scholarly journals An Algorithm for Mitigating Transient RFI in Pulsar Observation

2021 ◽  
Vol 922 (2) ◽  
pp. 94
Author(s):  
Yining Song ◽  
Zhiyong Liu ◽  
Na Wang ◽  
Jun Li ◽  
Rai Yuen
Keyword(s):  
Standard Deviation ◽  
Radio Telescope ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Time Of Arrival ◽  
Radio Frequency Interference ◽  
Observation Data ◽  
Signal To Noise ◽  
Pulse Profile ◽  
Pulsar Time

Abstract We propose an algorithm, referred to as the pulsar phase and standard deviation (PPSD), to mitigate transient radio frequency interference (RFI) in pulsar observations. PPSD uses the model for pulsar time of arrival to identify pulsar phase and extract the pulse profile to protect the original pulsar profile. PPSD sets a threshold based on the statistics empirical rule to label the transient RFI in the off-pulse data until all unlabelled off-pulse data obeys the white Gaussian noise (WGN) distribution. The transient RFI data is then substituted with WGN. Finally, we use PPSD to process the pulsar observation data obtained from the NanShan 25 m Radio Telescope. Our results show that PPSD can effectively mitigate the transient RFI and improve the signal-to-noise ratio of the pulsar observations.

scholarly journals Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5202 ◽  
Author(s):  
Yu ◽  
Ji ◽  
Xue ◽  
Wang
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Gaussian Noise ◽  
Quotient Space ◽  
Signal To Noise Ratio ◽  
Phase Error ◽  
Phase Shifting ◽  
Signal To Noise ◽  
Partial Differential ◽  
Filtering Method

Traditional filtering methods only focused on improving the peak signal-to-noise ratio of the single fringe pattern, which ignore the filtering effect on phase extraction. Fringe phase-shifting field based fuzzy quotient space-oriented partial differential equations filtering method is proposed to reduce the phase error caused by Gaussian noise while filtering. First, the phase error distribution that is caused by Gaussian noise is analyzed. Furthermore, by introducing the fringe phase-shifting field and the theory of fuzzy quotient space, the modified filtering direction can be adaptively obtained, which transforms the traditional single image filtering into multi-image filtering. Finally, the improved fourth-order oriented partial differential equations with fidelity item filtering method is established. Experiments demonstrated that the proposed method achieves a higher signal-to-noise ratio and lower phase error caused by noise, while also retaining more edge details.

scholarly journals Comparative Performance of Catalytic Fenton Oxidation with Zero-Valent Iron (Fe(0)) in Comparison with Ferrous Sulphate for the Removal of Micropollutants

2019 ◽  
Vol 9 (11) ◽  
pp. 2181
Author(s):  
Anuradha Goswami ◽  
Jia-Qian Jiang
Keyword(s):  
Standard Deviation ◽  
Signal To Noise Ratio ◽  
Ferrous Sulphate ◽  
Fenton Oxidation ◽  
Zero Valent Iron ◽  
Fenton Process ◽  
Signal To Noise ◽  
Experimental Conditions ◽  
Comparative Performance ◽  
Sludge Production

This research aims to depict the comparative performance of micropollutants’ removal by FeSO4- and zero-valent iron (Fe(0))-catalytic Fenton oxidation and to explore the possibilities of minimising the sludge production from the process. The emerging micropollutants used for the study were gabapentin, sulfamethoxazole, diuron, terbutryn and terbuthylazine. The Taguchi method, which evaluates the signal-to-noise ratio instead of the standard deviation, was used to develop robust experimental conditions. Though both FeSO4- and Fe(0)-catalytic Fenton oxidation were able to completely degrade the stated micropollutants, the Fe(0)-catalytic Fenton process delivered better removal of dissolved organic carbon (DOC; 70%) than FeSO4 catalytic Fenton oxidation (45%). Fe(0)-catalytic Fenton oxidation facilitated heterogeneous treatment functions, which eliminated toxicity from contaminated solution and there was no recognisable sludge production.

Static Thresholded Pulse Coupled Neural Networks in Contourlet Domain — A New Framework for Medical Image Denoising

2020 ◽  
Vol 20 (03) ◽  
pp. 2050025
Author(s):  
S. Shajun Nisha ◽  
S. P. Raja ◽  
A. Kasthuri
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Signal To Noise ◽  
Coupled Neural Networks ◽  
Noise Ratio ◽  
Pulse Coupled Neural Networks ◽  
Image Quality Index

Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.

Digital Modulation Recognition in Different Environments

2014 ◽  
Vol 1049-1050 ◽  
pp. 2084-2087 ◽  
Author(s):  
Rong Li
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Automatic Recognition ◽  
Modulation Recognition ◽  
Signal To Noise ◽  
Digital Modulation ◽  
Multipath Interference ◽  
Modulation Signal ◽  
Simulation Results ◽  
Recognition Success

For the using of multi-modulation, the precondition of receiving and demodulating signal is to determine the type of the modulation, so automatic recognition of modulation signal has significant influence on the analysis of the signals. In this paper, digital modulation recognition is studied respectively in different environment of White Gaussian Noise (WGN), stationary interference and multipath interference. The simulation results show that the recognition success rate is the highest in stationary interference environment and the lowest in multipath interference environment with the same signal to noise ratio (SNR).

Subspace-based estimation of time of arrival and Doppler shift for a signal of known waveform

2009 ◽  
Vol 1 (3) ◽  
pp. 209-214
Author(s):  
V.V. Latyshev
Keyword(s):  
Doppler Shift ◽  
Signal To Noise Ratio ◽  
Time Of Arrival ◽  
Computationally Efficient ◽  
Signal To Noise ◽  
Sufficient Statistics ◽  
Ml Estimation ◽  
Generalized Eigenvectors ◽  
Low Dimensional ◽  
Estimation Of Time

The subspace-based technique is used for the estimation of the time of arrival and Doppler shift of a signal of known waveform. The tool to find required subspaces is a special orthogonal decomposition of received data. It allows one to concentrate Fisher information on the desired parameter in just a few of the first terms of the decomposition. This approach offers a low-dimensional vector of sufficient statistics. It leads to computationally efficient Bayesian estimation. Besides, it results in expansion of the signal-to-noise ratio (SNR) range for effective maximum likelihood (ML) estimation. Finally, we can obtain independent time arrival and Doppler shift estimations based on generalized eigenvectors.

scholarly journals Evaluasi Modulasi MFSK untuk Transmisi Data Melalui Kanal Suara GSM

Jurnal INKOM ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 37
Author(s):  
Rika Sustika ◽  
Oka Mahendra
Keyword(s):  
Mobile Communication ◽  
Bit Error Rate ◽  
Error Rate ◽  
Gaussian Noise ◽  
Linear Prediction ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Signal To Noise ◽  
Global System ◽  
Shift Keying

Pada tulisan ini, dievaluasi performansi skema modulasi MFSK (M-ary Frequency Shift Keying) untuk aplikasi pengiriman data melalui kanal suara GSM (Global System for Mobile Communication). Parameter yang dievaluasi berupa kesalahan bit trasmisi yang dinyatakan dengan laju kesalahan bit atau bit error rate (BER). Evaluasi ini dilakukan untuk menentukan besarnya orde M yang akan dipilih pada aplikasi pengiriman data digital melalui kanal suara GSM. Pada proses simulasi, data digital dikodekan menjadi simbol-simbol lalu dimodulasi menggunakan modulator MFSK menjadi data menyerupai pembicaraan (suara). Suara yang dihasilkan dikodekan dengan algoritma CELP (Code Excited Linear Prediction), kemudian dikirimkan melalui udara yang dimodelkan sebagai kanal AWGN (Additive White Gaussian Noise). Di sisi penerima, sinyal terima yang menyerupai suara ini didemodulasi dan dikonversi kembali menjadi data digital. Dari simulasi menggunakan Eb/No (signal to noise ratio) sebesar 6 dB, diperoleh laju bit 2,5 kbps dengan BER 2,01 x 10-3 untuk M=4, 2,22 x 10-3 untuk M=8, dan 1,87 x 10-3 untuk M=16.

The “Bi-drifting” subpulses of PSR J0815+0939 observed with the Five-hundred-meter Aperture Spherical radio Telescope

2021 ◽  
Author(s):  
Lunhua Shang ◽  
Juntao Bai ◽  
Shijun Dang ◽  
Qijun Zhi
Keyword(s):  
Radio Telescope ◽  
Signal To Noise Ratio ◽  
Observation Data ◽  
Emission Model ◽  
Fluctuation Spectrum ◽  
Energy Distributions ◽  
Pulsar Emission ◽  
Emission Component ◽  
Peak Intensity ◽  
The Mean

Abstract We report the “Bi-drifting” subpulses observed in PSR J0815+0939 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The observation at band from 1050-1450MHz is evenly divided into two bands, i.e., the bands at center frequencies 1150MHz and 1350 MHz. The mean pulse profiles and the “Bi-drifting” subpulses at this two bands are investigated. It is found that the pulse profiles at this two frequencies show four emission components, and the peak separations between four emission components decrease with the increase of frequency. In addition, the ratio of peak intensity of each component to the intensity of component IV at 1150MHz is larger than that at 1350 MHz. We carry out an analysis of the longitude-resolved fluctuation spectrum and two-dimensional fluctuation spectrum for each emission component, and find that the P3 of components I, II and III are about 10.56, 10.57 and 10.59 s at 1150MHz and 1350 MHz. However, the reliable measurements of P3 of component IV and P2 for these four components were not obtained due to the low signal-to-noise ratio of observation data. The pulse energy distributions at frequencies 1150 and 1350MHz are presented, and it is found that no nulling phenomenon have been found in this pulsar. With our observation from the FAST, the “Bi-drifting” subpulse phenomenon of PSR J0815+0939 is expanded from 400MHz to 1350 MHz, which is helpful for the relevant researchers to test and constrain the pulsar emission model, especially the model of “Bi-drifting” subpulse.

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