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An automatic eddy detection algorithm based on quadrant angle of velocity vector and its application

INTERNATIONAL JOURNAL OF REMOTE SENSING 2025年第2期46卷 606-635页

作者： Wang, Yong Zhang, Shengjun Kong, Xiangxue Wang, Wanning Northeastern Univ Sch Resources & Civil Engn Shenyang Liaoning Peoples R China Liaoning Earthquake Agcy Liaoning Earthquake Emergency Serv Shenyang Liaoning Peoples R China

Eddies assume a pivotal role in both the oceanic heat cycle and marine dynamic processes. The development of real-time satellite observations, coupled with advancements in computer intelligence, has propelled automatic eddy detection algorithms to the forefront of ocean remote sensing research. Nevertheless, target omissions and accuracy degradations are inevitable for multiple detection algorithms due to strong morphological variations in ocean eddies. This paper proposes an automatic detection algorithm based on the quadrant angle of the velocity vector in the flow field. Firstly, a rectangular search box is established, and the corresponding quadrant angles at the four vertices are calculated. Secondly, the centres and types of eddies are determined according to the cumulative sum and variety rules of quadrant angles. Then the outermost boundary is identified by regularity of quadrant angles in eight directions expanding outward from the centre of the eddy using the stream function equation. The new algorithm is assessed and verified using geostrophic flow data derived from the CMEMS standard gridded sea level anomaly product. Furthermore, its detection capabilities are demonstrated through a comparative analysis with several other algorithms. All methods exhibit consistent efficacy in detecting the majority of eddies with similar spatial distributions. In cases where the new algorithm identifies a specific eddy not detected by the FF15 and ND10 algorithms, sea surface temperature data is employed for verification. The sea surface temperature distribution map, along with the obtained results, illustrates the superiority of the new algorithm and its adaptability to products with varying resolutions. Additionally, the results undergo verification through a manual detection method, revealing that the new algorithm achieves SDR of 91.73% and an EDR of 3.54%. This percentage significantly surpasses the lower acceptable limit of 80% for the SDR parameter. The new algor

关键词： Ocean eddy detection algorithm Sea level anomaly Quadrant angle SWOT

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detection algorithm for color dynamic images by multiple surveillance cameras under low luminance conditions based on fuzzy corresponding map

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APPLIED SOFT COMPUTING 2008年第4期8卷 1344-1353页

作者： Hatakeyama, Yutaka Mitsuta, Akimichi Hirota, Kaoru Kochi Univ Ctr Med Informat Sci Kochi Japan Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Interdiscip Grad Sch Sci & Engn Midori Ku Yokohama Kanagawa 2268502 Japan

An objects detection algorithm for color dynamic images from two cameras is proposed for a surveillance system under low illumination. It provides automatic calculation of a fuzzy corresponding map and color similarity for lower luminance conditions, which detects little chromatic regions in CCD camera images under lower illumination and presents regions with a possibility of occlusion situation. Experimental detection results for two dynamic images from real surveillance cameras in a downtown area in Japan under low luminance conditions show that the proposed algorithm has 15% improved accuracy compared with the independent detection algorithm in the same false alarm rate, which occlusion regions are correctly presented. Moreover, implementability for severe surveillance situation is discussed. The proposed algorithm is being considered for use in a low cost surveillance system at a relatively poor security downtown (shopping mall) area in Japan. (C) 2007 Elsevier B.V. All rights reserved.

关键词： detection algorithm multiple cameras camera correspondence similarity

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detection algorithm of Scene Boundary Based on Information Theory

Detection Algorithm of Scene Boundary Based on Information T...

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International Conference on Information Technology and Computer Science

作者： Shi, Yuexiang Yi, Can Xiangtan Univ Informat Engn Sch Xiangtan 411105 Peoples R China

In this paper, the new detection algorithm of scene boundary based on information theory has been presented. It uses the changed color of frame image as feature. With the detection of scene sudden change and gradual c... 详细信息

ISBN: (纸本)9780769536880

关键词： Information theory Scene boundary detection algorithm

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detection algorithm based on spatial statistics for IR target

Detection algorithm based on spatial statistics for IR targe...

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5th International Symposium on Photoelectronic detection and Imaging (ISPDI) - Infrared Imaging and Applications

作者： Zuo Shao-ping Meng Xiang-long Liu Jin-jiang Liu Guo-quan Li Hong-tao Baicheng Ordnance Test Ctr China Baicheng 137001 Peoples R China

ISBN: (纸本)9780819497765

A point target detection algorithm for IR image is proposed based on spatial statistics. Firstly, according to the spatial correlation of atmosphere background in infrared image, the spatial statistical analysis method is adopted, and a background suppression algorithm based on Kriging is put forward. Secondly, using peak detection algorithm merged with Kriging, the problem of high false-alarm probability for adaptive threshold filter is solved by dual channel filter. The result shows that the detection probability of the algorithm reaches to 99 percent when the input SCR is no less than 6 and probability of false alarm is between 1x10(-3) and 1x10(-4).

关键词： detection algorithm Spatial statistics Semivariogram Kriging Dual channel filter

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A study on the detection algorithm of an advanced ultrasonic signal for hydro-acoustic releaser

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2008年第5期6卷 767-775页

作者： Kim, Young- In Huh, Kyung-Moo Cho, Young-June Korea Inst Ind Technol Cheonan Si Chungcheongnam South Korea Dankook Univ Sch Elect Engn Cheonan Si Chungcheongnam South Korea Dankook Univ RIC Elect Parts Inspect Automat Cheonan Si Chungcheongnam South Korea

Methods used for exploring marine resources and spaces include positioning a probe under water and then recalling it after a specified time. Hydro-acoustic Releasers are commonly used for positioning and retrieving of Such exploration equipment. The most important factor in this kind of system is the reliability for recalling the instruments. The frequently used ultrasonic signal detection method call detect ultrasonic signals using a fixed comparator, but because Of increased rates of errors due to Outside interferences, information is repetitively acquired. This Study presents all effective ultrasonic signal detection algorithm using the characteristics of a resonance and adaptive comparator Combined with the FSK+ASK modulator. As a result, approximately, 8.8% Of ultrasonic wave communication errors caused by background noise and transmission losses were reduced for effectively detecting ultrasonic waves. Furthermore, the resonance circuit's quality factor was enhanced (Q = 120 to 160). As Such, the bias voltage of the transistor (Vb = 3.3 to 6.8V) was increased thereby enhancing the frequency's selectivity.

关键词： detection algorithm marine instrument probes remote control system ultrasonic signal

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A novel artificial bee colony detection algorithm for massive MIMO system

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2016年第17期16卷 3139-3152页

作者： Li, Lin Meng, Weixiao Ju, Shihao Harbin Inst Technol Commun Res Ctr Harbin Peoples R China Minist Publ Secur Key Lab Police Wireless Digital Commun Beijing Peoples R China

As a hot-spot of 5G, the research on detection algorithms for massive multiple input multiple output (MIMO) system is significant but difficult. The traditional MIMO detection algorithms or their improvements are not appropriate for large scaled antennas. In this paper, we propose artificial bee colony (ABC) detection algorithm for massive MIMO system. As one advanced technology of swarm intelligence, ABC algorithm is most efficient for large scaled constrained numerical combinatorial optimization problem. Therefore, we employ it to search the optimum solution vector in the modulation alphabet with linear detection result as initial. Simulation and data analysis prove the correctness and efficiency. Versus the scale of massive MIMO systems from 64 x 64 to 1024 x 1024 with uncoded four-quadrature-amplitude-modulation signals, the proposed ABC detection algorithm obtains bit error rate of 10(-5) at low average received signal-to-noise-ratio of 12 dB with rapid convergence rate, which approximates the optimum bit error rate performance of the maximum likelihood and achieves the theoretical optimum spectral efficiency with low required average received signal-to-noise-ratio of 10 dB in similar increasing regularity, over finite time of low polynomial computational complexity of O(N-T(2)) per symbol, where N-T denotes the transmitting antennas' number. The proposed ABC detection algorithm is efficient for massive MIMO system. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： massive multiple input multiple output (MIMO) system artificial bee colony (ABC) detection algorithm bit error rate (BER) computational comlexity spectral efficiency

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CNN Aided detection algorithm for Uplink Multiuser Modulation Encrypted Massive MIMO Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第3期73卷 3786-3803页

作者： Li, Lin Hou, Huijun Meng, Weixiao Nanjing Univ Posts & Telecommun Sch Internet Things Jiangsu Key Lab Broadband Wireless Commun & Intern Minist Educ Nanjing 210046 Peoples R China CETC 14th Res Inst Nanjing 210012 Peoples R China Harbin Inst Technol Commun Res Ctr Harbin 150001 Peoples R China

In the traditional physical layer encryption methods, both the delay and errors brought by the keys generation and interaction and those from the channel estimation are too high to be employed for the uplink multiuser massive multiple-input and multiple-output (MIMO) systems. Differently, this paper constructs a lightweight encryption scheme with the modulation random chaotic encryption (MRCE) signals in the uplink massive MIMO systems where the keys are not required to be known, a priori, at the base station (BS). Their generation is off-line and does not employ the channel state information, and there is no immediate interaction. Specially, as a deep learning based solution for detecting the MRCE signals in the uplink massive MIMO systems, the convolutional-neural-network aided nonlinear detection (CAD) algorithms are proposed in this paper. The simulation results showed their effectiveness. The anti-eavesdrop ability of the MRCE signals is verified even against eavesdroppers equipped with multiple antennas at high average signal to noise ratio (SNR). As shown in the simulation results, when the BS does not previously know the keys, the proposed CAD algorithms have a much better bit error rate (BER) performance and higher secrecy spectral efficiency (SSE) than the less efficient ones of their corresponding unassisted methods. The BER is closer to the theoretical lower bound of the optimum value obtained by the maximum likelihood method. They require lower average received SNR to converge to the theoretical maximum SSE given by the no error transmissions from the legitimate users to the BS. These performances also approach those of their corresponding unassisted algorithms without prior known keys. The proposed CAD algorithms have medium/strong robustness against the channel estimation error and medium/low polynomial computational complexity.

关键词： Massive multiple-input and multiple-output (MIMO) systems modulation encryption convolutional-neural-network detection algorithm

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Moving object detection algorithm and motion capture based on 3D LiDAR

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OPTICAL AND QUANTUM ELECTRONICS 2024年第4期56卷 585-585页

作者： Jiang, Jian Hunan Univ Arts & Sci Sch Comp & Elect Engn Changde 415000 Peoples R China

The application of autonomous driving technology in the field of transportation has become a hot research direction, and autonomous vehicles need to accurately detect and track moving targets around. As a kind of sensor widely used in the field of automatic driving, LiDAR has the characteristics of high precision and long distance detection. Therefore, this paper adopts a target detection algorithm based on three-dimensional LiDAR, which can identify moving targets accurately. Then the motion path of the detected target is captured and tracked by optical method, and the motion state of the target is monitored in real time. The experimental results show that the moving target detection algorithm and optical motion acquisition method based on 3D LiDAR can detect and track the moving target effectively, and capture its moving trajectory accurately. The application of this method to autonomous vehicles can improve vehicle perception and driving safety, and also provide a useful reference for other fields of moving object detection and tracking research.

关键词： Three-dimensional Laser radar Moving object detection algorithm Motion capture

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Network traffic anomaly detection algorithm using mahout classifier

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第1期37卷 137-144页

作者： Peng, Hua Liu, Liang Liu, Jiayong Lewis, Johnwb R. Sichuan Univ Coll Elect & Informat Engn Chengdu Sichuan Peoples R China Polk State Coll Haven FL 33881 USA

Now, the classifier network anomaly traffic detection method is generally considered to be a general method of good detection effect and high detection precision. In order to detect abnormal network traffic more efficiently, so as to ensure the security of Internet users, a network traffic anomaly detection algorithm based on Mahout Classifier is studied. Aiming at the problem of time correlation and the influence of abnormal samples on accuracy of detection statistics, and the first detection point is eliminated and the applicable detection point is added. The BP neural network algorithm and Bayesian network model are used to predict the abnormal probability of the anomaly node, the detection precision is optimized, and the exception points of the training set are reorganized. In view of the anomalies detected by anomaly detection models, an emergency response method is proposed, which not only detects anomalies, but also handles anomalies.

关键词： Mahout classifier network anomaly flow rate detection algorithm

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Improved detection algorithm for copy number variations based on hidden Markov model

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第13-14期79卷 9237-9253页

作者： Yang, Hai Zhu, Daming Shandong Univ Sch Comp Sci & Technol Qingdao 266237 Shandong Peoples R China

Aiming at the problems of parameter optimization and insufficient utilization of split reads in the detection for copy number variation (CNV), a new definition of relative read depth (RRD) and a randomized sampling strategy (RGN) are proposed in this paper. Compared to the raw read depth, the RRD parameter has weak correlation with GC content, mappability and the width of analysis windows tiled along the genome. The RGN strategy is based on the weighted sampling strategy which can speed up the read count data analysis. Subsequently, we propose an improved detection algorithm for CNV based on hidden Markov model (CNV-HMM). The HMM detects the abnormal signal of read count data and outputs the detection results of candidate CNVs. At the end of the algorithm, we filter out the results of candidate CNVs using the split reads to improve the performance of CNV-HMM algorithm. Finally, the experiment results show that our CNV-HMM algorithm has higher sensitivity and accuracy for CNVs detection than most of current detection algorithms and applicative both for diploid animal and plant.

关键词： detection algorithm Copy number variation Hidden Markov model Split read

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