From this page, you can have a look at my research and download some codes (written in Matlab).
Significant values are determined after calculating connectivity strength for surrogates derived by AAFT algorithm [17]. Replicator equations have been applied successfully in a variety of problems such as in game theory, to find maximum cliques in graph etc. We employed in recent papers a novel information-theoretic criterion, namely the Variation of Information (VI) [5], that has been introduced for comparing two different clusterings of the same data set and which measures the amount of information that is lost or gained in changing from clustering c1 to clustering c2.
Ordinal patterns analysis such as permutation entropy of the EEG series has been found useful to track brain dynamics and has been applied to detect changes in the dynamics of EEG data. I also included in the toolbox the implementation of a new method to visualize dependencies between two time series by applying the concept of cross recurrence plots to the local ordinal structure. In order to apply network analysis, it is important first to transform time series to functional connectivity graphs. A graph-theoretic algorithm was employed for identifying the most-cohesive group(s) of vertices given the weighted similarity (adjacency) matrix G of an FCG. A variety of synchronization measures are employed in various studies namely nonlinear interdependences, phase synchronizations, mutual information, cross correlation, coherence etc.
Analyzing the dynamics in a channel-wise fashion is an invalid approach for multisite encephalographic recordings, since it ignores any pattern of coordinated activity that might emerge from the coherent activation of distinct brain areas.
MATLAB® is a high-level language and interactive environment that enables you to perform computationally intensive tasks faster than with traditional programming languages such as C, C++, and Fortran.


Sign-up for ATI's E-Newsletters which include seminar announcements, tuition discounts and technical tips on the following training topics, about once per month for each topic. Comprehensive set of *.m files and function, including GUI, to calculate and plot Radar design and analysis issues.
Each folder contains a memo file with small examples of how to run the code and use the programs.
On the very first version of this toolbox, i implemented the entropy reduction rate (quantifies the deterministicity of a symbolic time series[18,19,20] )and the symbolic transfer entropy (original (TE) and one delay (D1TE); [21]). Moreover, i provided some related files such as the k-core decomposition (which can be used for the detection of hubs) and mst_density for the identification of the minimally rewired networks. We employed replicator dynamics in order to detect consistent hubs firstly across time and secondly across subjects.
We introduce commute times (CTs) as an alternative way to capture the true interplay between the nodes of a functional connectivity graph (FCG) . There is an m file where you can calculate TVFCGs (time varying functional connectivity graphs). The algorithm is based on the identification of the dominant set of nodes and, when repeatedly applied, facilitates effective clustering in a sequential mode of pairwise relational data. We suggest, here, the use of neural-gas algorithm see [16]) for encoding brain activity spatiotemporal dynamics in the form of a symbolic timeseries. This include, the radar equation, radar waveform analysis, pulse compression, matched filter, stretch processing, HRR, phased arrays, Kalaman filter, MTI, clutter analysis, Swerling models and Pd calaculations,PRN and Barker codes, SAR, etc.


The second was implemented by two master students where the for loop and if condition (of the first implementation) were replaced by matrix operations which makes it faster. The first one is named k core decomposition and the second one based on the percentage of the total shortest path lengths passing via a node. A codebook of k prototypes, best representing the instantaneous multichannel data, is first designed. He is a recognized Subject Matter Expert and is widely known for his three textbooks: Introduction to Radar Analysis, Radar Systems Analysis and Design Using MATLAB, and MATLAB Simulations for Radar Systems Design. Moreover, one can detect hubs based on the criterion proposed in [11] and applied in [1] which based on the mean and (1 or 2) standard deviations of the degree of the whole network.
The symbolic timeseries derived in this way is mapped to a network, the topology of which encapsulates the most important phase transitions of the underlying dynamical system [see original paper 15]. Mahafza’s background includes extensive work in the areas of Radar Technology, Radar Design and Analysis (including all sensor subcomponents), Radar Simulation and Model Design, Radar Signatures and Radar Algorithm Development (especially in the areas of advanced clutter rejection techniques and countermeasures).



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