Digital Signal Processing (DSP) Fundamentals培训




21 小时 通常来说是3天,包括中间休息。


  • Basic programming experience
  • Knowledge of basic electrical engineering concepts
  • An understanding of intermediate mathematics and physics concepts


  • Engineers
  • Computer Scientists


Digital Signal Processing (DSP) is an engineering practice that integrates mathematical concepts and electronics to manipulate electrical signal information. Using analysis results acquired from DSP, professionals can derive digital data that are employable for the optimization of modern technologies. DSP is an important operation in today's enterprise applications that outperforms analog signal processing in terms of high accuracy and data consistency.

This instructor-led, live training (online or onsite) is aimed at engineers and scientists who wish to learn and apply DSP implementations to efficiently handle different signal types and gain better control over multi-channel electronic systems.

By the end of this training, participants will be able to:

  • Setup and configure the necessary software platform and tools for Digital Signal Processing.
  • Understand the concepts and principles that are foundational to DSP and its applications.
  • Familiarize themselves with DSP components and employ them in electronics systems.
  • Generate algorithms and operational functions using the results from DSP.
  • Utilize the basic features of DSP software platforms and design signal filters.
  • Synthesize DSP simulations and implement various types of filters for DSP.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.



Overview of Core Electronics Concepts and Principles Applied in DSP

  • The relevance and depth of DSP in modern technologies

Recollecting Mathematics and Physics Fundamentals Implemented in DSP

  • Applying statistics, probability, and noise principles
  • Reviewing linear systems and superposition physics concepts
  • Understanding ADC and DAC manipulation

Overview of Useful DSP Software Platform and Tools

Getting Started with Digital Signal Processors Architecture

  • System requirements for DSP processors

Working with Convolution Properties and Algorithms

  • Delta function v.s. impulse response
  • Understanding the input side and output side algorithms
  • Comprehending electronics correlation and speed in DSP

Getting Started with the Discrete Fourier Transform

  • What are Fourier Transforms?
  • Manipulating DFT notations, format, and variables
  • Synthesizing and analyzing DFT calculations
  • Working with DFT properties and polar attributes

Applying DFT Principles and Processes to DSP Functionalities

  • Practicing spectral analysis and evaluating frequency responses

Operating Different DSP Domains and Choosing the Right Implementations

  • Time and space domains
  • Frequency and wavelet domains

Carrying Out Convolution Using the Frequency Domain

Employing Advanced Fourier Transform Properties and Applications

  • What are Fourier Transform pairs and how do they work?
  • Fast Fourier Transform v.s. Discrete Fourier Transform

Implementing Continuous Signal Processing

Overview of Digital Filters Types and its Primary Functions

Understanding How Information Filtering Works and Operating Basic Digital Filters

  • Step response v.s. noise reduction

Employing Moving Average Filters and Applying Convolution Implementations

Working with Window-Sinc Filters and Removing Frequency Components

Operating Recursive Filters and Chebyshev Filters

  • What is the recursive method in DSP?
  • What are Butterworth responses?

Choosing the Appropriate DSP Filter for an Electronics Application

Creating and Building a Custom DSP Filter Based on Arbitrary Frequency Response

  • Testing and optimizing a DSP filter

Integrating a Tested DSP Filter within the Preferred Electronics System

  • Understanding DSP for data compression, imaging techniques, and more
  • Carrying out exercises for various DSP applications and use cases

Overview of Advanced Software Tools for Achieving DSP Implementations

Implementing Complex DSP Techniques for Future Applications

  • Exploring complex Fourier Transforms, z-Domain, etc.


Summary and Conclusion









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