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检索条件"任意字段=Conference on Algorithms, Devices, and Systems for Optical Information Processing"

共 3196 条记录，以下是11-20 订阅

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All-optical tunable integrator based on microring resonator assisted by GST superlattice 2

All-optical tunable integrator based on microring resonator ...

引用

2nd International conference on optical Communication and optical information processing, OCOIP 2024

作者： Zhong, Wentao Dong, Yida Shen, Mei Lei, Lei Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen518060 China China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Academy for Advanced Interdisciplinary Studies Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen518000 China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

This paper presents the design and simulation analysis of a tunable optical integrator based on ZnS-SiO2/Ge2Sb2Te5 superlattice (GST-SL) phase change material (PCM). The transfer function of the integrator is realized through a silicon microring resonator cladded with a three-layer GST-SL (GST-SL3-MRR). The integration of the PCM into the microring resonator enables a reconfigurable Q-factor, allowing for tunable integration orders between 0.5th and 1st. Through laser-induced phase transitions between amorphous and crystalline states, the transmission spectrum of the MRR can be dynamically adjusted. Simulation results show that the integration time window reaches 300 ps for the first-order integration, and the tunable integration results closely align with the ideal mathematical predictions. demonstrating the device's highly tunable performance. Further simulations of applications for quickly solving ordinary differential equations highlight the potential applications of our device in areas such as optical signal processing and image recognition. © 2025 SPIE.

关键词： Zinc Selenide

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Research on high accuracy classification of temperature signals based on OFDR technology and EfficientNet 4

Research on high accuracy classification of temperature sign...

引用

4th International conference on Advanced Manufacturing Technology and Electronic information, AMTEI 2024

作者： Shang, Chuanyin Li, Peng Tong, Zhengrong Wang, Hao Li, Sibo Tianjin Key Laboratory of Thin Film Electronics and Communication Devices Engineering Research Center for Optoelectronic Devices and Communication Technology the Ministry of Education School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

The development of machine learning is driving revolutionary changes and unprecedented opportunities across various industries. This paper introduces a new temperature monitoring method that combines the efficient deep learning model EfficientNet with optical Frequency Domain Reflectometry (OFDR). We utilize distributed fiber-optic sensing technology based on OFDR to collect one-dimensional temperature signals under different temperature conditions, which we then transform into a two-dimensional image format suitable for machine learning processing. The dataset is subsequently trained using the EfficientNet deep learning model. The results show that the trained model achieves a high accuracy rate of 97.5% on the test dataset. This research not only demonstrates the feasibility of efficiently and accurately monitoring temperature changes but also provides new solutions and technological pathways for areas such as environmental monitoring, industrial automation, and safety systems. © 2025 SPIE.

关键词： Education and training Data modeling Performance modeling Fiber optics sensors Machine learning Matrices Fiber optics Image processing Reliability Signal processing

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Optimizing Neural Prosthetic Control systems Using Genetic Algorithm for Adaptive Signal processing

Optimizing Neural Prosthetic Control Systems Using Genetic A...

引用

2025 International conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Purnima, S. Sathiya, A. Poonkuzhali, P. Balakrishnan, Minu Saravanan, M. Saravanan, M. Jerusalem College of Engineering Dept. of Bme Chennai600 100 India Department of Ece Karur639113 India R.M.D. Engineering College Department of Ece Chennai601206 India Sri Eshwar College of Engineering Department of Information Technology Coimbatore641202 India Sri Eshwar college of engineering Department of Ece Coimbatore641202 India Annamalai University Faculty of Engineering & Technology Department of Ece Chidambaram608002 India

ISBN: (纸本)9798331509828

Neural prosthetic systems provide promising solutions for restoring motor functions in individuals with disabilities. However, optimizing the control systems that decode neural signals remains a key challenge due to the dynamic nature of neural activity. This paper proposes a novel approach using a Genetic Algorithm (GA) to optimize the parameters of adaptive signal processing algorithms in neural prosthetic systems. The GA enables continuous calibration and adjustment, improving the accuracy of neural signal decoding and control over time. Simulation results show significant improvements in the efficiency of signal decoding from EEG/EMG data, leading to smoother control of prosthetic devices. The proposed methodology offers an adaptive and user-specific solution, contributing to enhanced prosthetic functionality and user experience. © 2025 IEEE.

关键词： Genetic algorithms

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optical tunable differentiator based on microring resonator assisted by low-loss superlattice phase change material 2

Optical tunable differentiator based on microring resonator ...

引用

2nd International conference on optical Communication and optical information processing, OCOIP 2024

作者： Wu, Ziqi Dong, Yida Zhong, Wentao Shen, Mei Lei, Lei Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen518060 China 518060 China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Academy for Advanced Interdisciplinary Studies Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen518000 China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

This paper presents a novel microring resonator (MRR)-based all-optical tunable temporal differentiator. By modulating the crystallinity of the ZnS-SiO2/Ge2Sb2Te5 (GST-SL) phase change material (PCM), the coupling regime of the MRR can be precisely adjusted, achieving a differentiation order (α) range of 0.7-1.3. Experimental validation with non-return-to-zero (NRZ) and return-to-zero (RZ) signal types demonstrates a strong alignment with theoretical models, maintaining an error margin below 7.3%, which confirms the device's high adjustability and stability. The proposed tuning mechanism enables low-energy, high-speed optical control of MRR states, contributing to optimized chip area and power consumption. Supported by the unique characteristics of the superlattice structure, this all-optical tunable differentiator offers promising potential for large-scale AI-driven photonic integrated circuits, particularly in applications such as optical analog computing, optical image processing, and optical neural networks. © 2025 SPIE.

关键词： Photonic integrated circuits

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A Novel Environment Reconstruction Method based on Distributed NLOS Signals 16

A Novel Environment Reconstruction Method based on Distribut...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Yu, Fangrui Li, Junlin Xie, Wei Zhang, Kehao Wan, Qun School of Information and Communication Engineering Nation Key Laboratory on Blind Signal Processing University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9781510689251

Integrated Sensing and Communication (ISAC) is a rapidly evolving field with extensive application prospects. Environmental reconstruction (ER) is an important content of ISAC. Existing ER techniques often require large sample sizes or specific signals, which significantly limit their application. User Equipments (UEs) widely distributed in the environment and their communication signals provide an adequate measurement basis. However, there are challenges in utilizing communication signals due to limited research, and difficulties in identifying and extracting Non-Line-of-Sight (NLOS) signals. This paper proposes a distributed ER algorithm based on Angle of Arrival (AOA) and Time of Flight (TOF) information of NLOS signals, enabling identification and utilization of various order NLOS signals under complex NLOS conditions. A series of simulation delays validate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Non line of sight propagation Reflection Reconstruction algorithms Mirror pointing Signal processing Environmental sensing Wireless communications

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Machine Learning for Signal processing: Kernel-extreme Learning Machines with Augmented Complex-valued and Mixture Correntropy Techniques 16

Machine Learning for Signal Processing: Kernel-extreme Learn...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Lin, Bingqing Wang, Yan Guan, Yunhe Qian, Guobing College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9781510689251

In this paper, we propose novel machine learning algorithms for signal processing applications based on kernel-extreme learning machines (KELM) and its variants. Specifically, we focus on the use of complex domain and mixture correntropy techniques to enhance the performance and generalization ability of KELM. Two models are derived by augmenting a complex-valued extreme learning machine with kernel functions, minimizing human intervention while enhancing accuracy. Furthermore, we extend the mixture correntropy-based kernel extreme learning machine (MCKELM) to complex domains, resulting in the MCKCELM algorithm, capable of solving prediction problems in complex domains. To further improve the performance of MCKCELM, we apply an augmented matrix to derive two variant models, and demonstrate their convergence through experiments. All models presented in this paper are regularized models specifically designed for signal processing tasks. © 2025 SPIE.

关键词： Adversarial machine learning

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Design of PMCW Millimeter Wave Radar algorithms on an Embedded DSP 16

Design of PMCW Millimeter Wave Radar Algorithms on an Embedd...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Zhao, Jinghao Liu, Qiuming Tan, Bin School of Software engineering Jiangxi University of Science and Technology Nanchang China Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications Nanchang China Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics China School of Electronics and Information Engineering Jinggangshan University Ji’an China

ISBN: (纸本)9781510689251

The rapid advancements in autonomous driving technology necessitate the extensive deployment of automotive radars operating within the 77-81 GHz millimeter-wave band in the forthcoming years. In contrast to earlier Frequency Modulated Continuous Wave (FMCW) radars, Phase-Modulated Continuous Wave (PMCW) radars exhibit notable improvements in processing speed and flexibility, offering superior range and velocity resolution. These enhancements are critical for the precise detection and interpretation of dynamic and complex traffic scenarios. This paper initially presents the simulation and testing of algorithms for range and speed measurement using single-input single-output (SISO) PMCW millimeter-wave radar. Building upon these results, further simulations incorporating multi-input multi-output (MIMO) systems utilizing Hadamard codes are conducted to augment PMCW radar performance. The final phase of this study involves implementing the relevant signal processing algorithms on a custom-developed digital signal processor (DSP) named SWIFT. Experimental findings demonstrate that PMCW radar significantly mitigates multipath effects and clutter, maintaining high performance in complex environments. Furthermore, the algorithms executed on the DSP meet the anticipated performance standards. The proposed methodology not only validates the theoretical framework but also establishes a foundation for future hardware implementation. © 2025 SPIE.

关键词： Automotive radar

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

Position Determination from CSI Measurements of Multiple UEs...

引用

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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Research on Edge Computing Data processing Mechanism for Intelligent Terminal 3rd

Research on Edge Computing Data Processing Mechanism for Int...

引用

3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Zhang, Yefeng Hou, Rui Shi, Kexin Xu, Xiaoxing Xu, Jun State Grid Information and Telecommunication Group Co. Ltd. Beijing Branch Beijing100031 China Cloud Data Technology Co. Ltd. Beijing100031 China

ISBN: (纸本)9789819791231

Edge computing is a new computing mode, which aims to push computing and data processing to edge locations near data sources such as Internet of Things devices and intelligent terminals, so as to achieve low latency and efficient data processing. This research focuses on the edge computing environment for intelligent terminals, discusses the key data processing mechanisms, and conducts a series of experiments and evaluations to deeply understand its performance and effects. First, in terms of data collection and transmission, we studied how to effectively collect large-scale data from intelligent terminals and sensor devices, and transmit it to edge computing nodes through the network. The experimental results indicate that with the support of high-speed network connections, fast data transmission is crucial for real-time data processing. Secondly, in terms of data processing capability of edge computing nodes, we focus on real-time performance, data cleaning, pre-processing, analysis algorithms, and models. The experimental and evaluation results reveal the importance of appropriate hardware configuration and optimization algorithms in improving real-time performance and data quality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data processing mechanism Edge computing Intelligent terminal

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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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