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Courses

  • Introduction To Random Processes (20127)
  • תקציר הקורס:

    Abstract:

    Familiarity with random processes and their f?unction in electrical engineering.

    The course subjects include: probability theory and Bayes' law, random variables, distribution f?unction and density f?unctions, moments and characteristic f?unctions, f?unctions of multiple random variables, random processes, stationarity and ergodicity,

    autocorrelation and cross-correlation, power spectrum, passage of stationary random processes through LTI systems, Gaussian processes, narrow band processes, maximization criteria for signal to noise ratio, matched filter, filtering, prediction and estimation using minimum mean square error, Wiener filter, Markov process.
  • Introduction To Digital Signal Processing (20128)
  • תקציר הקורס:

    Abstract:

    The main objective of the course is to acquire basic tools and capabilities to analyze, characterize and design of digital signals and linear systems.

    Course topics: Mathematical background, basis in the discrete time domain,

    LTI systems in time domain, Discrete Fourier Transform (DFT), Discrete Time Fourier Transform (DTFT), LTI systems in the frequency domain,

    Bode’ curves, Z Transform and LTI systems in the Z Domain, poles and zeros map, basic methods for systems implementation, STFT, zero padding, windows, design of FIR linear phase filters.
  • Introduction To Information Theory (20214)
  • תקציר הקורס:

    Abstract:

    The focus of the course is to provide basics of information theory, such as, what is a source, what is channel, what is information

    and concepts of entropy, joint entropy and conditional entropy; distinguishing between memoryless sources

    and sources with memory; we will learn what are lossless and lossy coding. Noisy cannels and channel capacity will be discussed as well.
  • Random Signal Processing (20337)
  • תקציר הקורס:

    Abstract:

    Two main is goals of this course is to define and learn auto-regressive and Markov processes; learning the basics of estimation theory and parameters estimation of random processes.