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Research on SMA motor modelling and control algorithm for optical image stabilization

MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOsystems-information STORAGE AND processing systems 2025年第6期31卷 1381-1400页

作者： Li, Chen Qu, Yingmin Song, Zhengxun Liu, Xinliang Liu, Weiwen Changchun Univ Sci & Technol Int Res Ctr Nano Handling & Mfg China Changchun 130022 Jilin Peoples R China Changchun Univ Sci & Technol Zhongshan Inst Zhongshan 528437 Guangdong Peoples R China Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun 130022 Jilin Peoples R China

Cameras play an increasingly important role in mobile devices and intelligent systems. With the development of technology, the resolution of the camera continues to improve and the images become much clearer. However, image quality is easily affected by lens shake. optical stabilization technology improves the shooting stability as well as image quality of mobile devices by overcoming image blur which is caused by lens shake. In the current camera modules of smart devices, the voice coil motor (VCM) drives the lens movement. However, the traditional VCMs contain permanent magnets, thus generating stray magnetic fields and affecting the quality of captured images. Compared with VCM technology, shape memory alloy (SMA) motors driven by shape memory alloys have the advantages, such as lighter weight, higher accuracy, greater thrust, and lower energy consumption. But there are still some issues with the current SMA motors. To begin with, there is a control problem of SMA wire, and the current control algorithms are difficult to achieve high-precision control of SMA wire. What's more, the strain range of SMA wires used in current SMA motors is limited, so their application in certain fields are restricted. A SMA motor with a larger shrinkage of SMA wire was designed, which could be used in larger lenses and whose application range was extended. Given the hysteresis effect of SMA, a Hammerstein-like model was established to describe the dynamic hysteresis characteristics of SMA. The Prandtl Ishlinskii model was used to describe the static hysteresis model of SMA, and the Nonlinear AutoRegressive network with eXogenous inputs (NARX) model was used to describe the dynamic model of SMA and predict its properties. A composite control scheme based on inverse compensation was designed to eliminate the dynamic hysteresis phenomenon of SMA, using the PI inverse model as the feed-forward controller and BP-FOPID as the feedback controller. In a gesture to address the problem of mul

关键词： Permanent magnets

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MUSIC Subspace Pursuit Algorithm for DOA Estimation with Single Snapshot 15

MUSIC Subspace Pursuit Algorithm for DOA Estimation with Sin...

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15th International Conference on Signal processing systems, ICSPS 2023

作者： Cao, Ruoshi Zhao, Yongbo Gao, Di National Key Laboratory of Radar Signal Processing No.2 South Taibai Road Shaanxi Xi’an710071 China

ISBN: (纸本)9781510675056

Direction of arrival (DOA) estimation on uniform linear array with single snapshot has always been a hot topic in radar signal processing. The traditional subspace class estimation methods, such as multiple signal classification (MUSIC), and sparse recovery algorithms, such as subspace pursuit (SP) algorithm, are suitable for different situations, meanwhile have certain requirements for observation data and signal-to-noise ratio (SNR). Combining the advantages of these two methods, a novel MUSIC subspace pursuit (MSP) algorithm is proposed in this paper. Firstly, an error evaluation function of the residual signal is constructed. The angles measured by both methods are replaced and selected one by one. And then, the framework of the greedy algorithm is iteratively applied until convergence. In this algorithm, the subspace information of the signal is added to the greedy algorithm, and more conditions are used to improve the angle measurement accuracy with single snapshot. Simulation results illustrate that the proposed MSP algorithm outperforms the SP and MUSIC algorithms at different SNR levels and it keeps the faster reconstruction speed of SP algorithm. © 2024 SPIE.

关键词： Multiple signal classification

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Employing Machine Learning Paradigms for Optimized Hard Disk Failure Prediction in Shipboard systems 4

Employing Machine Learning Paradigms for Optimized Hard Disk...

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4th International Computational Imaging Conference, CITA 2024

作者： Ma, C. Li, F. Gao, D. Zhao, Y. Bao, T. Li, X. China Ship Research and Development Academy Beijing China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781510688834

This paper presents an optimized machine learning-based approach for hard disk failure prediction, specifically tailored for shipboard systems. In shipboard systems, unexpected hard disk failures can disrupt operations and lead to critical data loss;therefore, reliable failure prediction is essential. To address the impact of dataset imbalance on predictive performance, we employed the Synthetic Minority Over-sampling Technique (SMOTE) alongside a fault-backtracking method to transform the imbalanced dataset into a balanced one, thereby enhancing the algorithm's predictive accuracy. Four machine learning algorithms were utilized in this study: K-Nearest Neighbors (KNN), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Experimental results demonstrated that the RF algorithm achieved an accuracy of 97.1%, outperforming the other algorithms across all evaluation metrics. The contribution of this work lies in providing a feasible solution for hard disk failure prediction in shipboard systems through effective data processing strategies and algorithm selection, ultimately enhancing shipboard safety and reliability. © 2025 SPIE.

关键词： Hard disk storage

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Image enhancement network based on fiber topology-preserving multi-module fusion for neuron reconstruction

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BIOMEDICAL OPTICS EXPRESS 2025年第7期16卷 2584-2600页

作者： Chen, Wu Liao, Mingwei Bao, Shengda Sun, Chaoyi Jiang, Shan Gong, Hui Xiao, Chi Li, Anan Huazhong Univ Sci & Technol MOE Wuhan Natl Lab Optoelect Key Lab Biomed Photon Wuhan 430074 Peoples R China HUST Suzhou Inst Brainsmat JITRI Suzhou 215123 Peoples R China Hainan Univ Sch Biomed Engn State Key Lab Digital Med Engn Key Lab Biomed Engn Hainan Prov Sanya 572025 Peoples R China

The rapid development of optical labeling and imaging technologies has enabled scientists to capture three-dimensional images of mammalian brains at the single-neuron level. However, it has also brought about numerous technical challenges. In neuronal images, fibers exhibit lower fluorescence intensity than cell bodies and are densely packed, making it difficult to distinguish fiber signals from background noise or resolve connectivity accurately. While sparse high-brightness labeling and sensitive imaging technologies have partially addressed these issues, they have not been fundamentally resolved. This study focused on data post-processing and proposed an image enhancement method using a fiber topology-preserving multi-module fusion network. By incorporating a self-attention mechanism and a topology-preserving loss function, the method enhanced the signal-to-noise ratio and continuity of neuronal fibers. Applied to the three-dimensional reconstruction of complex fiber structures, it significantly improved the performance of existing algorithms, offering an effective technical approach for precise neuronal fiber structure analysis.

关键词： Image enhancement Imaging systems information processing Machine vision optical imaging Three dimensional reconstruction

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optical demultiplexing with fractal-structured beams in turbulent atmospheric environments 6

Optical demultiplexing with fractal-structured beams in turb...

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Environmental Effects on Light Propagation and Adaptive systems vi 2023

作者： Weng, Xiaojing Vuong, Luat T. Departments of Mechanical and Electrical and Computer Engineering University of California at Riverside RiversideCA92521 United States

ISBN: (纸本)9781510666917

When information is spatially repeated in self-similar fractal beam patterns, only a portion of the diffracted beam is needed to reconstruct the kernel data. What is unique to a fractal-encoding scheme is that the image demultiplexing process can be, to a first approximation, easily performed optically. In prior work, we experimentally and numerically study fractal-encoded optical beams and their mid- and far-field propagation without added turbulence. Here, we present preliminary simulations of fractal-encoded beams with high turbulence (C2n ≥ 10-14 m-2/3) where we achieve respectable bit error rates of 10-3. These results are impressive given that: data with low fractal orders is shown, simple threshold-algorithms are used (i.e., no machine learning), and only a third of the beam, off-axis, is needed. More robust channel encoding is associated with increased fractal orders, larger collection areas, and higher kernel singular value decomposition entropy. © 2023 SPIE.

关键词： Fractals

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Position Determination from CSI Measurements of Multiple UEs under Unaware Environment 16

Position Determination from CSI Measurements of Multiple UEs...

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16th International Conference on Signal processing systems, ICSPS 2024

作者： Wang, Chenchen Ma, Yubin Yu, Fangrui Wen, Hao Liu, Weiyu Wan, Qun Nation Key Laboratory on Blind Signal Processing School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9781510689251

In urban environment, communication signals encompass non-line-of-sight (NLOS) paths that contain environmental information. This paper investigates utilizing NLOS component signal to estimate the environment parameters, and uses these environmental parameters to achieve blind UEs (User Equipment) positioning. Specifically, this paper first analyzes the relationship between environmental parameters and the time of arrival (TOA) of NLOS signals. Second, an algorithm is proposed that utilizes the TOA of one-order NLOS signals in uplink signals between distributed UEs and base stations (BS) to estimate environmental parameters via maximum likelihood estimation. Subsequently, leveraging the obtained environmental parameters, achieving the positioning of blind UEs. Finally, numerical simulations validate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Reflection Non line of sight propagation Mirrors Reconstruction algorithms Environmental sensing Mirror surfaces Point clouds LIDAR Monte Carlo methods Numerical simulations

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The application of deep learning algorithms in power system load forecasting 6

The application of deep learning algorithms in power system ...

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6th International Conference on Wireless Communications and Smart Grid, ICWCSG 2024

作者： Wang, Shilin Cui, Wei Yang, Yang Meng, Xiangliang State Grid Information & Telecommunication Group Co. Ltd. No. 10 Xuanwumen Outer Street Xicheng District 95555-0345 Beijing100029 China

ISBN: (纸本)9781510682962

This article explores the application of deep learning (DL) algorithms in power system load forecasting. With the continuous advancement of the construction of new power systems, traditional load forecasting models designed based on time series analysis are no longer in line with the actual needs of the current power system. To improve the accuracy of load forecasting, this paper attempts to transform time series data into images for analysis, and then use deep learning methods widely used in the field of image processing for power load forecasting. Convolutional neural network (CNN), as a commonly used image processing algorithm, has been applied in time series data processing. However, data is still processed as a sequence matrix, and the advantages of CNN algorithm in processing image matrices have not been fully utilized. Therefore, this article proposes a CNN algorithm based on sequence to image conversion (STI-CNN). Firstly, the load sequence is converted into a load image. Then, a dual branch deep network model is used to accurately cluster the input data. Finally, the STI-CNN model is used for load prediction. Matlab simulation experiments show that the proposed STI-CNN model has excellent performance in different prediction metrics and has higher prediction accuracy. © 2024 SPIE.

关键词： Data modeling Data conversion Image processing systems modeling Convolutional neural networks Evolutionary algorithms Statistical modeling Education and training Support vector machines

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Lane detection networks based on deep neural networks and temporal information

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Alexandria Engineering Journal 2024年 98卷 10-18页

作者： Lin, Huei-Yung Chang, Chun-Ke Tran, Van Luan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106 Taiwan Department of Electrical Engineering National Chung Cheng University Chiayi621 Taiwan School of Engineering Eastern International University Binh Duong Viet Nam

In the past few years, the lane detection technique has become a key factor for autonomous driving systems and self-driving cars on the road. Among the various vehicle subsystems, the lane detection module is one of the essential parts of the Advanced Driver Assistance System (ADAS). Conventional lane detection approaches use machine vision algorithms to find straight lines in road scene images. However, it is challenging to identify straight or curved lane markings in complex environments. To deal with this problem, this paper presents a lane detection technique based on deep learning. It is combined with a 3D convolutional network, so the temporal information is added to the network architecture. Using the front camera images, the system can immediately detect the lane marking information ahead. Moreover, we propose an approach for improvement by adding the time axis to the network architecture. In addition to using 3D-ResNet50, the temporal convolution and spatial convolution are separated for processing. The accuracy is 91.34% improved after adding time and space split convolution with LeakyReLU. The experiments carried out using real scene images have demonstrated the feasibility of the proposed technique for applications to various complex scenes. © 2024 The Author(s)

关键词： Network architecture

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Optimization of PbS Colloidal Quantum Dots-based near-infrared photodetectors using pentacene

Optimization of PbS Colloidal Quantum Dots-based near-infrar...

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2024 International Conference on Optoelectronic information and optical Engineering, OIOE 2024

作者： Yi, Fangzhou Zhen, Dingting Peng, Zhannan Sun, Zhenhua Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781510680470

ISBN: (纸本)9781510680463

Lead sulfide colloidal quantum dots (PbS CQDs) offer the unique advantages of tunable bandgap, cost-effectiveness, and ease of processing. These characteristics make PbS CQDs an ideal candidate for the development of near-infrared (NIR) photodetectors, with promising applications in diverse fields such as fiber optic communication, biomedical imaging, and national defense. In this study, NIR photodetectors employing indium gallium zinc oxide (IGZO) as the carrier conductive layer, PbS CQDs as the photosensitive layer, and pentacene as the functional enhancement layer are fabricated and systematically studied. Comparative analysis unveiled That the p-type semiconductor property of the pentacene is leveraged to increase the photoresposne of the PbS CQDs-IGZO NIR photodetectors. This work not only sheds light on the structural advancements of photosensitive field-effect transistors but also provides valuable insights into optimizing their performance. © 2024 SPIE.

关键词： II-vi semiconductors

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Automation of optical tweezers: an enabler for single cell analysis and diagnostic 3

Automation of optical tweezers: an enabler for single cell a...

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Conference on Biophotonics in Point-of-Care III

作者： Jorge, Pedro Teixeira, Joana Rocha, vicente Ribeiro, Jose Silva, Nuno INESC TEC Rua Campo Alegre 687 P-4169007 Porto Portugal Univ Porto Fac Ciencias Dept Fis & Astron Rua Campo Alegre 687 P-4169007 Porto Portugal Univ Porto Dept Chem & Biochem CIQUP IMS Fac Sci P-4169007 Porto Portugal

ISBN: (纸本)9781510673359;9781510673342

Sensing at the single cell level can provide insights into its dynamics and heterogeneity, yielding information otherwise unattainable with traditional biological methods where average population behavior is observed. In this context, optical tweezers provide the ability to select, separate, manipulate and identify single cells or other types of microparticles, potentially enabling single cell diagnostics. Forward or backscatter analysis of the light interacting with the trapped cells can provide valuable insights on the cell optical, geometrical and mechanical properties. In particular, the combination of tweezers systems with advanced machine learning algorithms can enable single cell identification capabilities. However, typical processing pipelines require a training stage which often struggles when trying to generalize to new sets of data. In this context, fully automated tweezers system can provide mechanisms to obtain much larger datasets with minimum effort form the users, while eliminating procedural variability. In this work, a pipeline for full automation of optical tweezers systems is discussed. A performance comparison between manually operated and fully automated tweezers systems is presented, clearly showing advantages of the latter. A case study demonstrating the ability of the system to discriminate molecular binding events on microparticles is presented.

关键词： optical tweezers single cell automation machine learning

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