Signal Processing Training

Signal Processing Training

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Introduction:

Signal Processing Training course with hands-on (Online, Onsite, and Classroom Live!)

This important course brings together, in one place, signal processing concepts as well as mathematical techniques that are critical for understanding and effectively analyzing or designing the modern communications systems. It’s a great introduction to the subject for those who may not have been exposed to this material and an excellent refresher for those who learned it long time back in college. Both types of audiences will benefit from this course’s practical, application-centered instructional approach aimed at bridging the gap between theory and application. This Signal Processing Training course is a must for all whose work focuses on the analysis or design of existing or emerging communications systems.

What’s Included?

  • 4 days of Signal Processing Training with an expert instructor
  • Signal Processing Training Electronic Guide
  • Certificate of Completion
  • 100% Satisfaction Guarantee

Resources:

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Customize It:

  • If you are familiar with some aspects of  Signal Processing, we can omit or shorten their discussion.
  • We can adjust the emphasis placed on the various topics or build the Signal Processing course around the mix of technologies of interest to you (including technologies other than those included in this outline).
  • If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policy-maker), and present the Signal Processing course in manner understandable to lay audiences.

Audience/Target Group:

  • This Signal Processing course is aimed at those in the industry or government whose work involves the analysis or design of modern communications systems.

Prerequisites:

  • A Bachelor’s degree in Science, Mathematics, or Engineering or equivalent work experience.

Signal Processing Training – Course Outlines:

Discrete Time Signal Processing

  • Sampling Theorem:  Continuous and Discrete time
  • Interpolation and Up sampling
  • Decimation and Down sampling
  • ADC and DAC Convertors
  • Overview of Transforms
  • Convolution Operation
  • IIR and FIR Filter Structures
    • Pole-Zero Representations

Fourier and Z Transforms

  • Power Spectral Density (PSD)
  • Linear Filtering
  • Discrete Fourier Transforms (DFT)
  • FFT and IFFT

Probability Overview

  • Mean, Variance, Several Theorems
  • PDF Examples:  Gaussian, Erlang, Exponential, Uniform, etc.
  • Central Limit Theorem
  • Hypothesis Testing (MAP, ML)
  • Calculating Probability of Error
    • Digital Communications Systems Example
    • The importance of the PDF and CDF

Linear Algebra Methods

  • Dot Product and Cross Product
  • Matrix Inversion
  • Eigen Decomposition

Adaptive Signal Processing

  • Minimum Mean Square Error (MMSE)
  • Least Mean Squared (LMS) and NLMS
  • Recursive Least Squared (RLS)
  • Direct Matrix Inversion (DMI)
  • Maximum Likelihood Estimation (MLE)
  • Interpolation Techniques (Lagrange, Linear)

Equalization Methods

  • Decision Feedback Equalization (DFE)
  • Maximum Likelihood Sequence Equalizer (MLSE)

Communications Applications

  • DC Offset Estimation
  • Automatic Frequency Correction (AFC)
  • Channel Estimation
  • Likelihood Ratio Testing
  • Phase Noise

Estimators

  • Properties of Estimators
  • Digital Communications Application (BER)

Wrap-up

  • Course Recap and Q/A
  • Evaluations

Signal Processing Training Signal Processing Course Wrap-up

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