A uniform white-noise model for fixed-point roundoff errors in digital systems

Fixed-point roundoff noise in digital implementation of linear systems arises due to overflow, quantization of coefficients and input signals, and arithmetical errors. In uniform white-noise models, the last two types of roundoff errors are regarded as uniformly distributed independent random vectors on cubes of suitable size. For input signal quantization errors, the heuristic model is justified by a quantization theorem, which cannot be directly applied to arithmetical errors due to the complicated input-dependence of errors. The complete uniform white-noise model is shown to be valid in the sense of weak convergence of probabilistic measures as the lattice step tends to zero if the matrices of realization of the system in the state space satisfy certain nonresonance conditions and the finite-dimensional distributions of the input signal are absolutely continuous.

A model to forecast financial failure, in non financial galician SMES

We are concerned with providing more empirical evidence on forecast failure, developing forecast models, and examining the impact of events such as audit reports. A joint consideration of classic financial ratios and relevant external indicators leads us to build a basic prediction model focused in non-financial Galician SMEs. Explanatory variables are relevant financial indicators from the viewpoint of the financial logic and financial failure theory. The paper explores three mathematical models: discriminant analysis, Logit, and linear multivariate regression. We conclude that, even though they both offer high explanatory and predictive abilities, Logit and MDA models should be used and interpreted jointly.

Sistema de modelização tridimensional de galerias subterrâneas

A presente dissertação apresenta uma solução para o problema de modelização tridimensional de galerias subterrâneas. O trabalho desenvolvido emprega técnicas provenientes da área da robótica móvel para obtenção um sistema autónomo móvel de modelização, capaz de operar em ambientes não estruturados sem acesso a sistemas de posicionamento global, designadamente GPS. Um sistema de modelização móvel e autónomo pode ser bastante vantajoso, pois constitui um método rápido e simples de monitorização das estruturas e criação de representações virtuais das galerias com um elevado nível de detalhe. O sistema de modelização desloca-se no interior dos túneis para recolher informações sensoriais sobre a geometria da estrutura. A tarefa de organização destes dados com vista _a construção de um modelo coerente, exige um conhecimento exacto do percurso praticado pelo sistema, logo o problema de localização da plataforma sensorial tem que ser resolvido. A formulação de um sistema de localização autónoma tem que superar obstáculos que se manifestam vincadamente nos ambientes underground, tais como a monotonia estrutural e a já referida ausência de sistemas de posicionamento global. Neste contexto, foi abordado o conceito de SLAM (Simultaneous Loacalization and Mapping) para determinação da localização da plataforma sensorial em seis graus de liberdade. Seguindo a abordagem tradicional, o núcleo do algoritmo de SLAM consiste no filtro de Kalman estendido (EKF { Extended Kalman Filter ). O sistema proposto incorpora métodos avançados do estado da arte, designadamente a parametrização em profundidade inversa (Inverse Depth Parametrization) e o método de rejeição de outliers 1-Point RANSAC. A contribuição mais importante do método por nós proposto para o avanço do estado da arte foi a fusão da informação visual com a informação inercial. O algoritmo de localização foi testado com base em dados reais, adquiridos no interior de um túnel rodoviário. Os resultados obtidos permitem concluir que, ao fundir medidas inerciais com informações visuais, conseguimos evitar o fenómeno de degeneração do factor de escala, comum nas aplicações de localização através de sistemas puramente monoculares. Provámos simultaneamente que a correcção de um sistema de localização inercial através da consideração de informações visuais é eficaz, pois permite suprimir os desvios de trajectória que caracterizam os sistemas de dead reckoning. O algoritmo de modelização, com base na localização estimada, organiza no espaço tridimensional os dados geométricos adquiridos, resultando deste processo um modelo em nuvem de pontos, que posteriormente _e convertido numa malha triangular, atingindo-se assim uma representação mais realista do cenário original.

Sensing Cloud Optimization applied to a non-convex constrained economical dispatch

Conferência: 39th Annual Conference of the IEEE Industrial-Electronics-Society (IECON) - NOV 10-14, 2013

Logarithmically proportional objective function for planar surfaces recognition in 3D point cloud

3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating building models. This paper introduces a new logarithmically proportional objective function that can be used in both heuristic and metaheuristic (MH) algorithms to discover planar surfaces in a point cloud without exploiting any prior knowledge about those surfaces. It can also adopt itself to the structural density of a scanned construction. In this paper, a metaheuristic method, genetic algorithm (GA), is used to test this introduced objective function on a synthetic point cloud. The results obtained show the proposed method is capable to find all plane configurations of planar surfaces (with a wide variety of sizes) in the point cloud with a minor distance to the actual configurations. © 2014 IEEE.

Spectral solution for the air stripping pollutants removal dynamic model with non linear steady state conditions

This work deals with the numerical simulation of air stripping process for the pre-treatment of groundwater used in human consumption. The model established in steady state presents an exponential solution that is used, together with the Tau Method, to get a spectral approach of the solution of the system of partial differential equations associated to the model in transient state.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Natural antioxidants extraction and their incorporation into model pharmaceutical systems

Dissertation to obtain a Master Degree in Biotechnology

Development of new oxygen therapeutics using fluorinated ionic liquids

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau Mestre em Engenharia Biomédica

Revealing fungal activity in the presence of ionic liquids

Dissertation presented to obtain the Ph.D degree in Biochemistry

How do current term structure model behave beyond the last liquid point?: A comparison of the DNS and Smith-Wilson methods

A Work Project, presented as part of the requirements for the Award of a Master’s Double Degree in Finance from Maastricht University and NOVA – School of Business and Economics

Development and modulation of magnetic responsive supported ionic liquid membranes

The work presented in this thesis aims at developing a new separation process based on the application of supported magnetic ionic liquid membranes, SMILMs, using magnetic ionic liquids, MILs. MILs have attracted growing interest due to their ability to change their physicochemical characteristics when exposed to variable magnetic field conditions. The magnetic responsive behavior of MILs is thus expected to contribute for the development of more efficient separation processes, such as supported liquid membranes, where MILs may be used as a selective carrier. Driven by the MILs behavior, these membranes are expected to switch reversibly their permeability and selectivity by in situ and non-invasive adjustment of the conditions (e.g. intensity, direction vector and uniformity) of an external applied magnetic field. The development of these magnetic responsive membrane processes were anticipated by studies, performed along the first stage of this PhD work, aiming at getting a deep knowledge on the influence of magnetic field on MILs properties. The influence of the magnetic field on the molecular dynamics and structural rearrangement of MILs ionic network was assessed through a 1H-NMR technique. Through the 1H-NMR relaxometry analysis it was possible to estimate the self-diffusion profiles of two different model MILs, [Aliquat][FeCl4] and [P66614][FeCl4]. A comparative analysis was established between the behavior of magnetic and non-magnetic ionic liquids, MILs and ILs, to facilitate the perception of the magnetic field impact on MILs properties. In contrast to ILs, MILs show a specific relaxation mechanism, characterized by the magnetic dependence of their self-diffusion coefficients. MILs self-diffusion coefficients increased in the presence of magnetic field whereas ILs self-diffusion was not affected. In order to understand the reasons underlying the magnetic dependence of MILs self-diffusion, studies were performed to investigate the influence of the magnetic field on MILs’ viscosity. It was observed that the MIL´s viscosity decreases with the increase of the magnetic field, explaining the increase of MILs self-diffusion according to the modified Stokes- Einstein equation. Different gas and liquid transport studies were therefore performed aiming to determine the influence of the magnetic behavior of MILs on solute transport through SMILMs. Gas permeation studies were performed using pure CO2 andN2 gas streams and air, using a series of phosphonium cation based MILs, containing different paramagnetic anions. Transport studies were conducted in the presence and absence of magnetic field at a maximum intensity of 1.5T. The results revealed that gas permeability increased in the presence of the magnetic field, however, without affecting the membrane selectivity. The increase of gas permeability through SMILMs was related to the decrease of the MILs viscosity under magnetic field conditions.(...)

Design of bio-inspired ionic liquids for protein stabilisation

Ionic Liquids (ILs) consist in organic salts that are liquid at/or near room temperature. Since ILs are entirely composed of ions, the formation of ion pairs is expected to be one essential feature for describing solvation in ILs. In recent years, protein - ionic liquid (P-IL) interactions have been the subject of intensive studies mainly because of their capability to promote folding/unfolding of proteins. However, the ion pairs and their lifetimes in ILs in P-IL thematic is dismissed, since the action of ILs is therefore the result of a subtle equilibrium between anion-cation interaction, ion-solvent and ion-protein interaction. The work developed in this thesis innovates in this thematic, once the design of ILs for protein stabilisation was bio-inspired in the high concentration of organic charged metabolites found in cell milieu. Although this perception is overlooked, those combined concentrations have been estimated to be ~300 mM among the macromolecules at concentrations exceeding 300 g/L (macromolecular crowding) and transient ion-pair can naturally occur with a potential specific biological role. Hence the main objective of this work is to develop new bio-ILs with a detectable ion-pair and understand its effects on protein structure and stability, under crowding environment, using advanced NMR techniques and calorimetric techniques. The choline-glutamate ([Ch][Glu]) IL was synthesized and characterized. The ion-pair was detected in water solutions using mainly the selective NOE NMR technique. Through the same technique, it was possible to detect a similar ion-pair promotion under synthetic and natural crowding environments. Using NMR spectroscopy (protein diffusion, HSQC experiments, and hydrogen-deuterium exchange) and differential scanning calorimetry (DSC), the model protein GB1 (production and purification in isotopic enrichment media) it was studied in the presence of [Ch][Glu] under macromolecular crowding conditions (PEG, BSA, lysozyme). Under dilute condition, it is possible to assert that the [Ch][Glu] induces a preferential hydration by weak and non-specific interactions, which leads to a significant stabilisation. On the other hand, under crowding environment, the [Ch][Glu] ion pair is promoted, destabilising the protein by favourable weak hydrophobic interactions , which disrupt the hydration layer of the protein. However, this capability can mitigates the effect of protein crowders. Overall, this work explored the ion-pair existence and its consequences on proteins in conditions similar to cell milieu. In this way, the charged metabolites found in cell can be understood as key for protein stabilisation.

Mecanismos de Fotopercepción No-Visual y Control Circadiano en la Retina Interna de Vertebrados en Condiciones Fisiológicas y en un Modelo Animal de Ceguera. Mechanisms of Non-visual Perception and Circadian Control in the Vertebrate Inner Retina under Physiological Conditions and in Animal Model of Blindness.

La retina juega un rol esencial en el funcionamiento del sistema circadiano de los vertebrados al ser la encargada de sensar las condiciones de iluminación ambiental que ajustan el reloj interno con el fotoperíodo exterior a través de un circuito no-visual. Este circuito es independiente de la vía de formación de imágenes e involucra a las células ganglionares retinianas (CGRs) que proyectan a varias estructuras no-visuales del cerebro; esta vía es la encargada de regular el reflejo pupilar, la sincronización de los ritmos diarios de actividad, el sueño y la supresión de melatonina pineal. La retina contiene además un reloj autónomo que genera ritmos diarios autosostenidos en distintas funciones bioquímicas y fisiológicas, que le confiere la capacidad de predecir el tiempo y anticiparse en su fisiología a los cambios lumínicos a lo largo del ciclo día-noche. Este laboratorio ha demostrado por 1ra vez que las CGRs de pollo poseen osciladores endógenos que generan variaciones diarias en la biosíntesis de fosfolípidos (Guido et al, J Neurochem. 2001; Garbarino et al., J Neurosci Res. 2004a) y de la hormona melatonina con niveles máximos durante el día (Garbarino et al., J Biol Chem 2004b). Aún más, cultivos primarios de CGRs responden a la luz a través de una cascada bioquímica de fototransducción similar a la de invertebrados y que involucra la activación de la enzima fosfolipasa C (PLC) (Contin et al., FASEB J 2006). Estos cultivos fueron obtenidos a estadios embrionarios muy tempranos en dónde solo las CGRs son postmitóticas y mayoritariamente maduras. A estos estadios, los cultivos expresan marcadores de especificación de células ganglionares (pax6, brn3), la proteina Gq y los fotopigmentos melanopsina y criptocromos con gran homología con marcadores descriptos para fotorreceptores rabdoméricos de invertebrados (Contin et al, 2006). Recientemente comenzamos a investigar la percepción de luz en pollos GUCY1*, un modelo de ceguera, en animales que carecen de células fotorreceptoras-conos y bastones-funcionales. Resultados preliminares indicarían que la retina interna, y potencialmente las CGRs de estos animales conservarían la capacidad de responder a la luz regulando el reflejo pupilar y sincronizando los ritmos diarios de alimentación. La convergencia de osciladores y fotopigmentos en la población de CGRs podría contribuir al control temporal de la fisiología del organismo y regulación de funciones no-visuales. Son objetivos de este proyecto: a) Investigar el rol de las CGRs en el sistema circadiano estudiando: i- su habilidad para sintetizar melatonina y, su regulación por luz y dopamina; ii- su capacidad fotorreceptora intrínseca, investigando la presencia de fotopigmentos y componentes de la cascada de fototransducción fundamentalmente la vía de los fosfoinosítidos y la activación de PLC, mediante ensayos moleculares, bioquímicos y farmacológicos; b) Extender estos estudios a cultivos primarios de CGRs inmunopurificadas midiendo la respuesta a la luz sobre la síntesis de melatonina, y los niveles de los mensajeros 2rios Ca2+ y AMP cíclico, la inducción de genes tempranos y la regulación de la actividad NAT, enzima clave en la síntesis de melatonina; y c) Investigar la percepción de luz en pollos GUCY1*(ciegos), sobre distintas funciones no-visuales tales como el reflejo pupilar, la sincronización de los ritmos diarios de alimentación, la síntesis de melatonina y la expresión génica en animales expuestos a estimulación lumínica de distintas intensidades y longitudes de onda. Estos estudios permitirán construir el espectro de acción de la respuesta a la luz en los pollos ciegos a fin de identificar el/los fotopigmentos intervinientes en este fenómeno. Este proyecto profundizará el conocimiento sobre la capacidad fotorreceptora-no visual de la retina interna y particularmente de las CGRs, de la naturaleza de la cascada bioquímica que opera en las mismas y de los mecanismos de regeneración del cromóforo utilizado.

Non-linear model reduction and control of molten carbonate fuel cell systems with internal reforming fuel cell systems with internal reforming

Magdeburg, Univ., Diss, 2007