Radiation forecast is the milestone of solar energy industry, making possible existence whole market. Solar radiation models allow scientists and engineers to predict behaviour a PV system perform technical economic analysis. Despite numerous models, most them are highly complex or require massive amounts data, limiting start-ups. As result, state-of-the-art review was performed based on it thi...

This work considers the use of Total variation (TV) minimization in the recovery of a given gradient sparse vector from Gaussian linear measurements. It has been shown in recent studies that there exist a sharp phase transition behavior in TV minimization in asymptotic regimes. The phase transition curve specifies the boundary of success and failure of TV minimization for large number of measur...

A functional risk curve gives the probability of an undesirable event in function of the value of a critical parameter of a considered physical system. In several applicative situations, this curve is built using phenomenological numerical models which simulate complex physical phenomena. Facing to cpu-time expensive numerical models, we propose to use the Gaussian process model to build functi...

A novel minimization technique was developed. The proposed method is based on a Gaussian random search in the parameter space and it can handle wide range of problems, including bi-exponential curve fitting, reasonable time. It uses only function values, does not require gradients.

In this paper, we present a capacity analysis for broadband power line communication (PLC) channels, impaired by the Bernoulli–Gaussian impulsive noise, exploiting both orthogonal frequency division multiplexing (OFDM) and singlecarrier frequency-domain equalisation (SC-FDE) techniques. First, we investigate point-to-point communications and formulate the continuous signal model for OFDM and SC...

In these notes, we will talk about a different flavor of learning algorithms, known as Bayesian methods. Unlike classical learning algorithm, Bayesian algorithms do not attempt to identify “best-fit” models of the data (or similarly, make “best guess” predictions for new test inputs). Instead, they compute a posterior distribution over models (or similarly, compute posterior predictive distribu...

Fitting a Gaussian mixture model (GMM) to the smoothed speech spectrum allows an alternative set of features to be extracted from the speech signal. These features have been shown to possess information complementary to the standard MFCC parameterisation. This paper further investigates the use of these GMM features in combination with MFCCs. The extraction and use of a confidence metric to com...

We present local feature evaluation for a constrained local model (CLM) framework. We target facial images captured by a mobile camera such as a smartphone. When recognizing facial images captured by a mobile camera, changes in lighting conditions and image degradation from motion blur are considerable problems. CLM is effective for recognizing a facial expression because partial occlusions can...

We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares in light curve data. We assume flares are described by a model in which there is a rapid rise with a half-Gaussian profile, followed by an exponential decay. Our signal model also contains a polynomial background model required to fit underlying light curve variations in the data, which could otherwise partially mimic...

With large data collection projects such as the Dark Energy Survey underway, data from distant Supernovae (SNe) are becoming increasingly available. As the quantity of information increases, the ability to quickly and accurately classify SNe has become essential. An area of great interest is the development of a strictly photometric classification mechanism. The first step in the advancement of...