scholarly journals Statistical Properties of SNR for Compressed Measurements

2016 ◽  
Vol 2016 ◽  
pp. 1-6
Author(s):  
Yulong Gao ◽  
Yanping Chen ◽  
Linxiao Su
Keyword(s):  
Numerical Simulation ◽  
Signal Processing ◽  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Statistical Properties ◽  
Cumulative Distribution ◽  
Statistical Characteristics ◽  
Communication Signal ◽  
The Mean ◽  
Simulation Results

Some basic statistical properties of the compressed measurements are investigated. It is well known that the statistical properties are a foundation for analyzing the performance of signal detection and the applications of compressed sensing in communication signal processing. Firstly, we discuss the statistical properties of the compressed signal, the compressed noise, and their corresponding energy. And then, the statistical characteristics of SNR of the compressed measurements are calculated, including the mean and the variance. Finally, probability density function and cumulative distribution function of SNR are derived for the cases of the Gamma distribution and the Gaussian distribution. Numerical simulation results demonstrate the correctness of the theoretical analysis.

scholarly journals Nondecreasing Lower Bound on the Poisson Cumulative Distribution Function for z Standard Deviations Above the Mean

2013 ◽  
Vol 50 (4) ◽  
pp. 909-917
Author(s):  
M. Bondareva
Keyword(s):  
Distribution Function ◽  
Lower Bound ◽  
Cumulative Distribution Function ◽  
Random Variable ◽  
Step Function ◽  
Cumulative Distribution ◽  
Control Parameters ◽  
Poisson Random Variable ◽  
The Mean ◽  
Standard Deviations

In this paper we discuss a nondecreasing lower bound for the Poisson cumulative distribution function (CDF) at z standard deviations above the mean λ, where z and λ are parameters. This is important because the normal distribution as an approximation for the Poisson CDF may overestimate or underestimate its value. A sharp nondecreasing lower bound in the form of a step function is constructed. As a corollary of the bound's properties, for a given percent α and parameter λ, the minimal z is obtained such that, for any Poisson random variable with the mean greater or equal to λ, its αth percentile is at most z standard deviations above its mean. For Poisson distributed control parameters, the corollary allows simple policies measuring performance in terms of standard deviations from a benchmark.

scholarly journals Detection of Giant pulses from pulsar PSR B0950+08

2012 ◽  
Vol 8 (S291) ◽  
pp. 502-504
Author(s):  
T. V. Smirnova
Keyword(s):  
Distribution Function ◽  
Flux Density ◽  
Power Law ◽  
Pulse Energy ◽  
Cumulative Distribution Function ◽  
Cumulative Distribution ◽  
Maximum Peak ◽  
Giant Pulses ◽  
Peak Flux ◽  
The Mean

AbstractWe investigated pulse intensities of PSR B0950+08 at 112 MHz at various longitudes (phases) and detected very strong pulses exceeding the amplitude of the mean profile by more than one hundred times. The maximum peak flux density of a recorded pulse is 15240 Jy, and the energy of this pulse exceeds the mean pulse energy by a factor of 153. The analysis shows that the cumulative distribution function (CDF) of pulse intensities at the longitudes of the main pulse is described by a piece-wise power law, with a slope changing from n=−1.25 ± 0.04 to n=−1.84 ± 0.07 at I≥600 Jy. The CDF for pulses at the longitudes of the precursor has a power law with n=−1.5 ± 0.1. Detected giant pulses from this pulsar have the same signature as giant pulses of other pulsars.

Nondecreasing Lower Bound on the Poisson Cumulative Distribution Function for z Standard Deviations Above the Mean

2013 ◽  
Vol 50 (04) ◽  
pp. 909-917
Author(s):  
M. Bondareva
Keyword(s):  
Distribution Function ◽  
Lower Bound ◽  
Cumulative Distribution Function ◽  
Random Variable ◽  
Step Function ◽  
Cumulative Distribution ◽  
Control Parameters ◽  
Poisson Random Variable ◽  
The Mean ◽  
Standard Deviations

In this paper we discuss a nondecreasing lower bound for the Poisson cumulative distribution function (CDF) at z standard deviations above the mean λ, where z and λ are parameters. This is important because the normal distribution as an approximation for the Poisson CDF may overestimate or underestimate its value. A sharp nondecreasing lower bound in the form of a step function is constructed. As a corollary of the bound's properties, for a given percent α and parameter λ, the minimal z is obtained such that, for any Poisson random variable with the mean greater or equal to λ, its αth percentile is at most z standard deviations above its mean. For Poisson distributed control parameters, the corollary allows simple policies measuring performance in terms of standard deviations from a benchmark.

Understanding Peak Average Power Ratio in VFFT-OFDM Systems

2015 ◽  
Vol 776 ◽  
pp. 419-424 ◽  
Author(s):  
N.M.A.E.D. Wirastuti
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Statistical Approach ◽  
Average Power ◽  
Cumulative Distribution ◽  
Power Ratio ◽  
Ofdm Systems ◽  
Ofdm System ◽  
Complementary Cumulative Distribution Function ◽  
Simulation Results

This paper describes an impairment commonly encountered in an OFDM system that must be considered in the design that is peak average power ratio (PAPR). In these studies, a statistical approach to analysing PAPR is suggested. The PAPR statistics of OFDM and VFFT-OFDM are studied by simulation of the statistical distribution of the quantity that is the Complementary Cumulative Distribution Function (CCDF) of the PAPR. The simulation results show that the simulated CCDF of PAPR, the 1% PAPR of OFDM is about 11.40 dB, whereas for VFFT-OFDM this rises to less than 3% of the time. Simulations show that by employing VFFT in OFDM system results in a 0.55 dB deterioration in the PAPR 1% of time.

scholarly journals Inverse Analogue of Ailamujia Distribution with Statistical Properties and Applications

2020 ◽  
pp. 36-46
Author(s):  
Ahmad Aijaz ◽  
Afaq Ahmad ◽  
Rajnee Tripathi
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Real Life ◽  
Likelihood Estimation ◽  
Moment Generating Function ◽  
Statistical Properties ◽  
Cumulative Distribution ◽  
Life Data ◽  
Real Life Data ◽  
Method Of Maximum Likelihood

The present paper deals with the inverse analogue of Ailamujia distribution (IAD). Several statistical properties of the newly developed distribution has been discussed such as moments, moment generating function, survival measures, order statistics, shanon entropy, mode and median .The behavior of probability density function (p.d.f) and cumulative distribution function (c.d.f) are illustrated through graphs. The parameter of the newly developed distribution has been estimated by the well known method of maximum likelihood estimation. The importance of the established distribution has been shown through two real life data.

scholarly journals The Probability of a Confidence Interval Based on Minimal Estimates of the Mean and the Standard Deviation

2013 ◽  
Vol 2013 ◽  
pp. 1-4
Author(s):  
Louis M. Houston
Keyword(s):  
Computer Simulation ◽  
Distribution Function ◽  
Standard Deviation ◽  
Confidence Interval ◽  
Cumulative Distribution Function ◽  
Cumulative Distribution ◽  
Sample Standard Deviation ◽  
The Mean

Using two measurements, we produce an estimate of the mean and the sample standard deviation. We construct a confidence interval with these parameters and compute the probability of the confidence interval by using the cumulative distribution function and averaging over the parameters. The probability is in the form of an integral that we compare to a computer simulation.

DISSONANCE — A MEASURE OF VARIABILITY FOR ORDINAL RANDOM VARIABLES

2001 ◽  
Vol 09 (01) ◽  
pp. 39-53 ◽  
Author(s):  
RONALD R. YAGER
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Probability Distributions ◽  
Random Variables ◽  
Cumulative Distribution ◽  
General Properties

We look at the issue of obtaining a variance like measure associated with probability distributions over ordinal sets. We call these dissonance measures. We specify some general properties desired in these dissonance measures. The centrality of the cumulative distribution function in formulating the concept of dissonance is pointed out. We introduce some specific examples of measures of dissonance.

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