Signal Processing Basics in MATLAB

📡 Signal Processing Basics in MATLAB

Signal Processing deals with analysis, modification, and synthesis of signals such as sound, images, sensor data, ECG, communication signals, etc.

MATLAB is one of the most powerful tools for signal processing due to its built-in functions and toolboxes.
MATLAB is developed by MathWorks.

🔹 What Is a Signal?

A signal is a function that conveys information about a phenomenon.

Common Examples

Audio signal (speech, music)
ECG / EEG signals
Temperature sensor data
Communication signals

🔹 Types of Signals

Type	Description
Continuous-time	Defined for all time values
Discrete-time	Defined at specific intervals
Analog	Continuous amplitude
Digital	Discrete amplitude
Periodic	Repeats over time
Aperiodic	Does not repeat

1️⃣ Creating a Time-Domain Signal

🔸 Simple Sine Wave

📌 This is a time-domain representation.

2️⃣ Discrete-Time Signal

📌 stem() is used for discrete signals.

3️⃣ Basic Signal Operations

Amplitude Scaling

Time Shifting

Signal Addition

4️⃣ Noise in Signals

📌 Noise is unwanted disturbance.

5️⃣ Frequency Domain Analysis – FFT

The Fast Fourier Transform (FFT) converts a signal from time domain to frequency domain.

📌 Shows frequency components of the signal.

6️⃣ Filtering Signals (Basic Idea)

🔸 Simple Moving Average Filter

📌 Filtering removes unwanted frequencies/noise.

7️⃣ Sampling Concept

Sampling converts continuous signal → discrete signal
Must satisfy Nyquist Theorem:

Sampling frequency ≥ 2 × signal frequency

8️⃣ Real-Life Example (ECG-like Signal)

⚠️ Important Notes

plot() → continuous signals
stem() → discrete signals
FFT reveals frequency information
Noise is always present in real signals
Filtering is essential in practice

🎯 Interview Questions: Signal Processing Basics

🔹 Q1. What is a signal?

Answer:
A function that represents information over time or space.

🔹 Q2. Difference between analog and digital signal?

Answer:
Analog is continuous; digital is discrete.

🔹 Q3. What is time-domain representation?

Answer:
Signal shown as amplitude vs time.

🔹 Q4. What is FFT?

Answer:
Fast Fourier Transform converts signal to frequency domain.

🔹 Q5. Why is filtering required?

Answer:
To remove noise or unwanted frequencies.

🔹 Q6. What is Nyquist theorem?

Answer:
Sampling frequency must be at least twice the signal frequency.

✅ Summary

Signal processing analyzes real-world signals
MATLAB is ideal for signal generation & analysis
FFT reveals frequency components
Filtering improves signal quality
Foundation for DSP, communications, AI, medical systems

Signal Processing Basics in MATLAB