The following courses form a collection of resources on discrete-time signal processing. These materials cover essential topics including signal representation, digital filtering, discrete Fourier transforms, and multirate techniques—providing both foundational knowledge and practical applications for discrete-time signal processing.
6.341x Discrete-Time Signal Processing on MITx
6.341x is designed to provide both an in-depth and an intuitive understanding of the theory behind modern discrete-time signal processing systems and applications. The course begins with a review and extension of the basics of signal processing including a discussion of group delay and minimum-phase systems, and the use of discrete-time (DT) systems for processing of continuous-time (CT) signals. The course develops flow-graph and block diagram structures including lattice filters for implementing DT systems, and techniques for the design of DT filters. Parametric signal modeling and the efficient implementation of DT multirate and sampling rate conversion systems are discussed and developed. An in-depth development of the DFT and its computation as well as its use for spectral analysis and for filtering is presented. This component of the course includes a careful and insightful development of the relationship between the time-dependent Fourier transform and the use of filter banks for both spectral analysis and signal coding.
6.011 Introduction to Communication, Control, and Signal Processing, Spring 2010
This course examines signals, systems, and inference as unifying themes in communication, control, and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; group delay; state feedback and observers; probabilistic models; stochastic processes, correlation functions, power spectra, and spectral factorization; least-mean square error estimation; Wiener filtering; hypothesis testing; detection; and matched filters.
6.341 Discrete-Time Signal Processing, Fall 2005
This class addresses the representation, analysis, and design of discrete time signals and systems. The major concepts covered include discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flowgraph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform and FFT algorithm; short-time Fourier analysis and filter banks; multirate techniques; Hilbert transforms; and Cepstral analysis and various applications.
RES.6-008 Digital Signal Processing, 1987
This course was developed in 1987 by the MIT Center for Advanced Engineering Studies. It was designed as a distance-education course for engineers and scientists in the workplace.
Advances in integrated circuit technology have had a major impact on the technical areas to which digital signal processing techniques and hardware are being applied. A thorough understanding of digital signal processing fundamentals and techniques is essential for anyone whose work is concerned with signal processing applications.
Digital Signal Processing begins with a discussion of the analysis and representation of discrete-time signal systems, including discrete-time convolution, difference equations, the z-transform, and the discrete-time Fourier transform. Emphasis is placed on the similarities and distinctions between discrete-time and continuous-time signals and systems. The course proceeds to cover digital network and nonrecursive (finite impulse response) digital filters. Digital Signal Processing concludes with digital filter design and a discussion of the fast Fourier transform algorithm for computation of the discrete Fourier transform.
RES.6-007 Signals and Systems, 1987
This course was developed in 1987 by the MIT Center for Advanced Engineering Studies. It was designed as a distance-education course for engineers and scientists in the workplace.
Signals and Systems is an introduction to analog and digital signal processing, a topic that forms an integral part of engineering systems in many diverse areas, including seismic data processing, communications, speech processing, image processing, defense electronics, consumer electronics, and consumer products.
The course presents and integrates the basic concepts for both continuous-time and discrete-time signals and systems. Signal and system representations are developed for both time and frequency domains. These representations are related through the Fourier transform and its generalizations, which are explored in detail. Filtering and filter design, modulation, and sampling for both analog and digital systems, as well as exposition and demonstration of the basic concepts of feedback systems for both analog and digital systems, are discussed and illustrated.
