Learn More
Emboli classification is of high clinical importance for selecting appropriate treatment for patients. Several ultrasonic (US) methods using Doppler processing have been used for emboli detection and classification as solid or gaseous matter. We suggest in this experimental study exploiting the Radio-Frequency (RF) signal backscattered by the emboli since(More)
RBFN (Radial-Basis Function Networks) represent an attractive alternative to other neural network models. Their learning is usually split into an unsupervised part, where center and widths of the basis functions are set, and a linear supervised part for weight computation. Although available literature on RBFN learning widely covers how basis function(More)
In the human body, emboli can produce severe damage like stroke or heart attack. Commonly used Doppler detection techniques have shown their limits in the determination of the embolus nature. An alternative approach would be to examine Radio Frequency (RF) signal instead of Doppler signals. Under specific conditions of the ultrasound excitation wave,(More)
Data from spectrophotometers form spectra that are sets of a great number of exploitable variables in quantitative chemical analysis; calibration models using chemometric methods must be established to exploit these variables. In order to design these calibration models which are specific to each analyzed parameter, it is advisable to select a reduced(More)
Spectrophotometric data often comprise a great number of numerical components or variables that can be used in calibration models. When a large number of such variables are incorporated into a particular model, many difficulties arise, and it is often necessary to reduce the number of spectral variables. This paper proposes an incremental (Forward –(More)
In this paper, Enhanced Fisher linear discriminant Model (EFM) is presented as an alternative feature extraction algorithm to Principal Component Analysis (PCA) widely used in automatic face recognition/authentication tasks. We show that the promising EFM algorithm extracts from faces features that are relevant and efficient for authentication. This leads(More)
The classification of circulating microemboli, in the bloodstream, as gaseous or particulate matter is vital for selecting appropriate treatment for patients. Until now, Doppler techniques have shown some limitations to determine clearly the nature of circulating microemboli. The traditional techniques are largely based on the Fourier analysis. In this(More)
In this work, the active learning approach is adopted to address the problem of training sample collection for the estimation of chemical parameters for product quality control from spectroscopic data. In particular, two strategies for support vector regression (SVR) are proposed. The first method select samples distant in the kernel space from the current(More)
In this work, we investigate a new design of a multiblock general regression neural network applied to wind speed prediction in Algeria. The idea in our proposed method is to minimize the error of the prediction for wind speed in such a way as to minimize the quantity of training samples used, and thus to reduce the costs related to the training sample(More)