## Reconstruction and clustering with graph optimization and priors on gene networks and images (2017)

Collective analysis of multiple high-throughput gene expression datasets

Modern technologies have resulted in the production of numerous high-throughput biological datasets. However, the pace of development of capable computational methods does not cope with the pace of generation of new high-throughput datasets. Amongst the most popular biological high-throughput datasets are gene expression datasets (e.g. microarray datasets). This work targets this aspect by proposing a suite of computational methods which can analyse multiple gene expression datasets collectively. The focal method in this suite is the unification of clustering results from multiple datasets using external specifications (UNCLES). This method applies clustering to multiple heterogeneous datasets which measure the expression of the same set of genes separately and then combines the resulting partitions in accordance to one of two types of external specifications; type A identifies the subsets of genes that are consistently co-expressed in all of the given datasets while type ...

Abu-Jamous, Basel — Brunel University London

Segmentation par modèle déformable surfacique localement régularisé par spline

Image segmentation through deformable models is a method that localizes object boundaries. When difficult segmentation context are proposed because of noise or a lack of information, the use of prior knowledge in the deformation process increases segmentation accuracy. Medical imaging is often concerned by these context. Moreover, medical applications deal with large amounts of data. Then it is mandatory to use a robust and fast processing. This question lead us to a local regularisation of the deformable model. Highly based on the active contour framework, also known as \emph{snake}, we propose a new regularization scheme. This is done by filtering the displacements at each iteration. The filter is based on a smoothing spline kernel whose aim was to approximate a set of points rather than interpolating it. We point out the consistency of the regularization parameter in such a method. ...

Velut, Jerome — INSA-Lyon / CREATIS-LRMN

Signal processing algorithms for wireless acoustic sensor networks

Recent academic developments have initiated a paradigm shift in the way spatial sensor data can be acquired. Traditional localized and regularly arranged sensor arrays are replaced by sensor nodes that are randomly distributed over the entire spatial field, and which communicate with each other or with a master node through wireless communication links. Together, these nodes form a so-called ‘wireless sensor network’ (WSN). Each node of a WSN has a local sensor array and a signal processing unit to perform computations on the acquired data. The advantage of WSNs compared to traditional (wired) sensor arrays, is that many more sensors can be used that physically cover the full spatial field, which typically yields more variety (and thus more information) in the signals. It is likely that future data acquisition, control and physical monitoring, will heavily rely on this type of ...

Bertrand, Alexander — Katholieke Universiteit Leuven

This dissertation deals with the distributed processing techniques for parameter estimation and efficient data-gathering in wireless communication and sensor networks. The estimation problem consists in inferring a set of parameters from temporal and spatial noisy observations collected by different nodes that monitor an area or field. The objective is to derive an estimate that is as accurate as the one that would be obtained if each node had access to the information across the entire network. With the aim of enabling an energy aware and low-complexity distributed implementation of the estimation task, several useful optimization techniques that generally yield linear estimators were derived in the literature. Up to now, most of the works considered that the nodes are interested in estimating the same vector of global parameters. This scenario can be viewed as a special case of a more general ...

Bogdanovic, Nikola — University of Patras

Wireless Sensor Networks (WSNs) aim for accurate data gathering and representation of one or multiple physical variables from the environment, by means of sensor reading and wireless data packets transmission to a Data Fusion Center (DFC). There is no comprehensive common set of requirements for all WSN, as they are application dependent. Moreover, due to specific node capabilities or energy consumption constraints several tradeoffs have to be considered during the design, and particularly, the price of the sensor nodes is a determining factor. The distinction between small and large scale WSNs does not only refers to the quantity of sensor nodes, but also establishes the main design challenges in each case. For example, the node organization is a key issue in large scale WSNs, where many inexpensive nodes have to properly work in a coordinated manner. Regarding the amount of ...

Chidean, Mihaela I. — Rey Juan Carlos University

Parametric and non-parametric approaches for multisensor data fusion

Multisensor data fusion technology combines data and information from multiple sensors to achieve improved accuracies and better inference about the environment than could be achieved by the use of a single sensor alone. In this dissertation, we propose parametric and nonparametric multisensor data fusion algorithms with a broad range of applications. Image registration is a vital first step in fusing sensor data. Among the wide range of registration techniques that have been developed for various applications, mutual information based registration algorithms have been accepted as one of the most accurate and robust methods. Inspired by the mutual information based approaches, we propose to use the joint RÂ´enyi entropy as the dissimilarity metric between images. Since the RÂ´enyi entropy of an image can be estimated with the length of the minimum spanning tree over the corresponding graph, the proposed information-theoretic registration ...

Ma, Bing — University of Michigan

In recent years, advances in signal processing have led the wireless sensor networks to be capable of mobility. The signal processing in a wireless sensor network differs from that of a traditional wireless network mainly in two important aspects. Unlike traditional wireless networks, in a sensor network the signal processing is performed in a fully distributed manner as the sensor measurements in a distributed fashion across the network collected. Additionally, due to the limited onboard resource of a sensor network it is essential to develop energy and bandwidth efficient signal processing algorithms. The thesis is devoted to discuss the state of the arte of algorithms commonly known as tracking algorithms. Although tracking algorithms have only been attracting research and development attention recently, already a wide literature and great variety of proposed approaches regarding the topic exist. The dissertation focus on ...

Arienzo, Loredana — University of Salerno

Decentralized Estimation Under Communication Constraints

In this thesis, we consider the problem of decentralized estimation under communication constraints in the context of Collaborative Signal and Information Processing. Motivated by sensor network applications, a high volume of data collected at distinct locations and possibly in diverse modalities together with the spatially distributed nature and the resource limitations of the underlying system are of concern. Designing processing schemes which match the constraints imposed by the system while providing a reasonable accuracy has been a major challenge in which we are particularly interested in the tradeoff between the estimation performance and the utilization of communications subject to energy and bandwidth constraints. One remarkable approach for decentralized inference in sensor networks is to exploit graphical models together with message passing algorithms. In this framework, after the so-called information graph of the problem is constructed, it is mapped onto the ...

Uney, Murat — Middle East Technical University

Variational Sparse Bayesian Learning: Centralized and Distributed Processing

In this thesis we investigate centralized and distributed variants of sparse Bayesian learning (SBL), an effective probabilistic regression method used in machine learning. Since inference in an SBL model is not tractable in closed form, approximations are needed. We focus on the variational Bayesian approximation, as opposed to others used in the literature, for three reasons: First, it is a flexible general framework for approximate Bayesian inference that estimates probability densities including point estimates as a special case. Second, it has guaranteed convergence properties. And third, it is a deterministic approximation concept that is even applicable for high dimensional problems where non-deterministic sampling methods may be prohibitive. We resolve some inconsistencies in the literature involved in other SBL approximation techniques with regard to a proper Bayesian treatment and the incorporation of a very desired property, namely scale invariance. More specifically, ...

Buchgraber, Thomas — Graz University of Technology

Bayesian Compressed Sensing using Alpha-Stable Distributions

During the last decades, information is being gathered and processed at an explosive rate. This fact gives rise to a very important issue, that is, how to effectively and precisely describe the information content of a given source signal or an ensemble of source signals, such that it can be stored, processed or transmitted by taking into consideration the limitations and capabilities of the several digital devices. One of the fundamental principles of signal processing for decades is the Nyquist-Shannon sampling theorem, which states that the minimum number of samples needed to reconstruct a signal without error is dictated by its bandwidth. However, there are many cases in our everyday life in which sampling at the Nyquist rate results in too many data and thus, demanding an increased processing power, as well as storage requirements. A mathematical theory that emerged ...

Tzagkarakis, George — University of Crete

Acoustic sensor network geometry calibration and applications

In the modern world, we are increasingly surrounded by computation devices with communication links and one or more microphones. Such devices are, for example, smartphones, tablets, laptops or hearing aids. These devices can work together as nodes in an acoustic sensor network (ASN). Such networks are a growing platform that opens the possibility for many practical applications. ASN based speech enhancement, source localization, and event detection can be applied for teleconferencing, camera control, automation, or assisted living. For this kind of applications, the awareness of auditory objects and their spatial positioning are key properties. In order to provide these two kinds of information, novel methods have been developed in this thesis. Information on the type of auditory objects is provided by a novel real-time sound classification method. Information on the position of human speakers is provided by a novel localization ...

Plinge, Axel — TU Dortmund University

Transmission strategies for wireless energy harvesting nodes

Over the last few decades, transistor miniaturization has enabled a tremendous increase in the processing capability of commercial electronic devices, which, combined with the reduction of production costs, has tremendously fostered the usage of the Information and Communications Technologies (ICTs) both in terms of number of users and required data rates. In turn, this has led to a tremendous increment in the energetic demand of the ICT sector, which is expected to further grow during the upcoming years, reaching unsustainable levels of greenhouse gas emissions as reported by the European Council. Additionally, the autonomy of battery operated devices is getting reduced year after year since battery technology has not evolved fast enough to cope with the increase of energy consumption associated to the growth of the node’s processing capability. Energy harvesting, which is known as the process of collecting energy ...

Gregori, Maria — Centre Tecnològic de Telecomunicacions de Catalunya

Accelerating Monte Carlo methods for Bayesian inference in dynamical models

Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. In this thesis, we make use of Bayesian statistics to construct probabilistic models given prior information and historical data, which can be used for decision support and predictions. The main obstacle with this approach is that it often results in mathematical problems lacking analytical solutions. To cope with this, we make use of statistical simulation algorithms known as Monte Carlo methods to approximate the intractable solution. These methods enjoy well-understood statistical properties but are often computational prohibitive to employ. The main contribution of this thesis is the exploration of different strategies for accelerating inference methods based on sequential Monte Carlo ...

Dahlin, Johan — Linköping University

Methods for Comparative Analysis of Metagenomic Data

Modern research in environmental microbiology utilizes genomic data, especially sequencing of DNA, to describe microbial communities. The field studying all genetic material present in an environmental sample is referred to as metagenomics. This doctoral thesis deals with metagenomics from the perspective of bioinformatics that is unreplaceable during the data processing. In the theoretical part of this thesis, two different approaches of metagenomics are described including their main principles and weaknesses. The first approach, based on targeted sequencing, is a well-established field with a wide range of bioinformatics techniques. Yet, methods for comparison of samples from several environments can be highly improved. The approach introduced in this thesis uses unique transformation of data into a bipartite graph, where one partition is formed by taxa, while the other by samples or environments. Such a graph fully reflects qualitative as well as quantitative ...

Sedlar, Karel — Brno University of Technology, Department of Biomedical Engineering

On the Energy Efficiency of Cooperative Wireless Networks

The aim of this dissertation is the study of cooperative communications in wireless networks. In cooperative networks, each user transmits its own data and also aids the communication of other users. User cooperation is particularly attractive for the wireless medium, where every user listens to the transmission of other users. The main benefit of user cooperation in wireless networks is, probably, its efficacy to combat the wireless channel impairments. Path loss and shadowing effects are overcome using intermediate nodes, with better channel conditions, to retransmit the received signal to the estination. Further, the channel fading effect can be also mitigated by means of cooperative spatial diversity (the information arrives at the destination through multiple independent paths). These benefits result in an increase of the users spectral efficiency and/or savings on the overall network power resource. Besides these gains, the simple ...

Gomez-Vilardebo, Jesus — Universidad Politecnica de Madrid

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