Regulating the On-Surface LNA Probe Density for the Highest Target Recognition Efficiency

Langmuir ◽  
2014 ◽  
Vol 30 (34) ◽  
pp. 10389-10397 ◽  
Author(s):  
Sourav Mishra ◽  
Srabani Ghosh ◽  
Rupa Mukhopadhyay
Keyword(s):  
Target Recognition ◽  
Lna Probe ◽  
Recognition Efficiency

scholarly journals A Lightweight Convolutional Neural Network Based on Visual Attention for SAR Image Target Classification

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3039 ◽  
Author(s):  
Jiaqi Shao ◽  
Changwen Qu ◽  
Jianwei Li ◽  
Shujuan Peng
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Visual Attention ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Sar Image ◽  
Extraction Ability ◽  
Data Imbalance ◽  
Recognition Efficiency

With the continuous development of the convolutional neural network (CNN) concept and other deep learning technologies, target recognition in Synthetic Aperture Radar (SAR) images has entered a new stage. At present, shallow CNNs with simple structure are mostly applied in SAR image target recognition, even though their feature extraction ability is limited to a large extent. What’s more, research on improving SAR image target recognition efficiency and imbalanced data processing is relatively scarce. Thus, a lightweight CNN model for target recognition in SAR image is designed in this paper. First, based on visual attention mechanism, the channel attention by-pass and spatial attention by-pass are introduced to the network to enhance the feature extraction ability. Then, the depthwise separable convolution is used to replace the standard convolution to reduce the computation cost and heighten the recognition efficiency. Finally, a new weighted distance measure loss function is introduced to weaken the adverse effect of data imbalance on the recognition accuracy of minority class. A series of recognition experiments based on two open data sets of MSTAR and OpenSARShip are implemented. Experimental results show that compared with four advanced networks recently proposed, our network can greatly diminish the model size and iteration time while guaranteeing the recognition accuracy, and it can effectively alleviate the adverse effects of data imbalance on recognition results.

Linear Target Feature Extraction Applied in Target Identification

2014 ◽  
Vol 602-605 ◽  
pp. 1964-1967
Author(s):  
Man Zhao ◽  
Jin Jiang Cui ◽  
He Nan Wu ◽  
Guang Yang ◽  
Da Yong Jiang
Keyword(s):  
Target Identification ◽  
Target Recognition ◽  
Remote Sensing Image ◽  
Recognition System ◽  
Computer Processing ◽  
Target Feature ◽  
C Language ◽  
Line Extraction ◽  
Target Extraction ◽  
Recognition Efficiency

Linear target is the most widely used in remote sensing image. Effective extraction of the linear target can make us reduce a lot of practical work, thus greatly improve the target extraction and identification of timeliness. According to this situation, in the process of building a recognition system, the recognition efficiency can be realized by joining human recognize and identify, combining with the intelligence of computer processing and powerful place. So in this paper, the method based on edge detection and Hough transform algorithm is exploded. Line Extraction and Target Recognition System is developed. The system is realized under Windows operating system. The tool is Visual C++ 6.0 software. The platform is MFC functions. The system is written in C++ language. The characteristics of the system are the strong pertinence and the simple operation. When the system is applied safely, the results are definite and clear.

scholarly journals Assembly and translocation of a CRISPR-Cas primed acquisition complex

2017 ◽  
Author(s):  
Maxwell W. Brown ◽  
Kaylee E. Dillard ◽  
Yibei Xiao ◽  
Adam Dolan ◽  
Erik Hernandez ◽  
...  
Keyword(s):  
Single Molecule ◽  
Target Recognition ◽  
Adaptive Immune Response ◽  
Type I ◽  
Crispr Locus ◽  
One Dimensional ◽  
Adaptive Immune ◽  
Dna Loop ◽  
Recognition Efficiency

AbstractBacteria and archaea destroy foreign nucleic acids by mounting an RNA-based CRISPR-Cas adaptive immune response1–3. In type I CRISPR-Cas systems, the most frequently found type of CRISPR in bacteria and archaea3,4, foreign DNAs that trigger efficient immunity can also provoke primed acquisition of protospacers into the CRISPR locus5–12. Both interference and primed acquisition require Cascade (CRISPR-associated complex for antiviral defense) and the Cas3 helicase/nuclease. Primed acquisition also requires the Cas1-Cas2 integrase; however, the biophysical mechanisms of how interference and primed acquisition are coordinated have remained elusive. Here, we present single-molecule characterization of the type I-E Thermobifida fusca (Tfu) primed acquisition complex (PAC). TfuCascade rapidly samples non-specific DNA for its target via facilitated one-dimensional (1D) diffusion. An evolutionary-conserved positive patch on the Cse1 subunit increases the target recognition efficiency by promoting this 1D diffusion. Cas3 loads at target-bound Cascade and the Cascade/Cas3 complex initiates processive translocation via a looped DNA intermediate. Moving Cascade/Cas3 complexes stall and release the DNA loop at protein roadblocks. Cas1-Cas2 samples DNA transiently via 3D collisions, but stably associates with target-bound Cascade. Cas1-Cas2 also remains associated with translocating Cascade/Cas3, forming the PAC. By directly imaging all key subcomplexes involved in target recognition, interference, and primed acquisition, this work provides a molecular basis for the coordinated steps in CRISPR-based adaptive immunity.

Long range target recognition and identification of camouflaged armored vehicles.

1979 ◽  
Author(s):  
William L. Warnick ◽  
Garvin D. Chastain ◽  
William H. Ton
Keyword(s):  
Long Range ◽  
Target Recognition ◽  
Armored Vehicles

Target recognition on complex displays.

1959 ◽  
Author(s):  
Charles A. Baker ◽  
Dominic F. Morris ◽  
William C. Steedman
Keyword(s):  
Target Recognition

SVM-based global vision system of sports competition and action recognition

2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma
Keyword(s):  
Target Recognition ◽  
Vision System ◽  
Task Assignment ◽  
Recognition Algorithm ◽  
Control Factor ◽  
Training Time ◽  
Adaboost Algorithm ◽  
Advantages And Disadvantages ◽  
Time Control ◽  
Correlation Degree

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.

Sign in / Sign up

Export Citation Format

Share Document