Brain tissue classification of magnetic resonance images using partial volume modeling

This paper presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, we consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classes, but also mixtures, called mixclasses. A statistical model of the mixtures is proposed and studied by means of simulations. It is shown that it can be approximated by a Gaussian function under some conditions. The D'Agostino-Pearson normality test is used to assess the risk alpha of the approximation. In order to classify a brain into three types of brain tissue and deal with the problem of partial volume effects, the proposed algorithm uses two steps: 1) segmentation of the brain into pure and mixclasses using the mixture model; 2) reclassification of the mixclasses into the pure classes using knowledge about the obtained pure classes. Both steps use Markov random field (MRF) models. The multifractal dimension, describing the topology of the brain, is added to the MRFs to improve discrimination of the mixclasses. The algorithm is evaluated using both simulated images and real MR images with different T1-weighted acquisition sequences.     

Adaptive Nonlinear Control of Wind Energy Conversion System Involving Induction Generator

This paper presents a theoretical framework for adaptive control of a wind energy conversion system (WECS), involving a squirrel cage induction generator (SIG) connected with an AC/DC/AC IGBT‐based PWM converter. A multi‐loop nonlinear controller is designed to meet two main control objectives, i.e., (i) speed reference optimization in order to extract a maximum wind energy whatever the wind speed, and (ii) power factor correction (PFC) to avoid net harmonic pollution. These objectives must be achieved despite the mechanical parameters uncertainty. First, a nonlinear model of the whole controlled system is developed within the Park coordinates. Then, a multi‐loop nonlinear controller is synthesized using the adaptive backstepping design. A formal analysis based on Lyapunov stability is carried out to describe the control system performances. In addition to closed‐loop global asymptotic stability, it is proven that all control objectives (induction generator speed tracking, rotor flux regulation, DC link voltage regulation and unitary power factor) are asymptotically achieved.     

Wavelets and statistical analysis of functional magnetic resonance images of the human brain

Wavelets provide an orthonormal basis for multiresolution analysis and decorrelation or 'whitening' of nonstationary time series and spatial processes. Wavelets are particularly well suited to analysis of biological signals and images, such as human brain imaging data, which often have fractal or scale-invariant properties. We briefly define some key properties of the discrete wavelet transform (DWT) and review its applications to statistical analysis of functional magnetic resonance imaging (fMRI) data. We focus on time series resampling by 'wavestrapping' of wavelet coefficients, methods for efficient linear model estimation in the wavelet domain, and wavelet-based methods for multiple hypothesis testing, all of which are somewhat simplified by the decorrelating property of the DWT.     

GIS-based landslide susceptibility mapping in the Safi region,West Morocco

Bulletin of Engineering Geology and the Environment - This study aims to establish susceptibility maps for the Moroccan Safi region, which is affected by karstification processes. This process is...     

Nickel oxide optimization using Taguchi design for hydrogen detection

In this work, we established Taguchi's design of experiment as a statistical tool to decrease the amount of experiments, providing the practical optimal condition to operate a chemical sensor with high sensing quality. The main idea is to maximize the signal-to-noise “higher is better”, based on the optimization of texture coefficient extracted from XRD data using L9 (3³) orthogonal array, with three factors (concentration of NiO precursor, annealing temperature and annealing time). The process parameters are then varied in order to specify the best deposition parameters to obtain optimal texture coefficient of NiO thin films adopted for best oxidation/reduction reactions. After the calculation of S/N ratio based on the texture coefficient, we controlled the concentration and the annealing time to have good structural properties. The factors combination are (A₃B₁C₁) corresponding respectively to the concentration [Ni²⁺] = 0.08 M, the temperature T = 380 °C and the annealing time about 3 min. These factors are considered as the best combination of process parameters that leads to the optimal texture coefficient of NiO thin films. The gas testing was made on the sample with optimal conditions. This sample showed very low grain sizes with a film thickness equal to (380 nm). The gas testing under H₂, acetone and ethanol revealed a high conductance, with a response of 55, at the concentration of 500 ppm with T = 300 °C.     

Wavelets, ridgelets, and curvelets for Poisson noise removal.

In order to denoise Poisson count data, we introduce a variance stabilizing transform (VST) applied on a filtered discrete Poisson process, yielding a near Gaussian process with asymptotic constant variance. This new transform, which can be deemed as an extension of the Anscombe transform to filtered data, is simple, fast, and efficient in (very) low-count situations. We combine this VST with the filter banks of wavelets, ridgelets and curvelets, leading to multiscale VSTs (MS-VSTs) and nonlinear decomposition schemes. By doing so, the noise-contaminated coefficients of these MS-VST-modified transforms are asymptotically normally distributed with known variances. A classical hypothesis-testing framework is adopted to detect the significant coefficients, and a sparsity-driven iterative scheme reconstructs properly the final estimate. A range of examples show the power of this MS-VST approach for recovering important structures of various morphologies in (very) low-count images. These results also demonstrate that the MS-VST approach is competitive relative to many existing denoising methods.     

Morphological Diversity and Sparsity for Multichannel Data Restoration

Over the last decade, overcomplete dictionaries and the very sparse signal representations they make possible, have raised an intense interest from signal processing theory. In a wide range of signal processing problems, sparsity has been a crucial property leading to high performance. As multichannel data are of growing interest, it seems essential to devise sparsity-based tools accounting for such specific multichannel data. Sparsity has proved its efficiency in a wide range of inverse problems. Hereafter, we address some multichannel inverse problems issues such as multichannel morphological component separation and inpainting from the perspective of sparse representation. In this paper, we introduce a new sparsity-based multichannel analysis tool coined multichannel Morphological Component Analysis (mMCA). This new framework focuses on multichannel morphological diversity to better represent multichannel data. This paper presents conditions under which the mMCA converges and recovers the sparse multichannel representation. Several experiments are presented to demonstrate the applicability of our approach on a set of multichannel inverse problems such as morphological component decomposition and inpainting.

Sensorless induction machine observation with nonlinear magnetic characteristic

This paper addresses the problem of state estimation in induction motors. Generally, the motor observer design has been dealt based on standard models neglecting the saturation effect in the magnetic characteristic. As a matter of fact, magnetic saturation cannot be ignored especially when considering control strategies (speed and torque) that involve large flux variations. Such large flux variations are necessary to meet optimal operation conditions in the presence of wide range load torque changes. On the other hand, it is well known that use of mechanical (speed and torque) sensors entails reliability issues. In this paper, a new sensorless adaptive observer is designed for induction machine based on a model that accounts for the nonlinear feature in the magnetic circuit. The observer consists of two interconnected state‐dependent gain observers and is formally shown to provide accurate estimates of the mechanical and magnetic variables using only stator current and voltage measurements. Copyright © 2013 John Wiley & Sons, Ltd.     

Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients

We address the denoising of images contaminated with multiplicative noise, e.g. speckle noise. Classical ways to solve such problems are filtering, statistical (Bayesian) methods, variational methods, and methods that convert the multiplicative noise into additive noise (using a logarithmic function), apply a variational method on the log data or shrink their coefficients in a frame (e.g. a wavelet basis), and transform back the result using an exponential function.