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

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Research on Design and Optimization Methods of Intelligent Sensors Based on Optoelectronic Detection Technology

Research on Design and Optimization Methods of Intelligent S...

引用

Electrical Drives, Power Electronics & Engineering (EDPEE), International conference on

作者： Dinghe Zhou Junbo Huang Harbin Engineering University Harbin Heilongjiang China Hebei University of Technology Tianjin China

ISBN: (数字)9798350395631

ISBN: (纸本)9798350395648

In optoelectronic information science and engineering technology, optoelectronic detection technology is a very important technology. Photoelectric sensor (PS) refers to the process of using a photoelectric element as the basic detection component, converting the measured changes into changes in other signals, and then using the photoelectric element to convert the optical signal into an electrical signal. With the gradual improvement of modern science and technology, optoelectronic detection technology has further integrated modern technologies such as automation control systems and digital information processing technology, effectively combining optical and electronic technologies, greatly improving the accuracy and speed of optoelectronic detection. This article designs an intelligent sensor based on photoelectric detection technology. Through in-depth research on the basic principles of photoelectric detection technology, combined with microelectronics technology and integrated circuit design, a miniaturized and low-power intelligent sensor is achieved. Meanwhile, utilizing deep learning (DL) algorithms to optimize and calibrate sensors, improving their performance and accuracy. The experimental results show that the sensor designed in this article has high measurement accuracy and good application prospects.

关键词： Integrated circuit synthesis Performance evaluation Biomedical optical imaging Optimization methods optical variables measurement Market research Microelectronics

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RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking Dataset And A Benchmark

RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking D...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Heng Yu Ruiheng Zhang Haoyang Sun Zhe Cao Biwen Yang Jin Zhang Guanyu Liu Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

High-quality perception is crucial in autonomous driving and monitoring systems, where millimeter-wave radar and infrared cameras play important roles due to their robustness and reliability under harsh conditions. Both technologies can serve as low-cost supplements to optical image detection, improving overall system robustness. However, there is currently a lack of widely applicable feature-level fusion methods and multimodal datasets to effectively integrate visible light with these two heterogeneous data types for multiple tasks. In this work, we collect a new multimodal dataset, RADCI8, which synchronizes data from a camera, an infrared camera, and a radar for target detection and tracking. The dataset includes 2D image annotations, radar RAD tensor data with distance, angle, and Doppler information, as well as target ID annotations in both data formats. In addition, to address the incomplete use of radar data in previous fusion algorithms, we propose a detection method that fuses image and radar features using feature concatenation and an attention mechanism. Our proposed algorithm achieves 51.5% AP with an IOU of 50:95 on 2D bounding box prediction, significantly improving average detection accuracy over vision-based methods and maintaining robustness even when a single sensor degrades.

关键词： Tensors Radar detection Signal processing algorithms Radar imaging Radar tracking Cameras Prediction algorithms Robustness Doppler radar Synchronization

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The information Bottleneck Method in Deep Learning: Principles, Applications and Challenges

The Information Bottleneck Method in Deep Learning: Principl...

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IEEE International conference on Signal processing, information, Communication & systems (SPICSCON)

作者： Md Nurul Absar Siddiky Rashadul Hasan Badhon Muhammad Enayetur Rahman M M Salman Md Soyaib Hossain Sohag Dept. of ECE UNC at Charlotte Charlotte NC USA Dept. of ECE Old Dominion University Norfolk VA USA Dept. of ECE Khulna University Khulna Bangladesh Dept. of Physics and Optical Science UNC at Charlotte Charlotte NC USA

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

The information Bottleneck (IB) method provides a theoretical framework for balancing complexity and accuracy in machine learning models by compressing input data while retaining relevant information. This paper presents a compre-hensive review of the applications of the IB method in deep learning, focusing on its ability to enhance learning efficiency, generalization, and adversarial robustness. Variations like the Variational information Bottleneck (viB) are also explored for their success in improving neural network performance. Notably, the viB method performed superior on the MNIST dataset, outperforming conventional regularization techniques such as dropout and confidence penalty. In addition to achieving better generalization, the viB model demonstrates greater resilience to adversarial attacks, requiring more substantial input mod-ifications to mislead the model. However, significant challenges remain, including optimization complexities and scalability issues in large data sets. This paper also identifies future research di-rections that aim to address these limitations, such as developing more scalable IB algorithms and enhancing adversarial defense mechanisms, to fully realize the potential of IB-based learning models.

关键词： Deep learning Reviews Estimation Computer architecture Robustness Data models Complexity theory Optimization Mutual information Resilience

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Extended field of vision by using two synchronized camera acquisition off-axis holograms

Extended field of vision by using two synchronized camera ac...

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International conference on Image and Signal processing and their Applications (ISPA)

作者： Omar Chaab Fatan Bougheziou Malika Mimi Serge Simoëns Department of Sciences and Technology Signals and Systems Laboratory University Abdelhamid Ibn Badis Mostaganem Algeria Applied Optics Laboratory Institute of Optics and Fine Mechanics University Ferhat Abbas Setif Algeria Department of Electrical Engineering Signals and Systems Laboratory University Abdelhamid Ibn Badis Mostaganem Algeria Acoustic and Fluid Mechanics Laboratory UMR CNRS 5509 UCB Lyon I INSA Central School of Lyon Ecully Lyon France

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Digital holography (DH) is an imaging technique that allows the recording and the reconstruction of the complex optical field (complete information) in both amplitude and phase. Its main advantage is the possibility to modify the recorded wavefront to get free-aberration version. This technique represents the best contender for metrological applications especially in mechanical fluids where it gives an additional dimension for the determination of velocity field of different kind of fluid flow. In order to extend the fluid field of investigation, two synchronized cameras CCD and CMOS with different characteristic (pitch and size) are used for recording two views holograms; this task is implemented under Labview interface which also permits to record successive several holograms with a predefined time interval. The holograms are obtained with the help of holographic setup in off-axis configuration of two views (direct and orthogonal). In order to obtain the total field of view, the two reconstructed holograms must have the same output resolution.

关键词： Image resolution Heuristic algorithms Signal processing algorithms Transforms Holography Cameras Synchronization

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SAR Target Recognition from Subband and Subaperture Images via Complex Valued Convolutional Neural Networks

SAR Target Recognition from Subband and Subaperture Images v...

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Software System and information processing (ICSSIP), IEEE International conference on

作者： Jun Lou Jin Yang Ling Wang School of Electronic Information Hunan First Normal University Changsha China

ISBN: (数字)9798331506131

ISBN: (纸本)9798331506148

Synthetic aperture radar (SAR) images are different from optical images, as they reflect the scattering characteristics of targets and are complex data. Traditional real valued neural networks can only handle amplitude information and lose phase information. In addition, SAR images obtained by traditional imaging algorithms can only represent spatial scattering characteristics of targets, while the scattering characteristics of frequency and azimuth angle embed in two-dimensional SAR images, which cannot be utilized when directly using SAR images as inputs for neural networks. In response to the above issues, a method of SAR target recognition from subband and subaperture images via complex valued convolutional neural networks is proposed in this paper. The method uses subband and subaperture images at different frequencies and azimuth angles as multi-channel inputs for the complex valued neural convolutional network to achieve target recognition. By this method, not only is the spatial scattering information of the target utilized, but also the scattering information about frequency and azimuth angle is utilized. Not only is the amplitude information of the image utilized, but also the phase information of the image is utilized. Experiments on the MSTAR dataset have verified the effectiveness of this method.

关键词： Time-frequency analysis Uncertainty Target recognition Azimuth Neural networks Scattering Software systems Radar polarimetry Convolutional neural networks Synthetic aperture radar

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Research on Modulation Recognition Method in Low SNR Based on LSTM

Research on Modulation Recognition Method in Low SNR Based o...

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2021 International conference on Communication Technology and information Technology, ICCTIT 2021

作者： Zhang, Beiming Chen, Guoping Jiang, Chun State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai200240 China

Modulation mode recognition of radio signal is a committed step between signal detection and signal demodulation. At present, quite a lot studies have fully proved that deep learning algorithms can effectively identify the modulation pattern of radio signals. However, the sudden decline of recognition accuracy under the condition of low signal-to-noise ratio needs to be continuously studied and solved. Inspired by the excellent performance of recurrent neural network in signal recognition, this article optimizes and improves the existing system methods, realizes the noise reduction processing of low signal-to-noise ratio signals, and further solves the problem of low recognition accuracy. Through a large number of experimental tests using RML public dataset, the effectiveness of this paper is verified. The results show that the accuracy of modulation pattern recognition of low signal-to-noise ratio signals reaches an average of 27.2%. At last, the paper analyzes the existing problems and optimization points, and looks forward to the further research of relevant contents in the future. © Published under licence by IOP Publishing Ltd.

关键词： Pattern recognition

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Dynamic User Clustering for MIMO-NOMA Network: Deep Reinforcement Learning Approach

Dynamic User Clustering for MIMO-NOMA Network: Deep Reinforc...

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International conference on Wireless Communications and Signal processing (WCSP)

作者： Jiang Chang Shihao Zhu Qingzheng Tang Ming Zhao Jinkang Zhu CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Hefei China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

To enable the base station to serve more users, we implement multiple-input multiple-output non-orthogonal multiple access, which is capable of serving multiple users with a single beam. Therefore, it is crucial to group users according to their channel correlation before performing beamforming. Heuristic methods are commonly used, as the user clustering problem is NP-hard. However, these methods do not sufficiently utilize the information obtained through exploration. We use discrete soft actor-critic deep reinforcement learning for user clustering to achieve better performance. The performance of the heuristic method is affected by the predefined number of clusters as well, which makes it impractical. To address this issue, we modify the output of the clustering algorithm with the probability of a user belonging to a cluster and introduce several virtual clusters to generate empty clusters. This enhancement allows certain clusters to remain unselected by any user, leading to the formation of empty clusters, and enabling dynamic adjustment of the number of clusters based on real-time network conditions. Simulation results have demonstrated that our proposed dynamic clustering algorithm outperforms traditional clustering methods and leads to the optimal number of clusters.

关键词： NOMA Heuristic algorithms Simulation Signal processing algorithms Clustering algorithms Deep reinforcement learning MIMO Real-time systems Resource management MIMO communication

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Improving optical Character Recognition(OCR) Accuracy using Multi-Layer Perceptron(MLP)

Improving Optical Character Recognition(OCR) Accuracy using ...

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International conference on Trends in Electronics and Informatics (ICEI)

作者： M. Krishnamoorthi Karthi P Sri Ram M Sathyan T Vasanth Professor of the Department Dr.N.G.P Institute Of Technology Coimbatore India Department of Information Technology Dr.N.G.P Institute Of Technology Coimbatore India

optical Character Recognition (OCR) is a cutting-edge application that has been made possible due to advances in technology. optical Character Recognition involves using deep learning algorithms such as Multi-Layer Perceptron and Support Vector Machine to create a system that can recognize characters in an image. The optical Character Recognition system first segments each character individually as part of the pre-processing step, after which it post-processes the image to compare each character to the pre-processed data. In the modern world, optical Character Recognition is a crucial technology used in many industries to shorten turnaround times while increasing accuracy. However, one of the challenges faced with optical Character Recognition is that it takes longer to match exact results and produces results with lower precision and character misreadings. To address this challenge, the pre-processed data used by optical Character Recognition systems now includes practically all fonts. This means that the optical Character Recognition system has to match the characters with a much larger dataset, making the process more time-consuming and less accurate. To improve the accuracy and speed of optical Character Recognition systems, several methods have been employed. For instance, post-processing techniques such as error correction algorithms can be used to detect and correct errors in the optical Character Recognition output. Additionally, the use of deep learning techniques such as Conventional Neural Networks has been shown to improve the accuracy of optical Character Recognition systems. By employing these methods, optical Character Recognition can become an even more powerful tool that can shorten turnaround times while increasing accuracy.

关键词： Integrated optics Industries Deep learning Image recognition optical character recognition Neural networks optical imaging

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A Novel Approach to Depth Estimation and Disparity Map Generation in Stereoscopic Images Using Convolutional Neural Networks

A Novel Approach to Depth Estimation and Disparity Map Gener...

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Communication & Computing (WCONF), World conference on

作者： vipin Solanki Rahul Gupta Preeti Naval Department of Applied Science Sanskriti University Mathura Uttar Pradesh India Department of Management School of Mangement - PG JAIN (Deemed to be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

This paper provides a singular approach to depth estimation and disparity map technology in stereoscopic snap shots using convolutional neural networks (CNNs). Stereoscopic pictures are pairs brand new two or more photographs taken from barely extraordinary points in space., commonly utilized in 3-D film manufacturing. A good way to extract depth information from the 2 photographs., a stereo-disparity map can be generated by way of comparing the similarities among the two. The undertaking latest estimating the disparity map traditionally calls for tedious and computational heavy feature matching algorithms. This paper introduces a CNN-based disparity estimation technique that is both light-weight and quicker than traditional feature-matching algorithms. In particular., a completely-convolutional neural community (FCN) model is employed to regress the disparity map from an input stereoscopic photograph pair. To train this community., a large-scale artificial dataset is generated using simulation software program., presenting the essential ground fact. An extensive list state-of-the-art experiments and comparisons with approaches were conducted to evaluate its performance. Outcomes display that the proposed approach is capable of estimating displacement maps in stereo snap shots speedy and as it should be.

关键词： Navigation Stereo image processing Software algorithms Estimation optical imaging Software Real-time systems Convolutional neural networks optical films Tuning

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An Improved EMD Denoising Framework for Underwater Distributed Acoustic Sensing Signals by Exploring Permutation Entropy

An Improved EMD Denoising Framework for Underwater Distribut...

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IEEE International conference on information Communication and Signal processing (ICICSP)

作者： Jiantong Chen Siyuan Cang Huayong Yang Chao Li School of Mechanical and Electrical Engineering Guangdong University of Technology Guangzhou China Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) Guangzhou China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

This paper presents a novel denoising framework designed to address the issue of unknown noise interference in Distributed Acoustic fiber Sensing (DAS) signals. The proposed method combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Permutation Entropy (PE) with Wavelet Threshold Denoising (WTD). The CEEMDAN algorithm is utilized to decompose the DAS signal into multiple Intrinsic Mode Functions (IMFs), followed by the calculation of the PE value for each IMF. IMFs with a PE value exceeding 0.5 undergo denoising through wavelet transform, and are subsequently reconstructed. Our approach exhibits strong performance in analyzing DAS signals with seismic events, highlighting its effectiveness and reliability through its high signal-to-noise ratio.

关键词： Wavelet transforms Noise reduction Signal processing algorithms Acoustics Entropy Real-time systems optical fiber communication Sensors Reliability Signal to noise ratio

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