education Beijing100084 China Tsinghua University State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instrument Beijing100084 China Beijing Institute of Control Engineering Beijing100190 China Beijing Institute of Electronic System Engineering Beijing100854 China

Optical tweezers can manipulate tiny particles. However, the distortion caused by the scattering medium restricts the applications of optical tweezers. Wavefront shaping techniques including the transmission matrix (TM) method are powerful tools to achieve light focusing behind the scattering medium. In this paper, we propose a new kind of TM, named intensity transmission matrix (ITM). Only relying on the intensity distribution, we can calculate the ITM with only about 1/4 measurement time of the widely used four-phase method. Meanwhile, ITM method can avoid the energy loss in diffraction introduced by holographic modulation. Based on the ITM, we have implemented particle manipulation with a high degree of freedom on single and multiple particles. In addition, the manipulation range is enlarged over twenty times (compared with the memory effect) to 200 μm. Copyright © 2022, The Authors. All rights reserved.

关键词： Turbidity

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Energy Minimization with Secrecy Provisioning in Federated Learning-Assisted Marine Digital Twin Networks

Energy Minimization with Secrecy Provisioning in Federated L...

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IEEE International Conference on communications (ICC)

作者： Li Ping Qian Mingqing Li Xinyu Dong Yuan Wu Xiaoniu Yang College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China

Digital twin has been emerging as a promising paradigm that connects the physical entities and digital space, and continuously evolves and optimizes the physical systems. In this paper, we focus on studying the efficient data communication and computation when constructing the marine digital twin network with secrecy provisioning. Specifically, we leverage the federated learning (FL) to train the digital twin model. In the process of FL, all unmanned surface vehicles (USVs) deliver the trained models with non-orthogonal multiple access (NOMA) to the high altitude platform (HAP) for the global model aggregation. Considering the possible eavesdropping on the HAP, we utilize the chaotic sequences to spread the model information during the global model broadcasting. In this framework, we further want to minimize the total energy consumption of completing the digital twin training by jointly optimizing the global accuracy, local accuracy, HAP's transmission power, and model uploading duration subject to the secrecy provisioning and latency constraint. Despite the non-convexity, we propose a low-complexity search algorithm (LCS-Algorithm) to solve this joint optimization problem. Finally, the numerical results validate the performance of the proposed algorithm in terms of optimality and time efficiency.

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Learning-Driven Transmission Latency Minimization in EH-Relay Assisted IoT Networks

Learning-Driven Transmission Latency Minimization in EH-Rela...

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IEEE Conference on Global communications (GLOBECOM)

作者： Qianru Wang Li Ping Qian Mingqing Li Wei Jiang Yuan Wu Xiaoniu Yang College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China

Internet of Things (IoT) is one of the key applications of 5G, and the data transmission is the basis of IoT networks. In this paper, we investigate the data transmission scheme in non-orthogonal multiple access (NOMA) for IoT networks to minimize the transmission latency. In order to improve the communication efficiency between devices and the base station (BS) without more energy consumption, we deploy an energy harvesting (EH) relay node between devices and the BS for data transmitting and forwarding. Based on this networking model, we first aim at minimizing the transmission latency by jointly optimizing the transmit power of devices and the relay, forwarding ratios among devices, and forwarding time fraction when transmitting a fixed data bits from devices to the BS via the relay under the constraints of energy buffer and data buffer. Noted that the formulated problem is discrete-continuous mixed and non-convex, we apply the deep deterministic policy gradient (DDPG) algorithm in the framework of bisection searching to obtain the optimal solution. Specifically, the bisection searching is used to seek the possible transmission latency, and the DDPG is to check the feasibility of the chosen transmission latency. Finally, the effectiveness of the proposed model-data-driven algorithm is verified by comparing it with other benchmark algorithms, such as LINGO.

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Comparative Study of Three-Phase Bridge Converter Voltage control Techniques

Comparative Study of Three-Phase Bridge Converter Voltage Co...

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International Conference on Electrical, Computer and communication engineering (ECCE)

作者： Shah Mohazzem Hossain Mohammad Rasel Fahmida Amin Safkat Islam Dept. of Electrical Electronic and Communication Engineering Military Institute of Science and Technology Dhaka Bangladesh Dept. of High Voltage Substation Design and Quality Control PowerChina Hubei Electric Engineering Corporation Dhaka Bangladesh Dept. of High Voltage Substation Design and Quality Control Orient Energy Engineering Ltd Merul Badda East Gulshan Dhaka Bangladesh Dept. of Electrical Engineering and Control Systems University of Grenoble Alpes Grenoble France

Nowadays, voltage source converters are commonly used in renewable energy penetration and the smooth control of ac machines. Multiple strategies are followed in designing these converters. The primary purpose of the distinct design is to minimize the harmonic contents from the output waves of the converters and ensure maximum efficiency. In this paper, a three-phase bridge converter feeding a rectified dc input is interfaced with an RL load in the MATLAB/Simulink platform to compare the operational characteristics of these techniques. The total harmonic distortion is also analyzed in all these techniques to illustrate their performance in drive control. Moreover, the impact of varying dc-link voltage, load conditions, switching frequency and duty cycle on these techniques' operational char-acteristics and performances are investigated, primarily focusing on resulting harmonic contents evaluation.

关键词： Total harmonic distortion Renewable energy sources Switching frequency Bridge circuits Pulse width modulation Power system harmonics Harmonic analysis

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Multi-task Guided Blind Omnidirectional Image Quality Assessment with Feature Interaction

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Liu, Yun Li, Sifan Duan, Huiyu Zhou, Yu Fan, Daoxin Zhai, Guangtao Liaoning University Faculty of Information Shenyang110036 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai200240 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xuzhou First People’s Hospital Xuzhou221116 China

Omnidirectional image quality assessment (OIQA) has become an increasingly vital problem in recent years. Most previous blind OIQA methods only extract local features from the distorted viewports, or extract global features from the entire distorted image, lacking the interaction and fusion between local and global features. Moreover, the lack of reference information also limits their performance. Thus, we propose a blind OIQA model which consists of three novel modules, including a bidirectional pseudo-reference module, a Mamba-based global feature extraction module, and a multi-scale local-global feature aggregation module. Specifically, by considering the image distortion degradation process, a bidirectional pseudo-reference module capturing the error maps on viewports is first constructed to refine the multi-scale local visual features, which can supply rich quality degradation reference information without the reference image. To well complement the local features, the VMamba module is adopted to extract the representative multi-scale global visual features. Inspired by human hierarchical visual perception characteristics, a novel multi-scale aggregation module is built to strengthen the feature interaction and effective fusion which can extract deep semantic information. Finally, motivated by the multi-task managing mechanism of human brain, a multi-task learning module is introduced to assist the main quality assessment task by digging the hidden information in compression type and distortion degree. Extensive experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the blind OIQA task compared to other models. © 1991-2012 IEEE.

关键词： Image quality

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Bimanual gesture recognition based on convolution neural network

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International Journal of Wireless and Mobile Computing 2020年第4期18卷 311-319页

作者： Wu, Hao Li, Gongfa Sun, Ying Jiang, Guozhang Jiang, Du Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China

Gesture recognition is a key research field in the human-computer interaction. At present, most of researchers focus on one-handed gesture recognition, but do not pay much attention to bimanual (two hands) gesture recognition. This paper presents a deep learning-based solution to tackle the self-occlusion and self-similarity. To solve this problem, this paper uses Kinect to collect many colour and depth images of different gestures, and each gesture contains multiple sample individuals. Colour images and depth images are used to train the recognition model of bimanual gesture respectively, and then the colour image and depth image are fused, and the bimanual gesture recognition model is trained based on colour image and depth image fusion. Then, the bimanual recognition effects of the three models are compared. The experimental results show that, regardless of the single gesture precision or the mean average precision, the bimanual gesture recognition effect of the fused model is better than the gesture recognition models based on either colour image or depth image. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Deep learning

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Solar Radiation Forecasting Based on Random Forest and XGBoost

Solar Radiation Forecasting Based on Random Forest and XGBoo...

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International Conference on Green technology and Sustainable Development (GTSD)

作者： Nhat-Truong Nguyen Thai Cat Tuong Dao Le Nhut Tuyen Nguyen Thanh-Dat Nguyen Minh-Vuong Le Isobel Timothea French Van-Tuong Doan Van Hoang Kim Vo Nghi C. Tran Duc-Hung Pham Van-Trung Nguyen Department of Renewable Energy Ho Chi Minh City University of Technology and Education Vietnam Department of Control Engineering and Automation Ho Chi Minh City Industry and Trade College Vietnam LILAMA2 International Technology College Dongnai Viet Nam Institute of Cognitive Neuroscience National Central University Taiwan College of Information and Communication Technology Can Tho University Viet Nam Faculty Electrical and Electronic Engineering Hung Yen University of Technology and Education VietNam

ISBN: (数字)9798350386073

ISBN: (纸本)9798350386080

Renewable energy offers a viable alternative to depleting fossil fuels, contributing to global warming and greenhouse gas emissions. Thus, solar energy is widely favored as a renewable energy source. However, the output power of solar systems is unstable and is highly dependent on environmental circumstances such as radiation, humidity, temperature, and wind. Therefore, accurately estimating solar radiation is crucial for successful integration of the solar system into existing electrical grid systems. In this study, two different machine learning models, XGBoost and Random Forest, were used to predict solar radiation data in Ho Chi Minh City, Vietnam. The results show that RMSE and MAE of the Random Forest model (61.09 and 24.37) is smaller compared to XGBoost (61.31 and 25.08). Regression value of the two models is close to 0.955. In summary, this paper demonstrates that these two machine learning algorithms effectively forecast solar radiation, with the Random Forest model showing better performance.

关键词： Renewable energy sources Temperature distribution Temperature dependence Urban areas Humidity Predictive models Solar system

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Joint-Comb-Acquisition for High-Accelerating Doppler-Shift in Space communications

Joint-Comb-Acquisition for High-Accelerating Doppler-Shift i...

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IEEE International Conference on communications (ICC)

作者： Zhaowei Zhang Lin Liu Hui Liu Jun Yin Ruirui Chen Su Pan Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and Telecommunications Nanjing China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China School of Software Engineering Jinling Institute of Technology Nanjing China School of Information and Control Engineering China University of Mining and Technology Xuzhou China

In space communications, the signal suffers a long-distance transmission and a high dynamic movement. The long-distance transmission causes a very low signal-to-noise ratio (SNR) and the high dynamic movement causes a high-accelerating Doppler-shift. To address the low SNR, the conventional strategy is long-time accumulation. However, during the long-time accumulation period, the high-accelerating Doppler-shift brings about the serious energy dispersion problem. In this paper, we propose one joint-comb-acquisition (JCA) scheme to address the energy dispersion problem. The proposed JCA scheme, which uses multiple accumulation periods to jointly construct a narrower comb-search-range, excludes most noise elements. Also, the theoretical analysis derives the closed-form expression for acquisition probability. Moreover, the simulation results demonstrate the significant increase of acquisition probability varies from 4.6 to 47.2 percents, as compared with the existing schemes.

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Accelerating Diffusion Models Via Pre-Segmentation Diffusion Sampling for Medical Image Segmentation

Accelerating Diffusion Models Via Pre-Segmentation Diffusion...

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IEEE International Symposium on Biomedical Imaging

作者： Xutao Guo Yanwu Yang Chenfei Ye Shang Lu Bo Peng Hua Huang Yang Xiang Ting Ma Electronic & Informatin Engineering School Harbin Institute of Technology (Shenzhen) Shenzhen China Peng Cheng Laboratory Shenzhen China International Research Institute for Artifcial Intelligence Harbin Institute of Technology (Shenzhen) Shenzhen China Institute of Pathogen Biology Shenzhen Center for Disease Control and Prevention Shenzhen China Department of Radiology The Third People’s Hospital of Shenzhen Shenzhen China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology (Shenzhen) Shenzhen China

Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise uncertainty maps of the segmentation and allows an implicit ensemble of segmentations to boost the segmentation performance. However, DDPM requires many iterative denoising steps to generate segmentations from Gaussian noise, resulting in extremely inefficient inference. To mitigate the issue, we propose a principled acceleration strategy, called pre-segmentation diffusion sampling DDPM (PD-DDPM), which is specially used for medical image segmentation. The key idea is to obtain pre-segmentation results based on a separately trained segmentation network, and construct noise predictions (non-Gaussian distribution) according to the forward diffusion rule. We can then start with noisy predictions and use fewer reverse steps to generate segmentation results. Experiments show that PD-DDPM yields better segmentation results over representative baseline methods even if the number of reverse steps is significantly reduced. Moreover, PD-DDPM is orthogonal to existing advanced segmentation models, which can be combined to further improve the segmentation performance.

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Low-temperature spatial atomic layer deposited Ga2O3 films for high-performance flexible deep ultraviolet photodetector

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Materials Letters 2023年 340卷

作者： Hsu, Chia-Hsun Zhu, Run-Feng Kang, Pin-Chun Gao, Peng Wu, Wan-Yu Wuu, Dong-Sing Lien, Shui-Yang Zhu, Wen-Zhang Xiamen Key Laboratory of Development and Application for Advanced Semiconductor Coating Technology School of Opto-electronic and Communication Engineering Xiamen University of Technology Xiamen361026 China National Measurement and Testing Center for Flat Panel Display Industry Xiamen Institute of Measurement and Testing Xiamen361004 China Fujian Provincial Key Laboratory of Nanomaterials Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fujian Fuzhou350002 China Department of Materials Science and Engineering National United University Miaoli36063 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan Department of Materials Science and Engineering Da-Yeh University Changhua51591 Taiwan

This research aims to prepare amorphous gallium oxide (α-Ga2O3) films for use in flexible deep ultraviolet photodetectors. The films are deposited using high-speed spatial atomic layer deposition (SALD) at different substrate temperatures. The experimental results show that the saturating deposition rate at 145–155 °C is four to sixteen times higher than that of temporal ALD. Moreover, under the saturated SALD conditions the deposited film has less carbon impurities, oxygen-related defects, and greater compactness than under non-saturated conditions. Finally, a high-performance flexible deep ultraviolet α-Ga2O3 photodetector with fast decay time of 161 µs and low dark current density of 6.8 × 10-10 A/cm2 is achieved. © 2023 Elsevier B.V.

关键词： Gallium compounds

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