Tesis sobre el tema "Multisensory method"

This project is based on my personal experience of being tutor of students with special needs and of all ages. However, in this paper I focus on dyslexic students of the Lower Secondary School in the process of learning English as a foreign language. Moreover, i attempt to present how the phonological/orthographic teaching can help pupils to progress in all other skills. In the first part I provide insight into learning disabilities, in particular into dyslexia and its connected difficulties from both a learning and a psychological perspective. Moreover, I will introduce a part dedicated to the Italian question regarding dyslexic students at school compared to UK and USA. In the second part i focus on the main problems a dyslexic student has to face with the Italian mother tongue in order to understand the learning obstacles which can influence the study of English as a foreign language, examined in depth in the fourth chapter. The third chapter deals with dyslexia from a psychological perspective, taking into account that students have to cope with high levels of anxiety and stress; for this reason it is pivotal to highlight how working on self-esteem can influence both the students’ motivation and the scholastic achievement. As a consequence, an early diagnosis appears to be significant in the process of language acquisition. Thus, the Educational Helper’s role becomes a key figure to support dyslexic students at home to find useful strategies with the aim of becoming independent learners. Finally, the research project of two case studies attempts to provide a future suggestion in teaching English as a foreign language to dyslexic students, that means concentrating on phonological/orthographic competences and words acquisition through the use of multisensory techniques.

The objective of this research is to demonstrate how an underlying system's state vector distribution can be determined in a distributed heterogeneous sensor network with reduced subspace observability at the individual nodes. We show how the network, as a whole, is capable of observing the target state vector even if the individual nodes are not capable of observing it locally. The initialization algorithm presented in this work can generate the initial state vector distribution for networks with a variety of sensor types as long as the measurements at the individual nodes are known functions of the target state vector. Initialization is accomplished through a novel distributed implementation of the particle filter that involves serial particle proposal and weighting strategies, which can be accomplished without sharing raw data between individual nodes in the network. The algorithm is capable of handling missed detections and clutter as well as compensating for delays introduced by processing, communication and finite signal propagation velocities. If multiple events of interest occur, their individual states can be initialized simultaneously without requiring explicit data association across nodes. The resulting distributions can be used to initialize a variety of distributed joint tracking algorithms. In such applications, the initialization algorithm can initialize additional target tracks as targets come and go during the operation of the system with multiple targets under track.

For 500 years transient light phenomena (TLP) have been observed on the lunar surface by ground-based observers. The actual physical reason for most of these events is today still unknown. Current plans of NASA and SpaceX to send astronauts back to the Moon and already successful deep-space CubeSat mission will allow in the future research nanosatellite missions to the cislunar space. This thesis presents a new hardware and software concept for a future payload on such a nanosatellite. The main task was to develop and implement a high-performance image processing algorithm which task is to detect short brightening flashes on the lunar surface. Based on a review of historic reported phenomena, possible explanation theories for these phenomena and currently active and planed ground- or space-based observatories possible reference scenarios were analyzed. From the presented scenarios one, the detection of brightening events was chosen and requirements for this scenario stated. Afterwards, possible detectors, processing computers and image processing algorithms were researched and compared regarding the specified requirements. This analysis of available algorithm was used to develop a new high-performance detection algorithm to detect transient brightening events on the Moon. The implementation of this algorithm running on the processor and the internal GPU of a MacMini achieved a framerate of 55 FPS by processing images with a resolution of 4.2 megapixel. Its functionality and performance was verified on the remote telescope operated by the Chair of Space Technology of the University of Würzburg. Furthermore, the developed algorithm was also successfully ported on the Nvidia Jetson Nano and its performance compared with a FPGA based image processing algorithm. The results were used to chose a FPGA as the main processing computer of the payload. This concept uses two backside illuminated CMOS image sensor connected to a single FPGA. On the FPGA the developed image processing algorithm should be implemented. Further work is required to realize the proposed concept in building the actual hardware and porting the developed algorithm onto this platform.

L'impact des nuages de glace sur le cycle de l'eau et le bilan radiatif est encore incertain en raison de la complexité des processus nuageux qui rend difficile l'acquisition d'observations adéquates sur les propriétés des nuages de glace et leur représentation dans les modèles de circulation générale. Les instruments de télédétection actifs et passifs, tels que les radiomètres, les radars et les lidars, sont couramment utilisés pour les étudier. La restitution des propriétés microphysiques des nuages (extinction, contenu en glace, rayon effectif, ...) peut être effectuée à partir d'un seul instrument ou de la combinaison de plusieurs instruments. L’intérêt de l’utilisation de synergies instrumentales pour restituer les propriétés nuageuses réside dans le fait que cela permet de réduire les incertitudes dues aux lacunes des différents instruments pris séparément. La constellation de satellites A-Train a considérablement amélioré notre connaissance des nuages. Depuis 2006, le lidar à rétrodiffusion visible CALIOP embarqué à bord du satellite CALIPSO et le radar nuage à 94GHz CPR embarqué à bord du satellite CloudSat ont permis l’acquisition de profils nuageux sur l’ensemble du globe et de nombreuses méthodes synergiques de restitution ont été adaptées à ces instruments. En 2021 sera lancé un nouveau satellite, EarthCARE, embarquant des instruments de télédétection de pointe, notamment ATLID, un lidar à haute résolution spectrale (HSRL) à 355 nm et un radar nuage Doppler à 94 GHz. La mission principale de ce satellite est de quantifier les interactions entre les nuages, les aérosols et le bilan radiatif de la Terre afin d'améliorer les prévisions météorologiques et des modèles climatiques. Grâce à son instrumentation avancée installée sur une plate-forme unique, cette nouvelle mission devrait fournir des observations sans précédent des nuages depuis l'espace. Cependant, pour ce faire, les algorithmes synergiques développés pour les mesures de l'A-Train doivent être adaptés à cette nouvelle configuration instrumentale. Au cours de ma thèse, je me suis concentrée sur l'algorithme Varcloud développé en 2007 par Delanoë et Hogan et basé sur une technique variationnelle. La première partie du travail a consisté à adapter certains paramètres du modèle microphysique de l’algorithme aux études récentes d’une large base de données in situ. En particulier, les questions de la paramétrisation du rapport lidar et du choix de la relation masse-diamètre pour les cristaux de glace ont été abordées. La deuxième partie de mon travail a consisté à adapter l'algorithme de restitution Varcloud aux plates-formes aéroportées. Les plates-formes aéroportées sont idéales pour préparer et valider les missions spatiales, permettant de réaliser des mesures sous-trace, colocalisées avec les instruments spatiaux. En particulier, le HALO allemand et le Falcon 20 français ont des charges utiles très complémentaires et sont parfaitement conçus pour la préparation et la validation de la mission EarthCare. Les deux avions embarquent notamment un lidar à haute résolution spectrale (355 nm sur le Falcon et 532 nm sur le HALO) et un radar Doppler à 36 GHz (HALO) et 95 GHz (Falcon). À l'automne 2016, une campagne aéroportée dans laquelle les deux avions étaient impliqués s'est déroulée en Islande, à Keflavik, dans le cadre du projet NAWDEX. Les mesures recueillies au cours de cette campagne fournissent un ensemble de données intéressant pour caractériser la microphysique et la dynamique des nuages dans l'Atlantique Nord, région qui présent un grand intérêt pour les missions Cloudsat-CALIPSO et EarthCARE. En outre, une série de vols communs avec observation de la même scène nuageuse par les deux plates-formes ont été réalisées, fournissant des données permettant d'étudier l'influence de la configuration instrumentale sur les propriétés des nuages de glace restituées
The impact of ice clouds on the water cycle and radiative budget is still uncertain due to the complexity of cloud processes that makes it difficult to acquire adequate observations of ice cloud properties and parameterize them into General Circulation Models. Passive and active remote sensing instruments, radiometers, radars and lidars, are commonly used to study ice clouds. Inferring cloud microphysical properties (extinction, ice water content, effective radius, ...) can be done from one instrument only, or from the synergy of several. The interest of using instrumental synergies to retrieve cloud properties is that it can reduce the uncertainties due to the shortcomings of the different instruments taken separately. The A-Train constellation of satellites has considerably improved our knowledge of clouds. Since 2006, the 532nm backscattering lidar CALIOP on board the satellite CALIPSO and the 94GHz cloud radar CPR on board the satellite CloudSat have acquired cloud vertical profiles globally and many lidar-radar synergetic methods have been adapted to CloudSat and CALIPSO data. In 2021 will be launched a new satellite, EarthCARE, boarding state of the art remote sensing instrumentation, in particular ATLID, a High Spectral Resolution Lidar (HSRL) at 355nm and a Doppler cloud radar at 94 GHz. The main mission of this satellite is to quantify interactions between clouds, aerosols and the Earth's radiation budget in order to improve weather prediction and climate models. Thanks to its advanced instrumentation mounted on a single platform, this new mission is expected to provide unprecedented observations of clouds from space. However, to do so, the synergistic algorithms that were developed for A-Train measurements have to be adapted to this new instrumental configuration. During my PhD, I focused on the Varcloud algorithm that was developed in 2007 by Delanoë and Hogan, based on a variational technique. The first part of the work consisted in adapting some parameters of the microphysical model of the algorithm to recent studies of a large dataset of in-situ measurements. In particular, the questions of a parameterization of the lidar extinction-to-backscatter ratio and the choice of the mass-size relationship for ice crystals were addressed. The second part of my work consisted in adapting the Varcloud retrieval algorithm to airborne platforms. Airborne platforms are ideal to prepare and validate space missions, allowing for direct underpasses of spaceborne instruments. Moreover, German and French aircraft, respectively HALO and French Falcon 20 have very complementary payloads and are perfectly designed for the preparation, the calibration and the validation of EarthCare. Both aircraft board a high spectral resolution lidar (355 nm on the French Falcon and 532 nm on the HALO) and a Doppler radar at 36 GHz (HALO) and 95 GHz (Falcon). In fall 2016 a field campaign related to the NAWDEX project took place in Iceland, Keflavik with both aircraft involved. The measurements collected during this campaign provide an interesting dataset to characterize cloud microphysics and dynamics in the North Atlantic, which are of high interest regarding the Cloudsat-CALIPSO and EarthCARE missions. In addition, a series of common legs with the same cloud scene observed by both platforms were performed, providing data to study the influence of the instrumental configuration on the retrieved ice cloud properties

In the last decade the increasing availability of high resolution remote sensing data enabled precision forestry, which aims to obtain a precise reconstruction of the forest at stand, sub-stand or individual tree level. This calls for the need of developing techniques tailored on such new data that can achieve accurate forest parameters estimations. Moreover, in this context the integration of multiple remote sensing data brings to a more comprehensive representation of the forest structure. Accordingly, the goal of this thesis is the development of novel methods for the automatic estimation of forest parameters that can exploit the different properties of multiple remote sensing data sources. The thesis provides five main novel contributions to the state-of-the-art. The first contribution of the thesis addresses the problem of the single tree crowns segmentation in multilayered forest by using very high-density multireturn LiDAR data. The aim of the proposed method is to fully exploit the potential of these data to detect and delineate the single tree crowns of both dominant and sub-dominant trees by a hierarchical 3-D segmentation technique applied directly in the point cloud space. The second contribution of the thesis regards the estimation of the diameter at breast height (DBH) of each individual tree by using high-density LiDAR data. The proposed data-driven method extensively exploits the information provided by the high resolution data to model the main environmental variables that can affect the stems growth in terms of crown structure, topography and forest density. The third contribution of the thesis proposes a 3-D model based approach to the reconstruction of the tree top height by fusing low-density LiDAR data and high resolution optical images. The geometrical structure of the tree is reconstructed via a properly defined parametric model which drives the fusion of the data. Indeed, when high resolution LiDAR data is not available, the integration of different remote sensing data sources represents a valid solution to improve the parameter estimation. In this context, the fourth contribution of the thesis addresses the fusion of low-density airborne LiDAR data and terrestrial LiDAR data to perform localized forest analysis. The proposed technique automatically registers the two LiDAR point clouds by using the spatial pattern of the forest in order to integrate the data and to automatically estimate the crown parameters. The fusion of the LiDAR point clouds leads to a more comprehensive representation of the 3-D structure of the crowns. Finally, we introduce a sensor-driven domain adaptation method for the classification of forest areas sharing similar properties but located in different areas. The proposed method takes advantage from the availability of multiple remote sensing data to detect features subspaces where data manifolds are partially (or completely) aligned. Qualitative and quantitative experimental results obtained on large forest areas confirm the effectiveness of the methods developed in this thesis, which allow an improvement in terms of accuracy when compared to other state-of-the-art methods.

The purpose of this study is to examine the effect of experience staging techniques (personalization through co-creation and multisensory stimuli) on engagement level. This study also explores the possible contribution of experience staging techniques as practical tools that recreation professionals can use to better engage participants in recreation activities and events. A 2-way univariate ANOVA revealed no significant relationship between the use of co-creative and multisensory stimulating techniques and engagement levels in participants (F (3,200) = .263, p = .826, partial η2 = .004). Practical applications for recreation professionals and further research opportunities are discussed.

This dissertation explores data fusion methodology to deduce an overall inference from the data gathered from multiple heterogeneous sources. Typically, if there existed a data source in which the data were reliable and unbiased, then data fusion would not be necessary. Data fusion methodology combines data form multiple diverse sources so that the desired information - such as the population mean - is improved despite redundancies, inaccuracies, biases, and inflated variability in the data. Examples of data fusion include estimating average demand from similar sources, and integrating fatality counts from different media sources after a catastrophe. The approach in this study combines "inputs" from distinct sources so that the information is "fused." Another way of describing this process is "data integration." Important assumptions are 1. Several sources provide "inputs" for information used to estimate parameters of a probability distribution. 2. Since distributions for the data from the sources are heterogeneous, some sources are less reliable. 3. Distortions, bias, censorship, and systematic errors may be more prominent in data from certain sources. 4. The sample size of sources data, number of "inputs," may be very small. Examples of information from multiple sources are abundant: traffic information from sensors at intersections, multiple economic indicators from various sources, demand data for product using similar retail stores as sources, polling data from various sources, and disaster count of fatalities from different media sources after a catastrophic event. This dissertation seeks to address a gap in the operations literature by addressing three research questions regarding entropy base data fusion (EBDF) approaches to estimation. Three separate, but unifying, essays address the research questions for this dissertation. Essay 1 provides an overview of supporting literature for the research questions. A numerical analysis of airline maximum wait time data illustrates the underlying issues involved in EBDF methods. This essay addresses the research question: Why consider alternative entropy-based weighting methods? Essay 2 introduces 13 data fusion methods. A Monte Carlo simulation study examines the performance of these methods in estimating the mean parameter of a population with either a normal or lognormal distribution. This essay addresses the following research questions: 1. Can an alternative formulation for Shannon's entropy enhance the performance of Sheu (2010)'s data fusion approach? 2. Do symmetric and skewed distributions affect the 13 data fusion methods differently? 3. Do negative and positive biases affect the performance of the 13 methods differently? 4. Do entropy based data fusion methods outperform non-entropy based data fusion methods? 5. Which data fusion methods are recommended for symmetric and skewed data sets when no bias is present? What is the recommendation under conditions of few data sources? Essay 3 explores the use of the data fusion method estimates of the population mean in a newsvendor problem. A Monte Carlo simulation study investigates the accuracy of the using the estimates provided in Essay 2 as the parameter estimate for the distribution of demand that follows an exponential distribution. This essay addresses the following research questions: 1. Do data fusion methods with relatively strong performance in estimating the parameter mean estimate also provide relatively strong performance in estimating the optimal demand under a given ratio of overage and underage costs? 2. Do any of the data fusion methods deteriorate or improve with the introduction of positive and negative bias? 3. Do the alternative entropy formulations to Shannon's entropy enhance the performance of the methods on a relative basis? 4. Is the relative rank ordering performance of the data fusion methods different in Essay 2 and Essay 3 in the resulting performances of the methods? The contribution of this research is to introduce alternative EBDF methods, and to establish a framework for using EBDF methods in supply chain decision making. A comparative Monte Carlo simulation analysis study will provide a basis to investigate the robustness of the proposed data fusion methods for estimation of population parameters in a newsvendor problem with known distribution, but unknown parameter. A sensitivity analysis is conducted to determine the effect of multiple sources, sample size, and distributions.

In the recent years, remote sensing has faced a huge evolution. The constantly growing availability of remote sensing data has opened up new opportunities and laid the foundations for many new challenges. The continuous space missions and new constellations of satellites allow in fact more and more frequent acquisitions, at increasingly higher spatial resolutions, and at an almost total coverage of the globe. The availability of such an huge amount data has highlighted the need for automatic techniques capable of processing the data and exploiting all the available information. Meanwhile, the almost unlimited potential of machine learning has changed the world we live in. Artificial neural Networks have break trough everyday life, with applications that include computer vision, speech processing, autonomous driving but which are also the basis of commonly used tools such as online search engines. However, the vast majority of such models are of the supervised type and therefore their applicability rely on the availability of an enormous quantity of labeled data available to train the models themselves. Unfortunately, this is not the case with remote sensing, in which the enormous amounts of data are opposed to the almost total absence of ground truth. The purpose of this thesis is to find the way to exploit the most recent deep learning techniques, defining a common thread between two worlds, those of remote sensing and deep learning, which is often missing. In particular, this thesis proposes three novel contributions which face current issues in remote sensing. The first one is related to multisensor image registration and combines generative adversarial networks and non-linear optimization of crosscorrelation-like functionals to deal with the complexity of the setting. The proposed method was proved able to outperform state of the art approaches. The second novel contribution faces one of the main issues in deep learning for remote sensing: the scarcity of ground truth data for semantic segmentation. The proposed solution combines convolutional neural networks and probabilistic graphical models, two very active areas in machine learning for remote sensing, and approximate a fully connected conditional random field. The proposed method is capable of filling part of the gap which separate a densely trained model from a weakly trained one. Then, the third approach is aimed at the classification of high resolution satellite images for climate change purposes. It consist of a specific formulation of an energy minimization which allows to fuse multisensor information and the application a markov random field in a fast and efficient way for global scale applications. The results obtained in this thesis shows how deep learning methods based on artificial neural networks can be combined with statistical analysis to overcome their limitations, going beyond the classic benchmark environments and addressing practical, real and large-scale application cases.

L'objectif de la thèse est de développer des algorithmes de fusion de données recueillies à l’aide d'un réseau de capteurs sans fil afin de localiser plusieurs sources émettant un agent chimique ou biologique dans l'air. Ces capteurs détectent la concentration de la substance émise, transportée par advection et diffusion, au niveau de leurs positions et de communiquer cette information à un centre de traitement. L’information recueillie de façon collaborative est d'abord utilisée pour localiser les capteurs déployés au hasard et ensuite pour localiser les sources. Les applications comprennent, entre autres, la surveillance environnementale et la surveillance de sites sensibles ainsi que des applications de sécurité dans le cas d'une libération accidentelle ou intentionnelle d'un agent toxique. Toutefois, l'application considérée dans la thèse est celle de la détection et la localisation de mines terrestres. Dans cette approche, les mines sont considérées comme des sources émettrices de produits chimiques explosifs.La thèse comprend une contribution théorique où nous étendons l'algorithme de propagation de la croyance, un algorithme de fusion de données bien connu et largement utilisé pour l'estimation collaborative d'état dans les réseaux de capteurs, au cadre des méthodes à erreurs bornées. Le nouvel algorithme est testé sur le problème de l'auto-localisation dans les réseaux de capteurs statiques ainsi que l'application de suivi d'un objet mobile en utilisant un réseau de capteurs de distance. Autres contributions comprennent l'utilisation d'une approche probabiliste bayésienne avec des techniques d'analyse de données pour localiser un nombre inconnu de sources émettrices de vapeur
The aim of the thesis is to develop fusion algorithms for data collected from a wireless sensor network in order to locate multiple sources emitting some chemical or biological agent in the air. These sensors detect the concentration of the emitted substance, transported by advection and diffusion, at their positions and communicate this information to a treatment center. The information collected in a collaborative manner is used first to locate the randomly deployed sensors and second to locate the sources. Applications include, amongst others, environmental monitoring and surveillance of sensitive sites as well as security applications in the case of an accidental or intentional release of a toxic agent. However, the application we consider in the thesis is that of landmine detection and localization. In this approach, the land mines are considered as sources emitting explosive chemicals. The thesis includes a theoretical contribution where we extend the Belief Propagation algorithm, a well-known data fusion algorithm that is widely used for collaborative state estimation in sensor networks, to the bounded error framework. The novel algorithm is tested on the self-localization problem in static sensor networks as well as the application of tracking a mobile object using a network of range sensors. Other contributions include the use of a Bayesian probabilistic approach along with data analysis techniques to locate an unknown number of vapor emitting sources

Les systèmes de nez et de langues électroniques à base de capteurs chimiques et électrochimiques constituent une solution avantageuse pour la caractérisation des odeurs et des saveurs émanant des produits agroalimentaires. La sélectivité croisée de la matrice des capteurs couplée aux méthodes de reconnaissance de formes est l'élément clé dans la conception et le développement de ces systèmes. Dans cette optique, nous avons démontré la capacité d'un dispositif expérimental de nez électronique à discriminer entre les différents types de drogues, à analyser la fraîcheur des fromages, à identifier entre les fromages adultérés et à différentier entre les eaux potables et usées. Nous avons également réussi à classifier correctement les eaux potables (minérales, de source, gazeuse et de robinet) et usées par utilisation d'une langue électronique potentiométrique. Cette étude a été validée par la Chromatographie en Phase Gazeuse couplée à la Spectrométrie de Masse (CPG-MS). En outre, nous avons développé une langue électronique voltammétrique à base d'une électrode de Diamant Dopé au Bore pour différencier les phases de traitement des eaux usées domestiques et hospitaliers et pour identifier les différents métaux lourds (Pb, Hg, Cu, Cd, Ni et le Zn) contenus dans l'eau du fleuve Rhône. La Voltammétrie à Redissolution Anodique à Impulsion Différentielle (DPASV) a été utilisée comme une méthode électrochimique pour caractériser les eaux étudiées. Enfin, les systèmes multicapteurs hybrides se sont avérés un bon outil analytique pour caractériser les produits de l'industrie agroalimentaire tels que les jus tunisiens et les huiles d'olives marocaines
Electronic noses and tongues systems based on chemical and electrochemical sensors are an advantageous solution for the characterisation of odours and tastes that are emanating from food products. The cross-selectivity of the sensor array coupled with patter recognition methods is the key element in the design and development of these systems. In this context, we have demonstrated the ability of an electronic nose device to discriminate between different types of drugs, to analyse cheeses freshness, to identify adulterated cheeses and to differentiate between potable and wastewaters. We have also succeeded to correctly classify drinking waters (mineral, natural, sparkling and tap) and wastewaters by using a potentiometric electronic tongue. This study was validated by Gas Chromatography coupled with Mass Spectrometry (GC-MS). Furthermore, we have developed a voltammetric electronic tongue based on a Diamond Doped Boron electrode to differentiate treatment stages of domestic and hospital wastewaters and to identify different heavy metals (Pb, Hg, Cu, Cd, Ni and Zn) contained in Rhône river. The Differential Pulse Anodic Stripping Voltammetry (DPASV) was used as an electrochemical method to characterise the studied waters. Finally, the hybrid multisensor systems have proven to be good analytical tools to characterise the products of food industry such as Tunisian juices and Moroccan olive oils

Les capteurs et les biocapteurs sont des moyens d’analyse en plein essor à la fois rapides, sensibles, sélectifs et peu coûteux, applicables à des domaines très variés (agroalimentaire, environnement, biomédical…). Dans ce travail de recherche, nous nous sommes intéressés au développement de trois dispositifs à savoir un nez et une langue électroniques à base de systèmes multicapteurs pour l’analyse des odeurs et des saveurs ainsi que les biocapteurs. Les deux premiers dispositifs ont permis dans un premier temps de caractériser et de détecter les fraudes dans les produits de l’industrie agroalimentaire. Ainsi, nous sommes parvenus à détecter les pratiques frauduleuses dans l’huile d’argan par le nez et par la langue électroniques. Nous avons, par ailleurs, réussi à caractériser des miels de différentes origines géographiques et botaniques et à détecter l’adultération du miel pur par l’utilisation d’une langue électronique voltammétrique. Enfin, nous avons démontré l’efficacité des systèmes de nez et de langue électroniques à discriminer cinq marques de lait pasteurisé marocain. Des limitations du système de nez et de langue électroniques ont été révélées quant à la discrimination du lait pasteurisé (Jawda) en fonction des jours de stockage. Par contre, la fusion des données des deux systèmes moyennant un niveau d'abstraction intermédiaire a permis d’améliorer cette discrimination. Dans une deuxième étape, nous avons développé deux biocapteurs, le premier est basé sur l’utilisation de Polymère à Empreinte Moléculaire (MIP) dédié à la détection de la tétracycline dans le miel. Alors que le second est un immunocapteur conçu pour la détection de l’ochratoxine A. Le MIP, utilisé dans le premier cas, a été synthétisé à la surface d’électrodes en or par électropolymérisation des nanoparticules d'or fonctionnalisées par le p-aminothiophénol en présence de la tétracycline comme molécule empreinte. Dans le deuxième cas, un nouveau biocapteur capacitif basé sur l’utilisation d’un substrat de Nitrure de Silicium (Si3N4) combiné avec une nouvelle structure de nanoparticules magnétiques (MNPs) pour la détection de l’ochratoxine A a été conçu. En effet, Les MNPs possédant une terminaison carboxylique ont été liés de manière covalente à la monocouche auto-assemblée du silane-amine (3-aminopropyl triéthoxysilane APTES). Enfin, les anticorps anti-ochratoxine A ont été immobilisés sur les MNPs par liaison amide. Les performances des deux biocapteurs (limite de détection, sélectivité, reproductibilité) ont ensuite été évaluées
Sensors and Biosensors are rapid, sensitive, selective and low-cost analytical devices of growing interest for a wide range of application fields (e.g. food, environment, health …). This research focused on the development of three devices namely an electronic nose and tongue and electrochemical biosensors with applications in food analysis. The first two devices allowed the characterization and detection of frauds in the food field. Thus, we have been able to detect fraudulent practices in argan oil by using an electronic nose and tongue systems. In addition, the electronic tongue has successfully classified honeys of different geographical and botanical origins and detects the adulteration of pure honey. Finally, we have demonstrated the ability of the electronic nose and tongue systems to classify five brands of Moroccan pasteurized milk and to discriminate against them based on their storage days. In the second stage, we have developed two biosensors. The first one is based on a molecularly imprinted polymer (MIP) for the detection of tetracycline in honey. The second one is based on an immunosensor devoted for the detection of ochratoxin A. For the first biosensor, imprinted gold nanoparticles composites are assembled on Au surfaces by the electropolymerization of p-amino-thiophenol functionalized gold nanoparticles in the presence of the imprint molecule. In the second case, we have developed a novel capacitance electrochemical biosensor based on silicon nitride substrate (Si3N4) combined with a new structure of mangnetic nanoparticles (MNPs). Indeed, The MNPs with terminated carboxylic acid were covalently bonded to Si3N4 through a Self-Assembled Monolayers (SAMs) of the silane-amine (3-Aminopropyl) triethoxysilane (APTES). Finally anti-ochratoxin A antibodies were immobilized on MNPs by amide bonding. The performances of the two biosensors (limit of detection, selectivity, reproducibility) were then evaluated and the results are generally satisfactory

Faculty of Humanities School of Education 9805090w NATALIE@WBS.CO.ZA
Learning disabilities have become of increasing concern for educators. More and more children are having difficulty learning to read and write. This dissertation investigates what constitutes a learning disability, its etiology and whether or not it is possible to identify these disabilities in early childhood. The investigation further aims to discover if these learning disabilities are comprised of sub-disabilities and if these can be identified as such. To this end the research aims to determine the most appropriate remedial intervention strategies used for learning disabilities. Multisensory intervention is therefore explored. On the basis of this the Montessori Method is examined to ascertain whether or not the method can contribute to multisensory intervention at the preschool level. It is argued that the Montessori Method is admirably suited to making such a contribution. Further empirical research for these claims is indicated.

碩士
國立臺中教育大學
教師專業碩士學位學程
107
Phonological awareness is vital in early English acquisition and can be a solid foundation for reading and writing skills. The purpose of the action research was to explore the effects a series of multisensory-based English lessons had on the phonological awareness among second-grade students at elementary school. In total, 13 second-grade students from a rural elementary school in Kaohsiung participated in this study. It began from September to November in 2018, covering 29 teaching hours in 10 weeks. With the use of visual, auditory and tactile/kinesthetic senses in the multi-sensory method, the students participated in learning activities that centered around two subskills of phonological awareness: rhyme and oddity tasks. Both quantitative and qualitative data were collected. The quantitative data were the pre-and post-test scores of a phonological awareness test targeting the two subskills. The qualitative data included teaching reflections, class observation logs, worksheets and learning evaluation sheets. The test scores were analyzed with the Wilcoxon rank sum test using SPSS 20, and the qualitative data were categorized and analyzed to observe any pattern. The results of this study are as follows: 1.Multisensory phonological awareness instruction significantly improved students’ phonological awareness subskills: rhyme and oddity tasks 2.In using the multisensory teaching method, gestures that connect learning should be unified and simplified to facilitate learning. 3.The addition of visual aids such as texts and graphics during teaching can help retain students’ memory of sounds. 4.Senses should be selected according to the teaching objectives; not all senses need to be used simultaneously in multisensory instruction. 5.Sensory movements should be chosen based on the length of the sound and other factors of different phonological awareness subskills. Based on the results, this study concludes with implications and suggestions for English teachers and for future research.

Dopo un approfondito esame della letteratura, è emersa una carenza di strumenti in grado di misurare l'immagine di un’organizzazione (in particolare nei suoi aspetti maggiormente simbolici ed emotivi). Per superare questo problema, ho creato e testato un nuovo strumento, chiamato "Multi-Sensory Sort" (MuSeS). Allo stato attuale, difatti, la maggior parte degli strumenti finalizzati ad indagare l'immagine organizzativa si basa su questionari aventi scale di atteggiamento. Ciò presuppone che l'immagine organizzativa sia un costrutto consapevole e completamente verbalizzabile. MuSeS, invece, si basa su un’altra prospettiva, cercando di misurare gli aspetti maggiormente inconsapevoli e non-verbali dell'immagine organizzativa, utilizzando degli strumenti derivati sia dalla Psicologia che dal Marketing. MuSeS si compone di un insieme di tecniche proiettive basate su una stimolazione multisensoriale. MuSeS è stato testato su un totale di 198 persone. Un campione di 448 persone è stato inoltre utilizzato per i test di validazione. I risultati corroborano l’affidabilità (reliability) di MuSeS e la sua validità discriminante, concorrente, convergente e di contenuto. I risultati hanno mostrato, inoltre, come le MuSeS permetta di raccogliere dati in profondità, altrimenti difficili da ottenere attraverso altri tipi di indagine. Infine, vengono riportate le implicazioni teoriche e le possibili aree di ricerca future.
After a thorough review of the literature, a lack of tools able measure the Organizational Image (especially in its symbolic and emotive sides) was observed. In order to overcome this problem, I created and tested a new instrument, called “Multi-Sensory Sort” (MuSeS). Most of the tools created to investigate the Organizational Image are based on questionnaires with attitude scales; this assumes that the brand image is a conscious and fully verbalized construct. I started from another assumption, trying to measure the non-verbal and the unconscious brand image aspects, using instruments derived both from Psychology and Marketing. MuSeS, a direct methodology of exploring the consumer’s symbolic universe and the unconscious expectations, is composed of a set of projective techniques based on multisensory stimuli. MuSeS was tested on a total of 198 people. A sample of 448 was also used for the validation tests. Results supported MuSeS reliability and its discriminant, concurrent, convergent, and content validity. Results showed how MuSeS allows us to collect in depth data, otherwise difficult to obtain through other kinds of surveys. The theoretical implications are discussed along with the areas of future research.

by Wun-Cheung Tang.
Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.
Includes bibliographical references.
Abstract also in Chinese.
Abstracts --- p.1
Introduction --- p.3
Toeplitz and Circulant Matrices --- p.3
Conjugate Gradient Method --- p.6
Cosine Transform Preconditioner --- p.7
Regularization --- p.10
Summary --- p.13
Paper A --- p.19
Paper B --- p.36

碩士
國立中央大學
通訊工程學系
106
Wireless sensor network (WSN) is an active research area that has attracted much attention in recent years. Since the sensors used in a WSN have the properties of low cost, low bandwidth, low energy consumption, and anti-collision mechanism, WSN has been found in many applications. How to know the accurate positions of mobile terminals in a WSN is an important issue. This thesis studies an Received Signal Strength (RSS) technique to track mobile targets in a WSN and employs the Extended Kalman Filter (EKF) for position estimation of moving targets. In addition, there are usually multiple targets and clutter interferences in the tracking environment. To reduce the impact of interferences, we consider the Probabilistic Data Association Filter (PDAF) and Probability Hypothesis Density Filter (PHDF) to improve the tracking problem. Then, a data fusion center (FC) calculates target tracks with the weighted least squares method from the messages provided by multiple sensors.

Il programma Industry 4.0, in Germania, e le corrispondenti iniziative internazionali continueranno a trasformare la forza e l’ambiente di lavoro nell’industria fino al 2025. Parallelamente all'evoluzione del settore, la storia dell'interazione dell’operatori con le varie tecnologie di produzione industriale e digitale può essere riassunta come un'evoluzione generazionale verso la generazione dell’Operatore 4.0. Questo lavoro di tesi mira ad applicare le tecnologie abilitanti di Industry 4.0 per progettare e sviluppare, metodi e applicazioni a supporto della figura di Operator 4.0 rispetto a tre delle sue otto sfaccettature: l'Augmented Operator, il Virtual Operator e l'Healthy Operator. Nel Capitolo 1, presentiamo le ricerche svolte nel campo Industrial Augmented Reality. Descriviamo la tecnologia della Realtà Aumentata (AR) e la sua applicazione nel campo della Realtà Aumentata Industriale (IAR). Nel capitolo 2 presentiamo un prototipo di banco di lavoro badato su Spatial Augmented Reality (SAR) progettato nelle prime fasi di questa ricerca e descriviamo gli esperimenti effettuati per convalidare la sua efficienza come supporto per Operator 4.0. Nel Capitolo 3 descriviamo gli esperimenti effettuati per ottimizzare la leggibilità del testo mostrato nelle interfacce AR per Optical See-Through Displays. In questa ricerca, proponiamo nuovi indici estratti dalle immagini di background, visualizzati su uno schermo LCD, e li confrontiamo con quelli proposti in letteratura attraverso un test utente specifico. Nel Capitolo 4 presentiamo un framework AR per dispositivi palmari che aiuta gli utenti nella comprensione delle informazioni sugli impianti descritte tradizionalmente attraverso i Piping and Instrumentation Diagrams (P & ID) su supporto cartaceo. Nel Capitolo 5 descriviamo la ricerca svolta nel campo delle Human Machine Interfaces relativa all'uso delle interfacce utente naturali in realtà virtuale. Abbiamo progettato e sviluppato un'interfaccia gestuale per la navigazione di tour virtuali costituiti da immagini sferiche. Abbiamo confrontato l'interfaccia sviluppata con una classica controllata da mouse per valutare l'efficacia di tale interfaccia in termini di accettazione e coinvolgimento degli utenti. Nel Capitolo 6, descriviamo un framework generale per la progettazione di un vocabolario di gesti per la navigazione delle istruzioni tecniche nei manuali digitali per le operazioni di manutenzione. Viene anche proposta e utilizzata una procedura di validazione per confrontare i vocabolari gestuali in termini di fatica e carico cognitivo. Nel Capitolo 7, trattiamo l'aspetto dell’Healthy Operator. Descriviamo la progettazione e lo sviluppo di uno strumento software semi-automatico in grado di monitorare l'ergonomia dell'operatore nell’ambiente di lavoro valutando la metrica Rapid Upper Limb Assessment (RULA). Descriviamo il design e lo sviluppo del nostro prototipo software, il K2RULA, basato su un sensore a basso costo, il Microsoft Kinect v2. Successivamente, convalidiamo il nostro strumento con due esperimenti. Nel primo, lo confrontiamo con un sistema di tracking ottico, il golden standard di settore. Nel secondo confrontiamo i risultati restituiti dal prototipo con quelli calcolati da un valutatore esperto. Infine, traiamo le nostre conclusioni sul lavoro svolto e cerchiamo di tracciare un percorso per lo sviluppo futuro delle nostre ricerche.
The German program Industry 4.0 and the corresponding international initiatives will continue to transform the industrial workforce and their work environment through 2025. In parallel with the evolution of the industry, the history of the interaction of operators with various industrial and digital production technologies can be summarized as a generational evolution towards the Operator 4.0 generation. This work aims at applying the enabling technologies of Industry 4.0 in order to design and develop, methods and applications supporting the figure of the Operator 4.0 with respect three out of her/his eight facets – the Augmented Operator, the Virtual Operator, and the Healthy Operator. In Chapter 1, we introduce the researches carried out in the IAR field. We describe the Augmented Reality (AR) technology and its application in the field of the Industrial Augmented Reality (IAR). In chapter 2, we present a Spatial Augmented Reality (SAR) workbench prototype designed in the early stage of this research and we describe the experiments carried out to validate its efficiency as support to the Operator 4.0. In Chapter 3, we describe the experiments carried out to optimize legibility of text shown in AR interfaces for optical see-through displays. In this research, we propose novel indices extracted from the background images, displayed on an LCD screen, and we compare them with those proposed in the literature by designing a specific user test. In Chapter 4, we present an AR framework for handheld devices that enhance users in the comprehension of plant information traditionally conveyed through printed Piping and Instrumentation Diagrams (P&ID). In Chapter 5 we describe the research carried out in the field of HMI related to the use of Natural User Interfaces in Virtual Reality. We designed and developed a gesture interface for navigation of virtual tours made-up of spherical images. We compared the developed interface with a classical mouse-controlled one to evaluate the effectiveness of such an interface in terms of user acceptance and user engagement. In Chapter 6, we describe a general framework to design a mid-air gesture vocabulary for the navigation of technical instructions in digital manuals for maintenance operations. A validation procedure is also proposed and utilized to compare gesture vocabularies in terms of fatigue and cognitive load. In Chapter 7, we treat the facet of the Healthy Operator. We describe the design and development of a semi-automatic software tool able at monitoring the operator ergonomics in the shop floor by assessing Rapid Upper Limb Assessment (RULA) metrics. We describe the design and development of our software prototype – the K2RULA - based on a low- cost sensor, the Microsoft Kinect v2 depth-camera. Subsequently, we validate our tool with two experiments. In the first one, we compared the K2RULA grand-scores with those obtained with a reference optical motion capture system. In the second experiment, we evaluate the agreement of the grand-scores returned by the proposed application with those obtained by a RULA expert rater. Finally, we draw our conclusions regarding the work carried out and try to map out a path for the future development of our researches in these fields.