THE DEVELOPMENT OF A HOLISTIC EXPERT SYSTEM FOR INTEGRATED COASTAL ZONE MANAGEMENT

Coastal data and information comprise a massive and complex resource, which is vital to the practice of Integrated Coastal Zone Management (ICZM), an increasingly important application. ICZM is just as complex, but uses the holistic paradigm to deal with the sophistication. The application domain and its resource require a tool of matching characteristics, which is facilitated by the current wide availability of high performance computing. An object-oriented expert system, COAMES, has been constructed to prove this concept. The application of expert systems to ICZM in particular has been flagged as a viable challenge and yet very few have taken it up. COAMES uses the Dempster- Shafer theory of evidence to reason with uncertainty and importantly introduces the power of ignorance and integration to model the holistic approach. In addition, object orientation enables a modular approach, embodied in the inference engine - knowledge base separation. Two case studies have been developed to test COAMES. In both case studies, knowledge has been successfully used to drive data and actions using metadata. Thus a holism of data, information and knowledge has been achieved. Also, a technological holism has been proved through the effective classification of landforms on the rapidly eroding Holderness coast. A holism across disciplines and CZM institutions has been effected by intelligent metadata management of a Fal Estuary dataset. Finally, the differing spatial and temporal scales that the two case studies operate at implicitly demonstrate a holism of scale, though explicit means of managing scale were suggested. In all cases the same knowledge structure was used to effectively manage and disseminate coastal data, information and knowledge

THE ROLES OF ORTHOPAEDIC PATHOLOGY AND GENETIC DETERMINANTS IN EQUINE CERVICAL STENOTIC MYELOPATHY

Cervical stenotic myelopathy (CSM) is an important musculoskeletal and neurologic disease of the horse. Clinical disease occurs due to malformations of the vertebrae in the neck causing stenosis of the cervical vertebral canal and subsequent spinal cord compression. The disease is multifactorial in nature, therefore a clearer understanding of the etiology and pathogenesis of CSM will allow for improved management and therapeutic practices. This thesis examines issues of equine CSM diagnosis, skeletal tissue pathology, and inherited genetic determinants utilizing advances in biomedical imaging technologies and equine genomics. Magnetic resonance imaging (MRI) data provided a more complete assessment of the cervical column through image acquisition in multiple planes. First, MRI was compared to standing cervical radiographs for detection of stenosis. Using canal area or the cord canal area ratio, MRI more accurately predicted sites of compression in CSM cases. Secondly, articular process skeletal pathology localized on MRI was found to be more frequent and severe in CSM horses compared to controls. In addition, lesions were generalized throughout the cervical column and not limited to the spinal cord compression sites. A subset of lesions identified on MRI was evaluated using micro-CT and histopathology. Osteochondrosis, osseous cyst-like structures, fibrous tissue replacement of bone, and osteosclerosis were observed. These lesions support likely developmental aberrations of vertebral bone and cartilage maturation with secondary biomechanical influences. Bone cyst-like structures are a novel finding in this disease. Finally, the long-standing question of the contribution of genetic determinants to CSM was investigated using a genome wide association study (GWAS). Multiple significant loci were identified supporting the influence of a complex genetic trait in clinical disease. A simple Mendelian trait controlled by one gene is unlikely given the detection of variants across multiple chromosomes. Major contributions from this research include documentation of articular process bone and cartilage pathology in horses with CSM, support for abnormal cervical vertebrae development being an important contributing factor in the etiology and/or pathogenesis of equine CSM, and evidence that multiple genetic loci contribute to the CSM disease phenotype

Consensus ou fusion de segmentation pour quelques applications de détection ou de classification en imagerie

Récemment, des vraies mesures de distances, au sens d’un certain critère (et possédant de bonnes propriétés asymptotiques) ont été introduites entre des résultats de partitionnement (clustering) de donnés, quelquefois indexées spatialement comme le sont les images segmentées. À partir de ces métriques, le principe de segmentation moyenne (ou consensus) a été proposée en traitement d’images, comme étant la solution d’un problème d’optimisation et une façon simple et efficace d’améliorer le résultat final de segmentation ou de classification obtenues en moyennant (ou fusionnant) différentes segmentations de la même scène estimée grossièrement à partir de plusieurs algorithmes de segmentation simples (ou identiques mais utilisant différents paramètres internes). Ce principe qui peut se concevoir comme un débruitage de données d’abstraction élevée, s’est avéré récemment une alternative efficace et très parallélisable, comparativement aux méthodes utilisant des modèles de segmentation toujours plus complexes et plus coûteux en temps de calcul. Le principe de distance entre segmentations et de moyennage ou fusion de segmentations peut être exploité, directement ou facilement adapté, par tous les algorithmes ou les méthodes utilisées en imagerie numérique où les données peuvent en fait se substituer à des images segmentées. Cette thèse a pour but de démontrer cette assertion et de présenter différentes applications originales dans des domaines comme la visualisation et l’indexation dans les grandes bases d’images au sens du contenu segmenté de chaque image, et non plus au sens habituel de la couleur et de la texture, le traitement d’images pour améliorer sensiblement et facilement la performance des méthodes de détection du mouvement dans une séquence d’images ou finalement en analyse et classification d’images médicales avec une application permettant la détection automatique et la quantification de la maladie d’Alzheimer à partir d’images par résonance magnétique du cerveau.Recently, some true metrics in a criterion sense (with good asymptotic properties) were introduced between data partitions (or clusterings) even for data spatially ordered such as image segmentations. From these metrics, the notion of average clustering (or consensus segmentation) was then proposed in image processing as the solution of an optimization problem and a simple and effective way to improve the final result of segmentation or classification obtained by averaging (or fusing) different segmentations of the same scene which are roughly estimated from several simple segmentation models (or obtained with the same model but with different internal parameters). This principle, which can be conceived as a denoising of high abstraction data, has recently proved to be an effective and very parallelizable alternative, compared to methods using ever more complex and time-consuming segmentation models. The principle of distance between segmentations, and averaging of segmentations, in a criterion sense, can be exploited, directly or easily adapted, by all the algorithms or methods used in digital imaging where data can in fact be substituted to segmented images. This thesis proposal aims at demonstrating this assertion and to present different original applications in various fields in digital imagery such as the visualization and the indexation in the image databases, in the sense of the segmented contents of each image, and no longer in the common color and texture sense, or in image processing in order to sensibly and easily improve the detection of movement in the image sequence or finally in analysis and classification in medical imaging with an application allowing the automatic detection and quantification of Alzheimer’s disease

Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

Analysis of the developing brain using image registration

Imperial Users onl

A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques.

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Higher Committee for Education Development in Ira