Reducing power consumption is among the top concerns in Wireless Sensor Networks, as the lifetime of a Wireless Sensor Network depends on its power consumption. Directional antennas help achieve this goal contrary to the commonly used omnidirectional antennas that radiate electromagnetic power equally in all directions, by concentrating the radiated electromagnetic power only in particular directions. This enables increased communication range at no additional energy cost and reduces contention on the wireless medium. The SPIDA (SICS Parasitic Interference Directional Antenna) prototype is one of the few real-world prototypes of electronically switchable directional antennas for Wireless Sensor Networks. However, building several prototypes of SPIDA and conducting real-world experiments using them may be expensive and impractical. Modeling SPIDA based on real-world experiments avoids the expenses incurred by enabling simulation of large networks equipped with SPIDA. Such a model would then allow researchers to develop new algorithms and protocols that take advantage of the provided directional communication on existing Wireless Sensor Network simulators. In this thesis, a model of SPIDA for Wireless Sensor Networks is built based on thoroughly designed real-world experiments. The thesis builds a probabilistic model that accounts for variations in measurements, imperfections in the prototype construction, and fluctuations in experimental settings that affect the values of the measured metrics. The model can be integrated into existing Wireless Sensor Network simulators to foster the research of new algorithms and protocols that take advantage of directional communication. The model returns the values of signal strength and packet reception rate from a node equipped with SPIDA at a certain point in space given the two-dimensional distance coordinates of the point and the configuration of SPIDA as inputs. / Phone:+46765816263 Additional email: burkaja@yahoo.com

Participatory sensing (PS) systems are a new emerging sensing paradigm based on the participation of cellular users in a cooperative way. Due to the spatio-temporal granularity that a PS system can provide, it is now possible to detect and analyze events that occur at different scales, at a low cost. While PS systems present interesting characteristics, they also create new problems. Since the measuring devices are cheaper and they are in the hands of the users, PS systems face several design challenges related to the poor accuracy and high failure rate of the sensors, the possibility of malicious users tampering the data, the violation of the privacy of the users as well as methods to encourage the participation of the users, and the effective visualization of the data. This dissertation presents four main contributions in order to solve some of these challenges. This dissertation presents a framework to guide the design and implementation of PS applications considering all these aspects. The framework consists of five modules: sample size determination, data collection, data verification, data visualization, and density maps generation modules. The remaining contributions are mapped one-on-one to three of the modules of this framework: data verification, data visualization and density maps. Data verification, in the context of PS, consists of the process of detecting and removing spatial outliers to properly reconstruct the variables of interest. A new algorithm for spatial outliers detection and removal is proposed, implemented, and tested. This hybrid neighborhood-aware algorithm considers the uneven spatial density of the users, the number of malicious users, the level of conspiracy, and the lack of accuracy and malfunctioning sensors. The experimental results show that the proposed algorithm performs as good as the best estimator while reducing the execution time considerably. The problem of data visualization in the context of PS application is also of special interest. The characteristics of a typical PS application imply the generation of multivariate time-space series with many gaps in time and space. Considering this, a new method is presented based on the kriging technique along with Principal Component Analysis and Independent Component Analysis. Additionally, a new technique to interpolate data in time and space is proposed, which is more appropriate for PS systems. The results indicate that the accuracy of the estimates improves with the amount of data, i.e., one variable, multiple variables, and space and time data. Also, the results clearly show the advantage of a PS system compared with a traditional measuring system in terms of the precision and spatial resolution of the information provided to the users. One key challenge in PS systems is that of the determination of the locations and number of users where to obtain samples from so that the variables of interest can be accurately represented with a low number of participants. To address this challenge, the use of density maps is proposed, a technique that is based on the current estimations of the variable. The density maps are then utilized by the incentive mechanism in order to encourage the participation of those users indicated in the map. The experimental results show how the density maps greatly improve the quality of the estimations while maintaining a stable and low total number of users in the system. P-Sense, a PS system to monitor pollution levels, has been implemented and tested, and is used as a validation example for all the contributions presented here. P-Sense integrates gas and environmental sensors with a cell phone, in order to monitor air quality levels.

An automatic algorithm for accurate numerical gradient calculations has been developed. The algorithm is based on both finite differences and Chebyshev interpolation approximations. The novelty of the method is an automated tuning of the step size perturbation required for both methods. This automation guaranties the best possible solution using these approaches without the requirement of user inputs. The algorithm treats the functions as a black box, which makes it extremely useful when general and complex problems are considered. This is the case of spacecraft trajectory design problems and complex optimization systems. An efficient procedure for the automatic implementation is presented. Several examples based on an Earth-Moon free return trajectory are presented to validate and demonstrate the accuracy of the method. A state transition matrix (STM) procedure is developed as a reference for the validation of the method. / text

This dissertation explores macrosegregation in directionally solidified aluminum castings. Two methods of interpolating thermocouple data are presented. A method using Lagrangian polynomials to interpolate thermocouple profiles is described and gives the best results for steady state furnace conditions. Using cubic splines to interpolate temperatures works best under transient conditions. A simple model, neglecting convection, is presented for predicting macrosegregation during melting, holding, and solidification of a sample and is compared with existing models. The model is able to accurately capture macrosegregation in microgravity experiments and is verified by experimental results. A two dimensional model of solidification, including convection, is presented and used to simulate samples grown in microgravity and terrestrially. The terrestrial samples exhibit steepling convection, while the microgravity samples do not. Causes of the steepling convection are explored and quantitative comparisons are made against experimental samples, with good agreement. The role of the furnace temperature profile is discussed and it is shown how it can be used to manipulate the steepling convection. Simulations of directional solidification through changes in cross section are presented for four experiments in graphite molds and one hypothetical experiment in an alumina mold. When solidifying through a contraction in cross section, the mold material is shown to have a strong influence on the convection and resulting macrosegregation. When solidifying out of an expansion, there is less of a difference between the two mold materials. Qualitative comparisons are made against experimentally obtained microstructures and good agreement is found. Stray grains were found, at the expansion, in some of the experimental samples and an explanation based on the results of the simulations is given.

We provide a unifying projection-based framework for structure-preserving interpolatory model reduction of parameterized linear dynamical systems, i.e., systems having a structured dependence on parameters that we wish to retain in the reduced-order model. The parameter dependence may be linear or nonlinear and is retained in the reduced-order model. Moreover, we are able to give conditions under which the gradient and Hessian of the system response with respect to the system parameters is matched in the reduced-order model. We provide a systematic approach built on established interpolatory $\mathcal{H}_2$ optimal model reduction methods that will produce parameterized reduced-order models having high fidelity throughout a parameter range of interest. For single input/single output systems with parameters in the input/output maps, we provide reduced-order models that are \emph{optimal} with respect to an $\mathcal{H}_2\otimes\mathcal{L}_2$ joint error measure. The capabilities of these approaches are illustrated by several numerical examples from technical applications.

No description available.

Tese de Mestrado Integrado. Engenharia Mecânica. Faculdade de Engenharia. Universidade do Porto. 2013

We propose a method for seismic data interpolation based on 1) the reformulation of the problem as a stable signal recovery problem and 2) the fact that seismic data is sparsely represented by curvelets. This method does not require information on the seismic velocities. Most importantly, this formulation potentially leads to an explicit recovery condition. We also propose a large-scale problem solver for the l1-regularization minimization involved in the recovery and successfully illustrate the performance of our algorithm on 2D synthetic and real examples.

Seismic data is often irregularly and/or sparsely sampled along spatial coordinates. We show that these acquisition geometries are not necessarily a source of adversity in order to accurately reconstruct adequately-sampled data. We use two examples to illustrate that it may actually be better than equivalent regularly subsampled data. This comment was already made in earlier works by other authors. We explain this behavior by two key observations. Firstly, a noise-free underdetermined problem can be seen as a noisy well-determined problem. Secondly, regularly subsampling creates strong coherent acquisition noise (aliasing) difficult to remove unlike the noise created by irregularly subsampling that is typically weaker and Gaussian-like

In this overview of the recent Curvelet Reconstruction with Sparsity-promoting Inversion (CRSI) method, we present our latest 2-D and 3-D interpolation results on both synthetic and real datasets. We compare these results to interpolated data using other existing methods. Finally, we discuss the challenges related to sparsity-promoting solvers for the large-scale problems the industry faces.