Global ETD Search

51	Approaches to explore multiplex biological networks and application to study premature aging diseases / Approches pour explorer les réseaux biologiques multiplex et application aux maladies du vieillissement prématuré Valdeolivas Urbelz, Alberto 15 March 2019 (has links) Les gènes et les protéines n’agissent pas de manière isolée dans les cellules, mais interagissent plutôt pour faire leurs fonctions dans les processus biologiques. Ces interactions peuvent être représentées sous forme de grands réseaux dans lesquels les nœuds sont des gènes ou des protéines et les arêtes représentent leurs interactions. Diverses approches basées sur la théorie des graphes ont été développées pour extraire la connaissance fonctionnelle contenue dans ces réseaux. Néanmoins, ces méthodes ont été principalement appliquées à des réseaux individuels, en ignorant la diversité des interactions biologiques. Nous déclarons que ces différents types d’interactions peuvent être représentés sous la forme de réseaux multiplexes, c’est-à-dire des ensembles de réseaux partageant les mêmes nœuds, ce qui permet une description plus précise des systèmes biologiques. Cette thèse est focalisée sur le développement de nouveaux algorithmes étendant aux réseaux multiplexes certaines méthodes populaires de la théorie des graphes en biologie computationnelle, ainsi que sur leur application à l’étude des maladies humaines. Du côté des applications, nous nous concentrons sur les maladies liées au vieillissement prématuré, un groupe de maladies génétiques ressemblant à certains aspects du vieillissement physiologique à un âge précoce. Nous avons appliqué nos algorithmes pour détecter les modules associés à plus de 70 syndromes annotés avec un phénotype lié au vieillissement prématuré. Les résultats ont révélé le paysage des processus moléculaires perturbés dans ces maladies, qui peuvent être mis en parallèle avec les caractéristiques du vieillissement physiologique. / Genes and proteins do not act isolated in cells but rather interact to perform their functions in signaling pathways, molecular complexes, or, more generally, biological processes. These interactions can be represented as large networks in which nodes are genes or proteins and edges represent their interactions. Various graph-theory based approaches have been developed to extract the functional knowledge contained in biological networks. Nevertheless, these methods have been mainly applied to individual networks, ignoring the diversity of biological interactions. We state here that these different types of interactions can be represented as multiplex networks, i.e. collections of networks sharing the same nodes, leading to a more accurate description of biological systems. This thesis focuses on the extension from individual to multiplex networks of some of the state-of-the-art guilt-by-association methods in computational biology, and on their application to the study of human diseases. On the application side, we concentrate on premature aging diseases, a group of rare genetic disorders that resemble some aspects of physiological aging at an early age. In this framework, we applied our algorithms to detect the modules associated to more than 70 disorders annotated with at least one premature aging related phenotype. The results revealed the landscape of perturbed molecular processes in premature aging diseases, which can be paralleled with the hallmarks of physiological aging to help identifying common and specific features. Random Walk Réseaux biologiques Réseaux multiplex Cluster- ing Maladies du vieillissement prématuré Protéomique Phosphoprotéomique Cancer de la prostate Biological networks Multiplex networks Random walk with restart algorithm Clustering algorithms Premature aging diseases Proteomics Phoproteomics Prostate cancer
52	Error-Aware Density-Based Clustering of Imprecise Measurement Values Lehner, Wolfgang, Habich, Dirk, Volk, Peter B., Dittmann, Ralf, Utzny, Clemens 15 June 2022 (has links) Manufacturing process development is under constant pressure to achieve a good yield for stable processes. The development of new technologies, especially in the field of photomask and semiconductor development, is at its phys- ical limits. In this area, data, e.g. sensor data, has to be collected and analyzed for each process in order to ensure process quality. With increasing complexity of manufactur- ing processes, the volume of data that has to be evaluated rises accordingly. The complexity and data volume exceeds the possibility of a manual data analysis. At this point, data mining techniques become interesting. The application of current techniques is complex because most of the data is captured with sensor measurement tools. Therefore, every measured value contains a specific error. In this paper we propose an error-aware extension of the density-based al- gorithm DBSCAN. Furthermore, we present some quality measures which could be utilized for further interpretation of the determined clustering results. With this new cluster algorithm, we can ensure that masks are classified into the correct cluster with respect to the measurement errors, thus ensuring a more likely correlation between the masks. info:eu-repo/classification/ddc/004 ddc:004
53	A GENERAL FRAMEWORK FOR CUSTOMER CONTENT PRINT QUALITY DEFECT DETECTION AND ANALYSIS Runzhe Zhang (11442742) 11 July 2022 (has links) <p>Print quality (PQ) is one of the most significant issues with electrophotographic printers. There are many reasons for PQ issues, such as limitations of the electrophotographic process, faulty printer components, or other failures of the print mechanism. These reasons can produce different PQ issues, like streaks, bands, gray spots, text fading, and color fading defects. It is important to analyze the nature and causes of different print defects to more efficiently repair printers and improve the electrophotographic process. </p> <p><br></p> <p>We design a general framework for print quality detection and analysis of customer content. This print quality analysis framework inputs the original digital image saved on the computer and then the scanned image. This framework includes two main modules: image pre-processing, print defects feature vector extraction, and classification. The first module, image pre-processing, includes image registration, color calibration, and region of interest (ROI) extraction. The ROI extraction part is designed to extract four different kinds of ROI from the digital master image. Because different ROIs include different print defects, for example, the symbol ROI includes the text fading defect, and the raster ROI includes the color fading defect. The second module includes different ROI print defects detection and analysis algorithms. We classify different ROI print defects using their feature vector based on their severity. This module proposed four important defects detection methods: uniform color area streak detection, symbol ROI color text fading detection, raster ROI color fading detection using a novel unsupervised clustering method, and raster ROI streak detection. We will introduce the details of these algorithms in this thesis. </p> <p><br></p> <p>We will also show two other interesting print quality projects: print margin skew detection and print velocity simulation and estimation. Print margin skew detection proposes an algorithm that uses the Hough Lines Detection algorithm to detect printing margin and skew errors based on factual scanned image verification. In the print velocity simulation and estimation project, we propose a print velocity simulation tool, design a specific print velocity test page, and design a print velocity estimation algorithm using the dynamic time warping algorithm. </p> Computer vision Image processing Statistical data science image processing applications computer vision algorithms image quality analysis printing quality printing margin and skew streak banding color fading printing velocity classification algorithms clustering algorithms
54	Clustering Uncertain Data with Possible Worlds Lehner, Wolfgang, Volk, Peter Benjamin, Rosenthal, Frank, Hahmann, Martin, Habich, Dirk 16 August 2022 (has links) The topic of managing uncertain data has been explored in many ways. Different methodologies for data storage and query processing have been proposed. As the availability of management systems grows, the research on analytics of uncertain data is gaining in importance. Similar to the challenges faced in the field of data management, algorithms for uncertain data mining also have a high performance degradation compared to their certain algorithms. To overcome the problem of performance degradation, the MCDB approach was developed for uncertain data management based on the possible world scenario. As this methodology shows significant performance and scalability enhancement, we adopt this method for the field of mining on uncertain data. In this paper, we introduce a clustering methodology for uncertain data and illustrate current issues with this approach within the field of clustering uncertain data. info:eu-repo/classification/ddc/004 ddc:004
55	Machine Learning Techniques with Specific Application to the Early Olfactory System Auffarth, Benjamin January 2012 (has links) This thesis deals with machine learning techniques for the extraction of structure and the analysis of the vertebrate olfactory pathway based on related methods. Some of its main contributions are summarized below. We have performed a systematic investigation for classification in biomedical images with the goal of recognizing a material in these images by its texture. This investigation included (i) different measures for evaluating the importance of image descriptors (features), (ii) methods to select a feature set based on these evaluations, and (iii) classification algorithms. Image features were evaluated according to their estimated relevance for the classification task and their redundancy with other features. For this purpose, we proposed a framework for relevance and redundancy measures and, within this framework, we proposed two new measures. These were the value difference metric and the fit criterion. Both measures performed well in comparison with other previously used ones for evaluating features. We also proposed a Hopfield network as a method for feature selection, which in experiments gave one of the best results relative to other previously used approaches. We proposed a genetic algorithm for clustering and tested it on several realworld datasets. This genetic algorithm was novel in several ways, including (i) the use of intra-cluster distance as additional optimization criterion, (ii) an annealing procedure, and (iii) adaptation of mutation rates. As opposed to many conventional clustering algorithms, our optimization framework allowed us to use different cluster validation measures including those which do not rely on cluster centroids. We demonstrated the use of the clustering algorithm experimentally with several cluster validity measures as optimization criteria. We compared the performance of our clustering algorithm to that of the often-used fuzzy c-means algorithm on several standard machine learning datasets from the University of California/Urvine (UCI) and obtained good results. The organization of representations in the brain has been observed at several stages of processing to spatially decompose input from the environment into features that are somehow relevant from a behavioral or perceptual standpoint. For the perception of smells, the analysis of such an organization, however, is not as straightforward because of the missing metric. Some studies report spatial clusters for several combinations of physico-chemical properties in the olfactory bulb at the level of the glomeruli. We performed a systematic study of representations based on a dataset of activity-related images comprising more than 350 odorants and covering the whole spatial array of the first synaptic level in the olfactory system. We found clustered representations for several physico-chemical properties. We compared the relevance of these properties to activations and estimated the size of the coding zones. The results confirmed and extended previous studies on olfactory coding for physico-chemical properties. Particularly of interest was the spatial progression by carbon chain that we found. We discussed our estimates of relevance and coding size in the context of processing strategies. We think that the results obtained in this study could guide the search into olfactory coding primitives and the understanding of the stimulus space. In a second study on representations in the olfactory bulb, we grouped odorants together by perceptual categories, such as floral and fruity. By the application of the same statistical methods as in the previous study, we found clustered zones for these categories. Furthermore, we found that distances between spatial representations were related to perceptual differences in humans as reported in the literature. This was possibly the first time that such an analysis had been done. Apart from pointing towards a spatial decomposition by perceptual dimensions, results indicate that distance relationships between representations could be perceptually meaningful. In a third study, we modeled axon convergence from olfactory receptor neurons to the olfactory bulb. Sensory neurons were stimulated by a set of biologically-relevant odors, which were described by a set of physico-chemical properties that covaried with the neural and glomerular population activity in the olfactory bulb. Convergence was mediated by the covariance between olfactory neurons. In our model, we could replicate the formation of glomeruli and concentration coding as reported in the literature, and further, we found that the spatial relationships between representational zones resulting from our model correlated with reported perceptual differences between odor categories. This shows that natural statistics, including similarity of physico-chemical structure of odorants, can give rise to an ordered arrangement of representations at the olfactory bulb level where the distances between representations are perceptually relevant. / <p>QC 20120224</p> feature selection image features pattern classification relevance redundancy distributional similarity divergence measure genetic algorithms clustering algorithms annealing olfactory coding olfactory bulb odorants glomeruli property-activity relationship olfaction plasticity axonal guidance odor category perception spatial coding population coding memory organization odor quality
56	Agrupamento de sequências de miRNA utilizando aprendizado não-supervisionado baseado em grafos Kasahara, Viviani Akemi 12 August 2016 (has links) Submitted by Izabel Franco (izabel-franco@ufscar.br) on 2016-10-11T17:36:54Z No. of bitstreams: 1 DissVAK.pdf: 4608619 bytes, checksum: 3022034b9035e4e8caf1195902d24581 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-21T13:03:21Z (GMT) No. of bitstreams: 1 DissVAK.pdf: 4608619 bytes, checksum: 3022034b9035e4e8caf1195902d24581 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-21T13:03:27Z (GMT) No. of bitstreams: 1 DissVAK.pdf: 4608619 bytes, checksum: 3022034b9035e4e8caf1195902d24581 (MD5) / Made available in DSpace on 2016-10-21T13:03:34Z (GMT). No. of bitstreams: 1 DissVAK.pdf: 4608619 bytes, checksum: 3022034b9035e4e8caf1195902d24581 (MD5) Previous issue date: 2016-08-12 / Não recebi financiamento / Cluster analysis is the organization of a collection of patterns into clusters based on similarity which is determined by using properties of data. Clustering techniques can be useful in a variety of knowledge domains such as biotechnology, computer vision, document retrieval and many others. An interesting area of biology involves the concept of microRNAs (miRNAs) that are approximately 22 nucleotide-long non-coding RNA molecules that play important roles in gene regulation. Clustering miRNA sequences can help to understand and explore sequences belonging to the same cluster that has similar biological functions. This research work investigates and explores seven unsupervised clustering algorithms based on graphs that can be divided into three categories: algorithm based on region of influence, algorithm based on minimum spanning tree and spectral algorithm. To assess the contribution of the proposed algorithms, data from miRNA families stored in the online miRBase database were used in the conducted experiments. The results of these experiments were presented, analysed and evaluated using clustering validation indexes as well as visual analysis. / A análise de agrupamento é uma organização de coleção de padrões em grupos, baseando-se na similaridade das propriedades pertencentes aos dados. A técnica de agrupamento pode ser utilizado em muitas áreas de conhecimento como biotecnologia, visão computacional, recuperação de documentos, entre outras. Uma área interessante da biologia envolve o conceito de microRNAs (miRNAs), que são moléculas não-codificadas de RNA com aproximadamente 22 nucleotídeos e que desempenham um papel importante na regulação dos genes. O agrupamento de sequências de miRNA podem ajudar em sua exploração e entendimento, pois as sequências que pertencem ao mesmo grupo possuem uma função biológica similar. Esse trabalho explora e investiga sete algoritmos de agrupamentos não-supervisionados baseados em grafos que podem ser divididos em três categorias: algoritmos baseados em região de influência, algoritmos baseados em árvore spanning minimal e algoritmo espectral. Para avaliar a contribuição dos algoritmos propostos, os experimentos conduzidos utilizaram os dados das famílias de miRNAs disponíveis no banco de dados denominado miRBase. Os resultados dos experimentos foram apresentados, analisados e avaliados usando índices de validação de agrupamento e análise visual. Agrupamento de miRNA Mineração de dados Teoria dos grafos Algoritmo espectral Aprendizado não-supervisionado Clustering miRNA clustering Data mining Unsupervised learning Graph theory Graph based clustering algorithms Algorithm based on region of influence Algorithm based on minimum spanning tree Spectral algorithm
57	Spatially Correlated Data Accuracy Estimation Models in Wireless Sensor Networks Karjee, Jyotirmoy January 2013 (has links) (PDF) One of the major applications of wireless sensor networks is to sense accurate and reliable data from the physical environment with or without a priori knowledge of data statistics. To extract accurate data from the physical environment, we investigate spatial data correlation among sensor nodes to develop data accuracy models. We propose three data accuracy models namely Estimated Data Accuracy (EDA) model, Cluster based Data Accuracy (CDA) model and Distributed Cluster based Data Accuracy (DCDA) model with a priori knowledge of data statistics. Due to the deployment of high density of sensor nodes, observed data are highly correlated among sensor nodes which form distributed clusters in space. We describe two clustering algorithms called Deterministic Distributed Clustering (DDC) algorithm and Spatial Data Correlation based Distributed Clustering (SDCDC) algorithm implemented under CDA model and DCDA model respectively. Moreover, due to data correlation in the network, it has redundancy in data collected by sensor nodes. Hence, it is not necessary for all sensor nodes to transmit their highly correlated data to the central node (sink node or cluster head node). Even an optimal set of sensor nodes are capable of measuring accurate data and transmitting the accurate, precise data to the central node. This reduces data redundancy, energy consumption and data transmission cost to increase the lifetime of sensor networks. Finally, we propose a fourth accuracy model called Adaptive Data Accuracy (ADA) model that doesn't require any a priori knowledge of data statistics. ADA model can sense continuous data stream at regular time intervals to estimate accurate data from the environment and select an optimal set of sensor nodes for data transmission to the network. Data transmission can be further reduced for these optimal sensor nodes by transmitting a subset of sensor data using a methodology called Spatio-Temporal Data Prediction (STDP) model under data reduction strategies. Furthermore, we implement data accuracy model when the network is under a threat of malicious attack. Wireless Sensor Networks Sensor Network Models Data Accuracy Estimation Models Spatio-Temporal Data Estimation Adaptive Data Accuracy (ADA) Model Estimated Data Accuracy Model (EDA) Cluster Data Accuracy Model (CDA) Distributed Clustering Distributed Clusters - Data Estimation Communication Engineering
58	Correction and Optimization of 4D aircraft trajectories by sharing wind and temperature information / Correction et Optimisation de trajectoires d'avions 4D par partage des informations de vent et de température Legrand, Karim 28 June 2019 (has links) Cette thèse s'inscrit dans l'amélioration de la gestion du trafic aérien. Le vent et la température sont deux paramètres omniprésents, subis, et à l'origine de nombreux biais de prédiction qui altèrent le suivi des trajectoires. Nous proposons une méthode pour limiter ces biais. Le concept "Wind and Température Networking" améliore la prédiction de trajectoire en utilisant le vent et la température mesurés par les avions voisins. Nous détaillons les effets de la température sur l'avion, permettant sa prise en compte. L'évaluation du concept est faite sur 8000 vols. Nous traitons du calcul de trajectoires optimales en présence de vent prédit, pour remplacer les actuelles routes de l'Atlantique Nord, et aboutir à des groupes de trajectoires optimisées et robustes. Dans la conclusion, nous présentons d'autres champs d'applications du partage de vents, et abordons les besoins en nouvelles infrastructures et protocoles de communication, nécessaires à la prise en compte de ce nouveau concept. / This thesis is related to air traffic management systems current changes. On the ground and in flight, trajectory calculation methods and available data differ. Wind and temperature are two ubiquitous parameters that are subject to and cause prediction bias. We propose a concept to limit this bias. Our "Wind and Temperature Networking" concept improves trajectory prediction, using wind and temperature information from neighboring aircraft. We detail the effects of temperature on the aircraft performances, allowing for temperature to be taken into account. The concept evaluation is done on 8000 flights. We discuss the calculation of optimal trajectories in the presence of predicted winds, to replace the current North Atlantic Tracks, and to provide optimized and robust groups of trajectories. The conclusion of this thesis presents other fields of wind sharing applications, and addresses the need for new telecommunications infrastructures and protocols. Partage d'informations de vent Partage d'informations de température Trajectoires d'avion Navigation aérienne Algorithmes de classification Estimation de vent Prédiction de vent Estimation de température Prédiction de température Partage de vents Partage de températures Calcul de trajectoires d’urgence Estimation de champs de vents Estimation de champs de température Wind networking Temperature networking Flight trajectories Aircraft robust optimal trajectory Trajectory prediction (aerospace Trajectory optimisation (aerospace) Aircraft navigation Clustering algorithms Wind estimation Wind prediction Temperature estimation Temperature prediction Wind information sharing Temperature information sharing Flight trajectories wind profiles Flight trajectories temperature profiles Driftdown calculations Wind field estimations Temperature field estimations 629
59	Channel Probing for an Indoor Wireless Communications Channel Hunter, Brandon 13 March 2003 (has links) (PDF) The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of this parameter allows spatial diversity at the transmitter along with the receiver to be simulated. The process of going from raw measurement data to discrete arrivals and then to clustered arrivals is analyzed. Many possible errors associated with discrete arrival processing are discussed along with possible solutions. Four clustering methods are compared and their relative strengths and weaknesses are pointed out. The effects that errors in the clustering process have on parameter estimation and model performance are also simulated. multipath multipath propagation propagation environment indoor propagation indoor multipath indoor multipath propagation channel probing channel probing indoor wireless communications channel indoor wireless communications indoor wireless communications wireless communications wireless communications channel MIMO SISO multiple input multiple output single input single output antenna diversity antenna diversity antenna array channel modeling modeling model wireless propagation clustering methods clustering algorithms clustering parameter estimation parameter channel matrix h matrix ray tracing point spread function psf deconvolution clean algorithm clean algorithm antenna pattern network analyzer temporal pattern 2.4 ghz system 6 ghz system channel capacity capacity estimate channel capacity estimate propagation environment scattering transfer matrix matrix water filling solution water filling method water filling Saleh Valenzuela Saleh and Valenzuela Model arrival time of arrival angle of arrival direction of arrival arrival amplitude arrivals antenna positioner data simulations measurements angle of departure direction of departure correlation convolution errors error CFA CFAD constant false alarm constant false alarm detection signal signal dependent clutter clutter returns transmitter transmit receive receiver transmit antenna receive antenna spatial correlation system PSF system point spread function mapping fuzzy c-means clustering fuzzy clustering c-means clustering c-means fuzzy k-means euclidean distance time amplitude delay performance prediction performance prediction errors fuzzy weighted distance method rectangular method weighted rectangular method statistics statistical statistical model Electrical and Computer Engineering

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