xample illustrates how to use MATLAB to explore, extract, and display satellite remote sensing data sets distributed by the National Aeronautical and Space Administration (NASA) in Hierarchical Data Format (HDF).
Firstly, this is not a signal processing toolbox. Of course, once the data is loaded, there are many matlab functions available for data processing, but few of them are integrated into a GUI interface here. At present, there are no specific functions for processing raw EEG, such as filtering, averaging, etc. For examples of signal processing tools, see the matlab signal processing toolbox and the links below, especially EEGLAB.
This toolbox has been developed to facilitate quick and easy import, visualisation and measurement for ERP data. The toolbox can open and visualise ERP averaged data (Neuroscan, ascii formats), 2D/3D electrode coordinates and 3D cerebral tissue tesselations (meshes). All the features can be explored quickly and easily using the example data provided in the toolbox. The GUI interface is simple and intuitive. The following lists the features already available and some items that could be developed.
In order to provide you with the most up-to-date information possible in the Computer Vision Homepage, we have created a special page for all the submissions that have yet to be filed in their appropriate sub-pages. From time to time, our filing process g
by Andrew Moore (CMU), including tutorials on decision trees, information gain, cross validation, naive bayesian classifiers, hidden markov models, support vector machines, k-means and hierarchical clustering
The newsgroup 'comp.soft-sys.matlab' is a forum for discussing issues related to the use of Matlab, the scientific calculation and visualization package from MathWorks Inc. This includes discussion of similar software packages like Xmath, Octave, RLab. Appropriate discussion in the group will include both general Matlab issues and platform-specific questions, and discussion comparing Matlab to other systems.
MATLAB® and NumPy/SciPy have a lot in common. But there are many differences. NumPy and SciPy were created to do numerical and scientific computing in the most natural way with Python, not to be MATLAB® clones. This page is intended to be a place to collect wisdom about the differences, mostly for the purpose of helping proficient MATLAB® users become proficient NumPy and SciPy users. NumPyProConPage is another page for curious people who are thinking of adopting Python with NumPy and SciPy instead of MATLAB® and want to see a list of pros and cons.
ITK is a powerful open-source toolkit implementing state-of-the-art algorithms in medical image processing and analysis. MATLAB, on the other hand, is well-known for its easy-to-use, powerful prototyping capabilities that significantly improve productivity. With the help of MATITK, biomedical image computing researchers familiar with MATLAB can harness the power of ITK algorithms while avoiding learning C++ and dealing with low-level programming issues.
M. Gurav, P. MukeshTiwari, и P. Singh. International Journal on Recent and Innovation Trends in Computing and Communication, 3 (4):
1836--1840(апреля 2015)