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DSP Blockset Demos Overview
You can access the DSP Blockset demos by typing
demos
at the MATLAB command line. In the Demos window that opens, expand the Blocksets entry by double-clicking, and then click DSP to see the demos.
Explore all the demos to see how you can implement both basic and advanced DSP algorithms with the DSP Blockset. You can also use the demos as a base for building your own models. Simply select the section of the demo that you want to build on and copy it into your own model.
The available demos are listed below by category.
Adaptive Processing Demos
- Equalization: Demonstrates adaptive channel equalization by using the LMS algorithm to adaptively compute an estimate of an FIR equalization filter.
- Noise canceller (using either LMS or RLS): These demos use either the LMS or RLS algorithm to subtract noise from an input signal.
- Linear prediction: Uses the LMS adaptive FIR algorithm to adaptively compute the linear prediction coefficients for a noisy input signal.
- Time-delay estimation: Uses the LMS adaptive FIR algorithm to adaptively estimate the time delay for a noisy input signal.
- Tracking filter: Uses a Kalman filter to track the time-varying weights of a nonstationary fifth order FIR filter.
Audio Processing Demos
- Dynamic range compression: Compresses the dynamic range of a signal by modifying the range of the magnitude at each frequency bin. This nonlinear spectral modification is followed by an overlap-add FFT algorithm for reconstruction.
- Flanging: Introduces a "flanging" effect into a short segment of music.
- Reverberation: Uses the Integer Delay block to demonstrate the popular reverberation audio effect.
- LPC analysis and synthesis: Uses the Levinson solver and Time-Varying Lattice Filter for low-bandwidth transmission of speech.
- Waveform coding: This set of demos uses a variety of modulation methods to code a waveform using one bit per message sample:
Communications Demos
- SSB modulation: Demonstrates single sideband (SSB) modulation in sample-based and frame-based modes.
- WWV digital receiver: WWV is the call sign of a US Government radio station that transmits frequency reference standards and time code information with a timing accuracy of 10 microseconds and a frequency accuracy of 1 part in 100 billion. This demo simulates the transmission of a WWV signal and demonstrates implementation of the subsequent receiver and decoder blocks. The receiver design serves as a simple example of the use of Simulink, DSP Blockset, Stateflow® and Real-Time Workshop.
Filtering Demos
- Multirate filtering suite: Uses FIR decimation blocks in multiple stages to filter with very short bandwidths and low computational loads.
- FIR interpolation: Uses the FIR Interpolation block to demonstrate interpolation of a delayed sine wave signal.
- Overlap add/save: Demonstrates filtering of a sinusoid using the Overlap-Add and Overlap-Save FFT blocks.
- Sample rate conversion: Illustrates the efficiency of the FIR rate conversion block by comparing the block with the equivalent process of separate upsampling, FIR filtering, and downsampling.
Queues Demo
- Demo uses a Queue block with a system of selection switches to illustrate pushing and popping elements from a queue.
Sigma-Delta A/D Conversion Demo
- Demo illustrates analog-to-digital conversion using a sigma-delta algorithm implementation.
Sine Wave Generation Demo
- Demo compares different sine wave generation systems.
Spectral Analysis Demo
- Short-time FFT: Uses the Short-Time FFT block to compute and display a spectrogram.
- Comparison of techniques: Uses the Vector Scope block to simultaneously display spectral estimates computed by the Short-Time FFT, Burg Method, and Modified Covariance Method blocks.
Statistical Functions Demo
- Demo illustrates the behavior of several running-statistics blocks that are periodically reset every 100 input samples.
Wavelets Demos
- One-level PR filter bank: Uses the Dyadic Analysis and Dyadic Synthesis blocks to implement a perfect reconstruction filter bank.
- Wavelet function: Uses a sequence of FIR interpolation blocks to reconstruct a wavelet function from filter coefficients.
- Denoising: Uses Analysis and Synthesis blocks to remove noise from an input signal.
- Wavelet transmultiplexer (WTM): Illustrates the perfect reconstruction property of the discrete wavelet transform (DWT) by using a WTM to reconstruct three independent combined signals transmitted over a single communications link.
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