Amorçage cognitif pour amélioration de l’acquisition de la connaissance dans un système tutoriel intelligent

Cette thèse vise à définir une nouvelle méthode d’enseignement pour les systèmes tutoriels intelligents dans le but d’améliorer l’acquisition des connaissances. L’apprentissage est un phénomène complexe faisant intervenir des mécanismes émotionnels et cognitifs de nature consciente et inconsciente. Nous nous intéressons à mieux comprendre les mécanismes inconscients du raisonnement lors de l’acquisition des connaissances. L’importance de ces processus inconscients pour le raisonnement est bien documentée en neurosciences, mais demeure encore largement inexplorée dans notre domaine de recherche. Dans cette thèse, nous proposons la mise en place d’une nouvelle approche pédagogique dans le domaine de l’éducation implémentant une taxonomie neuroscientifique de la perception humaine. Nous montrons que cette nouvelle approche agit sur le raisonnement et, à tour de rôle, améliore l’apprentissage général et l’induction de la connaissance dans un environnement de résolution de problème. Dans une première partie, nous présentons l’implémentation de notre nouvelle méthode dans un système tutoriel visant à améliorer le raisonnement pour un meilleur apprentissage. De plus, compte tenu de l’importance des mécanismes émotionnels dans l’apprentissage, nous avons également procédé dans cette partie à la mesure des émotions par des capteurs physiologiques. L’efficacité de notre méthode pour l’apprentissage et son impact positif observé sur les émotions a été validée sur trente et un participants. Dans une seconde partie, nous allons plus loin dans notre recherche en adaptant notre méthode visant à améliorer le raisonnement pour une meilleure induction de la connaissance. L’induction est un type de raisonnement qui permet de construire des règles générales à partir d’exemples spécifiques ou de faits particuliers. Afin de mieux comprendre l’impact de notre méthode sur les processus cognitifs impliqués dans ce type de raisonnement, nous avons eu recours à des capteurs cérébraux pour mesurer l’activité du cerveau des utilisateurs. La validation de notre approche réalisée sur quarante-trois volontaires montre l’efficacité de notre méthode pour l’induction de la connaissance et la viabilité de mesurer le raisonnement par des mesures cérébrales suite à l’application appropriée d’algorithmes de traitement de signal. Suite à ces deux parties, nous clorons la thèse par une discussion applicative en décrivant la mise en place d’un nouveau système tutoriel intelligent intégrant les résultats de nos travaux.

Theory and numerical integration of subsurface light transport

En synthèse d’images, reproduire les effets complexes de la lumière sur des matériaux transluminescents, tels que la cire, le marbre ou la peau, contribue grandement au réalisme d’une image. Malheureusement, ce réalisme supplémentaire est couteux en temps de calcul. Les modèles basés sur la théorie de la diffusion visent à réduire ce coût en simulant le comportement physique du transport de la lumière sous surfacique tout en imposant des contraintes de variation sur la lumière incidente et sortante. Une composante importante de ces modèles est leur application à évaluer hiérarchiquement l’intégrale numérique de l’illumination sur la surface d’un objet. Cette thèse révise en premier lieu la littérature actuelle sur la simulation réaliste de la transluminescence, avant d’investiguer plus en profondeur leur application et les extensions des modèles de diffusion en synthèse d’images. Ainsi, nous proposons et évaluons une nouvelle technique d’intégration numérique hiérarchique utilisant une nouvelle analyse fréquentielle de la lumière sortante et incidente pour adapter efficacement le taux d’échantillonnage pendant l’intégration. Nous appliquons cette théorie à plusieurs modèles qui correspondent à l’état de l’art en diffusion, octroyant une amélioration possible à leur efficacité et précision.

Analysis of scoliosis trunk deformities using ICA

This paper describes a method for analyzing scoliosis trunk deformities using Independent Component Analysis (ICA). Our hypothesis is that ICA can capture the scoliosis deformities visible on the trunk. Unlike Principal Component Analysis (PCA), ICA gives local shape variation and assumes that the data distribution is not normal. 3D torso images of 56 subjects including 28 patients with adolescent idiopathic scoliosis and 28 healthy subjects are analyzed using ICA. First, we remark that the independent components capture the local scoliosis deformities as the shoulder variation, the scapula asymmetry and the waist deformation. Second, we note that the different scoliosis curve types are characterized by different combinations of specific independent components.

Modified Large Margin Nearest Neighbor Metric Learning for Regression

The main objective of this letter is to formulate a new approach of learning a Mahalanobis distance metric for nearest neighbor regression from a training sample set. We propose a modified version of the large margin nearest neighbor metric learning method to deal with regression problems. As an application, the prediction of post-operative trunk 3-D shapes in scoliosis surgery using nearest neighbor regression is described. Accuracy of the proposed method is quantitatively evaluated through experiments on real medical data.

Multimodal image fusion of anatomical structures for diagnosis, therapy planning and assistance

This paper provides an overview of work done in recent years by our research group to fuse multimodal images of the trunk of patients with Adolescent Idiopathic Scoliosis (AIS) treated at Sainte-Justine University Hospital Center (CHU). We first describe our surface acquisition system and introduce a set of clinical measurements (indices) based on the trunk's external shape, to quantify its degree of asymmetry. We then describe our 3D reconstruction system of the spine and rib cage from biplanar radiographs and present our methodology for multimodal fusion of MRI, X-ray and external surface images of the trunk We finally present a physical model of the human trunk including bone and soft tissue for the simulation of the surgical outcome on the external trunk shape in AIS.

PSWT Based Linear Predictive Coding and Development of a Parallel Multiple Subsequence Structure for DWT Computation

During 1990's the Wavelet Transform emerged as an important signal processing tool with potential applications in time-frequency analysis and non-stationary signal processing.Wavelets have gained popularity in broad range of disciplines like signal/image compression, medical diagnostics, boundary value problems, geophysical signal processing, statistical signal processing,pattern recognition,underwater acoustics etc.In 1993, G. Evangelista introduced the Pitch- synchronous Wavelet Transform, which is particularly suited for pseudo-periodic signal processing.The work presented in this thesis mainly concentrates on two interrelated topics in signal processing,viz. the Wavelet Transform based signal compression and the computation of Discrete Wavelet Transform. A new compression scheme is described in which the Pitch-Synchronous Wavelet Transform technique is combined with the popular linear Predictive Coding method for pseudo-periodic signal processing. Subsequently,A novel Parallel Multiple Subsequence structure is presented for the efficient computation of Wavelet Transform. Case studies also presented to highlight the potential applications.

Design Techniques for Sigmadelta based ADC for Wireless Applications

Analog-to digital Converters (ADC) have an important impact on the overall performance of signal processing system. This research is to explore efficient techniques for the design of sigma-delta ADC,specially for multi-standard wireless tranceivers. In particular, the aim is to develop novel models and algorithms to address this problem and to implement software tools which are avle to assist the designer's decisions in the system-level exploration phase. To this end, this thesis presents a framework of techniques to design sigma-delta analog to digital converters.A2-2-2 reconfigurable sigma-delta modulator is proposed which can meet the design specifications of the three wireless communication standards namely GSM,WCDMA and WLAN. A sigma-delta modulator design tool is developed using the Graphical User Interface Development Environment (GUIDE) In MATLAB.Genetic Algorithm(GA) based search method is introduced to find the optimum value of the scaling coefficients and to maximize the dynamic range in a sigma-delta modulator.

Investigations of Nonlinear Optical Properties of certain organic photonic materials using Z-Scan and DFWM techniques

Nonlinear optical processes in organic compounds have attracted considerable interest in the field of science and technology because of their compelling technological promises in fields of optical communication,computing,switching and signal processing.As a result of the synthesis of novel organic compounds with varying degree of nonlinear optical strength, many practical devices based on these are getting realised giving new theoretical insights into the nonolinear optical behaviour of materials.Organic compounds like phthalocyanines and porphyrins have evoked great deal of interest in the field of photonic technology.The present thesis describes the results obtained from the investigations carried out on the nonlinear optical properties of certain organo-metallic compounds using Z-Scan and DFWM techniques.

Design and Development of Fiber Optic Sensors for Trace Detection of Certain Environmental Pollutants

In recent years,photonics has emerged as an essential technology related to such diverse fields like laser technology,fiber optics,communication,optical signal processing,computing,entertainment,consumer electronics etc.Availabilities of semiconductor lasers and low loss fibers have also revolutionized the field of sensor technology including telemetry. There exist fiber optic sensors which are sensitive,reliable.light weight and accurate devices which find applications in wide range of areas like biomedicine,aviation,surgery,pollution monitoring etc.,apart from areas in basic sciences.The present thesis deals with the design,fabrication and characterization of a variety of cost effective and sensitive fiber optic sensors for the trace detetction of certain environment pollutants in air and water.The sensor design is carried out using the techniques like evanescent waves,micro bending and long period gratings.

Photothermal beam deflection for non-destructive evaluation of semiconductor thin films

Non-destructive testing (NDT) is the use of non-invasive techniques to determine the integrity of a material, component, or structure. Engineers and scientists use NDT in a variety of applications, including medical imaging, materials analysis, and process control.Photothermal beam deflection technique is one of the most promising NDT technologies. Tremendous R&D effort has been made for improving the efficiency and simplicity of this technique. It is a popular technique because it can probe surfaces irrespective of the size of the sample and its surroundings. This technique has been used to characterize several semiconductor materials, because of its non-destructive and non-contact evaluation strategy. Its application further extends to analysis of wide variety of materials. Instrumentation of a NDT technique is very crucial for any material analysis. Chapter two explores the various excitation sources, source modulation techniques, detection and signal processing schemes currently practised. The features of the experimental arrangement including the steps for alignment, automation, data acquisition and data analysis are explained giving due importance to details.Theoretical studies form the backbone of photothermal techniques. The outcome of a theoretical work is the foundation of an application.The reliability of the theoretical model developed and used is proven from the studies done on crystalline.The technique is applied for analysis of transport properties such as thermal diffusivity, mobility, surface recombination velocity and minority carrier life time of the material and thermal imaging of solar cell absorber layer materials like CuInS2, CuInSe2 and SnS thin films.analysis of In2S3 thin films, which are used as buffer layer material in solar cells. The various influences of film composition, chlorine and silver incorporation in this material is brought out from the measurement of transport properties and analysis of sub band gap levels.The application of photothermal deflection technique for characterization of solar cells is a relatively new area that requires considerable attention.The application of photothermal deflection technique for characterization of solar cells is a relatively new area that requires considerable attention. Chapter six thus elucidates the theoretical aspects of application of photothermal techniques for solar cell analysis. The experimental design and method for determination of solar cell efficiency, optimum load resistance and series resistance with results from the analysis of CuInS2/In2S3 based solar cell forms the skeleton of this chapter.

Realisation of a Target Classifier For Noise Sources in the Ocean

This thesis addresses one of the emerging topics in Sonar Signal Processing.,viz.the implementation of a target classifier for the noise sources in the ocean, as the operator assisted classification turns out to be tedious,laborious and time consuming.In the work reported in this thesis,various judiciously chosen components of the feature vector are used for realizing the newly proposed Hierarchical Target Trimming Model.The performance of the proposed classifier has been compared with the Euclidean distance and Fuzzy K-Nearest Neighbour Model classifiers and is found to have better success rates.The procedures for generating the Target Feature Record or the Feature vector from the spectral,cepstral and bispectral features have also been suggested.The Feature vector ,so generated from the noise data waveform is compared with the feature vectors available in the knowledge base and the most matching pattern is identified,for the purpose of target classification.In an attempt to improve the success rate of the Feature Vector based classifier,the proposed system has been augmented with the HMM based Classifier.Institutions where both the classifier decisions disagree,a contention resolving mechanism built around the DUET algorithm has been suggested.

Development of a New Transform:MRT

Fourier transform methods are employed heavily in digital signal processing. Discrete Fourier Transform (DFT) is among the most commonly used digital signal transforms. The exponential kernel of the DFT has the properties of symmetry and periodicity. Fast Fourier Transform (FFT) methods for fast DFT computation exploit these kernel properties in different ways. In this thesis, an approach of grouping data on the basis of the corresponding phase of the exponential kernel of the DFT is exploited to introduce a new digital signal transform, named the M-dimensional Real Transform (MRT), for l-D and 2-D signals. The new transform is developed using number theoretic principles as regards its specific features. A few properties of the transform are explored, and an inverse transform presented. A fundamental assumption is that the size of the input signal be even. The transform computation involves only real additions. The MRT is an integer-to-integer transform. There are two kinds of redundancy, complete redundancy & derived redundancy, in MRT. Redundancy is analyzed and removed to arrive at a more compact version called the Unique MRT (UMRT). l-D UMRT is a non-expansive transform for all signal sizes, while the 2-D UMRT is non-expansive for signal sizes that are powers of 2. The 2-D UMRT is applied in image processing applications like image compression and orientation analysis. The MRT & UMRT, being general transforms, will find potential applications in various fields of signal and image processing.

Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.

Development and Evaluation of Blind Identification Techniques for Nonlinear Systems

Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.

Performance analysis of FIR digital filter design: RNS versus traditional

Residue Number System (RNS) based Finite Impulse Response (FIR) digital filters and traditional FIR filters. This research is motivated by the importance of an efficient filter implementation for digital signal processing. The comparison is done in terms of speed and area requirement for various filter specifications. RNS based FIR filters operate more than three times faster and consumes only about 60% of the area than traditional filter when number of filter taps is more than 32. The area for RNS filter is increasing at a lesser rate than that for traditional resulting in lower power consumption. RNS is a nonweighted number system without carry propogation between different residue digits.This enables simultaneous parallel processing on all the digits resulting in high speed addition and multiplication in the RNS domain