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Effects of different scattering components in speckle autocorrelation imaging

Optics Letters 2025年第3期50卷 880-883页

作者： Xie, Gengchen Zhang, Xuyu Zhang, Dawei Han, Shensheng Liu, Honglin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Hangzhou Institute for Advanced Study Hangzhou310024 China

Various methods of imaging through scattering media suffer a rapid degradation in image quality as medium thickness increases. To address this issue, based on speckle autocorrelation imaging we designed a double-layer diffuser experiment to study the effects of different scattering components on imaging. The results show that both singly scattered light and doubly scattered light can transmit information. However, the difference in memory effect range leads to diverse abilities in information extraction, thereby affecting image recovery. This study may provide insights into the mechanisms of information transmission within thick scattering media and methods to extract information from multiply scattered light to reconstruct images. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Speckle

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On a novel tracking differentiator design based on iterative learning in a moving window

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Control Theory and technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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Novel Design of Grey Wolf Optimization Heuristics for High Resolution Direction of Arrival Estimation in Acoustic Plane Waves

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Wireless Personal Communications 2023年第4期128卷 2507-2529页

作者： Ahmed, Nauman Wang, Huigang Raja, Muhammad Asif Zahoor Ali, Wasiq Kashif, Muhammad Zaman, Fawad School of Marine Science and Technology Northwestern Polytechnical University Xi’an China Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan COMSATS University Islamabad Attock Campus Attock Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad Pakistan

High resolution direction of arrival estimation for closely spaced target in low signal to noise ration (SNR) is an active research area in underwater acoustic and SONAR signal processing The resolution performance of classical/traditional subspace based algorithms including MUSIC and ESPRIT procedures degraded under sever scenarios of low signal to noise ratio (SNR), less number of samples/snapshots and short distant targets. In this work, optimization courage of swarming intelligence of grey wolf optimization (GWO) is carried out for viable DOA estimation in the challenging instances of worst underwater scenarios using a uniform linear array (ULA). The high resolution DOA for short distant emitters is achieved using less number of samples vitally with GWO by inspecting the global minima of the highly nonlinear objective model of ULA having multi local minimas. The performance is analyzed for different number of targets employing estimation accuracy, resolution certainty, variability analysis, frequency distribution of RMSE, robustness against different SNR of additive-white Gaussian measurement noise and comparative studies with MUSIC and Particle Swarm Optimization PSO with the validation strength of Cramer Rao bound reveals the valuation of the scheme. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Direction of arrival

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Environmental Sound Classification Based on Multi-channel Features and Hybrid Attention 17

Environmental Sound Classification Based on Multi-channel Fe...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Liu, Guishan Zhou, Shuai Peng, Zhangjun Li, Li School of Computer Science and Technology Southwest University of Science and Technology Sichuan Mianyang621000 China College of Mechanical and Electrical Engineering Sichuan Electronic Machinery Vocational and Technical College Sichuan Mianyang621000 China Sichuan Autonomous Controllable Artificial Intelligence Engineering Technology Center Sichuan Mianyang621000 China

ISBN: (纸本)9798331507398

Environmental sound classification (ESC) is an important research direction, but it remains challenging due to the wide variety of environmental sounds which are difficult to uniformly represent and can be easily interfered by noise. The ESC has limitations in accurately separating and recognizing sounds in multi-class complex scenarios, especially under conditions of noise interference or overlapping sound sources. To improve the accuracy of ESC recognition, this paper proposes a classification method that utilizes a multi-channel feature and a mixed attention model. Firstly, the time-level and frequency-domain signals of the ESC are extracted, and filters are applied to extract spectral features and reconstruct into a feature graph. The limitations of single-feature representation are addressed by leveraging the complementarity between the three-channel features. Secondly, a hybrid classification model composed of channel and time-frequency concentration module classification model is introduced. The channel attention model calculates the feature map and assigns weights to different channels, and the channel features with more effective information and better resolution of this kind of sound can be allocated more weights. The time-frequency concentration module, however, focuses on more effective information in the time domain and frequency domain. The method proposed in this paper can effectively suppress the background noise, eliminate redundancy, and improve the convergence speed and classification accuracy. The recognition accuracies on ESC-10 and ESC-50 datasets reach 96.25% and 89.56% respectively, and 98.40% on the Urbansound8k dataset. © 2024 IEEE.

关键词： Frequency domain analysis

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IoT-Based Intelligent Energy Management for EV Charging Stations

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第11期51卷 1853-1861页

作者： Dasi, Swathi Bondalapati, Swarupa Rani Subbaraju, Mummidi P. Nimma, Divya Jangir, Pradeep Reddy, R. Vijaya Kumar Zareena, N. Electronics and Communication Engineering Department Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Electrical and Electronics Engineering Department Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Electrical and Electronics Engineering Department Aditya University Andhra Pradesh Surampalem India University of Southern Mississippi United States Biosciences Department Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India associated with Applied Science Research Center Applied Science Private University Amman Jordan. Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Computer Science and Engineering Department R.V.R. & J. C. College of Engineering Guntur India

The electronics panels and controllers used in electric vehicle (EV) charging stations need to function securely and optimally in countries such as India, where the external temperature remains high for a significant part of the year. This study is the first to use a solar-battery storage integrated switchable glazing architecture to supply HVAC (heating, ventilation, and air conditioning) in the control rooms of electric vehicle charging stations throughout the day. The EV charging station has been equipped with a rooftop-mounted solar PV source as part of an initiative to promote renewable energy and sustainable forms of mobility. The utilization of a vanadium redox flow battery (VRFB) by the system ensures energy security through the provision of a durable solution for storing energy over an extended period of time. A smart scheduling system utilizing IoT technology was demonstrated to effectively meet the glazing load need of buildings in real-time, taking into account dynamic climatic conditions. This system incorporates solar PV, VRFB storage, and the neighborhood power grid. The efficiency of the suggested method was validated using four separate transitory scenarios: sunny, intermittently cloudy, prolongedly cloudy, and low solar radiation with frequent grid outages. Due to its versatility to be applied in various situations, the suggested method has the potential to greatly enhance capacity. © (2024), (International Association of Engineers). All rights reserved.

关键词： Air conditioning

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Interior Scene Intelligent Generated for Smart Homes Based on GAN-ID Algorithms

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1046-1055页

作者： Zhang, Fu-Quan Ma, Lin-Juan Zhou, Jun-Xian Wang, Run-Hong Zhang, Guang-Zhen College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing Information System Minjiang University Fuzhou China Fujian Province University Fuzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China Minjiang University Fuzhou China Information Media School of Tianjin Technician Institute of Mechanical & Electrical Technology Tianjin China

The purpose of this paper is to present a new GAN-ID algorithm to gener-ate interior scene intelligently based on Artificial Intelligence of Things (AIoT). Although great progress has been made when GAN algorithm applied in smart homes and smart cities, the issue of the trade-off between the accuracy and the efficiency in interior scene has been overlooked. A number of experiments were performed to check the performance and it is indicated that the results of the proposed GAN-ID has great advantages in model sample generation than other common methods when generating interior scene intelligently, which is similar to state-of-the-art. It is meaningful when it can be applied in smart cities and smart homes filed based on AIoT in the future. © 2024, J. Network Intell. All rights reserved.

关键词： Automation

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Graph-based deep learning models in the prediction of early-stage Alzheimers 46

Graph-based deep learning models in the prediction of early-...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Thapaliya, Bishal Wu, Zundong Sapkota, Ram Ray, Bhaskar Suresh, Pranav Ghimire, Santosh Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States Tribhuvan University Department of Applied Sciences and Chemical Engineering Nepal

ISBN: (纸本)9798350371499

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications. © 2024 IEEE.

关键词： Prediction models

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Tabletop Automated TCSPS Measurement System for Characterizing Single-Photon Emitters

Tabletop Automated TCSPS Measurement System for Characterizi...

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2024 Latin America Optics and Photonics Conference, LAOP 2024

作者： Lagutchev, Alexei S. Bogdanov, Simeon I. Boltasseva, Alexandra Shalaev, Vladimir M. School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Oak RidgeTN37931 United States Department of Electrical and Computer Engineering Nick Holonyak Jr. Micro and Nanotechnology Laboratory Illinois Quantum Information Science and Technology Center University of Illinois at Urbana-Champaign UrbanaIL60801 United States

We report on a table-top system for characterizing single photon emitter fluorescence lifetimes, spectra, photon correlation and spin properties. Measurements are facilitated by automation and machine learning. ©...

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Machine learning assisted prediction for hydrogen production of advanced photovoltaic technologies

DeCarbon

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DeCarbon 2024年第2期4卷 76-89页

作者： Qiang Yang Zhu Ma Lihong Bai Qiuyue Yuan Fuchun Gou Yanlin Li Zhuowei Du Yi Chen Xingchong Liu Jian Yu Xiaoqian Zhou Cheng Qian Zichen Liu Zilu Tian Anan Zhang Kuan Sun Liming Ding Chun Tang Taoli Meng Fan Min Ying Zhou School of Electrical Engineering and Information Southwest Petroleum UniversityChengdu 610500China School of New Energy and Materials Southwest Petroleum UniversityChengdu 610500China Key Laboratory of Low-grade Energy Utilization Technologies and Systems School of Energy and Power EngineeringChongqing UniversityChongqing 400044China Center for Excellence in Nanoscience(CAS) Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijing 100190China Guangdong Hongfeng Energy Develoment Co. Ltd.Dongguan 523000China School of Computer Science Institute for Artificial IntelligenceSouthwest Petroleum UniversityChengdu 610500China

The photovoltaic(PV)water electrolysis method currently stands as the most promising approach for green hydrogen *** rapid iteration of photovoltaic technologies has significantly affected on the technical and economic evaluation for photovoltaic hydrogen *** this work,the photovoltaic hydrogen production of three most advanced silicon photovoltaic technologies is systematically compared for the first time under the climatic conditions of the Kucha ***-weather stable hydrogen production control system with optimal charging and discharging strategies is constructed to realize stable and efficient hydrogen energy *** machine learning(ML)algorithms are used to forecast the performance in power generation and hydrogen production of a 100 MW photovoltaic hydrogen production and energy storage(PH-S)system throughout its operational *** long short-term memory(LSTM)algorithm exhibits the best performance,achieving mean absolute error(MAE)of 0.0415,root mean square error(RMSE)of 0.0891,and coefficient of determination(R2)of *** terms of cost-effectiveness,heterojunction with intrinsic thin layer(HJT)PV technology achieves the lowest levelized cost of electricity(LCOE)and hydrogen(LCOH)at 0.025$/kWh and 6.95$/kg,*** to the sensitivity analysis,when the cost of proton exchange membrane electrolysis(PEMEC)reduced 50%,the LCOH for PH-S system decreased 21.40%.This study provides valuable insights for the practical implementation of large-scale photovoltaic hydrogen production and cost reduction in PH-S systems.

关键词： Photovoltaic technology Machine-learning Hydrogen production Economic evaluation

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Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation

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IEEE Transactions on Cybernetics 2024年第8期54卷 4643-4651页

作者： Jaw, Da-Wei Huang, Shih-Chia Lu, Zhi-Hui Fung, Benjamin C. M. Kuo, Sy-Yen National Taiwan University Department of Electrical Engineering Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan Fudan University School of Computer Science The Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of Education Shanghai200433 China McGill University School of Information Studies MontrealQCH3A 1X1 Canada

Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with sufficient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low- and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low- and sufficient-luminance domains. © 2013 IEEE.

关键词： Feature extraction

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