As science advances, novel experiments are becoming more and more complex, requiring a zoo of control devices and electronics executing complicated sequences of steps. Device availability and monetary constrains usually lead to a highly heterogeneous setup with components from several different manufacturers using many different protocols and interfacing mechanisms. This often results in control software being puzzled together to use and provide a multitude of interfacing and control functionality, each using their own calling conventions, data structures, etc. To make matters worse, usually a group of relatively independent programmers is trying to write and maintain the code base. Often this causes extensive duplication of effort as program segments are hard to reuse, since unpredictable changes to the segments by the original authors might compromise other code using these segments.
The FastICA package is a free (GPL) MATLAB program that implements the fast fixed-point algorithm for independent component analysis and projection pursuit. It features an easy-to-use graphical user interface, and a computationally powerful algorithm.
The tca package is a Matlab program that implements the tree-dependent
component analysis (TCA) algorithms that extends the independent
component analysis (ICA), where instead of looking for a linear transform
that makes the data components independent, we are looking for components
that can be best fitted in a tree structured graphical model. The TCA model
can be applied in any situation where the data can be assumed to have been
transformed by an unknown linear transformation.
he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization.
The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
FatFs06.rar
FatFs is a generic file system module to implement the FAT file system to small embedded systems. The FatFs is written in compliance with ANSI C, therefore it is independent of hardware architecture. It can be incorporated into cheap microcontrollers, such as 8051, PIC, AVR, SH, Z80, H8, ARM and etc..., without any change.
