Journal: Sensors (Basel, Switzerland)

Article Title: Peripheral Network Connectivity Analyses for the Real-Time Tracking of Coupled Bodies in Motion

doi: 10.3390/s18093117

Figure Lengend Snippet: The data collection and representation tools. ( A ) Two professional dancers in T-pose while being calibrated within the Phase Space. Cameras capturing the motion are marked and suits contain 38 LEDs in each dancer’s body. Data is sampled at 960 Hz. ( B ) ( Left ) panel: Skeleton showing the distribution of LEDs from 1–38 across the body segments. ( Right ) panel: Our avatar designed in Matlab using a forward kinematics model in to track the various parameters of interest (see movie from the Phase Space and Bot and Dolly in Link 1 of the .).

Article Snippet: The number of overlapping frames is randomly chosen using the continuous Gamma family of probability distributions, generated with the MATLAB function gamrand , according to the empirically estimated Gamma-mean and Gamma-variance, using the MLE approach, as explained above (fitting the Gamma shape and scale parameters obtainable from the whole data set to attain the mean and variance).

Techniques:

Journal: Frontiers in Computational Neuroscience

Article Title: Comparison of IT Neural Response Statistics with Simulations

doi: 10.3389/fncom.2017.00060

Figure Lengend Snippet: Kurtosis and PTI of responses generated by gamma distributions different from that used in Lehky et al. . (A,B) Kurtosis and PTI by a gamma distribution with a = gamrnd (8.0, 1.0) and b = gamrnd (2.0, 0.5), respectively. (C,D) Kurtosis and PTI by a gamma distribution with a = gamrnd (8.0, 1.0) and b = gamrnd (3.0, 1.0), respectively.

Article Snippet: And in Method-II, the Matlab gamma random number generator gamrnd is used both to generate synthetic responses and the two gamma-distribution parameters, and the Matlab Poisson random number generator poissrnd is used to generate noisy responses.

Techniques: Generated