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Small Moving Target Recognition in Star Image with TRM EI SCIE
期刊论文 | 2021 , 35 (2) | International Journal of Pattern Recognition and Artificial Intelligence
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Abstract :

Recognition of small moving targets in space has become one of the frontier scientific researches in recent decade. Most of them focus on detection and recognition in star image with sidereal stare mode. However, in this research field, few researches are about detection and recognition in star image with track rate mode. In this paper, a novel approach is proposed to recognize the moving target in single frame by machine learning method based on elliptical characteristic extraction of star points. The technical path about recognition of moving target in space is redesigned instead of traditional processing approaches. Elliptical characteristics of each star point can be successfully extracted from single image. Machine learning can achieve the classification goal in order to make sure that all moving targets can be extracted. The experiments show that our proposed approach can have better performance in star images with different qualities. © 2021 World Scientific Publishing Company.

Keyword :

Stars Machine learning

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GB/T 7714 Du, Yun , Wen, Desheng , Liu, Guizhong et al. Small Moving Target Recognition in Star Image with TRM [J]. | International Journal of Pattern Recognition and Artificial Intelligence , 2021 , 35 (2) .
MLA Du, Yun et al. "Small Moving Target Recognition in Star Image with TRM" . | International Journal of Pattern Recognition and Artificial Intelligence 35 . 2 (2021) .
APA Du, Yun , Wen, Desheng , Liu, Guizhong , Qiu, Shi . Small Moving Target Recognition in Star Image with TRM . | International Journal of Pattern Recognition and Artificial Intelligence , 2021 , 35 (2) .
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End-to-End Correlation Tracking With Enhanced Multi-Level Feature Fusion EI SCIE
期刊论文 | 2021 , 9 , 128827-128840 | IEEE ACCESS
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Abstract :

Discriminative correlation filters (DCF) have drawn increasing interest in visual tracking. In particular, a few recent works treat DCF as a special layer and add it into a Siamese network for visual tracking. However, most of them adopt shallow networks to learn target representations, which lack robust semantic information of deeper layers and make these works fail to handle significant appearance changes. In this paper, we design a novel Siamese network to fuse high-level semantic features and low-level spatial detail features for correlation tracking. Specifically, to introduce more semantic information into low-level features, we specially design a residual semantic embedding module to adaptively involve more semantic information from high-level features to guide the feature fusion. Furthermore, we adopt an effective and efficient channel attention mechanism to filter out noise information and make the network focus more on valuable features that are beneficial for visual tracking. The overall architecture is trained end-to-end offline to adaptively learn target representations, which are not only enabled to encode high-level semantic features and low-level spatial detail features, but also closely related to correlation filters. Experimental results on widely used OTB2013, OTB2015, VOT2016, TC-128, and UAV123 benchmarks show that our proposed tracker performs favorably against several state-of-the-art trackers.

Keyword :

Information filters Feature extraction Target tracking deep features Fuses multi-level feature fusion Visualization Correlation Semantics correlation filters Visual tracking

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GB/T 7714 Liu, Guangen , Liu, Guizhong . End-to-End Correlation Tracking With Enhanced Multi-Level Feature Fusion [J]. | IEEE ACCESS , 2021 , 9 : 128827-128840 .
MLA Liu, Guangen et al. "End-to-End Correlation Tracking With Enhanced Multi-Level Feature Fusion" . | IEEE ACCESS 9 (2021) : 128827-128840 .
APA Liu, Guangen , Liu, Guizhong . End-to-End Correlation Tracking With Enhanced Multi-Level Feature Fusion . | IEEE ACCESS , 2021 , 9 , 128827-128840 .
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Method to Improve the Detection Accuracy of Quadrant Detector Based on Neural Network EI SCIE
期刊论文 | 2021 , 33 (22) , 1254-1257 | IEEE PHOTONICS TECHNOLOGY LETTERS
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Abstract :

The quadrant detector (QD), has developed into a core detector in the free space optical communication system. The light power received by the detector surface will be very weak after long distance transmission of laser, it brings great challenges to the high precision spot position detection of the detector. Therefore, this letter proposes a method to improve the spot position detection accuracy of the QD through artificial neural network. The neural network can solve the impact of multiple different factors on the detection accuracy of the detector at one time, which can save a lot of time and cost. Moreover, the test results of the detection accuracy of the network show that the neural network has significantly improved the detection accuracy of the spot position of the QD.

Keyword :

Position measurement Laser beams Optical fiber amplifiers neural network Surface treatment Detectors Neural networks Training quadrant detector (QD) Free space optical communication (FSOC)

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GB/T 7714 Wang, Xuan , Su, Xiuqin , Liu, Guizhong et al. Method to Improve the Detection Accuracy of Quadrant Detector Based on Neural Network [J]. | IEEE PHOTONICS TECHNOLOGY LETTERS , 2021 , 33 (22) : 1254-1257 .
MLA Wang, Xuan et al. "Method to Improve the Detection Accuracy of Quadrant Detector Based on Neural Network" . | IEEE PHOTONICS TECHNOLOGY LETTERS 33 . 22 (2021) : 1254-1257 .
APA Wang, Xuan , Su, Xiuqin , Liu, Guizhong , Han, Junfeng , Zhu, Wenhua , Liu, Zengxin . Method to Improve the Detection Accuracy of Quadrant Detector Based on Neural Network . | IEEE PHOTONICS TECHNOLOGY LETTERS , 2021 , 33 (22) , 1254-1257 .
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Energy-Efficient Task Offloading and Resource Allocation via Deep Reinforcement Learning for Augmented Reality in Mobile Edge Networks EI SCIE
期刊论文 | 2021 , 8 (13) , 10843-10856 | IEEE INTERNET OF THINGS JOURNAL
WoS CC Cited Count: 1
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Abstract :

The augmented reality (AR) applications have been widely used in the field of Internet of Things (IoT) because of good immersion experience for users, but their ultralow delay demand and high energy consumption bring a huge challenge to the current communication system and terminal power. The emergence of mobile-edge computing (MEC) provides a good thinking to solve this challenge. In this article, we study an energy-efficient task offloading and resource allocation scheme for AR in both the single-MEC and multi-MEC systems. First, a more specific and detailed AR application model is established as a directed acyclic graph according to its internal functionality. Second, based on this AR model, a joint optimization problem of task offloading and resource allocation is formulated to minimize the energy consumption of each user subject to the latency requirement and the limited resources. The problem is a mixed multiuser competition and cooperation problem, which involves the task offloading decision, uplink/downlink transmission resources allocation, and computing resources allocation of users and MEC server. Since it is an NP-hard problem and the communication environment is dynamic, it is difficult for genetic algorithms or heuristic algorithms to solve. Therefore, we propose an intelligent and efficient resource allocation and task offloading algorithm based on the deep reinforcement learning framework of multiagent deep deterministic policy gradient (MADDPG) in a dynamic communication environment. Finally, simulation results show that the proposed algorithm can greatly reduce the energy consumption of each user terminal.

Keyword :

task offloading deep reinforcement learning Task analysis Servers resource allocation Computational modeling Internet of Things (IoT) Heuristic algorithms multiagent deep deterministic policy gradient (MADDPG) Optimization mobile-edge computing (MEC) Energy consumption Resource management Augmented reality (AR)

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GB/T 7714 Chen, Xing , Liu, Guizhong . Energy-Efficient Task Offloading and Resource Allocation via Deep Reinforcement Learning for Augmented Reality in Mobile Edge Networks [J]. | IEEE INTERNET OF THINGS JOURNAL , 2021 , 8 (13) : 10843-10856 .
MLA Chen, Xing et al. "Energy-Efficient Task Offloading and Resource Allocation via Deep Reinforcement Learning for Augmented Reality in Mobile Edge Networks" . | IEEE INTERNET OF THINGS JOURNAL 8 . 13 (2021) : 10843-10856 .
APA Chen, Xing , Liu, Guizhong . Energy-Efficient Task Offloading and Resource Allocation via Deep Reinforcement Learning for Augmented Reality in Mobile Edge Networks . | IEEE INTERNET OF THINGS JOURNAL , 2021 , 8 (13) , 10843-10856 .
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A novel approach for space debris recognition based on the full information vectors of star points EI SCIE Scopus
期刊论文 | 2020 , 71 | Journal of Visual Communication and Image Representation | IF: 2.678
WoS CC Cited Count: 1 SCOPUS Cited Count: 1
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Abstract :

The recognition and detection of space debris has become one of significant research fields recently. Compared with natural images, effective information are very few contained in star images. In the past years, the gray values of star points and the continuity of sequential star images are utilized by numerous algorithms to carry out the recognition and detection through fusion of consecutive star images, which have been achieved good performance. However, with the rapid increase of star image data, those algorithms seem to be inadequate in recognition ability. In this paper, we propose one novel approach based on the full information vectors of star points to recognize moving targets with the machine learning method which is never utilized in space debris recognition field. Besides gray values, we further deeply excavate the characteristics of each star point in a single frame by the equal probability density curve of Gaussian distribution. The elliptical pattern characteristic vectors of star points can be input into the machine learning method for classification of static stars and moving targets in a single frame. Finally, trajectories of moving targets can be determined within 3 frames by the full information vectors. Therefore, traditional processing methods are abandoned and the proposed brand new approach redefines the recognition technical route of space debris. The experimental results demonstrate that moving targets can be successfully recognized in a single frame and the coverage rate of moving targets can reach 100%. Compared with other traditional methods, the proposed approach has better performance and more robustness. © 2019 Elsevier Inc.

Keyword :

Vectors Stars Machine learning Probability distributions Vector spaces Space debris

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GB/T 7714 Du, Yun , Wen, Desheng , Liu, Guizhong et al. A novel approach for space debris recognition based on the full information vectors of star points [J]. | Journal of Visual Communication and Image Representation , 2020 , 71 .
MLA Du, Yun et al. "A novel approach for space debris recognition based on the full information vectors of star points" . | Journal of Visual Communication and Image Representation 71 (2020) .
APA Du, Yun , Wen, Desheng , Liu, Guizhong , Qiu, Shi , Yao, Dalei , Yi, Hongwei et al. A novel approach for space debris recognition based on the full information vectors of star points . | Journal of Visual Communication and Image Representation , 2020 , 71 .
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Monocular 3D detection for autonomous vehicles by cascaded geometric constraints and depurated using 3D results EI Scopus
会议论文 | 2020 , 954-959 | 3rd International Conference on Unmanned Systems, ICUS 2020
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3D object detection is a key task in 3D vision perception of autonomous vehicles. In this paper, we present a novel two-stage 3D object detection method aimed to get a more accurate 3D location of an object. We modify existing 3D properties regressing network by adding two additional components, viewpoints classification and the center projection regression of a 3D box's bottom face (CBF). The center projection is associated with a similar triangle constraint to acquire an initial 3D location of a closed-form solution. For no truncated objects, the previous predicted location is involved in the initial value of over-determined equations constructed by the 2D-3D boxes fitting constraint with the configuration determined by the classified viewpoint. Then the recovered 3D information is utilized to purify the detection results. Results of comparison with state-of-the-art methods on the KITTI dataset show that although conceptually simple, our method outperforms more complex and computationally expensive methods. Furthermore, our method can filter out false alarms and false detection in both 2D and 3D results. © 2020 IEEE.

Keyword :

Autonomous vehicles Object recognition Location Object detection

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GB/T 7714 Jiaojiao, Fang , Linglao, Zhou , Guizhong, Liu . Monocular 3D detection for autonomous vehicles by cascaded geometric constraints and depurated using 3D results [C] . 2020 : 954-959 .
MLA Jiaojiao, Fang et al. "Monocular 3D detection for autonomous vehicles by cascaded geometric constraints and depurated using 3D results" . (2020) : 954-959 .
APA Jiaojiao, Fang , Linglao, Zhou , Guizhong, Liu . Monocular 3D detection for autonomous vehicles by cascaded geometric constraints and depurated using 3D results . (2020) : 954-959 .
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Research on photoelectric signal preprocessing of four-quadrant detector in free space optical communication system EI Scopus
会议论文 | 2020 , 628-632 | 5th IEEE International Conference on Signal and Image Processing, ICSIP 2020
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Abstract :

In the free space optical communication system, the detection accuracy and detection speed of the beam deflection angle have an important influence on the tracking accuracy and tracking speed of the precision tracking system. The beam deflection angle is equivalent to the position detection of the laser spot on the photo-sensitive surface of the precision tracking detector. Using a four-quadrant detector to detect the spot position error in real time can effectively eliminate the spot jitter error through the correction mechanism. By analyzing the signal characteristics of the four-quadrant detector, a photoelectric signal preprocessing circuit for the four-quadrant detector output is designed and implemented. Including the transimpedance amplifier module, low-pass filter module, main amplification module. Finally, the spot position is calculated by the photocurrent in the four quadrants. Through theoretical calculation, physical modeling, simulation, the designed signal preprocessing circuit is verified by simulation. The simulation results show that the magnification of designed signal preprocessing circuit is 2×108 and the bandwidth is 1.97MHz, which can provide a strong guarantee for the calculation of the spot position of the four-quadrant detector. © 2020 IEEE.

Keyword :

Signal detection Optical data processing Low pass filters Optical communication Image processing Photoelectricity Operational amplifiers Photocurrents

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GB/T 7714 Wang, Xuan , Su, Xiuqin , Liu, Guizhong et al. Research on photoelectric signal preprocessing of four-quadrant detector in free space optical communication system [C] . 2020 : 628-632 .
MLA Wang, Xuan et al. "Research on photoelectric signal preprocessing of four-quadrant detector in free space optical communication system" . (2020) : 628-632 .
APA Wang, Xuan , Su, Xiuqin , Liu, Guizhong , Han, Junfeng , Wang, Rui . Research on photoelectric signal preprocessing of four-quadrant detector in free space optical communication system . (2020) : 628-632 .
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Investigation of high-precision algorithm for the spot position detection for four-quadrant detector EI SCIE
期刊论文 | 2020 , 203 | Optik | IF: 2.443
WoS CC Cited Count: 2
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Abstract :

In this paper, we propose a new polynomial fitting algorithm to improve the spot position detection accuracy based on four-Quadrant Detector (4QD) when the circular spot with Gaussian energy is used as the incident light model. The traditional polynomial fitting method is difficult to ensure high spot position detection accuracy in a wide detection range. To solve this problem, we analyze and compare the characteristics of different algorithms for spot position detection, and consider the influence of the 4QD gap size in the model. Based on the initial solution of the geometric approximation method, we introduce the error compensation factor function, a new spot position detection model is designed. The results of simulation and experiment show that the new algorithm can greatly reduce the position detection error of 4QD for Gaussian spot. When the radius of incident spot is 0.5 mm and within the detection range of [-0.5 mm∼0.5 mm], the maximum error is 0.001353 mm and the root-mean-square error is 0.0004596 mm with the new five-order polynomial fitting algorithm which are reduced 56.7% and 69.7% than traditional nine-order polynomial fitting algorithm. Moreover, the computational complexity of the new algorithm is much less than traditional algorithm and the new algorithm also has good prospects in laser communication, high energy laser weapons or others. © 2019 Elsevier GmbH

Keyword :

Gaussian distribution Polynomials Error detection High energy lasers Mean square error Error compensation

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GB/T 7714 Xuan, Wang , Xiuqin, Su , Guizhong, Liu et al. Investigation of high-precision algorithm for the spot position detection for four-quadrant detector [J]. | Optik , 2020 , 203 .
MLA Xuan, Wang et al. "Investigation of high-precision algorithm for the spot position detection for four-quadrant detector" . | Optik 203 (2020) .
APA Xuan, Wang , Xiuqin, Su , Guizhong, Liu , Junfeng, Han , Rui, Wang . Investigation of high-precision algorithm for the spot position detection for four-quadrant detector . | Optik , 2020 , 203 .
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A Method for Improving the Detection Accuracy of the Spot Position of the Four-Quadrant Detector in a Free Space Optical Communication System. EI PubMed SCIE Scopus
期刊论文 | 2020 , 20 (24) | Sensors | IF: 3.576
SCOPUS Cited Count: 1
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Abstract :

In a free space optical communication system, the beacon light will lose most of its energy after long-distance transmission, and the background light from the universe will strongly interfere with it. The four-quadrant detector (4QD) has been widely used in optical communication systems as a high-precision spot position detection sensor. However, if the light signal falling on the 4QD is too weak, the electrical signal of the output position will be very weak, and it will easily be affected by or even submerged in noise. To solve this problem, we propose a method for improving the spot position detection accuracy. First, we analyzed the solution relationship between the actual position of the spot and the output signal of the 4QD, with a Gaussian spot as the incident light model. The output current signal of the detector was then transimpedance-amplified by an analog circuit and the output voltage signal with noise was digitally filtered. An error compensation factor and the gap size of the detector were introduced into the traditional spot position detection model. High-precision spot position information for the 4QD in a complex environment was then obtained using the improved spot position detection model. Experimental results show that the maximum spot position detection error for this method was only 0.0277 mm, and the root mean square error was 0.0065 mm, when the 4QD was in a high background noise environment. The spot position detection accuracy was significantly improved compared with traditional detection algorithms. Real-time detection can therefore be achieved in practical applications.

Keyword :

high background noise quadrant detectors Gaussian spot

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GB/T 7714 Wang Xuan , Su Xiuqin , Liu Guizhong et al. A Method for Improving the Detection Accuracy of the Spot Position of the Four-Quadrant Detector in a Free Space Optical Communication System. [J]. | Sensors , 2020 , 20 (24) .
MLA Wang Xuan et al. "A Method for Improving the Detection Accuracy of the Spot Position of the Four-Quadrant Detector in a Free Space Optical Communication System." . | Sensors 20 . 24 (2020) .
APA Wang Xuan , Su Xiuqin , Liu Guizhong , Han Junfeng , Wang Kaidi , Zhu Wenhua . A Method for Improving the Detection Accuracy of the Spot Position of the Four-Quadrant Detector in a Free Space Optical Communication System. . | Sensors , 2020 , 20 (24) .
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Determination of Noise Index of Piezoelectric Ceramic Drive System Based on Coupled Electro-mechanical Model CPCI-S
会议论文 | 2019 , 406-411 | IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
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Piezoelectric ceramic ( PZT) driver is a key component in PZT micro-displacement systems, and the noise index determines the accuracy of the output displacement of the PTZ actuator. In fast steering mirror(FSM) systems, the response of noise in a mechanical system is bound to affect resolution of the system. This paper analyzes the PZT driver system from the perspective of coupled electro-mechanical. By establishing a mathematical model of the coupled electro-mechanical system, we determine the energy of the output noise of the drive system in the mechanical system and the transfer of the electrical noise energy on the electromechanical system actuator. It is then determined that the power consumption of the PTZ actuator is mainly composed of two parts: the energy used to drive the deformation of the system and the dissipation of heat due to structural damping. The noise index of the PTZ driver system is 192.3dB when the accuracy of the output displacement is 1 nm through the energy conversion. The method is universal to be applied to determine the system noise index in various mechatronic systems, as well as to more complex mechanical systems.

Keyword :

coupled electro-mechanical Piezoelectric ceramic driver noise index

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GB/T 7714 Wang, Xuan , Su, Xiuqin , Liu, Guizhong et al. Determination of Noise Index of Piezoelectric Ceramic Drive System Based on Coupled Electro-mechanical Model [C] . 2019 : 406-411 .
MLA Wang, Xuan et al. "Determination of Noise Index of Piezoelectric Ceramic Drive System Based on Coupled Electro-mechanical Model" . (2019) : 406-411 .
APA Wang, Xuan , Su, Xiuqin , Liu, Guizhong , Han, Junfeng , Yu, Tingting . Determination of Noise Index of Piezoelectric Ceramic Drive System Based on Coupled Electro-mechanical Model . (2019) : 406-411 .
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