The document discusses different classifications of signals: 1. Signals can be multichannel/multidimensional involving multiple sources or sensors, such as ground acceleration from an earthquake with primary and secondary longitudinal and transverse waves, as well as surface waves. Multidimensional signals include black and white or color TV signals. 2. Signals can be continuous-time or discrete-time, with continuous-time signals defined for every time value and discrete-time signals defined at specific time values that can be represented by sequences of real or complex numbers. 3. Signals can be continuous valued, taking on any value in a range, or discrete valued, taking values from a finite set through quantization.

1. DIGITAL SIGNAL PROCESSING
INTRODUCTION
BY
MIAN AHMED YASER
AP ECE CUI LAHORE

2. 1.2: CLASSIFICATION OF SIGNALS
Signal processing and analyzing techniques are specific for particular
classes of signals.
Classification of signals:
1. Multichannel and Multidimensional signals
2. Continuous –Time versus Discrete-Time Signals
3. Continuous values versus Discrete Valued Signals
4. Deterministic versus Random Signals

3. 1.2.1 Multichannel and Multidimentional
Signals
• Real values signal
• Complex valued signal
• Vector Signals

4. Vector signals-
Multichannel signals
• Signals generated by multiple
sources or multiple sensors
• Ground acceleration due to
earthquake
• Primary wave
• Longitudinal and
Transversal
• Secondary wave
• Longitudinal and
Transversal
• Surface wave

6. Multidimensional Signals
• Black and White TV signal I(x,y,t)
• Color TV Signal

7. 1.2.2 Continuous Time Versus Discrete-Time
Signals
• Continuous Time Signal or Analog Signal are defined for every value of
time within an interval of (a,b). These signals are functions of a
continuous variable, e.g. Speech signals, or signals like 𝑥1 𝑡 =
𝐶𝑜𝑠 π𝑡, 𝑥2 𝑡 = 𝑒
− | |
• Discrete Time Signals are defined at certain specific values of time,
𝑥 𝑡𝑛 = 𝑒
− |𝑡𝑛|, n=0, ±1, ±2, …………
•Discrete-Time signals can be represented by a sequence of real or
complex numbers
• 𝑥(𝑛) represents a discrete-time signal instead of 𝑥 𝑡
• If time instants tn are equally spaced i.e. 𝑡𝑛 = 𝑛𝑇

8. Discrete-Time signal example

9. SAMPLING
• Selecting values of an analog signals at discrete-Time instants

10. 1.2.3 Continuous Valued Versus Discrete-Valued Signals
• If a signal takes on all possible values on a finite or an infinite rage, it is a continuous
valued signal
• If a signal takes values from a finite set of possible values then it is a discrete valued
signal- QUANTIZATION

11. 1.2.4 Deterministic Versus Random Signals