scholarly journals Low Distortion of Noise Filter Realization with 6.34 V/μs Fast Slew Rate and 120 mVp-p Output Noise Signal

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1008
Author(s):  
Fang-Ming Yu ◽  
Kun-Cheng Lee ◽  
Ko-Wen Jwo ◽  
Rong-Seng Chang ◽  
Jun-Yi Lin
Keyword(s):  
Input Signal ◽  
Gaussian Noise ◽  
Fast Response ◽  
Noise Signal ◽  
High Order ◽  
Phase Delay ◽  
Slew Rate ◽  
Noise Filter ◽  
Output Noise ◽  
Actual Signal

In order to reduce Gaussian noise, this paper proposes a method via taking the average of the upper and lower envelopes generated by capturing the high and low peaks of the input signal. The designed fast response filter has no cut-off frequency, so the high order harmonics of the actual signal remain unchanged. Therefore, it can immediately respond to the changes of input signal and retain the integrity of the actual signal. In addition, it has only a small phase delay. The slew rate, phase delay and frequency response can be confirmed from the simulation results of Multisim 13.0. The filter outlined in this article can retain the high order harmonics of the original signal, achieving a slew rate of 6.34 V/μs and an almost zero phase difference. When using our filter to physically test the input signal with a noise level of 3 Vp-p Gaussian noise, a reduced noise signal of 120 mVp-p is obtained. The noise can be suppressed by up to 4% of the raw signal.

Quantum random number Gaussian noise signal generator

2019 ◽  
Vol 27 (7) ◽  
pp. 1492-1499
Author(s):  
余恒炜 YU Heng-wei ◽  
孙晓娟 SUN Xiao-juan ◽  
王星辰 WANG Xing-chen ◽  
蒋 科 JIANG Ke ◽  
吴 忧 WU-You ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Random Number ◽  
Noise Signal ◽  
Signal Generator

Gaussian Noise Filter Using Variable Step Time Matrix of PCNN

2011 ◽  
Vol 48-49 ◽  
pp. 551-554 ◽  
Author(s):  
Yuan Yuan Cheng ◽  
Hai Yan Li ◽  
Qi Xiao ◽  
Yu Feng Zhang ◽  
Xin Ling Shi
Keyword(s):  
Gaussian Noise ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Filter ◽  
Variable Step ◽  
Mean Filter ◽  
Reduction Methods ◽  
Novel Method

A novel method was brought forward for the purpose of filtering Gaussian noise effectively by using variable step time matrix of the simplified pulse coupled neural network (PCNN). Firstly, the time matrix of PCNN, related to the grayscale and spatial information of an image, is calculated to identify the noise polluted pixels. Subsequently, a variable step, a long step for strong noise and a short step for weak noise, based on the time matrix is applied to modify the grayscale of noised pixels in a sliding window. And then wiener filter is used to the image to further filter the noise. Experiments show that the proposed filter can remove Gaussian noise effectively than other noise reduction methods such as median filter, mean filter, wiener filter etc, and the filtered image is smooth and the details and edges are sharp. Compared with existing PCNN based Gaussian noise filter, the proposed filter gets higher Peak Signal-to-Noise Ratio (PSNR) and better performance.

The Radar Signal Detection Based on Higher Order Statistics

2013 ◽  
Vol 310 ◽  
pp. 421-423
Author(s):  
Chun Yu Wang ◽  
Xing Long Qi ◽  
Run Lan Tian ◽  
Lin Ren
Keyword(s):  
Signal Detection ◽  
Order Statistics ◽  
Gaussian Noise ◽  
Signal Detection Theory ◽  
Detection Method ◽  
Noise Signal ◽  
Higher Order ◽  
Radar Signal ◽  
Higher Order Statistics ◽  
Colored Gaussian Noise

Radar signal detection theory is significant for the radar signal detection, and there are many radar signal detection method at present. In this paper, higher order statistics was used to achieve the radar signal detection. It analyzed the basic theory of higher order statistics and higher order statistics in radar signal detection. And it achieved radar signal detection in the MATLAB software, colored Gaussian noise signal detection method based on dual-spectrum was used to detect the radar signal mixed with man-made noise.

Sensor Noise Assessment Approach in a Patterned Region

2013 ◽  
Vol 717 ◽  
pp. 529-532
Author(s):  
Gwang Gil Jeon
Keyword(s):  
Input Signal ◽  
Gaussian Noise ◽  
Noise Level ◽  
Large Range ◽  
Noise Variance ◽  
Noise Levels ◽  
Pass Filter ◽  
Assessment Approach ◽  
Noise Assessment ◽  
High Pass

In this paper, we propose a simple noise assessment approach for sensors. We assume the input signal is contaminated by white additive and zero mean Gaussian noise which. To measure the noise level, we the intensity-homogeneous blocks are found first, and then we evaluate the noise variance in the blocks. To determined intensity-homogeneous blocks, we use high pass filter. Experimental results show that the presented method provides good performance for natural and artificial images over a large range of noise levels.

scholarly journals IC 741 and IC AMP02FP Based Signal Conditioning Circuit for Electrostatic Sensor using Multisim

2020 ◽  
Vol 9 (5) ◽  
pp. 166-168
Keyword(s):  
Electric Charge ◽  
Input Signal ◽  
Noise Signal ◽  
Low Noise ◽  
Signal Conditioning ◽  
Gain Bandwidth ◽  
Voltage Amplifier ◽  
Signal Conditioning Circuit ◽  
Conditioning Circuit

The Electrostatic sensors are extensively used in military, radar and electronic applications which can sense the electric charge from the moving charged motes. The signal conditioning circuit for an electrostatic sensor is a voltage amplifier that can intensify a small input signal. This paper proposal is to design a low noise signal conditioning circuit using Multisim and to study the performance of its gain, bandwidth and noise properties.

Acquisition Method of Typical Time Course for Stationary Signal Basing on Frequency Weighting

2015 ◽  
Vol 734 ◽  
pp. 577-580
Author(s):  
Dan Lu ◽  
Yi Qi Zhou ◽  
Lei Liu ◽  
Yue Biao Chen
Keyword(s):  
Time Course ◽  
Spectral Characteristics ◽  
Noise Signal ◽  
Test Method ◽  
Measured Signal ◽  
Statistical Parameters ◽  
Typical Time ◽  
Actual Signal ◽  
Frequency Weighting ◽  
Acquisition Method

The cab acoustic response, which could be considered to be random and stationary, determines the riding comfort of the excavator. In order to get the most useful information from the actual cab noise signal, an acquisition method of typical time course for a stationary signal was proposed in this paper. Firstly, a series of de-noising preprocessing was applied on the test signal. Then, the stationarity and ergodicity of that were examined via runs test method. Finally, combining with FFT, IFFT and the frequency weighting, the typical time course was obtained. The results show that the statistical parameters and spectral characteristics of the obtained typical time course are consistent with those of the measured signal, which means that the obtained one could be regarded as a representation of the actual signal for further analyzing more effectively.

SYNCHRONIZING CHAOS IN DRIVEN CHUA'S CIRCUIT

1993 ◽  
Vol 03 (04) ◽  
pp. 1057-1066 ◽  
Author(s):  
K. MURALI ◽  
M. LAKSHMANAN
Keyword(s):  
Adaptive Control ◽  
Gaussian Noise ◽  
Control Algorithm ◽  
Noise Signal ◽  
The Novel ◽  
Chua’S Circuit ◽  
Chua's Circuit ◽  
Adaptive Control Algorithm

In this paper, we report the numerical observations of synchronization in driven Chua's circuit using the criterion of Pecora and Carroll. Also, synchronization behavior of this system is investigated under the influence of Gaussian noise signal and we point out that it is possible to obtain synchronization even in this environment. More interestingly, we also point out the novel possibility of controlling unsynchronized motion using the adaptive control algorithm.

R2C: improvingab initioresidue contact map prediction using dynamic fusion strategy and Gaussian noise filter

2016 ◽  
Vol 32 (16) ◽  
pp. 2435-2443 ◽  
Author(s):  
Jing Yang ◽  
Qi-Yu Jin ◽  
Biao Zhang ◽  
Hong-Bin Shen
Keyword(s):  
Gaussian Noise ◽  
Contact Map ◽  
Fusion Strategy ◽  
Noise Filter
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