The PSTHs (lower panels) show firing rates after Gaussian kernel smoothing with 50 ms standard deviation, averaged across trials with identical object and on-the-object cue/target positions. Every panel includes the PSTHs for the two on-the-object cues/targets which according to the object-...
They used Gaussian error with zero mean to diversify their samples. They optimize the schedule of EWH in a house equipped with PV under dynamic pricing. The user preference is modeled with a temperature dead-band between 60 and 80°C. In [45] proposed a method to control EWH under ...
First, we propose the use of a non-Gaussian long-range dependent process to model Internet traffic aggre... A Scherrer,N Larrieu,P Owezarski,... - 《IEEE Transactions on Dependable & Secure Computing》 被引量: 216发表: 2007年 Method for laying out the infrastructure of a cellular ...
(6) Input the hidden variable into the decoder and decode it through a Gaussian distribution to output the data. (7) Train the model with a random gradient iteration until a clear image is generated. We use the incremental update training method to improve the accuracy of the auxiliary model...
A similar peak ∼2.8 eV was identified from a gaussian deconvolution of the FeCAB spectrum, and it contributes some intensity in-between the two main peaks for many complexes. A pronounced peak in the blue spectral region between 3.1 and 3.2 eV is seen for all complexes. This peak thus ...
As a result, the alignment described in Equation (5) serves as a data-driven estimator, enabling us to quantify the similarity between the random variables X and Y. 2.2. Gaussian Functional Connectivity from EEG Records Let us examine a collection of multichannel EEG recordings referred to as ...
As a result, the alignment described in Equation (5) serves as a data-driven estimator, enabling us to quantify the similarity between the random variables X and Y. 2.2. Gaussian Functional Connectivity from EEG Records Let us examine a collection of multichannel EEG recordings referred to as ...
As a result, the alignment described in Equation (5) serves as a data-driven estimator, enabling us to quantify the similarity between the random variables X and Y. 2.2. Gaussian Functional Connectivity from EEG Records Let us examine a collection of multichannel EEG recordings referred to as ...
CWT image-based feature sets have been demonstrated to improve performance by augmenting them with additive white Gaussian noise. Such fingerprinting techniques require radio-maps for recognizing user positions, and the most basic method of generating a radio-map is point-by-point calibration, which ...