Numerical Tests-Based Assessment of the Procedures of Blurry and Noisy Image Enhancement

An extension of the test-based method of multi-aspect numerical assessment of the quality of image enhancement procedures on blurry and noisy images is presented. The principles of construction of the tests affected by various types and intensity of blurring and noising are described. Linear models of blurring have been defined. Formal definitions of measurable parameters characterizing the quality of image enhancement procedures are proposed. Comments on the errors and applicability limitations of the proposed testing method are also given. In a numerical experiment, the utility of the method by comparative assessment of 18 various image enhancement procedures is proven. Remarks concerning the properties of several widely-known image enhancement procedures are formulated. Concluding remarks about the utility of the presented method of image enhancement procedures assessment are given

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

A Process for Extracting Knowledge in Design for the Developing World

The aim of this study was to develop the process necessary to identify design knowledge shared across product classes and contexts in Design for the Developing World. A process for extracting design knowledge in the field of Design for the Developing World was developed based on the Knowledge Discovery in Databases framework. This process was applied to extract knowledge from a sample dataset of 48 products and small-scale technologies. Unsupervised cluster analysis revealed two distinct product groups, cluster X-AA and cluster Z-AC-AD. Unique attributes of cluster XX-AA include local manufacture, local maintenance and service, human-power, distribution by a non-governmental organization, income-generation, and application in water/sanitation or agriculture sectors. The label Locally Oriented Design for the Developing World was assigned to this group based on the dominant features represented. Unique attributes of cluster Z-AC-AD include electric-power, distribution by a private organization, and application in the health or energy/communication sectors. The label Globally Oriented Design for the Developing World was assigned to this group. These findings were corroborated by additional analyses that suggest certain design knowledge is shared across classes and contexts within groups of products. The results suggest that at least two of these groups exist, which can serve as an initial framework for organizing the literature related to inter-context and inter-class design knowledge. Design knowledge was extracted from each group by collecting known approaches, principles, and methods from available literature. This knowledge may be applied as design guidance in future work by identifying a product group corresponding to the design scenario and sourcing the related set of knowledge

Développement et validation d'une classification des résidences privées avec services accueillant des personnes âgées

Au Québec, près de 120,000 personnes âgées vivent dans plus de 2,000 résidences privées avec services. Ces milieux de vie substituts se présentent sous plusieurs formes, car ils se sont développés, jusqu'à tout récemment, sans aucun contrôle étatique. Il en résulte une importante variabilité dans, par exemple, les services offerts, les ratios de personnel, l'aménagement physique, les critères d'admission et les capacités d'accueil. L'hétérogénéité de ces milieux rend leur représentation et leur comparaison laborieuses, autant pour les professionnels de la santé que pour les personnes âgées et leur famille. Le développement de classifcation est particulièrement utile lorsqu'on souhaite se représenter un ensemble hétérogène, comme c'est le cas des résidences privées avec services accueillant des personnes âgées (RPA). L'objectif général de l'étude est de développer et de valider, à l'aide d'analyses de classification automatisée (ACA), une classification des RPA basée sur des caractéristiques de leur environnement physique et organisationnel. La présente thèse intègre trois articles scientifiques, en lien avec chacun des trois objectifs spécifiques de l'étude. Le premier article détaille la méthodologie et les résultats liés à l'identification des caractéristiques nécessaires à la représentation d'une RPA. Cent soixante-quinze variables ont été identifiées. Le deuxième article décrit le développement d'un questionnaire pour mesurer ces variables, l'étude de la fidélité test-retest du questionnaire ainsi que l'appréciation de sa cohérence interne. Somme toute, le questionnaire de l'environnement physique et organisationnel (EPO) présente de bonnes propriétés métrologiques. Enfin, le troisième article présente de manière détaillée le développement et la validation de la classification. À cet effet, 552 des 1,928 propriétaires de RPA admissibles ont complété le questionnaire EPO afin de fournir les informations nécessaires à la création de la classification. Différentes méthodes d'ACA ont été employées. Trois classifications plausibles ont été soumises à un groupe d'experts composé de huit intervenants cliniques. La classification retenue par ce comité est issue de la méthode hiérarchique de Ward combinée à la méthode non hiérarchique k-means. La classification est composée de cinq groupes de RPA qui se distinguent, entre autres, par les services offerts, la clientèle accueillie et le niveau de support à l'autonomie fonctionnelle. Ces travaux de recherche sont les premiers à regrouper les RPA du Québec en groupes homogènes mutuellement exclusifs sur la base de leur environnement physique et organisationnel. Une fois implantée, cette classification fournira des informations précieuses aux intervenants, aux gestionnaires ainsi qu'aux personnes âgées et leur famille. Elle favorisera davantage de congruence entre les besoins cliniques identifiés, les préférences de la personne âgée et le choix d'une RPA optimale

Data Clustering and Partial Supervision with Some Parallel Developments

Data Clustering and Partial Supell'ision with SOllie Parallel Developments by Sameh A. Salem Clustering is an important and irreplaceable step towards the search for structures in the data. Many different clustering algorithms have been proposed. Yet, the sources of variability in most clustering algorithms affect the reliability of their results. Moreover, the majority tend to be based on the knowledge of the number of clusters as one of the input parameters. Unfortunately, there are many scenarios, where this knowledge may not be available. In addition, clustering algorithms are very computationally intensive which leads to a major challenging problem in scaling up to large datasets. This thesis gives possible solutions for such problems. First, new measures - called clustering performance measures (CPMs) - for assessing the reliability of a clustering algorithm are introduced. These CPMs can be used to evaluate: I) clustering algorithms that have a structure bias to certain type of data distribution as well as those that have no such biases, 2) clustering algorithms that have initialisation dependency as well as the clustering algorithms that have a unique solution for a given set of parameter values with no initialisation dependency. Then, a novel clustering algorithm, which is a RAdius based Clustering ALgorithm (RACAL), is proposed. RACAL uses a distance based principle to map the distributions of the data assuming that clusters are determined by a distance parameter, without having to specify the number of clusters. Furthermore, RACAL is enhanced by a validity index to choose the best clustering result, i.e. result has compact clusters with wide cluster separations, for a given input parameter. Comparisons with other clustering algorithms indicate the applicability and reliability of the proposed clustering algorithm. Additionally, an adaptive partial supervision strategy is proposed for using in conjunction with RACAL_to make it act as a classifier. Results from RACAL with partial supervision, RACAL-PS, indicate its robustness in classification. Additionally, a parallel version of RACAL (P-RACAL) is proposed. The parallel evaluations of P-RACAL indicate that P-RACAL is scalable in terms of speedup and scaleup, which gives the ability to handle large datasets of high dimensions in a reasonable time. Next, a novel clustering algorithm, which achieves clustering without any control of cluster sizes, is introduced. This algorithm, which is called Nearest Neighbour Clustering, Algorithm (NNCA), uses the same concept as the K-Nearest Neighbour (KNN) classifier with the advantage that the algorithm needs no training set and it is completely unsupervised. Additionally, NNCA is augmented with a partial supervision strategy, NNCA-PS, to act as a classifier. Comparisons with other methods indicate the robustness of the proposed method in classification. Additionally, experiments on parallel environment indicate the suitability and scalability of the parallel NNCA, P-NNCA, in handling large datasets. Further investigations on more challenging data are carried out. In this context, microarray data is considered. In such data, the number of clusters is not clearly defined. This points directly towards the clustering algorithms that does not require the knowledge of the number of clusters. Therefore, the efficacy of one of these algorithms is examined. Finally, a novel integrated clustering performance measure (lCPM) is proposed to be used as a guideline for choosing the proper clustering algorithm that has the ability to extract useful biological information in a particular dataset. Supplied by The British Library - 'The world's knowledge' Supplied by The British Library - 'The world's knowledge