Research for scan detection algorithm of high-speed links based on honeypot

2010 ◽  
Author(s):  
Xinliang Wang ◽  
Fang Liu ◽  
LuYing Chen ◽  
Zhenming Lei
Keyword(s):  
High Speed ◽  
Detection Algorithm ◽  
Scan Detection

scholarly journals High-Speed Target Detection Algorithm Based on Sparse Fourier Transform

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 37828-37836 ◽  
Author(s):  
Cunsuo Pang ◽  
Shengheng Liu ◽  
Yan Han
Keyword(s):  
Fourier Transform ◽  
Target Detection ◽  
High Speed ◽  
Detection Algorithm

Based on the CO2 Gas Shielded Welding Molten Pool Image Edge Detection Algorithm

2013 ◽  
Vol 437 ◽  
pp. 840-844 ◽  
Author(s):  
Xiao Gang Liu ◽  
Bing Zhao
Keyword(s):  
Edge Detection ◽  
Molten Pool ◽  
High Speed ◽  
Vision System ◽  
Detection Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Defect Recognition ◽  
Pool Image

This paper use the passive vision system through high-speed camera collects molten pool images; and then according to the frequency domain characteristics of the weld pool image Butterworth low-pass filter; gradient method for image enhancement obtained after pretreatment. Research Roberts, Sobel, Prewitt, Log, Zerocross, and Canny 6 both traditional differential operator edge detection processing results. Through comparison and analysis of choosing threshold for [0.1, 0. Canny operator can get the ideal molten pool edge character, for subsequent welding molten pool defect recognition provides favorable conditions.

Lane Detection Algorithm Based on Genetic Algorithm and its Parallel Computing Realization

2012 ◽  
Vol 479-481 ◽  
pp. 65-70
Author(s):  
Xiao Hui Zhang ◽  
Liu Qing ◽  
Mu Li
Keyword(s):  
Genetic Algorithm ◽  
Data Transmission ◽  
High Speed ◽  
High Performance ◽  
Large Data ◽  
Detection Algorithm ◽  
Lane Detection ◽  
The Road ◽  
Time Problem ◽  
High Speed Data

Based on the target detection of alignment template, the paper designs a lane alignment template by using correlation matching method, and combines with genetic algorithm for template stochastic matching and optimization to realize the lane detection. In order to solve the real-time problem of lane detection algorithm based on genetic algorithm, this paper uses the high performance multi-core DSP chip TMS320C6474 as the core, combines with high-speed data transmission technology of Rapid10, realizes the hardware parallel processing of the lane detection algorithm. By Rapid10 bus, the data transmission speed between the DSP and the DSP can reach 3.125Gbps, it basically realizes transmission without delay, and thereby solves the high speed transmission of the large data quantity between processor. The experimental results show that, no matter the calculated lane line, or the running time is better than the single DSP and PC at the parallel C6474 platform. In addition, the road detection is accurate and reliable, and it has good robustness.

Study of BLAST Signal Detection Algorithm in LTE System

2013 ◽  
Vol 756-759 ◽  
pp. 3183-3188
Author(s):  
Tao Lei ◽  
Deng Ping He ◽  
Fang Tang Chen
Keyword(s):  
Signal Detection ◽  
High Speed ◽  
Data Communication ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Detection Algorithms ◽  
High Speed Data ◽  
Simulation Results ◽  
Better Than

BLAST can achieve high speed data communication. Its signal detection directly affects performance of BLAST receiver. This paper introduced several signal detection algorithmsZF algorithm, MMSE algorithm, ZF-SIC algorithm and MMSE-SIC algorithm. The simulation results show that the traditional ZF algorithm has the worst performance, the traditional MMSE algorithm and the ZF-SIC algorithm is similar, but with the increase of the SNR, the performance of ZF-SIC algorithm is better than MMSE algorithm. MMSE-SIC algorithm has the best detection performance in these detection algorithms.

scholarly journals Concurrent Validity of an Automated Footprint Detection Algorithm to Measure Plantar Contact Area During Walking

2019 ◽  
Vol 109 (6) ◽  
pp. 416-425 ◽  
Author(s):  
Daniel E. Lidstone ◽  
Louise M. Porcher ◽  
Jessica DeBerardinis ◽  
Janet S. Dufek ◽  
Mohamed B. Trabia
Keyword(s):  
Contact Area ◽  
Gold Standard ◽  
Concurrent Validity ◽  
High Speed ◽  
Intraclass Correlation ◽  
Detection Algorithm ◽  
Automatic Identification ◽  
Identification Algorithm ◽  
Gold Standard Method ◽  
Strong Agreement

Background: Monitoring footprints during walking can lead to better identification of foot structure and abnormalities. Current techniques for footprint measurements are either static or dynamic, with low resolution. This work presents an approach to monitor the plantar contact area when walking using high-speed videography. Methods: Footprint images were collected by asking the participants to walk across a custom-built acrylic walkway with a high-resolution digital camera placed directly underneath the walkway. This study proposes an automated footprint identification algorithm (Automatic Identification Algorithm) to measure the footprint throughout the stance phase of walking. This algorithm used coloration of the plantar tissue that was in contact with the acrylic walkway to distinguish the plantar contact area from other regions of the foot that were not in contact. Results: The intraclass correlation coefficient (ICC) demonstrated strong agreement between the proposed automated approach and the gold standard manual method (ICC = 0.939). Strong agreement between the two methods also was found for each phase of stance (ICC > 0.78). Conclusions: The proposed automated footprint detection technique identified the plantar contact area during walking with strong agreement with a manual gold standard method. This is the first study to demonstrate the concurrent validity of an automated identification algorithm to measure the plantar contact area during walking.

scholarly journals A Deep Learning-Based Fragment Detection Approach for the Arena Fragmentation Test

2020 ◽  
Vol 10 (14) ◽  
pp. 4744
Author(s):  
Hyukzae Lee ◽  
Jonghee Kim ◽  
Chanho Jung ◽  
Yongchan Park ◽  
Woong Park ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Object Detection ◽  
High Speed ◽  
Detection Algorithm ◽  
Learning Technologies ◽  
Experimental Conditions ◽  
Fragmentation Test ◽  
Detection Approach ◽  
Previous Image

The arena fragmentation test (AFT) is one of the tests used to design an effective warhead. Conventionally, complex and expensive measuring equipment is used for testing a warhead and measuring important factors such as the size, velocity, and the spatial distribution of fragments where the fragments penetrate steel target plates. In this paper, instead of using specific sensors and equipment, we proposed the use of a deep learning-based object detection algorithm to detect fragments in the AFT. To this end, we acquired many high-speed videos and built an AFT image dataset with bounding boxes of warhead fragments. Our method fine-tuned an existing object detection network named the Faster R-convolutional neural network (CNN) on this dataset with modification of the network’s anchor boxes. We also employed a novel temporal filtering method, which was demonstrated as an effective non-fragment filtering scheme in our recent previous image processing-based fragment detection approach, to capture only the first penetrating fragments from all detected fragments. We showed that the performance of the proposed method was comparable to that of a sensor-based system under the same experimental conditions. We also demonstrated that the use of deep learning technologies in the task of AFT significantly enhanced the performance via a quantitative comparison between our proposed method and our recent previous image processing-based method. In other words, our proposed method outperformed the previous image processing-based method. The proposed method produced outstanding results in terms of finding the exact fragment positions.

Combined Back-EMF Estimator with High-Frequency Signal Injection for Wide Speed Range Sensorless Control of PMLSM

2013 ◽  
Vol 753-755 ◽  
pp. 1405-1408
Author(s):  
Hua Cai Lu ◽  
Xuan Yu Yao ◽  
Juan Ti
Keyword(s):  
High Frequency ◽  
High Speed ◽  
Sensorless Control ◽  
Estimation Method ◽  
Detection Algorithm ◽  
Estimation Accuracy ◽  
High Frequency Signal ◽  
Weighting Method ◽  
Transition Speed ◽  
Signal Injection

This paper describes a composite sensorless position and speed detection algorithm designed for permanent magnet linear synchronous motor (PMLSM). A high-frequency voltage signal injection method is used at starting and low speed, and a back-EMF integrate method is used at high speed, and the two kinds of method are fused by weighting method in the transition speed area. Simulation results show that estimation accuracy of this composite estimation method is satisfactory, and the sensorless control system based on this method has good dynamic response characteristics within full speed.

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