THE ROLE OF XML IN THE MODELING PROCESS OF A VIRTUAL BUSINESS

The aim of this paper is to describe the XML stack of languages used in the implementation process of a web application. This application is based on a three tier architecture named XRX. In this type of architecture there is no need for data model transformations between the tiers of the architecture like in the classical architecture. So the applications developed according XRX architecture become more flexible, efficient and simple.XML, XPath, XQuery, XSLT, XForms, XRX, UBL

GNSS Signal spoofing detection

This thesis elaborates on the implementation of spoofing detection techniques for GPS L1 C/A signals, topic which is up to the minute in the GNSS community. The interest of this topic has its origin on the fact that, currently, there is a large number of applications relying on GNSS communications. Moreover, the public character of the communication details and specifications have exposed the communications to spoofing agents, which, with a relatively cheap equipment, are capable of controlling the tracking loops of a victim receiver and, as a result, manipulate the its timing or navigation solution. In front of this issue, this project aims to contribute on the spoofing detection community by implementing, in the recognized Borre¿s GNSS receiver software, and testing some techniques. To do so, the project is organized in three sections; the preliminary study of the state of the art and the software that will be considered as the starting point, the spoofing signal analysis and the implementation of the selected spoofing detection techniques, and the result¿s evaluation

Exploiting XML Technologies in Medical Information Systems

Integration of clinical research data and routine care data, in order to streamline the process of conducting clinical studies, has been a problem for quite a while now. The Single Source project at the University of Münster aims at contributing to this area. The approach is based on a vast usage of XML technology together with a novel integration architecture. The emphasis in this paper is on the former: The seamless usage of XML technology throughout the entire application is presented, and mismatches of programming paradigms are averted by exploiting the features of XML, XQuery and XForms. In particular, this is demonstrated by the example of a component used for handling forms, by how it is built and used in the entire scenario

Short-Range Millimeter-Wave Sensing and Imaging: Theory, Experiments and Super-Resolution Algorithms

Recent advancements in silicon technology offer the possibility of realizing low-cost and highly integrated radar sensor and imaging systems in mm-wave (between 30 and 300 GHz) and beyond. Such active short-range mm-wave systems have a wide range of applications including medical imaging, security scanning, autonomous vehicle navigation, and human gesture recognition. Moving to higher frequencies provides us with the spectral and spatial degrees of freedom that we need for high resolution imaging and sensing application. Increased bandwidth availability enhances range resolution by increasing the degrees of freedom in the time-frequency domain. Cross-range resolution is enhanced by the increase in the number of spatial degrees of freedom for a constrained form factor. The focus of this thesis is to explore system design and algorithmic development to utilize the available degrees of freedom in mm-wave frequencies in order to realize imaging and sensing capabilities under cost, complexity and form factor constraints. We first consider the fundamental problem of estimating frequencies and gains in a noisy mixture of sinusoids. This problem is ubiquitous in radar sensing applications, including target range and velocity estimation using standard radar waveforms (e.g., chirp or stepped frequency continuous wave), and direction of arrival estimation using an array of antenna elements. We have developed a fast and robust iterative algorithm for super-resolving the frequencies and gains, and have demonstrated near-optimal performance in terms of frequency estimation accuracy by benchmarking against the Cramer Rao Bound in various scenarios.Next, we explore cross-range radar imaging using an array of antenna elements under severe cost, complexity and form factor constraints. We show that we must account for such constraints in a manner that is quite different from that of conventional radar, and introduce new models and algorithms validated by experimental results. In order to relax the synchronization requirements across multiple transceiver elements we have considered the monostatic architecture in which only the co-located elements are synchronized. We investigate the impact of sparse spatial sampling by reducing the number of array antenna elements, and show that ``sparse monostatic'' architecture leads to grating lobe artifact, which introduces ambiguity in the detection/estimation of point targets in the scene. At short ranges, however, targets are ``low-pass'' and contain extended features (consisting of a continuum of points), and are not well-modeled by a small number of point scatterers. We introduce the concept of ``spatial aggregation,'' which provides the flexibility of constructing a dictionary in which each atom corresponds to a collection of point scatterers, and demonstrate its effectiveness in suppressing the grating lobes and preserving the information in the scene.Finally, we take a more fundamental and systematic approach based on singular decomposition of the imaging system, to understand the information capacity and the limits of performance for various geometries. In general, a scene can be described by an infinite number of independent parameters. However, the number of independent parameters that can be measured through an imaging system (also known as the degrees of freedom of the system) is typically finite, and is constrained by the geometry and wavelength. We introduce a measure to predict the number of spatial degrees of freedom of 1D imaging systems for both monostatic and multistatic array architectures. Our analysis reveals that there is no fundamental benefit in multistatic architecture compared to monostatic in terms of achievable degrees of freedom. The real benefit of multistatic architecture from a practical point of view, is in being able to design sparse transmit and receive antenna arrays that are capable of achieving the available degrees of freedom. Moreover, our analytical framework opens up new avenues to investigate image formation techniques that aim to reconstruct the reflectivity function of the scene by solving an inverse scattering problem, and provides crucial insights on the achievable resolution

Smart forms: a survey to state and test the most major electronic forms technologies that are based on W3C standards

Smart Forms are efficient and powerful electronic forms that could be used for the interactions between end users and web applications systems. Several electronic forms software products that use W3C technologies are presented to meet the demands of users. This thesis aims to study and test the major electronic forms technologies that are based on W3C standards. It discusses the main electronic forms features and experiments them with some essential applications. This research produces deep understanding of the most electronic forms technologies that are based on W3C standards and their important features, which make an electronic form smart form. In addition, it opens developments prospects for other researchers to develop some applications ideas that could contribute in the electronic forms domain

Randomized Dynamic Mode Decomposition

This paper presents a randomized algorithm for computing the near-optimal low-rank dynamic mode decomposition (DMD). Randomized algorithms are emerging techniques to compute low-rank matrix approximations at a fraction of the cost of deterministic algorithms, easing the computational challenges arising in the area of `big data'. The idea is to derive a small matrix from the high-dimensional data, which is then used to efficiently compute the dynamic modes and eigenvalues. The algorithm is presented in a modular probabilistic framework, and the approximation quality can be controlled via oversampling and power iterations. The effectiveness of the resulting randomized DMD algorithm is demonstrated on several benchmark examples of increasing complexity, providing an accurate and efficient approach to extract spatiotemporal coherent structures from big data in a framework that scales with the intrinsic rank of the data, rather than the ambient measurement dimension. For this work we assume that the dynamics of the problem under consideration is evolving on a low-dimensional subspace that is well characterized by a fast decaying singular value spectrum