Sunday, March 7, 2010

The MathWorks releases 2010a of MATLAB and Simulink product families

NATICK, USA: The MathWorks hasannounced the Release 2010a (R2010a) of its MATLAB and Simulink product families.

Key in this release are new streaming capabilities for signal processing and video processing in MATLAB, nonlinear solvers for standard and large-scale optimization, and expanded Simulink support for large teams designing complex systems. R2010a also introduces Simulink PLC Coder, which helps industrial control system engineers generate IEC 61131 structured text. This release updates 83 other products, including PolySpace code verification products.

Advancements for the MATLAB family that are part of the R2010a release include:
Signal Processing Blockset and Video and Image Processing Blockset: New System objects for stream processing in MATLAB. Supporting more than 140 algorithms, System objects use less memory, improve the handling of lengthy signal and video data streams, and simplify the development of streaming algorithms.

Symbolic Math Toolbox: New interface with Simscape to automatically generate Simscape language equations for physical modeling.

Global Optimization Toolbox and Optimization Toolbox: New nonlinear solvers for more complex and realistic problems and the ability to use parallel computing to accelerate time-to-solution.

SimBiology: Stochastic approximation expectation-maximization (SAEM), dosing schedules support, and performance enhancements for improved data fitting and modeling for pharmacokinetics (PK) and pharmacodynamics (PD).

R2010a reflects a continued focus on performance within the MATLAB product family, including multicore support and performance enhancements for more than 50 functions in Image Processing Toolbox, and additional multithreaded math functions in MATLAB. There are also enhancements to file sharing, path management, and the desktop in MATLAB.

With this release, Simulink offers expanded support for large design teams, enabling them to design complex systems more efficiently. These capabilities include tunable parameter structures for management of large parameter sets and triggered model blocks and function-call branching for component-based modeling.

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