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Key Features
The DSP Blockset extends the Simulink environment by providing core components and algorithms for DSP systems. You can use blocks from the DSP Blockset in the same way that you would use any other Simulink blocks, combining them with blocks from other libraries to create sophisticated DSP systems. A few of the important features are:Frame-Based Operations
Most real-time DSP systems optimize throughput rates by processing data in "batch" or "frame-based" mode, where each batch or frame is a collection of consecutive signal samples that have been buffered into a single unit. By propagating these multisample frames instead of the individual signal samples, the DSP system can best take advantage of the speed of DSP algorithm execution, while simultaneously reducing the demands placed on the data acquisition (DAQ) hardware. The DSP Blockset delivers this same high level of performance for both simulation and code generation by incorporating frame-processing capability into all of its blocks. A completely frame-based model can run several times faster than the same model processing sample-by-sample; even faster if the data source is frame based. See "Understanding Sample Rates" and "Understanding Samples and Frames" in Chapter 2 for complete information.Matrix Support
The DSP Blockset supports two-dimensional matrices. Typical uses of the matrix format are:A matrix can be used in its traditional mathematical capacity, as a simple structured array of numbers. The matrix values might represent the pixel brightnesses from a charge-coupled device (CCD) camera, a collection of measurements from several tests, or any other group of values. Most blocks for general matrix operations are found in the Matrix Functions and Linear Algebra libraries.
A number of the matrix factorization blocks in the Linear Algebra library store the submatrix factors (i.e., lower and upper submatrices) in a single compound matrix.
The standard format for multichannel frame-based data is a matrix containing each channel's data in a separate column. A matrix with three columns, for example, contains three channels of data, one frame per channel. The number of rows in such a matrix is the number of samples in each frame.
Adaptive and Multirate Filtering
The Adaptive Filters and Multirate Filters libraries provide key tools for the construction of advanced DSP systems. Adaptive filter blocks are parameterized to support the rapid tailoring of DSP algorithms to application-specific environments, and effortless "what if" experimentation. The multirate filtering algorithms employ polyphase implementations for efficient simulation and real-time code execution.Statistical Operations
Use the blocks in the Statistics library for basic statistical analysis. These blocks calculate measures of central tendency and spread (e.g., mean, standard deviation, and so on), as well as the frequency distribution of input values (histogram).Linear Algebra
The Linear Algebra library provides a wide variety of matrix factorization methods, and equation solvers based on these methods. The popular Cholesky, LU, LDL, and QR factorizations are all available.Parametric Estimation
The Parametric Estimation library provides a number of methods for modeling a signal as the output of an AR system. The methods include the Burg AR Estimator, Covariance AR Estimator, Modified Covariance AR Estimator, and Yule-Walker AR Estimator, which allow you to compute the AR system parameters based on forward error minimization, backward error minimization, or both.Real-Time Code Generation
You can also use the separate Real-Time Workshop product to generate optimized, compact, C code for models containing blocks from the DSP Blockset.