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Signal Processing with MATLAB
Signal Processing and Communications
Signal Processing with MATLAB
Learn to analyze signals and design signal processing systems
Complimentary Services: Post training email support & 1-hr consultation session within 1 month after the course completion!
Course
Highlights
This two-day course shows how to analyze signals and design signal processing systems using MATLAB®, Signal Processing Toolbox™, and DSP System Toolbox™.
Topics include:
- Creating and analyzing signals
- Performing spectral analysis
- Designing and analyzing filters
- Designing multirate filters
- Designing adaptive filters
Who Should
Attend
MATLAB Fundamentals or equivalent experience using MATLAB, and a good understanding of signal processing theory, including linear systems, spectral analysis, and filter design
Course
Prerequisites
MATLAB Fundamentals or equivalent experience using MATLAB, and a good understanding of signal processing theory, including linear systems, spectral analysis, and filter design
Course
Benefits
After completing this training, you will gain knowledge and skills on:
- Signal generation through file import, sampled and synthesized signals
- Visualization of the signals in time-domain and frequency domain
- Identifying the characteristics of different spectrum analysis methods
- Understanding of Linear-time invariant systems
- Designing different kind of filters and understand the benefits and the drawback of different kind of filters
Partners
Upcoming Program
- Please keep me posted on the next schedule
- Please contact me to arrange customized/ in-house training
Course Outline
Signals in MATLAB
Objective: Generate sampled and synthesized signals from the command line and visualize them. Create noise signals for a given specification. Perform signal processing operations like resampling, modulation, and correlation.
- Creating discrete signals
- Sampling and resampling
- Visualizing signals
- Modeling noise
- Performing resampling, modulation, and correlation
- Generating streaming signals
Spectral Analysis
Objective: Understand different spectral analysis techniques and the use of windowing and zero padding. Become familiar with the spectral analysis tools in MATLAB and explore nonparametric (direct) and parametric (model-based) techniques of spectral analysis.
- Discrete Fourier transform
- Windowing and zero padding
- Power spectral density estimation
- Time-varying spectra
- Using a spectrum analyzer in MATLAB
Linear Time Invariant Systems
Objective: Represent linear time-invariant (LTI) systems in MATLAB and compute and visualize different characterizations of LTI systems.
- LTI system representations
- z-transform
- Frequency and impulse response
- Visualizing filter properties
- Applying filters to finite and streaming signals
Filter Design
Objective: Design filters interactively using the Filter Design and Analysis app. Design filters from the command line using filter specification objects.
- Filter specifications
- Interactive filter design
- Common filter design functions
- Filter design with filter specification objects
- Reducing filter delay
- Frequency-domain filtering
The Signal Analysis App
Objective: Learn to use a powerful all-in-one app for importing and visualizing multiple signals, performing spectral analysis on them, and designing and applying filters to the signals. Make simple statistical and cursor measurements on signals.
- Browse signals and make simple measurements
- Perform interactive spectral analysis
- Design and apply filters to signals interactively
Multirate Filters
Objective: Understand principles of polyphase multirate filter design. Design multirate interpolating and decimating filters. Design multistage and narrow-band filters.
- Downsampling and upsampling
- Noble identities and polyphase FIR structures
- Polyphase decimators and interpolators
- Design multistage and interpolated FIR filters
Adaptive Filter Design
Objective: Design adaptive filters for system identification and noise cancellation.
- Basics of adaptive filtering
- Perform system identification
- Perform noise cancellation
- Improve adaptive filter efficiency