- Statistics - Discussion
- Z table
- Weak Law of Large Numbers
- Venn Diagram
- Variance
- Type I & II Error
- Trimmed Mean
- Transformations
- Ti 83 Exponential Regression
- T-Distribution Table
- Sum of Square
- Student T Test
- Stratified sampling
- Stem and Leaf Plot
- Statistics Notation
- Statistics Formulas
- Statistical Significance
- Standard normal table
- Standard Error ( SE )
- Standard Deviation
- Skewness
- Simple random sampling
- Signal to Noise Ratio
- Shannon Wiener Diversity Index
- Scatterplots
- Sampling methods
- Sample planning
- Root Mean Square
- Residual sum of squares
- Residual analysis
- Required Sample Size
- Reliability Coefficient
- Relative Standard Deviation
- Regression Intercept Confidence Interval
- Rayleigh Distribution
- Range Rule of Thumb
- Quartile Deviation
- Qualitative Data Vs Quantitative Data
- Quadratic Regression Equation
- Process Sigma
- Process Capability (Cp) & Process Performance (Pp)
- Probability Density Function
- Probability Bayes Theorem
- Probability Multiplecative Theorem
- Probability Additive Theorem
- Probability
- Power Calculator
- Pooled Variance (r)
- Poisson Distribution
- Pie Chart
- Permutation with Replacement
- Permutation
- Outlier Function
- One Proportion Z Test
- Odd and Even Permutation
- Normal Distribution
- Negative Binomial Distribution
- Multinomial Distribution
- Means Difference
- Mean Deviation
- Mcnemar Test
- Logistic Regression
- Log Gamma Distribution
- Linear regression
- Laplace Distribution
- Kurtosis
- Kolmogorov Smirnov Test
- Inverse Gamma Distribution
- Interval Estimation
- Individual Series Arithmetic Mode
- Individual Series Arithmetic Median
- Individual Series Arithmetic Mean
- Hypothesis testing
- Hypergeometric Distribution
- Histograms
- Harmonic Resonance Frequency
- Harmonic Number
- Harmonic Mean
- Gumbel Distribution
- Grand Mean
- Goodness of Fit
- Geometric Probability Distribution
- Geometric Mean
- Gamma Distribution
- Frequency Distribution
- Factorial
- F Test Table
- F distribution
- Exponential distribution
- Dot Plot
- Discrete Series Arithmetic Mode
- Discrete Series Arithmetic Median
- Discrete Series Arithmetic Mean
- Deciles Statistics
- Data Patterns
- Data collection - Case Study Method
- Data collection - Observation
- Data collection - Questionaire Designing
- Data collection
- Cumulative Poisson Distribution
- Cumulative plots
- Correlation Co-efficient
- Co-efficient of Variation
- Cumulative Frequency
- Continuous Series Arithmetic Mode
- Continuous Series Arithmetic Median
- Continuous Series Arithmetic Mean
- Continuous Uniform Distribution
- Comparing plots
- Combination with replacement
- Combination
- Cluster sampling
- Circular Permutation
- Chi Squared table
- Chi-squared Distribution
- Central limit theorem
- Boxplots
- Black-Scholes model
- Binomial Distribution
- Beta Distribution
- Best Point Estimation
- Bar Graph
- Arithmetic Range
- Arithmetic Mode
- Arithmetic Median
- Arithmetic Mean
- Analysis of Variance
- Adjusted R-Squared
- Home
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
Statistics - Signal to Noise Ratio
Sign to-commotion proportion (contracted SNR) is a measure utipzed as a part of science and designing that analyzes the level of a coveted sign to the level of foundation clamor. It is characterized as the proportion of sign energy to the clamor power, regularly communicated in decibels. A proportion higher than 1:1 (more prominent than 0 dB) shows more flag than clamor. While SNR is regularly cited for electrical signs, it can be connected to any type of sign, (for example, isotope levels in an ice center or biochemical motioning between cells).
Signal-to-noise ratio is defined as the ratio of the power of a signal (meaningful information) and the power of background noise (unwanted signal):
${SNR = frac{P_{signal}}{P_{noise}}}$If the variance of the signal and noise are known, and the signal is zero:
${SNR = frac{sigma^2_{signal}}{sigma^2_{noise}}}$If the signal and the noise are measured across the same impedance, then the SNR can be obtained by calculating the square of the ampptude ratio:
${SNR = frac{P_{signal}}{P_{noise}} = {(frac{A_{signal}}{A_{noise}})}^2} $Where A is root mean square (RMS) ampptude (for example, RMS voltage).
Decibels
Because many signals have a very wide dynamic range, signals are often expressed using the logarithmic decibel scale. Based upon the definition of decibel, signal and noise may be expressed in decibels (dB) as
${P_{signal,dB} = 10log_{10}(P_{signal})} $and
${P_{noise,dB} = 10log_{10}(P_{noise})} $In a similar manner, SNR may be expressed in decibels as
${SNR_{dB} = 10log_{10}(SNR)} $Using the definition of SNR
${SNR_{dB} = 10log_{10}(frac{P_{signal}}{P_{noise}})} $Using the quotient rule for logarithms
${10log_{10}(frac{P_{signal}}{P_{noise}}) = 10log_{10}(P_{signal}) - 10log_{10}(P_{noise})} $Substituting the definitions of SNR, signal, and noise in decibels into the above equation results in an important formula for calculating the signal to noise ratio in decibels, when the signal and noise are also in decibels:
${SNR_{dB} = P_{signal,dB} - P_{noise,dB}} $In the above formula, P is measured in units of power, such as Watts or mill watts, and signal-to-noise ratio is a pure number.
However, when the signal and noise are measured in Volts or Amperes, which are measures of ampptudes, they must be squared to be proportionate to power as shown below:
${SNR_{dB} = 10log_{10}[{(frac{A_{signal}}{A_{noise}})}^2] \[7pt] = 20log_{10}(frac{A_{signal}}{A_{noise}}) \[7pt] = A_{signal,dB} - A_{noise,dB}} $Example
Problem Statement:
Compute the SNR of a 2.5 kHz sinusoid sampled at 48 kHz. Add white noise with standard deviation 0.001. Set the random number generator to the default settings for reproducible results.
Solution:
${ F_i = 2500; F_s = 48e3; N = 1024; \[7pt] x = sin(2 imes pi imes frac{F_i}{F_s} imes (1:N)) + 0.001 imes randn(1,N); \[7pt] SNR = snr(x,Fs) \[7pt] SNR = 57.7103}$ Advertisements