A simulation on improvement of the accuracy and the stability of stereo maching using triplet linear array sensor data.

1987 ◽  
Vol 26 (2) ◽  
pp. 4-10 ◽  
Author(s):  
Ryosuke Shibasaki ◽  
Shunji Murai
Keyword(s):  
Linear Array ◽  
Sensor Data ◽  
Array Sensor ◽  
The Stability

scholarly journals Multi-sensor data fusion based on consistency test and sliding window variance weighted algorithm in sensor networks

2013 ◽  
Vol 10 (1) ◽  
pp. 197-214 ◽  
Author(s):  
Jian Shu ◽  
Ming Hong ◽  
Wei Zheng ◽  
Li-Min Sun ◽  
Xu Ge
Keyword(s):  
Sensor Networks ◽  
Internal Noise ◽  
Sliding Window ◽  
Test Method ◽  
Sensor Data ◽  
Data Simulation ◽  
Consistency Test ◽  
Multi Sensor Data Fusion ◽  
Weighted Algorithm ◽  
The Stability

In order to solve the problem that the accuracy of sensor data is reducing due to zero offset and the stability is decreasing in wireless sensor networks, a novel algorithm is proposed based on consistency test and sliding-windowed variance weighted. The internal noise is considered to be the main factor of the problem in this paper. And we can use consistency test method to diagnose whether the mean of sensor data is offset. So the abnormal data is amended or removed. Then, the result of fused data can be calculated by using sliding window variance weighted algorithm according to normal and amended data. Simulation results show that the misdiagnosis rate of the abnormal data can be reduced to 3% by using improved consistency test with the threshold set to [0.05, 0.15], so the abnormal sensor data can be diagnosed more accurately and the stability can be increased. The accuracy of the fused data can be improved effectively when the window length is set to 2. Under the condition that the abnormal sensor data has been amended or removed, the proposed algorithm has better performances on precision compared with other existing algorithms.

scholarly journals Indoor Human Detection Based on Thermal Array Sensor Data and Adaptive Background Estimation

2017 ◽  
Vol 05 (04) ◽  
pp. 16-28 ◽  
Author(s):  
Anna A. Trofimova ◽  
Andrea Masciadri ◽  
Fabio Veronese ◽  
Fabio Salice
Keyword(s):  
Human Detection ◽  
Sensor Data ◽  
Background Estimation ◽  
Array Sensor

scholarly journals Compressive Imaging of Moving Object Based on Linear Array Sensor

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Changjun Zha ◽  
Yao Li ◽  
Jinyao Gui ◽  
Huimin Duan ◽  
Tailong Xu
Keyword(s):  
Moving Objects ◽  
Linear Array ◽  
Matching Pursuit ◽  
Moving Object ◽  
Imaging Method ◽  
Compressive Imaging ◽  
Array Sensor ◽  
Object Based ◽  
Direct Separation ◽  
Matching Pursuit Algorithm

Using the characteristics of a moving object, this paper presents a compressive imaging method for moving objects based on a linear array sensor. The method uses a higher sampling frequency and a traditional algorithm to recover the image through a column-by-column process. During the compressive sampling stage, the output values of the linear array sensor are multiplied by a coefficient that is a measurement matrix element, and then the measurement value can be acquired by adding all the multiplication values together. During the reconstruction stage, the orthogonal matching pursuit algorithm is used to recover the original image when all the measurement values are obtained. Numerical simulations and experimental results show that the proposed compressive imaging method not only effectively captures the information required from the moving object for image reconstruction but also achieves direct separation of the moving object from a static scene.

scholarly journals Pyroelectric linear array sensor for object recognition

2014 ◽  
Vol 53 (2) ◽  
pp. 023101 ◽  
Author(s):  
Srikant Chari ◽  
Eddie L. Jacobs ◽  
Divya Choudhary
Keyword(s):  
Object Recognition ◽  
Linear Array ◽  
Array Sensor
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