Signal Processing

Signal Processing is a fundamental course that explores the techniques used to analyze, manipulate, and transform signals in various forms such as sound, image, and video. This course introduces the key concepts and methodologies that enable the extraction of useful information from signals, and focuses on both the theoretical and practical aspects of the field. Signal processing is widely used in a variety of applications, including communications, audio processing, image analysis, radar systems, and medical signal processing.

Course Objectives:
By the end of this course, students will:

1. Understand the fundamentals of signal representation and manipulation.
2. Gain hands-on experience in designing filters and analyzing signals in both the time and frequency domains.
3. Develop skills in the application of digital signal processing (DSP) techniques for real-world signal analysis problems.
4. Understand the principles behind common algorithms used in signal processing, including Fourier transforms, wavelet transforms, and filtering techniques.
5. Be able to apply signal processing techniques to practical scenarios, such as audio filtering, speech recognition, and image compression.

Course Outline:

1. Introduction to Signals:
   • Continuous and Discrete Signals
   • Signal Representation: Time-domain and Frequency-domain
2. Fourier Analysis:
   • Fourier Series and Fourier Transform
   • Discrete Fourier Transform (DFT)
   • Fast Fourier Transform (FFT)
3. Digital Signal Processing:
   • Sampling and Quantization
   • Z-Transform and Its Applications
   • Digital Filters: FIR and IIR Filters
4. Filter Design and Analysis:
   • Filter Characteristics
   • Filter Implementation
   • Signal Convolution
5. Advanced Signal Processing Techniques:
   • Wavelet Transform
   • Signal Compression
   • Speech and Audio Signal Processing
6. Applications of Signal Processing:
   • Communication Systems
   • Image and Video Processing
   • Biomedical Signal Processing
   • Radar and Sonar Signal Processing
7. Case Studies and Practical Applications:
   • Practical signal processing applications in industry
   • MATLAB/Simulink for signal analysis

Pre-requisites:

• Basic understanding of mathematics, including calculus and linear algebra.
• Introduction to Electrical Engineering or related field knowledge.

Learning Outcomes:
Students will acquire the ability to:

• Analyze and manipulate both continuous and discrete-time signals.
• Apply Fourier transforms and filter design techniques to solve signal processing challenges.
• Implement signal processing algorithms using tools like MATLAB or Python.
• Apply advanced techniques in digital signal processing to real-world scenarios.

Target Audience:
This course is ideal for students in Electrical Engineering, Computer Science, Telecommunications, and other fields that require a solid understanding of signal processing principles.

Course Delivery:

• Lectures (Theory)
• Laboratory sessions (Practical Applications)
• Assignments and Case Studies
• Hands-on projects using software tools like MATLAB

Assessment:

• Midterm Exams
• Practical Laboratory Reports
• Final Project (Real-world signal processing application)