SU-G-JeP3-01: A Method to Quantify Lung SBRT Target Localization Accuracy Based On Digitally Reconstructed Fluoroscopy

2016 ◽  
Vol 43 (6Part27) ◽  
pp. 3670-3670
Author(s):  
K Lafata ◽  
L Ren ◽  
J Cai ◽  
F Yin
Keyword(s):  
Target Localization ◽  
Localization Accuracy ◽  
Lung Sbrt

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

Impact analysis of attitude determination accuracy to target localization accuracy on Multi-BD receivers

2016 ◽  
Vol 31 (9) ◽  
pp. 902-912
Author(s):  
蔡明兵 CAI Ming-bing ◽  
王超 WANG Chao ◽  
刘晶红 LIU Jing-hong ◽  
周前飞 ZHOU Qian-fei ◽  
宋悦铭 SONG Yue-ming
Keyword(s):  
Impact Analysis ◽  
Attitude Determination ◽  
Target Localization ◽  
Localization Accuracy

Evaluation of patient‐specific MR distortion correction schemes for improved target localization accuracy in SRS

2020 ◽  
Author(s):  
Dimitrios Dellios ◽  
Eleftherios P. Pappas ◽  
Ioannis Seimenis ◽  
Chryssa Paraskevopoulou ◽  
Kostas I. Lampropoulos ◽  
...  
Keyword(s):  
Distortion Correction ◽  
Target Localization ◽  
Patient Specific ◽  
Localization Accuracy

Spatial Cueing and the Relation between the Accuracy of “Where” and “What” Decisions in Visual Search

1989 ◽  
Vol 41 (4) ◽  
pp. 747-773 ◽  
Author(s):  
Hermann J. Müller ◽  
Patrick M. A. Rabbitt
Keyword(s):  
Visual Search ◽  
Shape Analysis ◽  
Target Localization ◽  
Target Orientation ◽  
Spatial Cueing ◽  
Localization Accuracy ◽  
Accurate Localization ◽  
Orientation Experiment ◽  
Candidate Location ◽  
Accurate Matching

To study the processes underlying selective attention in visual search, the relation between the accuracy of “where” (location) and “what” (same/different orientation matching) decisions was analysed under various display conditions. Target-non-target discriminability was varied by contrasting single and multiple element displays; further, attention was directly manipulated by spatial cueing. In Experiment 1, analyses for both single and multiple displays showed that localization accuracy remained above chance when same/different matching failed; the inverse also obtained. It seems that accurate matching is not a prerequisite for target localization, nor is accurate localization a prerequisite for same/different matching. However, localization is a prerequisite for the accurate recognition of target orientation (Experiment 2). In this case, it seems that features critical for localization “call” attention to a particular candidate location. This facilitates further (shape) analysis of the stimulus that is found there. This orienting process is by-passed if attention is cued to the location in advance.

SU-E-J-164: Evaluation of Target Localization Accuracy of ExacTrac System for Spine SBRT

2011 ◽  
Vol 38 (6Part9) ◽  
pp. 3481-3481
Author(s):  
S Venkataraman ◽  
D Sasaki ◽  
J Butler ◽  
G Schroeder ◽  
M West ◽  
...  
Keyword(s):  
Target Localization ◽  
Localization Accuracy

scholarly journals Wireless Sensor Network Target Localization Algorithm Based on Two- and Three-Dimensional Delaunay Partitions

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Chenguang Shao
Keyword(s):  
Three Dimensional ◽  
Target Localization ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Accuracy ◽  
Localization Method ◽  
Anchor Nodes ◽  
2D And 3D ◽  
The Relationship

The target localization algorithm is critical in the field of wireless sensor networks (WSNs) and is widely used in many applications. In the conventional localization method, the location distribution of the anchor nodes is fixed and cannot be adjusted dynamically according to the deployment environment. The resulting localization accuracy is not high, and the localization algorithm is not applicable to three-dimensional (3D) conditions. Therefore, a Delaunay-triangulation-based WSN localization method, which can be adapted to two-dimensional (2D) and 3D conditions, was proposed. Based on the location of the target node, we searched for the triangle or tetrahedron surrounding the target node and designed the localization algorithm in stages to accurately calculate the coordinate value of the target. The relationship between the number of target nodes and the number of generated graphs was analysed through numerous experiments, and the proposed 2D localization algorithm was verified by extending it the 3D coordinate system. Experimental results revealed that the proposed algorithm can effectively improve the flexibility of the anchor node layout and target localization accuracy.

SU-E-J-30: The Effect of Planning CT Slice Thickness On Evaluation of SRS Target Localization Accuracy

2015 ◽  
Vol 42 (6) ◽  
pp. 3270-3270
Author(s):  
J Briceno ◽  
H Li ◽  
Y Huang ◽  
N Wen ◽  
I Chetty
Keyword(s):  
Target Localization ◽  
Slice Thickness ◽  
Localization Accuracy ◽  
Planning Ct

scholarly journals Target Localization Methods Based on Iterative Super-Resolution for Bistatic MIMO Radar

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 341
Author(s):  
Jianhe Du ◽  
Meng Han ◽  
Libiao Jin ◽  
Yan Hua ◽  
Shufeng Li
Keyword(s):  
Multiple Input Multiple Output ◽  
Super Resolution ◽  
Single Pulse ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Target Localization ◽  
Localization Accuracy ◽  
Bistatic Mimo Radar ◽  
Multiple Pulse ◽  
Direction Of Departure

The direction-of-departure (DOD) and the direction-of-arrival (DOA) are important localization parameters in bistatic MIMO radar. In this paper, we are interested in DOD/DOA estimation of both single-pulse and multiple-pulse multiple-input multiple-output (MIMO) radars. An iterative super-resolution target localization method is firstly proposed for single-pulse bistatic MIMO radar. During the iterative process, the estimated DOD and DOA can be moved from initial angles to their true values with high probability, and thus can achieve super-resolution estimation. It works well even if the number of targets is unknown. We then extend the proposed method to multiple-pulse configuration to estimate target numbers and localize targets. Compared with existing methods, both of our proposed algorithms have a higher localization accuracy and a more stable performance. Moreover, the proposed algorithms work well even with low sampling numbers and unknown target numbers. Simulation results demonstrate the effectiveness of the proposed methods.

Improving Multimodal Localization Through Self-Supervision

2020 ◽  
Vol 2020 (6) ◽  
pp. 14-1-14-8
Author(s):  
Robert Relyea ◽  
Darshan Bhanushali ◽  
Karan Manghi ◽  
Abhishek Vashist ◽  
Clark Hochgraf ◽  
...  
Keyword(s):  
Inventory Management ◽  
Ground Truth ◽  
Target Localization ◽  
Training Data ◽  
Image Feature ◽  
Rfid Tags ◽  
Ground Truth Data ◽  
Localization Accuracy ◽  
Robot Positioning ◽  
Metal Wires

Modern warehouses utilize fleets of robots for inventory management. To ensure efficient and safe operation, real-time localization of each agent is essential. Most robots follow metal tracks buried in the floor and use a grid of precisely mounted RFID tags for localization. As robotic agents in warehouses and manufacturing plants become ubiquitous, it would be advantageous to eliminate the need for these metal wires and RFID tags. Not only do they suffer from significant installation costs, the removal of wires would allow agents to travel to any area inside the building. Sensors including cameras and LiDAR have provided meaningful localization information for many different positioning system implementations. Fusing localization features from multiple sensor sources is a challenging task especially when the target localization task’s dataset is small. We propose a deep-learning based localization system which fuses features from an omnidirectional camera image and a 3D LiDAR point cloud to create a robust robot positioning model. Although the usage of vision and LiDAR eliminate the need for the precisely installed RFID tags, they do require the collection and annotation of ground truth training data. Deep neural networks thrive on lots of supervised data, and the collection of this data can be time consuming. Using a dataset collected in a warehouse environment, we evaluate the performance of two individual sensor models for localization accuracy. To minimize the need for extensive ground truth data collection, we introduce a self-supervised pretraining regimen to populate the image feature extraction network with meaningful weights before training on the target localization task with limited data. In this research, we demonstrate how our self-supervision improves accuracy and convergence of localization models without the need for additional sample annotation.

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