**Operations in Continuous Time Signals:**

### Periodic & Non-Periodic Signals:

- A signal is a periodic signal if it completes a pattern within a measurable time frame, called a period and repeats that pattern over identical subsequent periods.
- The
**period**is the smallest value of*T*satisfying*g(t + T) = g(t)*for all*t*. The period is defined so because if*g(t + T) = g(t)*for all*t*, it can be verified that*g(t + T') = g(t)*for all*t*where*T' = 2T, 3T, 4T, ...*In essence, it's the smallest amount of time it takes for the function to repeat itself. If the period of a function is finite, the function is called "periodic". - Functions that never repeat themselves have an infinite period, and are known as "aperiodic functions".

### Even & Odd Signals:

A function even function if it is symmetric about the y-axis. While, A signal is odd if it is inversely symmetrical about the *y*-axis.

Even Signal, f(x) = f(-x)

Odd Signal, f(x) = - f(-x)

**Note:** Some functions are neither even nor odd. These functions can be written as a sum of even and odd functions. A function *f(x)* can be expressed in terms of sum of an odd function and an even function.

**Invertibility and Inverse Systems:**

A system is invertible if distinct inputs results distinct outputs. As shown in the figure for the continuous-time case, if a system is invertible, then an inverse system exists that, when cascaded with the original system, results an output w(t) equal to the input x(t) to the first system.

An example of an invertible continuous-time system is **y(t) = 2x(t)**,

for which the inverse system is **w(t) = 1/2 y(t)**

**Causal System: **

A system is causal if the output depends only on the input at the present time and in the past. Such systems are often referred as non anticipative, as the system output does not anticipate future values of the input. Similarly, if two inputs to a causal system are identical up to some point in time t_{o} or n_{o} the corresponding outputs must also be equal up to this same time.

**y _{1}(t) = 2x(t) + x(t-1) + [x(t)]^{2 }⇒ Causal Signal **

**y _{1}(t) = 2x(t) + x(t-1) + [x(t+2)] ⇒ Non-Causal Signal **

**Homogeneity (Scaling):**

A system is said to be homogeneous if, for any input signal X(t), i.e. When the input signal is scaled, the output signal is scaled by the same factor.

** Time-Shifting / Time Reversal / ****Time Scaling **

Time Shifting can be understood as shifting the signal in time. When a constant is added to the time, we obtain the advanced signal, & when we decrease the time, we get the delayed signal.

### Time Scaling:

Due to the scaling in time the output Signal may shrink or stretch it depends on the numerical value of scaling factor.

### Time Inversion:

Time Inversion referred as flipping the signal about the y-axis.

**The Correlation Functions**

Correlation is a mathematical operation that is similar to convolution. As convolution, in correlation two signals are used to produce a third signal. The third signal is referred to as the cross-correlation of the two input signals. When a signal is correlated with itself, the resultant signal is called autocorrelation.

There are three basic definitions to define correlation function

**(a) For an infinite duration waveform:-**

** **

It can be considered as a “power” based definition.

**(b) For the finite duration waveform**:- If the waveform exists only in the interval t_{1} ≤t ≤ t_{2.}

It cab be considered as an “energy” based definition.

**(c) For the periodic waveform:-** f(t) is periodic with period T then.

for an arbitrary t_{0}, it again can be considered as a “power” based definition.

**Example**:- Obtain the auto-correlation function of the square pulse which have amplitude a and duration T as shown in figure below.

The wave form has a finite duration, and the auto-correlation function is

The auto-correlation function is developed graphically below

**Properties of the Auto-correlation Function**

- The auto-correlation functions φff (τ ) and ρff (τ ) are even functions, that is

φ_{ff} (−τ ) = φ_{ff} (τ ), and ρ_{ff} (−τ ) = ρ_{ff} (τ )

- A maximum value of ρ
_{ff}(τ ) (or φ_{ff}(τ ) occurs at delay τ = 0,

|ρ_{ff}(τ )| ≤ ρ_{ff} (0), and |φ_{ff} (τ )| ≤ φ_{ff }(0)

and we note that is the “energy” of the waveform.

Similarly

- ρ
_{ff}(τ) contains no phase information, and is independent of the time origin. - If f(t) is periodic with period T, φ
_{ff}(τ) is also periodic with period T. - If f(t) has zero mean (µ = 0), and f(t) is non-periodic, lim ρ
_{ff}(τ ) = 0.

** Note on the relative “widths” of the Auto-correlation and Power/Energy Spectra**

- As in the case of Fourier analysis of waveforms, there is a general reciprocal relationship between the width of a signal's spectrum and the width of its auto-correlation function. A narrow autocorrelation function generally implies a “broad” spectrum

and a “broad” autocorrelation function generally implies a narrow-band waveform.

From the limit, if φ_{ff} (τ)= δ(τ), then Φ_{ff} (j Ω)=1, and the spectrum is said to be “white”.

**The Cross-Correlation Function**

The cross-correlation is referred as a measure of self-similarity between two waveforms f(t) and g(t). As in the case of the auto-correlation functions we need two definitions:

in the case of infinite duration waveforms, and

for finite duration waveforms.

**Example:-** Find the cross-correlation function between the two functions as shown in figure.

It is clear in figure that g(t) is a delayed version of f(t). The cross-correlation is

where the peak occurs at τ = T_{2}−T_{1} (the delay between the two signals).

**Properties of the Cross-Correlation Function**

- φ
_{fg}(τ ) = φ_{gf}(−τ), and the cross-correlation function is not necessarily an even function. - If φ
_{fg}(τ ) = 0 for all τ , then f(t) and g(t) are said to be uncorrelated. - If g(t) = a.f(t−T), where a is a constant, that is g(t) is a scaled and delayed version of f(t), then φ
_{ff}(τ) will have its maximum value at τ = T. - Cross-correlation is often used in optimal estimation of delay, such as in echolocation (radar, sonar), and in GPS receivers.

**The Cross-Power/Energy Spectrum**

We define the cross-power/energy density spectra as the Fourier transforms of the cross-correlation functions:

R_{fg }(jΩ) = (F−jΩ).G(jΩ)

Note that although R_{ff} (jΩ) is real and even because ρ_{ff}(τ) is real and even, this is not the case with the cross-power/energy spectra,Φ_{fg }(jΩ) and R_{fg}(jΩ),and they are in general complex.

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