An Improved Mean Filtering Algorithm for Measuring Data of Robotic Arm Force Sensor

2021 ◽  
Author(s):  
Li-hong Li ◽  
Ji-fu Li ◽  
Jia-cai Gao
Keyword(s):  
Force Sensor ◽  
Robotic Arm ◽  
Filtering Algorithm ◽  
Mean Filtering

scholarly journals Non-local mean filtering algorithm based on deep learning

2018 ◽  
Vol 232 ◽  
pp. 03025
Author(s):  
Baozhong Liu ◽  
Jianbin Liu
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Denoising Method ◽  
Filtering Algorithm ◽  
Local Mean ◽  
Non Local ◽  
Reduction Effect ◽  
Mean Filtering

Aimed at the problem that the traditional image denoising algorithm is not effective in noise reduction, a new image denoising method is proposed. The method combines deep learning and non-local mean filtering algorithms to denoise the noisy image to obtain better noise reduction effect. By comparing with Gaussian filtering algorithm, median filtering algorithm, bilateral filtering algorithm and early non-local mean filtering algorithm, the noise reduction effect of the new algorithm is better than the traditional method and the peak signal to noise ratio is compared with the early non-local mean algorithm. The performance is better.

scholarly journals A Novel Compact Multi-Axis Force Sensor

2020 ◽  
Vol 10 (12) ◽  
pp. 1-12
Author(s):  
Yash Gujarati ◽  
◽  
Ravindra Thamma
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Data Acquisition ◽  
Strain Gauge ◽  
Force Sensor ◽  
Robotic Arm ◽  
Element Analysis ◽  
Stage Of Development ◽  
Accuracy And Repeatability

This paper presents the development of a sixaxis force/torque (FTS) sensor using crossbeams for a robotic arm. The sensor produced in this paper is a new unique design that was developed under rigorous trial and testing using finite element analysis (FEA) at every stage of development. Additionally, the FTS presented uses strain gauge technology and data-acquisition (DAQ) to measure and record forces in Fx, Fy, and Fz direction along with torque in Mx, My, and Mz direction. FTS was tested, calibrated, and fitted on a robotic arm to test its accuracy and repeatability

scholarly journals IMPLEMENTATION OF MODIFIED MEDIAN FILTERING ALGORITHM FOR SALT & PEPPER NOISE REDUCTION ON IMAGE

2017 ◽  
Author(s):  
Robbi Rahim ◽  
Ali Ikhwan
Keyword(s):  
Image Quality ◽  
Noise Reduction ◽  
Special Technique ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Analog Signals ◽  
Pixel Value ◽  
The Mean ◽  
Spatial Window ◽  
Mean Filtering

Noise is one form of issue in the image, salt & pepper noise is the kind of noise that can be made using a special technique or also due to the conversion from analog signals to digital, the noise can be improved by using algorithms such as the mean filtering, the mid-point filtering and median filtering, median filtering algorithm is widely used for repair image quality, this article will discuss the modification of the median filtering to improve noise in the image by taking the average of neighboring pixels by 2 points from the value of the center clockwise, the value is taken to be processed to retrieve the value of the middle and then the overall result value will be divided to replace the center pixel value 3x3 spatial window.

Application of Improved Median Filtering Algorithm to Image De-Noising

2014 ◽  
Vol 998-999 ◽  
pp. 838-841
Author(s):  
Wen Long Jiang ◽  
Guang Lin Li ◽  
Wei Bing Luo
Keyword(s):  
Impulse Noise ◽  
Median Filter ◽  
Median Filtering ◽  
Real Time Processing ◽  
Time Processing ◽  
Filtering Algorithm ◽  
Weighted Median ◽  
The Mean ◽  
Weighted Median Filter ◽  
Mean Filtering

Based on the shortcomings of standard median filtering and combined with the mean filtering, this paper puts forward two improved median filtering algorithms referred as the weighted fast median filtering algorithm and the weighted adaptive median filtering algorithm. The experiment results with MATLAB show that weighted fast median filtering algorithm has a significant effect on low-density impulse noise, and accelerates the speed of real-time processing. The weighted median filter could effectively eliminate the high-density impulse noise from polluted images, and better maintains the details of the original image.

The Realization of Fuzzy Adaptive Filtering Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1733-1737
Author(s):  
Ji Guang Liu ◽  
Hai Yang Wang
Keyword(s):  
Noise Reduction ◽  
Adaptive Filtering ◽  
Experimental Simulation ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Whole Process ◽  
Actual Use ◽  
The Mean ◽  
Mean Filtering ◽  
Fuzzy Adaptive

This paper introduces a kind of fuzzy adaptive filtering algorithm. The whole process is divided into four steps. Plenty experimental simulation have been made, which has a good results using these methods. On this the premise which the signal detail is not damaged, this filtering algorithm can not only remove pulse but also has a higher capability of noise reduction. It have been verified by actual use and experimental simulation that this filtering algorithm not only has the all advantages of mean filtering and median filtering but can avoid edge blurry of signal, which can’t be realized using the mean filtering and the median filtering under bigger windows .

Adaptive weighted mean filtering algorithm based on city block distance

2013 ◽  
Vol 33 (11) ◽  
pp. 3197-3200
Author(s):  
Meng CAO ◽  
Youhui ZHANG ◽  
Zhiwei WANG ◽  
Rui DONG ◽  
Yingjuan ZHENG
Keyword(s):  
Weighted Mean ◽  
City Block ◽  
Filtering Algorithm ◽  
City Block Distance ◽  
Mean Filtering

Research on a Denoising Method of the Force Sensor Signal Based on Multiple Filter

2015 ◽  
Vol 727-728 ◽  
pp. 620-625
Author(s):  
Xiao Jian Wu ◽  
Guo Kun Zuo ◽  
Zhong Zhu Yang ◽  
Shang Qing Xiao
Keyword(s):  
Motion Control ◽  
Digital Filtering ◽  
Force Sensor ◽  
Robot Motion ◽  
Rehabilitation Robot ◽  
Denoising Method ◽  
Filtering Algorithm ◽  
First Order ◽  
Advantages And Disadvantages ◽  
The Common

Forcesensor is very important in the rehabilitation robot. However, the force sensorsignals will introduce noise easily on the process of transmitting, amplifyingand sampling, which is very unfavorable for the robot motion control. Thispaper reviews the common digital filtering algorithm and analyzes theirprinciples, advantages and disadvantages, then designs an improved filteringalgorithm combines the limiting filtering algorithm withthe first-order lag filtering algorithm. At last, this paper verifies thefiltering effect by theoretical analysis, simulation and experiments.

An Improved Hybrid Median-Mean Filter Algorithm

2014 ◽  
Vol 701-702 ◽  
pp. 288-292
Author(s):  
Fang Jia ◽  
De Cheng Xu ◽  
Xin Fu
Keyword(s):  
Gaussian Noise ◽  
Median Filter ◽  
Experimental Results ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Mean Filter ◽  
Image Details ◽  
Mean Filtering

In the process of imaging, digitalization and transmission, images are generally contaminated by Gaussian noise and salt & pepper noise, which cannot be eliminated completely at the same time only by Mean filter or Median filter. Aiming at solving this problem, an improved hybrid median-mean filter algorithm based on the Improved Median Filtering (IMF) algorithm is proposed in this paper. The experimental results show that the new algorithm shows better performance than either Median filtering algorithm or Mean filtering algorithm, which can not only get rid of Gaussian noise and salt & pepper noise simultaneously, but also minimize the contradictions between noise erasing and image details protecting effectively.

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