30.115 Digital Signal Processing
This course is a fundamental and essential course for engineering students. The concepts and skills learned here form the backbone of modern engineering, across wide areas such as robotics, biomedical engineering, communications, and AI. From the basics of discrete-time signals, sampling, and quantization to advanced tools like the Z-transform, Fourier analysis, and digital filter design, students will gain both theoretical understanding and practical experience through lab sessions and a design project. These tools also serve as the basis for innovating in AI and deep learning, where convolution, filtering, and time and frequency domain analysis are used in tasks such as speech recognition, image processing, and sensor data interpretation. By mastering DSP, students will develop algorithmic thinking and hands-on skills applicable to future engineering work, giving them a solid base to innovate in AI, signal processing, and beyond, while also satisfying the requirement in their job applications as engineers in various industries.
Goal
The aim of this course is to provide students with a solid understanding of digital signal processing principles and techniques, while developing practical skills through group-based lab sessions and a design project. By mastering these concepts, students will build a strong foundation for innovation in modern engineering applications such as robotics, biomedical engineering, communications, and AI, and acquire the algorithmic thinking and hands-on experience necessary for future engineering practice and career development across diverse industries.
Learning objectives
- Generate various discrete-time signal sequences and perform operations to process them
- Analyse time domain and frequency domain characteristics of digital signals
- Formulate and design structures for implementing the discrete-time systems
- Design digital filters (FIR & IIR) with given specifications
Measurable outcomes
- Explain the sampling theorem, compare the features of discrete-time signals versus continuous-time signals
- Compare the characteristics of linear and non-linear, time-invariant and time-variant discrete-time systems
- Calculate DFT and IDFT of given signal sequences
- Apply the FFT algorithm and demonstrate its advantages over the DFT
- Design FIR and IIR filters to meet the required magnitude and phase responses
Pedagogy
Cohort based learning, with group-based lab exercises and design project.
Grading
- Homework (15%)
- Lab Exercises using Matlab DSP Design Toolbox (Group-based, 25%)
- Design Project (Group-based,15%)
- Midterm Exam (20%)
- Final Exam (20%)
- Class Participation (5%)
Prerequisite
Text and references
- Digital Signal Processing (4th Edition);
Authors: John G. Proakis, Dimitris K Manolakis
ISBN-13: 978-0131873742