Dedication We dedicate this book to our family members who complete us. In particular, M. Ben-chohra's dedication is to his wife, Kheira, and his children, Mohamed, Maroua, and Abdelillah; J. Henderson dedicates to his wife, Darlene, and his descendants, Kathy, Contents Preface xi 1. Preliminaries 1 1.1. Definitions and results for multivalued analysis 1 1.2. Fixed point theorems 4 1.3. Semigroups 7 1.4. Some additional lemmas and notions 9 2. Impulsive ordinary differential equations & inclusions 11 2.1. Introduction 11 2.2. Impulsive ordinary differential equations 12 2.3. Impulsive ordinary differential inclusions 24 2.4. Ordinary damped differential inclusions 49 2.5. Notes and remarks 62 3. Impulsive functional differential equations & inclusions 63 3.1. Introduction 63 3.2. Impulsive functional differential equations 63 3.3. Impulsive neutral differential equations 74 3.4. Impulsive functional differential inclusions 80 3.5. Impulsive neutral functional DIs 95 3.6. Impulsive semilinear functional DIs 107 3.7. Notes and remarks 118 4. Impulsive differential inclusions with nonlocal conditions 119 4.1. Introduction 119 4.2. Nonlocal impulsive semilinear differential inclusions 119 4.3. Existence results for impulsive functional semilinear differential inclusions with nonlocal conditions 136 4.4. Notes and remarks 145 5. Positive solutions for impulsive differential equations 147 5.1. Introduction 147 5.2. Positive solutions for impulsive functional differential equations 147 5.3. Positive solutions for impulsive boundary value problems 154 5.4. Double positive solutions for impulsive boundary value problems 159 5.5. Notes and remarks 165 viii Contents 6. Boundary value problems for impulsive differential inclusions 167 6.1. Introduction 167 6.2. First-order impulsive differential inclusions with periodic boundary conditions 167 6.3. Upper-and lower-solutions method for impulsive differential inclusions with nonlinear boundary conditions 184 6.4. Second-order boundary value problems 191 6.5. Notes and remarks 198 7. Nonresonance impulsive differential inclusions 199 7.1. Introduction 199 7.2. Nonresonance first-order impulsive functional differential inclusions with periodic boundary conditions 199 7.3. Nonresonance second-order impulsive functional differential inclusions with periodic boundary conditions 209 7.4. Nonresonance higher-order boundary value problems for impulsive functional differential inclusions 217 7.5. Notes and remarks 227 8. Impulsive differential equations & inclusions with variable times 229 8.1. Introduction 229 8.2. First-order impulsive differential equations with variable times 229 8.3. Higher-order impulsive differential equations with variable times 235 8.4. Boundary value problems for differential inclusions with variable times 241 8.5. Notes and remarks 252 9. Nondensely defined impulsive differential equations & inclusions 253 9.1. Introduction 253 9.2. Nondensely defined impulsive semilinear differential equations with nonlocal conditions 253 9.3. Nondensely defined …

Impulsive Differential Equations and Inclusions

Computer Aided Civil and Infrastructure Engineering

Intraocular Tuberculosis—An Update

The patterns of ultrasonic backscattered echoes represent valuable information pertaining to the geometric shape, size, and orientation of the reflectors as well as the microstructure of the propagation path. Accurate estimation of the ultrasonic echo pattern is essential in determining the object/propagation path properties. In this study, we model ultrasonic backscattered echoes in terms of superimposed Gaussian echoes corrupted by noise. Each Gaussian echo in the model is a nonlinear function of a set of parameters: echo bandwidth, arrival time, center frequency, amplitude, and phase. These parameters are sensitive to the echo shape and can be linked to the physical properties of reflectors and frequency characteristics of the propagation path. We address the estimation of these parameters using the maximum likelihood estimation (MLE) principle, assuming that all of the parameters describing the shape of the echo are unknown but deterministic. In cases for which noise is characterized as white Gaussian, the MLE problem simplifies to a least squares (LS) estimation problem. The iterative LS optimization algorithms when applied to superimposed echoes suffer from the problem of convergence and exponential growth in computation as the number of echoes increases. In this investigation, we have developed expectation maximization (EM)-based algorithms to estimate ultrasonic signals in terms of Gaussian echoes. The EM algorithms translate the complicated superimposed echoes estimation into isolated echo estimations, providing computational versatility. The algorithm outperforms the LS methods in terms of independence to the initial guess and convergence to the optimal solution, and it resolves closely spaced overlapping echoes.

Model-based estimation of ultrasonic echoes. Part I: Analysis and algorithms

Multi-objective optimization using Taguchi based grey relational analysis for micro-milling of Al 7075 material with ball nose end mill

Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera–subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.

Past, Present, and Future of Face Recognition: A Review

Deep learning for real-time semantic segmentation: Application in ultrasound imaging

Detection process approach of tool wear in high speed milling

In the field of biological tissue characterization, fundamental acoustic attenuation properties have been demonstrated to have diagnostic importance. Attenuation caused by scattering and absorption shifts the instantaneous spectrum to the lower frequencies. Due to the time-dependence of the spectrum, the attenuation phenomenon is a time-variant process. This downward shift may be evaluated either by the maximum energy frequency of the spectrum or by the center frequency. In order to improve, in strongly attenuating media, the results given by the short-time Fourier analysis and the short-time parametric analysis, we propose two approaches adapted to this time-variant process: an adaptive method and a time-varying method. Signals backscattered by an homogeneous medium of scatterers are modeled by a computer algorithm with attenuation values ranging from 1 to 5 dB/cm MHz and a 45 MHz transducer center frequency. Under these conditions, the preliminary results obtained with the proposed time-variant methods, compared with the classical short-time Fourier analysis and the short-time auto-regressive (AR) analysis, are superior in terms of standard deviation (SD) of the attenuation coefficient estimate. This study, based on nonstationary AR spectral estimation, promises encouraging perspectives for in vitro and in vivo applications both in weakly and highly attenuating media.

/pdf/time-varying-autoregressive-spectral-estimation-for-56no0yefom.pdf

Time-varying autoregressive spectral estimation for ultrasound attenuation in tissue characterization

Several studies have been carried out in the last twenty years on the characterization and detection of cerebral artery emboli. From the detection point of view the existing methods are largely based on the classical Fourier analysis of which the well known limitations provide poor accuracy. This paper first recalls existing methods based on Fourier, Wigner-Ville and wavelet approaches. It then presents new emboli detection methods based on parametric signal processing approaches. The basic idea of these parametric methods is to compare the Doppler embolic signal to its autoregressive model. The detection principle consists in constructing a decision information which contains the signature of the micro-embolus being sought. The detection is finally evaluated using receiver operating characteristic (ROC) curves. Comparison between the new methods and classical approaches is performed using a realistic embolic signal simulation. Furthermore, to validate the authors' theoretical study, they tested their new algorithms using in vivo signals. This comparison shows the significant inaccuracy of existing methods to detect micro-emboli.

/pdf/micro-emboli-detection-an-ultrasound-doppler-signal-2x0ifpmjy8.pdf

Abdeldjalil Ouahabi

Papers

Past, Present, and Future of Face Recognition: A Review

Deep learning for real-time semantic segmentation: Application in ultrasound imaging

Detection process approach of tool wear in high speed milling

Time-varying autoregressive spectral estimation for ultrasound attenuation in tissue characterization

Micro-emboli detection: an ultrasound Doppler signal processing viewpoint