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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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DSP implementation of signal processing for two-dimensional (2-D) phased array digital multi-beam system 13

DSP implementation of signal processing for two-dimensional ...

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13th International conference on Signal processing systems, ICSPS 2021

作者： Sun, Dengbo Li, Peng Wang, Teng Hu, Jingwei Xie, Renhong Rui, Yibin School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

This paper illustrates the improved signal processing system architecture and processing flow for a two-dimensional (2- D) phased array digital multi-beam radar. 2D phased array digital multi-beam radar is required to achieve electronically scanning in both azimuth and elevation, which demands higher processing performance of the system. To improve the real-time performance of the system, we modify the signal processing system architecture. In this paper, we will focus on DSP to demonstrate the utility of the improved system architecture and processing flow. And a new detection strategy which combines dual-threshold variable index constant false alarm (DT-vi-CFAR) and 2-D Cell Average CFAR (CACFAR) is proposed. Practical validation shows that this detection strategy significantly reduces the complexity compared to traditional 2-D CA-CFAR, while improving the detection probability compared to traditional DT-vi-CFAR. © COPYRIGHT SPIE.

关键词： Computer architecture

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Calculation of a phase diffractive optical element that forms a given set of spheroidal functions 8

Calculation of a phase diffractive optical element that form...

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8th International conference on information Technology and Nanotechnology, ITNT 2022

作者： Krupnikov, Andrey Kirilenko, Mikhail Samara National Research University Samara Russia Samara National Research University Samara Russia Image Processing Systems Institute RAS Branch of the FSRC 'Crystallography and Photonics' RAS Samara Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

We consider spheroidal functions calculated by the method of solving the equation for eigenvalues and eigenvectors of the finite Fourier transform. With the help of iterative algorithms, a phase-only diffractive optical element was calculated to form a superposition of eigenmodes of the optical system, which are spheroidal functions. © 2022 IEEE.

关键词： optical systems

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Research on Polarization Imaging Target Detection Method Based on Multi-view 3

Research on Polarization Imaging Target Detection Method Bas...

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3rd International conference on Computer Graphics, Image, and virtualization, ICCGIV 2023

作者： Cui, Hao Wang, Xin Pu, Xiankun Shi, Lei Zhou, Zhiqiang Gao, Jun School of Computer and Information Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China

ISBN: (纸本)9781510671720

Traditional multi-view target detection can only obtain the light intensity information of the target, and its detection ability is very limited in complex environments;Traditional polarization target detection mainly relies on a single camera to obtain the polarization information of the target, making it difficult to directly obtain all the polarization information of the target in some complex backgrounds. In order to overcome the limitations of the two detection methods mentioned above, this paper studies a multi angle polarization target detection method based on a polarization array system. This method mainly uses traditional multi perspective image processing algorithms to process polarized images and achieve polarization image stitching. To address the issue of the inability to directly concatenate polarized images in low light environments, this paper proposes the introduction of image enhancement algorithms to achieve the concatenation and target detection of multi angle polarized images in low light environments. Based on the characteristics of polarization feature images, a fusion method based on polarization features was studied to improve the contrast between the target and the background. © 2023 SPIE.

关键词： Image enhancement

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Speech recognition of speaker identity based on Convolutional neural networks

Speech recognition of speaker identity based on Convolutiona...

引用

2022 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2022

作者： An, Hangdong School of Information Technology and Electrical Engineering The University of Queensland QLD4072 Australia

ISBN: (纸本)9781510663350

Recent advances in the field of speaker recognition (SR) have shown very accurate and high-performance algorithms. When the amount of data is small, its performance drops dramatically [1]. Today, when testing and training involve only small amounts of speech data, identifying the speaker remains a key consideration, as real-life applications can only access speech data for a limited period. Speech recognition-based security systems are one of the main areas of research. In this paper, we will use a graphical CNN algorithm for speaker recognition and compare the effect of different times of speech on the accuracy of the model. The results show that better results can be obtained by using the speech feature data with a deep learning CNN model, with an average accuracy of 86.3%. In addition, the accuracy of speech recognition can be better improved by comparing multiple sets of speech segments with smaller results in this study. © 2023 SPIE.

关键词： Speech recognition

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Winter wheat phenology classification using stacking ensemble learning algorithm based on Sentinel-1A SAR images 5

Winter wheat phenology classification using stacking ensembl...

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5th International conference on Geoscience and Remote Sensing Mapping, ICGRSM 2023

作者： Duan, Haoxin Yang, Huijin Zhao, Jianhui Li, Ning School of Computer and Information Engineering Henan University Kaifeng China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781510672789

Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Synthetic Aperture Radar (SAR) is a kind of microwave remote sensing equipment, which has the advantage of all-weather and all-day, and can realize large-scale and periodic crop phenological monitoring. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. A stacking ensemble learning algorithm using time series Sentinel-1A SAR images for winter wheat phenology classification was proposed in this paper based on multiple machine learning models, including Random Forest (RF), Support Vector Machine (SVM), K-nearest Neighbor(K-NN), Naive Bayes (NB) and BP Neural Network (BP) models. The experimental results showed that, comparing with each single model, the stacking ensemble learning algorithm proposed in this paper had the optimal performance, with the highest overall recognition accuracy of 81.40%, demonstrating its effectiveness and application potential for winter wheat phenology identification. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Synthetic aperture radar

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Restricted Boltzmann Machine Classifier for Nonlinear Compensation in DP-16QAM Single and WDM Coherent optical Communication systems

Restricted Boltzmann Machine Classifier for Nonlinear Compen...

引用

2023 conference on Lasers and Electro-Optics Europe and European Quantum Electronics conference, CLEO/Europe-EQEC 2023

作者： Singh, Arshdeep Gautam, Naveenta Lall, Brejesh Choudhary, Amol Department of Electrical Engineering Indian Institute of Technology Delhi India Group Indian Institute of Technology Delhi India Bharti School of Telecommunication Technology and Management Indian Institute of Technology Delhi India

ISBN: (纸本)9798350345995

With the rapid growth of machine learning, neural network (NN)-based nonlinear compensation (NLC) for coherent optical communication systems have become widespread due to its robust nonlinear fitting capability, reduced complexity and real-time operation. NNs can adaptively evaluate nonlinear impairment and reduce computational complexity while improving performance as compared to digital signal processing (DSP)-based NLC algorithms such as digital back propagation (DBP) [1,2]. In this work, an NLC scheme based on Bernoulli-Restricted Boltzmann Machine (RBM) is proposed. This method factors the present symbol's nonlinear degradation to recover the transmitted symbols and aims to combine RBM's effective information extraction capabilities with a logistic regressor. The proposed semi-supervised model is based on a two-layered RBM which works in an unsupervised manner by learning the probability distribution over its sample training data inputs. The embeddings of RBM are fed to a logistic regressor (LR) based on LBFGS solver [3], for classification along with a target vector which consists of the true class labels. RBM-LR operates to discover latent factors in the data that can further be used to create a nonlinear decision boundary [4]. © 2023 IEEE.

关键词： Digital signal processing

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Intelligent topology decision optimization method based on knowledge map enhancement algorithm

Intelligent topology decision optimization method based on k...

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2024 International conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Liu, Yi Tao, Rongming Zhang, Yijing Liu, Jiachen East Branch of State Grid Corporation of China Pudong New Area Shanghai200120 China

ISBN: (纸本)9781510682597

In order to solve the problem of how to effectively optimize the topological structure in complex systems, improve the accuracy and reliability of decision-making, and reduce the false alarm rate and false alarm rate, an intelligent topological structure decision-making optimization method based on knowledge map enhancement algorithm is proposed. Build an algorithm to enhance the knowledge map of intelligent topology decision-making, on this basis, build an intelligent topology and analyze its characteristics;According to the output results of intelligent topology, the decision-making optimization process of intelligent topology is designed. The experimental results show that after the optimization of this method, the storage space usage of each node has changed significantly, the accuracy of intelligent topology decision-making has been improved obviously, and the false alarm rate and false alarm rate have been greatly reduced, which has a good optimization effect. © 2024 SPIE.

关键词： Decision making Mathematical optimization Evolutionary algorithms Data modeling Machine learning Data processing Sampling rates Detection and tracking algorithms Power supplies Signals intelligence

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Development of an Entry-Exit Decision Algorithm for Bus Passenger Counting Using Cameras 16

Development of an Entry-Exit Decision Algorithm for Bus Pass...

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2024 16th International conference on Graphics and Image processing, ICGIP 2024

作者： Nishiura, Tsubasa Yokoyama, Soichiro Yamashita, Tomohisa Kawamura, Hidenori Sato, Yoshimi Hasegawa, Rei Hirasawa, Miyuki Faculty of Information Science and Technology Hokkaido University Kita 14 Nishi 9 Kita-ku Hokkaido Sapporo060-0814 Japan C’s Lab Co. Ltd. 3 Kita 1-jo Nishi 7-chome Chuo-ku Hokkaido Sapporo060-0001 Japan

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

In recent years, entry-exit management technologies have gained attention due to the rise of smart cities and the proliferation of IoT devices. These technologies are used not only in indoor environments like offices and retail stores but also in outdoor settings such as public transportation. Particularly in public transport, there is growing demand for Origin-Destination (OD) data collection to better understand passenger movement. In this study, we developed a system to estimate the number of passengers boarding and alighting from buses by installing cameras at the bus doors and capturing video footage of passengers. The system utilizes person detection and tracking algorithms to estimate passenger trajectories and applies a deep neural network (DNN) model to classify whether a detected individual is boarding or alighting. We introduced a DNN model for binary classification to determine whether individuals are inside or outside the bus, using bounding box and time information. By performing a grid search, we identified optimal model parameters. The system was tested using both YOLOR and the latest YOLOv10 object detectors, and the MPNTrack person tracking algorithm. The combination of YOLOv10 and the DNN model achieved an error rate of 11.7% for the entrance camera, while YOLOR and the DNN model achieved an error rate of 0.803% for the exit camera, significantly improving the accuracy over previous methods. This study demonstrates that integrating object detection, tracking, and DNN-based classification improves the accuracy of passenger counting systems, which is crucial for the effective management of public transportation systems. © 2025 SPIE.

关键词： Deep neural networks

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An effective dynamic SLAM algorithm integrating YOLOv7 model 15

An effective dynamic SLAM algorithm integrating YOLOv7 model

引用

15th International conference on Graphics and Image processing, ICGIP 2023

作者： Fan, Yisheng Xue, Xiaorong Zhao, Zhiyuan Liu, Zheyu School of Electronics and Information Engineering Liaoning University of Technology Liaoning China

ISBN: (纸本)9781510675018

Dynamic objects are ubiquitous in real-world environments. However, traditional Simultaneous Localization and Mapping (SLAM) algorithms assume static scenes and fail on active tasks. Therefore, we need SLAM systems to achieve a precise and efficient localization and mapping in dynamic environments. Toward this goal, this paper proposes a dynamic SLAM algorithm incorporating a target detection model, YOLOv7. The general process of the algorithm is as follows: Firstly, the real-time target detector YOLOv7 segments the input image and divides it into static and dynamic parts. Secondly, feature information of the image frame is extracted. If the feature information is located in the dynamical part of the image, it will be eliminated for achieving an accurate tracking and mapping in the following steps. Next, tracking and mapping based on the image frame without dynamic objects are performed. Finally, global Bundle Adjustment (BA) optimization is performed to obtain more accurate pose and map information. The experimental results on TUM and KITTI show that the proposed algorithm in this paper has achieved the same accuracy as the DynaSLAM. In real-time, this algorithm has more advanced efficiency advantages on KITTI and TUM than the DynaSLAM algorithm and Dynamic ORB_SLAM. In particular, it has improved the speed by 33.03% on the TUM compared to Dynamic ORB_SLAM. Therefore, the proposed algorithm ensures that the SLAM system can achieve high accuracy in dynamic scenarios while maximizing speed. © 2024 SPIE. All rights reserved.

关键词： Mapping

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Physics-Informed Deep Learning-Based Monocular vision and IMU Fusion for Extracting Position and Heading Data of Multirotors 12

Physics-Informed Deep Learning-Based Monocular Vision and IM...

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12th RSI International conference on Robotics and Mechatronics, ICRoM 2024

作者： Rezaei, Arshia Radfar, Erfan Talaeizadeh, Amin Alasty, Aria Department of Mechanical Engineering Iran

ISBN: (纸本)9798331529734

Multirotor Unmanned Aerial Vehicles (UAV)s flight performance rely on accurate estimation of their attitude and position for stable and robust control, which is essential for precise path following and management. Traditional methods for estimating six Degrees of Freedom (DoF) parameters, such as position, orientation, and velocity, often combine data from Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and vision sensors using complex algorithms for fusion and filtering. This approach can lead to challenges in real-time processing and robustness to environmental variations. In this paper, we propose a novel approach that leverages deep learning and incorporates a physics-informed loss function based on monocular vision data to enhance the accuracy of these sensors by eliciting position and heading data from a trained model of flow detection, enhanced with a mathematical model of optical flow. We implement a fusion of camera, IMU, magnetometer, and ultrasonic sensors for robust and accurate 6 DoF data acquisition in multirotor systems. Our proposed framework utilizes Convolutional Neural Networks (CNN)s to directly learn the mapping between sensor data and 6 DoF parameters. By jointly processing visual and inertial data in an end-to-end manner, our model exploits the complementary information provided by each sensor modality, thereby enhancing estimation accuracy and robustness to external disturbances. We demonstrate the effectiveness of our approach through extensive experiments on real-world multirotor platforms, show-casing significant improvements in pose and motion estimation accuracy compared to conventional methods. Our fusion deep learning framework offers a promising pathway for enhancing the autonomy and performance of multi rotor UAVs. © 2024 IEEE.

关键词： optical flows

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