scholarly journals Analysis of NMR Spectrometer Receiver Noise Figure

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Peter Andris ◽  
Earl F. Emery ◽  
Ivan Frollo
Keyword(s):  
Experimental Data ◽  
Noise Figure ◽  
Noise Signal ◽  
Power Calculation ◽  
Noise Power ◽  
Mean Square ◽  
Specific Task ◽  
Nuclear Magnetic Resonance Spectrometer ◽  
System Noise ◽  
Measurement And Evaluation

This article describes the measurement and evaluation of the system noise figure of a nuclear magnetic resonance spectrometer. A method was used which involved the console of the spectrometer, calibrated for real voltage (in volts), and used for noise signal digitisation and measurement. The resulting digitised signal was exported and processed, and the root mean square value calculated. This value was utilised for the noise power calculation, which was compared with the theoretical value and the noise figure calculated. The method presented enables analysis of the bandwidth of the noise. Many of the equations that are commonly used for signal processing have been derived for the specific task and require verification. We have verified the technique using a commercial console with two preamplifier pairs. The experimental data agree well with the theoretical values, confirming that the presented method is a valid, simple, and fast tool for the inspection of the spectrometer receiver.

Optimization of Signal Processing Applications Using Parameterized Error Models for Approximate Adders

2021 ◽  
Vol 20 (2) ◽  
pp. 1-25
Author(s):  
Celia Dharmaraj ◽  
Vinita Vasudevan ◽  
Nitin Chandrachoodan
Keyword(s):  
Signal Processing ◽  
Circuit Design ◽  
Computational Efficiency ◽  
Noise Power ◽  
Mean Square ◽  
Error Statistics ◽  
Error Models ◽  
Optimization Framework ◽  
Accuracy Constraint ◽  
Multi Level

Approximate circuit design has gained significance in recent years targeting error-tolerant applications. In the literature, there have been several attempts at optimizing the number of approximate bits of each approximate adder in a system for a given accuracy constraint. For computational efficiency, the error models used in these routines are simple expressions obtained using regression or by assuming inputs or the error is uniformly distributed. In this article, we first demonstrate that for many approximate adders, these assumptions lead to an inaccurate prediction of error statistics for multi-level circuits. We show that mean error and mean square error can be computed accurately if static probabilities of adders at all stages are taken into account. Therefore, in a system with a certain type of approximate adder, any optimization framework needs to take into account not just the functionality of the adder but also its position in the circuit, functionality of its parents, and the number of approximate bits in the parent blocks. We propose a method to derive parameterized error models for various types of approximate adders. We incorporate these models within an optimization framework and demonstrate that the noise power is computed accurately.

Larger-Eddy Simulation of Velocity Fluctuation in a Mixing T-Junction

2012 ◽  
Vol 152-154 ◽  
pp. 1313-1318
Author(s):  
Tao Lu ◽  
Su Mei Liu ◽  
Ping Wang ◽  
Wei Yyu Zhu
Keyword(s):  
Experimental Data ◽  
Velocity Fluctuation ◽  
Flow Model ◽  
Numerical Results ◽  
Mean Square ◽  
Main Duct ◽  
Velocity Fluctuations ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Les Model

Velocity fluctuations in a mixing T-junction were simulated in FLUENT using large-eddy simulation (LES) turbulent flow model with sub-grid scale (SGS) Smagorinsky–Lilly (SL) model. The normalized mean and root mean square velocities are used to describe the time-averaged velocities and the velocities fluctuation intensities. Comparison of the numerical results with experimental data shows that the LES model is valid for predicting the flow of mixing in a T-junction junction. The numerical results reveal the velocity distributions and fluctuations are basically symmetrical and the fluctuation at the upstream of the downstream of the main duct is stronger than that at the downstream of the downstream of the main duct.

scholarly journals Investigation of a parameter estimation method for contaminant transport in aquifers

2001 ◽  
Vol 3 (4) ◽  
pp. 203-213 ◽  
Author(s):  
Channa Rajanayaka ◽  
Don Kulasiri
Keyword(s):  
Experimental Data ◽  
Hydraulic Conductivity ◽  
Contaminant Transport ◽  
Dispersion Coefficient ◽  
Estimation Method ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
System Noise ◽  
Estimated Parameters ◽  
Comparison Of The Results

Real world groundwater aquifers are heterogeneous and system variables are not uniformly distributed across the aquifer. Therefore, in the modelling of the contaminant transport, we need to consider the uncertainty associated with the system. Unny presented a method to describe the system by stochastic differential equations and then to estimate the parameters by using the maximum likelihood approach. In this paper, this method was explored by using artificial and experimental data. First a set of data was used to explore the effect of system noise on estimated parameters. The experimental data was used to compare the estimated parameters with the calibrated results. Estimates obtained from artificial data show reasonable accuracy when the system noise is present. The accuracy of the estimates has an inverse relationship to the noise. Hydraulic conductivity estimates in a one-parameter situation give more accurate results than in a two-parameter situation. The effect of the noise on estimates of the longitudinal dispersion coefficient is less compared to the effect on hydraulic conductivity estimates. Comparison of the results of the experimental dataset shows that estimates of the longitudinal dispersion coefficient are similar to the aquifer calibrated results. However, hydraulic conductivity does not provide a similar level of accuracy. The main advantage of the estimation method presented here is its direct dependence on field observations in the presence of reasonably large noise levels.

scholarly journals Noise reduction in ECG Signals for Bio-telemetryb

2019 ◽  
Vol 9 (1) ◽  
pp. 505
Author(s):  
V. Jagan Naveen ◽  
K. Murali Krishna ◽  
K. Raja Rajeswari
Keyword(s):  
Reducing Power ◽  
Ecg Signal ◽  
Noise Power ◽  
Signal Power ◽  
Mean Square ◽  
Rls Algorithm ◽  
Biomedical Signal ◽  
Ecg Signals ◽  
Power Line Interference ◽  
Line Interference

<p><span lang="EN-US">In Biotelemetry, Biomedical signal such as ECG is extremely important in the diagnosis of patients in remote location and is recorded commonly with noise. Considered attention is required for analysis of ECG signal to find the patho-physiology and status of patient. In this paper, LMS and RLS algorithm are implemented on adaptive FIR filter for reducing power line interference (50Hz) and (AWGN) noise on ECG signals .The ECG signals are randomly chosen from MIT_BIH data base and de-noising using algorithms. The peaks and heart rate of the ECG signal are estimated. The measurements are taken in terms of Signal Power, Noise Power and   Mean Square Error.</span></p>

Design and performance analysis of live model of Bessel beamformer for adaptive array system

2014 ◽  
Vol 33 (4) ◽  
pp. 1434-1447 ◽  
Author(s):  
M. Yasin ◽  
Pervez Akhtar
Keyword(s):  
Experimental Data ◽  
Signal Processing ◽  
Base Station ◽  
Real System ◽  
Signal Recovery ◽  
Mean Square ◽  
Content Type ◽  
Live Model ◽  
Audio Data ◽  
Directive Gain

Purpose – The purpose of this paper is to design and analyze the performance of live model of Bessel beamformer for thorough comprehension of beamforming in adaptive environment and compared with live model of least mean square (LMS) in terms of gain and mean square error (MSE). It presents the principal elements of communication system. The performance of designed live model is tested for its efficiency in terms of signal recovery, directive gain by minimizing MSE using the “wavrecord” function to bring live audio data in WAV format into the MATLAB workspace. These adaptive techniques are illustrated by appropriate examples. Design/methodology/approach – The proposed algorithm framework relies on MATLAB software with the goal to obtain high efficiency in terms of signal recovery, directive gain by minimizing MSE using the “wavrecord” function to bring live audio data in WAV format. It is assumed that this audio signal is only the message or the baseband signal received by the computer. Here the authors consider computer (laptop) as a base station containing adaptive signal processing algorithm and source (mobile phone) as a desired user, so the experiment setup is designed for uplink application (user to base station) to differentiate between desired signal, multipath and interfering signals as well as to calculate their directions of arrival. Findings – The presented adaptive live model is reliable, robust and lead to a substantial reduction of MSE, signal recovery in comparison with the LMS technique. The paper contains experimental data. Obtained results are presented clearly and the conclusion comes directly from the presented experimental data. The paper shows that the presented method leads to superior results in comparison with the popular LMS method and can be used as a better alternative in many practical applications. Research limitations/implications – The adaptive processes described in the paper are still limited to simulation. It is because of the non-availability of real system for testing, therefore chosen research approach that is platform of MATLAB is opted for simulation. Therefore, researchers are encouraged to test the proposed algorithms on real system if possible. Practical implications – The paper contains experimental data. The paper's impact on the society is acceptable. These implications are consistent with the findings and the conclusions of the paper. However, there is a need to extend this paper to a next level by implementing the proposed algorithms in the real time environment using FPGA technology. Social implications – This research will improve the signal quality of wireless cellular system by increasing capacity and will reduce the total cost of the system so that cost toward subscribers be decreased. Originality/value – The live model presented in this paper is shown to provide better results. It is the original work and can provide scientific contribution to signal processing community.

Modeling glucose disposal in diabetic dogs fed mixed meals

1984 ◽  
Vol 246 (1) ◽  
pp. E52-E61
Author(s):  
Y. Yamasaki ◽  
J. Tiran ◽  
A. M. Albisser
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Insulin Infusion ◽  
Glucose Disposal ◽  
Mean Square ◽  
Mixed Meal ◽  
Peripheral Plasma ◽  
Intravenous Insulin ◽  
Hepatic Glucose ◽  
Diabetic Dogs

We have utilized a previously described mathematical model to study glucose disposal in fed, conscious, ambulatory, diabetic dogs. The model was applied to estimate the daily disposition of ingested glucose in the periphery, liver, and urine following a regular mixed meal containing 130 g of carbohydrate. Experimental data was obtained from 11 pancreatectomized animals. Both the portal and peripheral routes were used for intravenous insulin infusion and the daily profiles of peripheral plasma glucose and insulin concentrations measured. Total calories in mixed meals derived from carbohydrates (37%), fat (30%), and protein (30%). When judged according to the root-mean-square differences, agreement was excellent between model-predicted and experimentally observed glucose as well as insulin concentrations. This agreement occurred whether or not, in addition to basal insulin, meal insulin was also given. Using the model, we then predicted in detail the rates of glucose uptake in peripheral tissue, liver, and kidneys. With portally infused insulin resulting in diurnal glycemic normalization, the net daily hepatic glucose balance was physiological, being close to zero. Remarkably, with peripheral insulin infusions there was an unphysiological net negative hepatic glucose balance of 10 g/day.

scholarly journals Noise Power Spectrum Scene-Dependency in Simulated Image Capture Systems

2020 ◽  
Vol 2020 (9) ◽  
pp. 345-1-345-7
Author(s):  
Edward W. S. Fry ◽  
Sophie Triantaphillidou ◽  
Robin B. Jenkin ◽  
Ralph E. Jacobson ◽  
John R. Jarvis
Keyword(s):  
Power Spectrum ◽  
Transfer Functions ◽  
Noise Power ◽  
Noise Power Spectrum ◽  
Modulation Transfer ◽  
Simulated Image ◽  
Image Capture ◽  
System Noise ◽  
Relative Standard ◽  
Average System

The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply nonlinear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The sceneand- process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-processdependent Modulation Transfer Functions (SPD-MTF).

scholarly journals Specific residue: application of orthogonal contrasts when heteroscedasticity is present

2010 ◽  
Vol 67 (1) ◽  
pp. 117-125
Author(s):  
Maria Cristina Stolf Nogueira
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Analysis Of Variance ◽  
Residue Analysis ◽  
Sum Of Squares ◽  
Block Designs ◽  
Mean Square ◽  
Orthogonal Contrasts ◽  
Variance Homogeneity ◽  
Analyze Data

When experimental data are submitted to analysis of variance, the assumption of data homoscedasticity (variance homogeneity among treatments), associated to the adopted mathematical model must be satisfied. This verification is necessary to ensure the correct test for the analysis. In some cases, when data homoscedascity is not observed, errors may invalidate the analysis. An alternative to overcome this difficulty is the application of the specific residue analysis, which consists of the decomposition of the residual sum of squares in its components, in order to adequately test the correspondent orthogonal contrasts of interest between treatment means. Although the decomposition of the residual sum of squares is a seldom used procedure, it is useful for a better understanding of the residual mean square nature and to validate the tests to be applied. The objective of this review is to illustrate the specific residue application as a valid and adequate alternative to analyze data from experiments following completely randomized and randomized complete block designs in the presence of heteroscedasticity.

scholarly journals A Seven-Parameter BRDF Model with Double-Peak Characteristic Suitable for Sandy Soil

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Yu-Feng Yang ◽  
Wen-Shuai Li ◽  
Han-Zhi Zhang
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Sandy Soil ◽  
Incident Angle ◽  
Mean Square ◽  
Double Peak ◽  
Practical Project ◽  
Parameter Values ◽  
Mean Square Errors ◽  
Combined Data

A seven-parameter BRDF model with double-peak characteristic was proposed in this paper, which can fit double-peak data. The global genetic algorithm was used to model the BRDF experimental data of sandy soil, and the parameter values and relative mean square error of the seven-parameter BRDF model were obtained. The results proved the correctness of the model, and the relative mean square errors of this model are, respectively, 0.30%, 0.22%, 0.26%, and 0.25% corresponding to the incident angles of 15°, 30°, 45°, and 60°. Additionally, we also combined data from four incident angles to derive seven parameters, which do not depend on incident angle, and the overall error is 1.79%. Finally, in order to intuitively show the BRDF of sandy soil, 3D BRDF graph of sandy soil with different incident angles is, respectively, given. It will be of great significance in practical project applications.

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