Probability, Random Variables, and Random Processes: Theory and Signal Processing Applications

INDEX

μ-law quantizer

F-distribution

z-transform

z-plane

unit circle

and power spectral density (PSD)

bilateral

Cauchy residue theorem

inverse

mapping from s-domain

Parseval’s theorem

partial fraction expansion (PFE)

poles and zeros

region of convergence (ROC)

table of properties

table of transform pairs

unilateral

adaptive beamformer

adaptive filtering

algorithm iteration number

infinite-impulse response (IIR)

bias of equation-error

equation-error formulation

local minima

output-error formulation

least mean-square (LMS) algorithm

least-mean-fourth (LMF) algorithm

Newton’s method (NM)

perceptron

performance surface

contours of constant mean-square error (MSE)

stability bounds

steepest descent (SD)

step-size parameter

time constant

additive white Gaussian noise (AWGN) channel

and capacity

alphabet of outcomes

antenna array

angle of arrival (AOA)

calibrated

constant modulus (CM)

direction finding (DF)

direction vectors

source signals

uniform linear (ULA)

Apéry’s constant

arcsine distribution

atoms

autocorrelation

function

time average

matrix

eigenfunctions

eigenstructure

Hessian

properties

singular

autocovariance

function

matrix

autoregressive (AR) model

autoregressive moving-average (ARMA) model

axioms of probability

bandwidth

half-power (3-dB)

noise-equivalent (NE)

root-mean-square (RMS)

Bartlett window

basis functions

Basu’s theorem

Bayes’ formula (rule)

beamformer

adaptive

constant modulus (CM) array

multistage

output signal-to-noise ratio (SNR)

shift factors

signal canceller ...