What is semi random telegraph signal process?

What is semi random telegraph signal process?

Define semi random telegraph signal process and random telegraph signal process and Prove also that the former is evolutionary and later is WSS. Sol : If {N(t) } is a poisson process and X(t) = (-1)N(t), then {X(t)} is called a semi random telegraph signal process.

What is random Telegraph signal?

In very small electronic devices the alternate capture and emission of carriers at an individual defect site generates discrete switching in the device resistance—referred to as a random telegraph signal (RTS).

What is telegraph noise?

INTRODUCTION. Random telegraph noise (RTN) is a phenomenon in which MOSFET drain current (ID) exhibits random discrete fluctuations or switching events as a function of time [1-3]. These fluctuations have been shown to be significant in highly scaled devices in which the channel length and width are reduced [3, 4].

Is Poisson process is a WSS process?

If a process is wss then its mean, variance, autocorrelation function and other first and second order statistical measures are independent of time. We have seen that a Poisson random process has mean µ(t) = λt, so it is not stationary in any sense. wss process.

When can a random process is said to be an ergodic process?

A random process is said to be ergodic if the time averages of the process tend to the appropriate ensemble averages. This definition implies that with probability 1, any ensemble average of {X(t)} can be determined from a single sample function of {X(t)}.

What causes flicker noise?

Flicker noise can be expressed in the form [15]. Flicker noise is believed to be caused by charge carriers that are randomly trapped and released between the interfaces of two materials. This phenomenon typically occurs in semiconductors that are used in instrumentation amplifiers to record electrical signals.

Is white noise a WSS?

So, yes, any process that is called “white” is inherently WSS.

Is white noise always Gaussian?

Noise having a continuous distribution, such as a normal distribution, can of course be white. It is often incorrectly assumed that Gaussian noise (i.e., noise with a Gaussian amplitude distribution – see normal distribution) necessarily refers to white noise, yet neither property implies the other.

Why do we need ergodicity?

The idea behind ergodicity is that, while collecting more and more observations, we keep learning something new about the process. In other words, if I pick two random variables of the process which are sufficiently ‘far apart’, their distributions should be independent among each others.