SIGN

Question 1

signal

Answer 1

A signal is a function representing a physical quantity or variable. Typically it contains information about the behaviour or nature of the
phenomenon.

Question 2

signal processing

Answer 2

In signal processing we try to translate a system or a signal into
mathematics. The goal of signal processing is to replace complex signals with simpler ones we understand (decomposition).

Question 3

Continuous signal

Answer 3

• Signal x (t)
• t is a continuous variable

Question 4

Discrete signal

Answer 4

• Signal x[n]
• t is a discrete time variable

Question 5

Periodic

Answer 5

A continuous-time signal is said to be periodic with period T if there is a positive nonzero value of T

Question 6

System

Answer 6

A system is a mathematical model of a physical process that relates an input (or excitation) signal to an output (or response) signal.

Question 7

Impulse decomposition

Answer 7

Impulse decomposition provides a way to analyse signals one sample at a time.

Question 8

Impulse response

Answer 8

The impulse response is the signal that exits a system when a delta function (unit impulse) is the input.

Question 9

Impulse stappenplan

Answer 9

1) The input signal can be decomposed into a set of impulses, each of which can be viewed as a scaled and shifted delta function.
2) The output resulting from each impulse is a scaled and shifted version of the impulse response.
3) The overall output signal can be found by adding these scaled and shifted impulse responses

Question 10

Convolution

Answer 10

Convolution is a formal mathematical operation, just as multiplication, addition, and integration. Addition takes two numbers and produces a third number, while convolution takes two signals and produces a third signal

Question 11

Input side algorithm

Answer 11

analysing how each sample in the input signal contributes to many points in the output signal

Question 12

Output side algorithm

Answer 12

ooking at individual samples in the output signal, and finding the contributing points from the input

Question 13

Properties convolution

Answer 13

Cummutative
Associative
Distributive

Question 14

A set of weighing coefficients

Answer 14

each sample in the output signal is equal to a sum of weighted inputs

Question 15

Propreties of LTI systems

Answer 15

1. Memory(less)
2. Causality
3. Linearity
4. Time-invariant

Question 16

1. Memory(less)

Answer 16

A system is said to be memoryless if the output at any time depends only on the input at the same time. Otherwise, the system is said to have memory. Examples of systems with memory, are systems described with an integral or sum;

Question 17

2. Causality

Answer 17

A system is said to be causal if its output at the present time depends on only the present and/or
past values of the input. Thus, in a causal system, it is not possible to obtain an output before an
input is applied to the system. A system is called non-causal if its output at the present time depends
on future values of the input. Note that all memoryless systems are causal, but not vice versa.

Question 18

3. Linearity

Answer 18

A system is linear if it satisfies the superposition property

Question 19

4. Time-invariant

Answer 19

A system is called time-invariant if a time shift (delay or advance) in the input signal causes the same time shift in the output signal.

Question 20

Fourier Series

Answer 20

any periodic signal could be expressed as a sum of sinusoids

Question 21

Sinusoidal fidelity

Answer 21

If the input to a linear system is a sinusoidal wave, the output will also be a sinusoidal wave, and at exactly the same frequency as the input.

Question 22

Discrete Fourier Transformaties

Answer 22

The Fast Fourier Transform, a fast algorithm for computing the Discrete Fourier Transform, is used commonly in engineering applications where the signal processing of measured data is performed.

Question 23

Fourier series soorten

Answer 23

• Complex fourier
• Trigometric fourier series (real fourier series)
• Harmonic form fourier series