105 research outputs found

    Dynamic Response of Silo Supporting Structure under Pulsating Loads

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    Silo quaking is a time varying mass structural dynamic problem and the existence of a silo quake spectrum is confirmed in this research. The outcomes confirm that silo quaking can be prevented by providing the silo structure with sufficient mass, stiffness and damping to counterbalance the effects of pulsating forces and mass losses. Furthermore, dynamic structural analysis algorithms and software need to be developed to solve time varying mass structural dynamic problems

    Application of Singular Spectrum Analysis (SSA), Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD) for automated solvent suppression and automated baseline and phase correction from multi-dimensional NMR spectra

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    A common problem on protein structure determination by NMR spectroscopy is due to the solvent artifact. Typically, a deuterated solvent is used instead of normal water. However, several experimental methods have been developed to suppress the solvent signal in the case that one has to use a protonated solvent or if the signals of the remaining protons even in a highly deuterated sample are still too strong. For a protein dissolved in 90% H2O / 10% D2O, the concentration of solvent protons is about five orders of magnitude greater than the concentration of the protons of interest in the solute. Therefore, the evaluation of multi-dimensional NMR spectra may be incomplete since certain resonances of interest (e.g. Hα proton resonances) are hidden by the solvent signal and since signal parts of the solvent may be misinterpreted as cross peaks originating from the protein. The experimental solvent suppression procedures typically are not able to recover these significant protein signals. Many post-processing methods have been designed in order to overcome this problem. In this work, several algorithms for the suppression of the water signal have been developed and compared. In particular, it has been shown that the Singular Spectrum Analysis (SSA) can be applied advantageously to remove the solvent artifact from NMR spectra of any dimensionality both digitally and analogically acquired. In particular, the investigated time domain signals (FIDs) are decomposed into water and protein related components by means of an initial embedding of the data in the space of time-delayed coordinates. Eigenvalue decomposition is applied on these data and the component with the highest variance (typically represented by the dominant solvent signal) is neglected before reverting the embedding. Pre-processing (group delay management and signal normalization) and post-processing (inverse normalization, Fourier transformation and phase and baseline corrections) of the NMR data is mandatory in order to obtain a better performance of the suppression. The optimal embedding dimension has been empirically determined in accordance to a specific qualitative and quantitative analysis of the extracted components applied on a back-calculated two-dimensional spectrum of HPr protein from Staphylococcus aureus. Moreover, the investigation of experimental data (three-dimensional 1H13C HCCH-TOCSY spectrum of Trx protein from Plasmodium falciparum and two-dimensional NOESY and TOCSY spectra of HPr protein from Staphylococcus aureus) has revealed the ability of the algorithm to recover resonances hidden underneath the water signal. Pathological diseases and the effects of drugs and lifestyle can be detected from NMR spectroscopy applied on samples containing biofluids (e.g. urine, blood, saliva). The detection of signals of interest in such spectra can be hampered by the solvent as well. The SSA has also been successfully applied to one-dimensional urine, blood and cell spectra. The algorithm for automated solvent suppression has been introduced in the AUREMOL software package (AUREMOL_SSA). It is optionally followed by an automated baseline correction in the frequency domain (AUREMOL_ALS) that can be also used out the former algorithm. The automated recognition of baseline points is differently performed in dependence on the dimensionality of the data. In order to investigate the limitations of the SSA, it has been applied to spectra whose dominant signal is not the solvent (as in case of watergate solvent suppression and in case of back-calculated data not including any experimental water signal) determining the optimal solvent-to-solute ratio. The Independent Component Analysis (ICA) represents a valid alternative for water suppression when the solvent signal is not the dominant one in the spectra (when it is smaller than the half of the strongest solute resonance). In particular, two components are obtained: the solvent and the solute. The ICA needs as input at least as many different spectra (mixtures) as the number of components (source signals), thus the definition of a suitable protocol for generating a dataset of one-dimensional ICA-tailored inputs is straightforward. The ICA has revealed to overcome the SSA limitations and to be able to recover resonances of interest that cannot be detected applying the SSA. The ICA avoids all the pre- and post-processing steps, since it is directly applied in the frequency domain. On the other hand, the selection of the component to be removed is automatically detected in the SSA case (having the highest variance). In the ICA, a visual inspection of the extracted components is still required considering that the output is permutable and scale and sign ambiguities may occur. The Empirical Mode Decomposition (EMD) has revealed to be more suitable for automated phase correction than for solvent suppression purposes. It decomposes the FID into several intrinsic mode functions (IMFs) whose frequency of oscillation decreases from the first to the last ones (that identifies the solvent signal). The automatically identified non-baseline regions in the Fourier transform of the sum of the first IMFs are separately evaluated and genetic algorithms are applied in order to determine the zero- and first-order terms suitable for an optimal phase correction. The SSA and the ALS algorithms have been applied before assigning the two-dimensional NOESY spectrum (with the program KNOWNOE) of the PSCD4-domain of the pleuralin protein in order to increase the number of already existing distance restraints. A new routine to derive 3JHNHα couplings from torsion angles (Karplus relation) and vice versa, has been introduced in the AUREMOL software. Using the newly developed tools a refined three-dimensional structure of the PSCD4-domain could be obtained

    BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

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    The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

    Advanced Techniques for Ground Penetrating Radar Imaging

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    Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

    Micro/Nano Manufacturing

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    Micro manufacturing involves dealing with the fabrication of structures in the size range of 0.1 to 1000 µm. The scope of nano manufacturing extends the size range of manufactured features to even smaller length scales—below 100 nm. A strict borderline between micro and nano manufacturing can hardly be drawn, such that both domains are treated as complementary and mutually beneficial within a closely interconnected scientific community. Both micro and nano manufacturing can be considered as important enablers for high-end products. This Special Issue of Applied Sciences is dedicated to recent advances in research and development within the field of micro and nano manufacturing. The included papers report recent findings and advances in manufacturing technologies for producing products with micro and nano scale features and structures as well as applications underpinned by the advances in these technologies

    Sensor Signal and Information Processing II

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    In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

    Understanding magmatic processes and seismo-volcano source localization with multicomponent seismic arrays

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    Dans cette thèse, nous étudions le problème de la localisation de sources sismo-volcanique, à partir des données enregistrées par des réseaux de capteurs composés de nouveaux sismomètres à trois composantes (3C). Nous nous concentrerons sur le volcan Ubinas, l'un des plus actifs au Pérou. Nous développons une nouvelle approche (MUSIC-3C) basée sur la méthode MUSIC permetant de retourner les 3 paramètres utiles (lenteur, azimut et incidence). Pour valider notre méthodologie, nous analysons des sources synthétiques propagées en tenant compte de la topographie du volcan Ubinas. Dans cette expérience, les données synthétiques ont été générées pour plusieurs sources situées à différentes profondeurs sous le cratère Ubinas. Nous utilisons l'algorithme MUSIC-3C pour les relocaliser. Nous traitons également des données réelles provenant d'une expérience de terrain menée sur le volcan Ubinas (Pérou) en 2009 par les équipes de recherche de l'IRD-France (Institut de Recherche pour le Déveleppment), UCD l'Irlande (projet VOLUME) et l'Institut de Géophysique du Pérou (IGP). Nous utilisons l'algorithme MUSIC-3C pour localiser les événements explosifs (type vulcanien), ce qui nous permet d'identifier et d'analyser les processus physiques de ces événements, à la suite de cette analyse, nous avons trouvé deux sources pour chaque explosion situées à 300 m et 1100 m en dessous du fond du cratère actif. Basé sur les mécanismes éruptifs proposés pour d'autres volcans du même type, nous interprétons la position de ces sources ainsi que les limites du conduit éruptif impliqué dans le processus de fragmentation.In this thesis, we study the seismo-volcanic source localization using data recorded by new sensor arrays composed of three-component (3C) seismometers deployed on Ubinas stratovolcano (Peru). We develop a new framework (MUSIC-3C) of source localization method based on the well-known MUSIC algorithm. To investigate the performance of the MUSIC-3C method, we use synthetic datasets designed from eight broadband isotropic seismic sources located beneath the crater floor at different depths. The fundamental scheme of the MUSIC-3C method exploits the fact of the cross-spectral matrix of 3C array data, corresponding to the first seismic signal arrivals, provides of useful vector components (slowness, back-azimuth and incidence angle) from the seismic source. Application of the MUSIC-3C method on synthetic datasets shows the recovery of source positions. Real data used in this study was collected during seismic measurements with two seismic antennas deployed at Ubinas volcano in 2009, whose experiment conduced by volcanic teams of IRD-France (l'Institute de Recherche pour le Déveleppment), Geophysics group University College Dublin Ireland and Geophysical Institute of Peru (IGP). We apply the MUSIC-3C algorithm to investigate wave fields associated with the magmatic activity of Ubinas volcano. These analysis evidence a complex mechanism of vulcanian eruptions in which their seismic sources are found at two separated sources located at depths of 300 m and 1100 m beneath the crater floor. This implies the reproduction of similar mechanisms into the conduit. Based on the eruptive mechanisms proposed for other volcanoes of the same type, we interpret the position of this sources as the limits of the conduit portion that was involved in the fragmentation process.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

    Models and Analysis of Vocal Emissions for Biomedical Applications

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    The Models and Analysis of Vocal Emissions with Biomedical Applications (MAVEBA) workshop came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

    Spatio-temporal rainfall estimation and nowcasting for flash flood forecasting.

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    Thesis (Ph.D.Eng.)-University of KwaZulu-Natal, Durban, 2007.Floods cannot be prevented, but their devastating effects can be minimized if advance warning of the event is available. The South African Disaster Management Act (Act 57 of 2002) advocates a paradigm shift from the current "bucket and blanket brigade" response-based mind set to one where disaster prevention or mitigation are the preferred options. It is in the context of mitigating the effects of floods that the development and implementation of a reliable flood forecasting system has major significance. In the case of flash floods, a few hours lead time can afford disaster managers the opportunity to take steps which may significantly reduce loss of life and damage to property. The engineering challenges in developing and implementing such a system are numerous. In this thesis, the design and implement at ion of a flash flood forecasting system in South Africa is critically examined. The technical aspect s relating to spatio-temporal rainfall estimation and now casting are a key area in which new contributions are made. In particular, field and optical flow advection algorithms are adapted and refined to help predict future path s of storms; fast and pragmatic algorithms for combining rain gauge and remote sensing (rada r and satellite) estimates are re fined and validated; a two-dimensional adaptation of Empirical Mode Decomposition is devised to extract the temporally persistent structure embedded in rainfall fields. A second area of significant contribution relates to real-time fore cast updates, made in response to the most recent observed information. A number of techniques embedded in the rich Kalm an and adaptive filtering literature are adopted for this purpose. The work captures the current "state of play" in the South African context and hopes to provide a blueprint for future development of an essential tool for disaster management. There are a number of natural spin-offs from this work for related field s in water resources management

    Stability of Articulated Revetments Against Wave Attack on Shallow Soft Soil Slopes

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    Continuously-connected, articulated revetment systems have potential to decrease the weight of armor cover in resisting wave attack, compared to traditional designs. Modes of instability for sloping revetments include uplift, sliding, and toe roll-up. Design methods are summarized by McDonnell (1998), Pilarczyk (1998), and Herbich (1999). Russo (2003) conducted a field prototype scale investigation on performance of Articulated Concrete Mattresses (ACMs) in coastal Louisiana, which demonstrated this structure’s ability to resist a range of wave loading conditions, and inspired scoping of further research to quantify structure performance beyond known limits. Present research expanded earlier works by examining fundamental physical processes of wave loading near the theoretical threshold of structure incipient motion. The motivation for further investigation and modeling modes of failure is to: (1) demonstrate a method to support the design selection process, (2) optimize revetment dimensions when articulated block is considered the most appropriate application, and (3) meet earthen slope protection requirements with relatively low ground pressures exerted by the armor layer for use in soft soil conditions. A new structure performance metric is derived as the physically dimensionless “hydromechanic potential,” which is used to quantify structure movement as an interconnected system under wave attack. Research involved using a spectral hydromechanics analytical approach, with instrumented physical model results, to demonstrate a capability for constraining uncertainty on the behavior of revetments in specified conditions. Physical modeling was conducted based on dimensional analysis and similitude criteria. Physical modeling and spectral analysis were based on principles of hydrodynamics and structure mechanics of articulated revetment system configurations at incipient motion under irregular wave conditions. Theoretical equilibrium exists when destabilizing wave loading forces are in balance with restoring gravitational forces of the structure. Tests of prior works, conducted through traditional methods, were generally able to measure structure performance under wave attack to between 3.7 and 8 of the ratio of destabilizing-to-restoring forces. Despite being the best available physical data measurable to-date, Herbich (1999) characterized structure performance in this range for design as “doubtful”. Results of this dissertation research indicated that a new lower limit is detectable at the threshold of equilibrium based on hydromechanic potential
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