TRAVELING SIGNAL‐TO‐NOISE RATIO AND SIGNAL POWER ESTIMATES

Undesirable seismic noise of a nondeterministic type must be destroyed by making use of its statistical properties. Averaging of one sort or another provides methods for performing this noise removal. Our purpose here is to present a method for direct estimation of signal strength versus seismogram time, with stepout as a parameter. After describing the method and its expected behavior to some extent, we illustrate its application to a set of three noisy records.

Download Full-text

Analisis Kualitas Sinyal Jaringan WLAN Terhadap Layout Ruangan di Politeknik Negeri Lhokseumawe

Jurnal Litek : Jurnal Listrik Telekomunikasi Elektronika ◽

10.30811/litek.v16i1.1464 ◽

2019 ◽

Vol 16 (1) ◽

pp. 7

Author(s):

Aida Wulandari ◽

Yassir Yassir ◽

Hanafi Hanafi

Keyword(s):

Signal To Noise Ratio ◽

Signal Strength ◽

Signal To Noise ◽

Noise Ratio

Pemanfaatan Wi-Fi dalam sistem komunikasi data berbasis wireless, menjadi pilihan oleh banyak pengguna karena keunggulan mobilitasnya. Dalam sistem komunikasi ini, kualitas signal strength dan SNR menjadi sangat penting dalam mencapai layanan sistem komunikasi yang handal. Kehandalan tersebut dapat diketahui dengan melakukan uji kualitas signal strength dan SNR yang dapat diberikan oleh sebuah perangkat Wi-FI melalui pengukuran. Pengukuran kualitas signal strength dan SNR dilaksanakan pada tiga model ruangan berbeda. Ruang pertama adalah ruang indoor terbuka bertempat di auditorium, ruang kedua adalah ruang semi indoor bertempat di ruang perpustakaan, dan ruang ketiga adalah ruang indoor tertutup bertempat di Gedung 3 Jurusan Teknik Elektro. Sistem menggunakan Wi-Fi yang beroperasi pada frekuensi kerja 2,4 GHz. Pengumpulan data dilakukan pada beberapa titik dalam ruangan dengan memvariasikan jarak antara pemancar dan penerima. Nilai signal strenght pada ruang indoor terbuka tertinggi diperoleh -49 dBm dan terendah -62 dBm, nilai signal strength pada ruang semi indoor tertinggi -51,6 dBm dan terendah 91,2 dBm, dan nilai signal strength pada ruang indoor tertutup -70,4 dBm dan terendah -85 dBm. Nilai SNR tertinggi pada ruang indoor terbuka diperoleh 48 dB dan terendah 38,6 dB, nilai SNR tertinggi pada ruang semi indoor diperoleh 47,2 dB dan terendah 20 dB, dan nilai SNR tertinggi pada ruang indoor tertutup diperoleh 31 dB dan terendah 21,2 dB.Kata-kata kunci: Wi-Fi, Signal Strength, Signal To Noise Ratio, pathloss, frekuensi, WLAN, hardware, sofware

Download Full-text

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

International Journal of Image and Graphics ◽

10.1142/s0219467820500254 ◽

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.

Download Full-text

Noise Removal from Brain MRI Images Using Adaptive Bayesian Shrinkage

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8446 ◽

2020 ◽

Vol 17 (4) ◽

pp. 1818-1825

Author(s):

S. Josephine ◽

S. Murugan

Keyword(s):

Signal To Noise Ratio ◽

Brain Mri ◽

Noise Removal ◽

Wavelet Thresholding ◽

Signal To Noise ◽

Pixel Intensity ◽

Noisy Pixel ◽

Shrinkage Method ◽

Bayesian Shrinkage ◽

Noise Ratio

In MR machine, surface coils, especially phased-arrays are used extensively for acquiring MR images with high spatial resolution. The signal intensities on images acquired using these coils have a non-uniform map due to coil sensitivity profile. Although these smooth intensity variations have little impact on visual diagnosis, they become critical issues when quantitative information is needed from the images. Sometimes, medical images are captured by low signal to noise ratio (SNR). The low SNR makes it difficult to detect anatomical structures because tissue characterization fails on those images. Hence, denoising are essential processes before further processing or analysis will be conducted. They found that the noise in MR image is of Rician distribution. Hence, general filters cannot be used to remove these types of noises. The linear spatial filtering technique blurs the object boundaries and degrades the sharp details. The existing works proved that Wavelet based works eliminates the noise coefficient that called wavelet thresholding. Wavelet thresholding estimates the noise level from high frequency content and estimates the threshold value by comparing the estimated noisy wavelet coefficient with other wavelet coefficients and eliminate the noisy pixel intensity value. Bayesian Shrinkage rule is one of the widely used methods. It uses for Gaussian type of noise, the proposed method introduced some adaptive technique in Bayesian Shrinkage method to remove Rician type of noises from MRI images. The results were verified using quantitative parameters such as Peak Signal to Noise Ratio (PSNR). The proposed Adaptive Bayesian Shrinkage Method (ABSM) outperformed existing methods.

Download Full-text

Coherent Power Measurements with a Compact Airborne Ka-Band Precipitation Radar

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-17-0058.1 ◽

2018 ◽

Vol 35 (1) ◽

pp. 3-20 ◽

Cited By ~ 3

Author(s):

Andrew L. Pazmany ◽

Samuel J. Haimov

Keyword(s):

Signal To Noise Ratio ◽

Power Estimation ◽

Noise Power ◽

Signal Power ◽

Signal To Noise ◽

Precipitation Radar ◽

Noise Component ◽

Ka Band ◽

Noise Ratio ◽

Conventional Signal

AbstractCoherent power is an alternative to the conventional noise-subtracted power technique for measuring weather radar signal power. The inherent noise-canceling feature of coherent power eliminates the need for estimating and subtracting the noise component, which is required when performing conventional signal power estimation at low signal-to-noise ratio. The coherent power technique is particularly useful when averaging a high number of samples to improve sensitivity to weak signals. In such cases, the signal power is small compared to the noise power and the required accuracy of the estimated noise power may be difficult to achieve. This paper compares conventional signal power estimation with the coherent power measurement technique by investigating bias, standard deviation, and probability of false alarm and detection rates as a function of signal-to-noise ratio and threshold level. This comparison is performed using analytical expressions, numerical simulations, and analysis of cloud and precipitation data collected with the airborne solid-state Ka-band precipitation radar (KPR) operated by the University of Wyoming.

Download Full-text

Development of Stand Alone Application Tool for Processing and Quality Measurement of Weld Imperfection Image Captured by μ-Focused Digital Radiography Using MATLAB- Based Graphical User Interface

The Journal of Engineering Research [TJER] ◽

10.24200/tjer.vol9iss2pp64-79 ◽

2012 ◽

Vol 9 (2) ◽

pp. 64 ◽

Cited By ~ 2

Author(s):

PZ Nadila ◽

YHP Manurung ◽

SA Halim ◽

SK Abas ◽

G Tham ◽

...

Keyword(s):

Image Processing ◽

User Interface ◽

Image Quality ◽

Digital Radiography ◽

Graphical User Interface ◽

Signal To Noise Ratio ◽

Quality Measurement ◽

Noise Removal ◽

Signal To Noise ◽

Noise Ratio

Digital radiography incresingly is being applied in the fabrication industry. Compared to film- based radiography, digitally radiographed images can be acquired with less time and fewer exposures. However, noises can simply occur on the digital image resulting in a low-quality result. Due to this and the system’s complexity, parameters’ sensitivity, and environmental effects, the results can be difficult to interpret, even for a radiographer. Therefore, the need of an application tool to improve and evaluate the image is becoming urgent. In this research, a user-friendly tool for image processing and image quality measurement was developed. The resulting tool contains important components needed by radiograph inspectors in analyzing defects and recording the results. This tool was written by using image processing and the graphical user interface development environment and compiler (GUIDE) toolbox available in Matrix Laboratory (MATLAB) R2008a. In image processing methods, contrast adjustment, and noise removal, edge detection was applied. In image quality measurement methods, mean square error (MSE), peak signal-to-noise ratio (PSNR), modulation transfer function (MTF), normalized signal-to-noise ratio (SNRnorm), sensitivity and unsharpness were used to measure the image quality. The graphical user interface (GUI) wass then compiled to build a Windows, stand-alone application that enables this tool to be executed independently without the installation of MATLAB.

Download Full-text

A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images

Journal of Intelligent Systems ◽

10.1515/jisys-2017-0402 ◽

2018 ◽

Vol 29 (1) ◽

pp. 189-201 ◽

Cited By ~ 9

Author(s):

Sima Sahu ◽

Harsh Vikram Singh ◽

Basant Kumar ◽

Amit Kumar Singh

Keyword(s):

Magnetic Resonance ◽

Bayesian Approach ◽

Gaussian Noise ◽

Signal To Noise Ratio ◽

Noise Removal ◽

White Gaussian Noise ◽

Error Estimator ◽

Wavelet Coefficients ◽

Signal To Noise ◽

Noise Ratio

Abstract A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging (MRI). In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, signal variances, and noise variances of the distribution. The minimum mean square error estimator is used for estimating the true wavelet coefficients. The proposed method is simulated on MRI. Performance and image quality parameters show that the proposed method has the capability to reduce the noise more effectively than other state-of-the-art methods. The proposed method provides 8.83%, 2.02%, 6.61%, and 30.74% improvement in peak signal-to-noise ratio, structure similarity index, Pratt’s figure of merit, and Bhattacharyya coefficient, respectively, over existing well-accepted methods. The effectiveness of the proposed method is evaluated by using the mean squared difference (MSD) parameter. MSD shows the degree of dissimilarity and is 0.000324 for the proposed method, which is less than that of the other existing methods and proves the effectiveness of the proposed method. Experimental results show that the proposed method is capable of achieving better signal-to-noise ratio performance than other tested de-noising methods.

Download Full-text

Design of Enhanced Rotation Locked Loop for Roll Angle Estimation of Rotating Vehicle in a Weak GPS Signal Environment

Sensors ◽

10.3390/s19010080 ◽

2018 ◽

Vol 19 (1) ◽

pp. 80

Author(s):

Hun Im ◽

Deok Lim ◽

Sang Lee

Keyword(s):

Computer Simulations ◽

Signal To Noise Ratio ◽

Signal Strength ◽

Roll Angle ◽

Weak Signal ◽

Signal To Noise ◽

Angle Estimation ◽

Signal Environment ◽

Noise Ratio

In order to estimate the roll angle of a rotating vehicle, an enhanced rotation locked loop (RLL) algorithm is proposed in this paper. The RLL algorithm estimates the roll angle by using the property that the power of the GPS signal measured at the receiver of a rotating vehicle changes periodically. However, in case the received GPS power is decreased, the performance of the conventional RLL algorithm degrades, or it cannot estimate the roll angle anymore, therefore, for operating the RLL algorithm in a weak signal environment, this paper designs a method to increase the signal-to-noise ratio (SNR) by overlapping multiple GPS signals’ correlator outputs and a method to compensate the decreased response of a rotation discriminator at low-signal strength. Through computer simulations, the performance of the proposed algorithm is verified and it is shown that the roll angle can be estimated stably even at a weak signal environment down to 29 dB–Hz of C/N0.

Download Full-text

VHF signal power suppression in stratiform and convective precipitation

Annales Geophysicae ◽

10.5194/angeo-24-23-2006 ◽

2006 ◽

Vol 24 (1) ◽

pp. 23-35 ◽

Cited By ~ 3

Author(s):

A. J. McDonald ◽

K. P. Monahan ◽

D. A. Hooper ◽

C. Gaffard

Keyword(s):

Signal To Noise Ratio ◽

Convective Precipitation ◽

Small Scale ◽

Signal Power ◽

Signal To Noise ◽

Bright Band ◽

Signal Suppression ◽

Stratiform Precipitation ◽

Noise Ratio

Abstract. Previous studies have indicated that VHF clear-air radar return strengths are reduced during periods of precipitation. This study aims to examine whether the type of precipitation, stratiform and convective precipitation types are identified, has any impact on the relationships previously observed and to examine the possible mechanisms which produce this phenomenon. This study uses a combination of UHF and VHF wind-profiler data to define periods associated with stratiform and convective precipitation. This identification is achieved using an algorithm which examines the range squared corrected signal to noise ratio of the UHF returns for a bright band signature for stratiform precipitation. Regions associated with convective rainfall have been defined by identifying regions of enhanced range corrected signal to noise ratio that do not display a bright band structure and that are relatively uniform until a region above the melting layer. This study uses a total of 68 days, which incorporated significant periods of surface rainfall, between 31 August 2000 and 28 February 2002 inclusive from Aberystwyth (52.4° N, 4.1° W). Examination suggests that both precipitation types produce similar magnitude reductions in VHF signal power on average. However, the frequency of occurrence of statistically significant reductions in VHF signal power are very different. In the altitude range 2-4 km stratiform precipitation is related to VHF signal suppression approximately 50% of the time while in convective precipitation suppression is observed only 27% of the time. This statistical result suggests that evaporation, which occurs more often in stratiform precipitation, is important in reducing the small-scale irregularities in humidity and thereby the radio refractive index. A detailed case study presented also suggests that evaporation reducing small-scale irregularities in humidity may contribute to the observed VHF signal suppression.

Download Full-text