If you click on the link in the Known Software Problems section of the DSP Blockset 5.1 (13SP1) Release Notes, you are taken to a page that contains incorrect information. The actual known software problems for DSP Blockset 5.1 (13SP1) are described below.
1) Autocorrelation Block with Constant Sample Time Might Cause a Segmentation Violation While Generating C Code
Generating code from a model containing the Autocorrelation block with constant sample time and the Inline parameters check box selected in the Simulation Parameters dialog box might cause a segmentation violation.
2) Data Type Override Cannot Be Used for Fixed-Point DSP Blocks in Subsystems
The Data type override parameter of the Fixed-Point Settings interface does not currently work for fixed-point DSP Blockset blocks in subsystems.
3) DSP Fixed-Point Attributes Block Does Not Work Inside a Masked Subsystem
The DSP Fixed-Point Attributes (DFPA) block does not function inside a masked subsystem or a library-linked subsystem. No error is thrown, but after the model is saved and reloaded, or after any names are changed in the DFPA path, the blocks will have no effect and will not show up in the DFPA GUI.
4) DSP Fixed-Point Attributes Block Shows Incorrect Initial Values
The Rounding mode and Overflow mode popups in the DFPA GUI do not show the correct values when the GUI is first launched. The two modes are 'Unset,' but 'Floor' and 'Wrap' are shown in the popups. Regardless of what is shown, these two modes are treated as 'Unset' when the GUI is first launched. If you select and then unselect the relevant check box, these popups will show the correct values. You only need to do this once per MATLAB session.
5) DSP Gain Block Errors Out for Continuous Sample Times
The DSP Gain block will only work with discrete-time signals.
6) Some Fixed-Point Optimizations Produce Wrong Results in Filter Realization Wizard
When you apply optimizations for 1, -1, and 0 gains on fixed-point realizations of lattice structures in the Filter Realization Wizard, some Convert blocks are removed. The optimized model then produces significantly different results when compared to the results from the non-optimized model.
