Synthesis of a Digital Low-Pass Quasi-Gaussian Filter for Noise Reduction in Poisson Observations

2021 ◽  
Vol 57 (4) ◽  
pp. 437-444
Author(s):  
V. E. Chinkin ◽  
V. G. Getmanov ◽  
I. I. Yashin
Keyword(s):  
Noise Reduction ◽  
Gaussian Filter ◽  
Low Pass

SYNTHESIS OF A DIGITAL LOW-PASS QUASI-GAUSSIAN FILTER FOR NOISE REDUCTION IN POISSON OBSERVATIONS

2021 ◽  
Vol 57 (4) ◽  
pp. 118-125
Author(s):  
V. E. Chinkin ◽  
V. G. Getmanov ◽  
I. I. Yashin
Keyword(s):  
Noise Reduction ◽  
Gaussian Filter ◽  
Low Pass

scholarly journals Low-Pass Filtering Method for Poisson Data Time Series

2021 ◽  
Vol 11 (10) ◽  
pp. 4524
Author(s):  
Victor Getmanov ◽  
Vladislav Chinkin ◽  
Roman Sidorov ◽  
Alexei Gvishiani ◽  
Mikhail Dobrovolsky ◽  
...  
Keyword(s):  
Time Series ◽  
Finite Impulse Response ◽  
Digital Processing ◽  
Distributed Data ◽  
Gaussian Filter ◽  
Experimental Physics ◽  
Filtering Method ◽  
Filter Synthesis ◽  
Low Pass ◽  
Poisson Data

Problems of digital processing of Poisson-distributed data time series from various counters of radiation particles, photons, slow neutrons etc. are relevant for experimental physics and measuring technology. A low-pass filtering method for normalized Poisson-distributed data time series is proposed. A digital quasi-Gaussian filter is designed, with a finite impulse response and non-negative weights. The quasi-Gaussian filter synthesis is implemented using the technology of stochastic global minimization and modification of the annealing simulation algorithm. The results of testing the filtering method and the quasi-Gaussian filter on model and experimental normalized Poisson data from the URAGAN muon hodoscope, that have confirmed their effectiveness, are presented.

scholarly journals Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Soojun Kim ◽  
Huiseong Noh ◽  
Narae Kang ◽  
Keonhaeng Lee ◽  
Yonsoo Kim ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Standard Error ◽  
Rainfall Series ◽  
Radar Rainfall ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Filtering Technique ◽  
Filtering Techniques

The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF.

scholarly journals A novel edge detection method based on efficient gaussian binomial filter

2021 ◽  
Vol 7 (2) ◽  
pp. 211
Author(s):  
El Houssain Ait Mansour ◽  
Francois Bretaudeau
Keyword(s):  
Edge Detection ◽  
High Speed ◽  
Detection Method ◽  
Detection Methods ◽  
Gaussian Filter ◽  
Image Edge Detection ◽  
Low Pass ◽  
Image Edge ◽  
Edge Detection Method ◽  
Low Pass Filters

Most basic and recent image edge detection methods are based on exploiting spatial high-frequency to localize efficiency the boundaries and image discontinuities. These approaches are strictly sensitive to noise, and their performance decrease with the increasing noise level. This research suggests a novel and robust approach based on a binomial Gaussian filter for edge detection. We propose a scheme-based Gaussian filter that employs low-pass filters to reduce noise and gradient image differentiation to perform edge recovering. The results presented illustrate that the proposed approach outperforms the basic method for edge detection. The global scheme may be implemented efficiently with high speed using the proposed novel binomial Gaussian filter.

Efficiency Improvement of Delta Sigma Modulator-Based Transmitter Using Complex Delta Sigma Modulator and Noise Reduction Loop

2020 ◽  
Vol 29 (16) ◽  
pp. 2050267
Author(s):  
Nasser Erfani Majd ◽  
Amin Aeenmehr
Keyword(s):  
Noise Reduction ◽  
Average Power ◽  
Quantization Noise ◽  
Coding Efficiency ◽  
Delta Sigma Modulator ◽  
Delta Sigma ◽  
Low Pass ◽  
Polar Low ◽  
Oversampling Ratio

This paper proposes an architecture to enhance coding efficiency (CE) of the Delta Sigma Modulator (DSM) transmitters. In this architecture, a complex–low pass delta sigma modulator (LPDSM) is used instead of existing Cartesian–LPDSM and polar–low pass envelope delta sigma modulator (LPEDSM). Simulation results show that for Uplink long-term evolution (LTE) signal with 1.92[Formula: see text]MHz bandwidth and 7.8-dB peak to average power ratio (PAPR), the CE for the complex–LPDSM-based transmitter is equal to 41.7% in compare to 9.7% CE for Cartesian–LPDSM transmitter. Also, due to the resolving of noise convolution problem, the complex–LPDSM-based transmitter baseband part needs lower oversampling ratio (OSR) and clock speed than polar–LPEDSM transmitter baseband part to achieve the same signal-to-noise and distortion ratio (SNDR). In the next step, a quantization noise reduction loop is implemented in this architecture. By using this technique for an Uplink LTE signal with 1.92[Formula: see text]MHz bandwidth, with the same PAPR and OSR of 16, the CE is improved from 41.7% to 56.1% with 40[Formula: see text]dB SNDR.

Coding Efficiency and Bandwidth Enhancement in Polar Delta-Sigma Modulator Transmitter Using Quantization Noise Reduction and Parallel Processing Techniques

2017 ◽  
Vol 26 (05) ◽  
pp. 1750085 ◽  
Author(s):  
Nasser Erfani Majd ◽  
Hassan Ghafoori Fard ◽  
Abbas Mohammadi
Keyword(s):  
Noise Reduction ◽  
Average Power ◽  
Quantization Noise ◽  
Bandwidth Enhancement ◽  
Coding Efficiency ◽  
Clock Speed ◽  
Delta Sigma Modulator ◽  
Delta Sigma ◽  
Low Pass ◽  
Processing Techniques

This paper introduces an architecture to enhance coding efficiency (CE) and bandwidth of the delta-sigma modulator (DSM) transmitters. In this architecture a low-pass envelope DSM (LPEDSM) is used instead of the traditional Cartesian low-pass DSM (LPDSM) to reduce the quantization noise and to improve the coding efficiency. The simulation results show that for an uplink long-term evolution (LTE) signal with 1.92[Formula: see text]MHz bandwidth, 7.8[Formula: see text]dB peak-to-average power ratio (PAPR), and an oversampling ratio (OSR) of 32, the CE for the polar LPEDSM transmitter is equal to 41.72% in comparison to 9.7% CE for the Cartesian LPDSM transmitter. In the next step, the CE and bandwidth of the transmitter are improved at the same time by using the quantization noise reduction technique in the polar LPEDSM transmitter with parallel baseband. By using this combined technique in the four-branch transmitter baseband part for an uplink LTE signal with 7.68[Formula: see text]MHz bandwidth, 7.8[Formula: see text]dB PAPR, and an OSR of 32, the CE is improved from 42.59% to 55.86% with 40[Formula: see text]dB signal-to-noise-and-distortion ratio (SNDR) while the clock speed is only 61.44[Formula: see text]MHz which is four times lower than the clock speed requirement of the conventional transmitter baseband part to achieve the same SNDR.

Sign in / Sign up

Export Citation Format

Share Document