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  • Courseware 
    Digital Signal Processing

    Digital Signal Processing

    The course treats: the discrete Fourier Transform (DFT), the Fast Fourier Transform (FFT), their application in OFDM and DSL; elements of estimation theory and their application in communications; linear prediction, parametric methods, the Yule-Walker equations, the Levinson algorithm, the Schur algorithm; detection and estimation filters; non-parametric estimation; selective filtering, application to beamforming.

    Subjects:
    Electrical Engineering
    Keywords:
    Signal processing
    Resource Type:
    Courseware
     
  • Courseware 
    Biomedical Signal and Image Processing

    Biomedical Signal and Image Processing

    This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.

    Subjects:
    Biomedical Engineering and Medical Imaging
    Keywords:
    Biomedical engineering Signal processing Image processing
    Resource Type:
    Courseware
     
  • Courseware 
    Signals and Systems

    Signals and Systems

    This course covers the fundamentals of signal and system analysis, focusing on representations of discrete-time and continuous-time signals (singularity functions, complex exponentials and geometrics, Fourier representations, Laplace and Z transforms, sampling) and representations of linear, time-invariant systems (difference and differential equations, block diagrams, system functions, poles and zeros, convolution, impulse and step responses, frequency responses). Applications are drawn broadly from engineering and physics, including feedback and control, communications, and signal processing.

    Subjects:
    Electrical Engineering
    Keywords:
    Signal processing System analysis
    Resource Type:
    Courseware
     
  • Courseware 
    Signals, Systems and Inference

    Signals, Systems and Inference

    This course covers signals, systems and inference 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; and group delay. State feedback and observers. Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error estimation; Wiener filtering. Hypothesis testing; detection; matched filters.

    Subjects:
    Electronic and Information Engineering|Electrical Engineering
    Keywords:
    Signal processing Signal theory (Telecommunication) System analysis
    Resource Type:
    Courseware
     
  • MOOC 
    Signals and Systems, Part 1

    Signals and Systems, Part 1

    We encounter signals and systems extensively in our day-to-day lives, from making a phone call, listening to a song, editing photos, manipulating audio files, using speech recognition softwares like Siri and Google now, to taking EEGs, ECGs and X-Ray images. Each of these involves gathering, storing, transmitting and processing information from the physical world. This course will equip you to deal with these tasks efficiently by learning the basic mathematical framework of signals and systems. This course is divided into two parts. In this part (EE210.1x), we will explore the various properties of signals and systems, characterization of Linear Shift Invariant Systems, convolution and Fourier Transform, while the next part (EE210.2x), will deal with the Sampling theorem, Z-Transform, discrete Fourier transform and Laplace transform. Ideas introduced in this course will be useful in understanding further electrical engineering courses which deal with control systems, communication systems, power systems, digital signal processing, statistical signal analysis and digital message transmission. The concepts taught in this course are also useful to students of other disciplines like mechanical, chemical, aerospace and other branches of engineering and science.

    Course related:
    EE3008A Linear Systems and Signal Processing
    Subjects:
    Electrical Engineering
    Keywords:
    Signal processing
    Resource Type:
    MOOC