Winding patterns for actively shielded shim coils with asymmetric target-fields

A method is presented for calculating the currents and winding patterns required to design independent zonal and tesseral shim coils for magnetic resonance imaging. Both actively shielded and unshielded configurations are considered, and the region of interest can be located asymmetrically with respect to the coil's length. Streamline, target-field and Fourier-series methods are utilized. The desired target-field is specified at two cylindrical radii, on and inside a circular conducting cylinder of length 2L and radius a. The specification is over some asymmetric portion pL < z < qL of the coil's length (-1 < p < q < 1). Arbitrary functions are used in the outer sections, -L < z < pL and qL < z < L, to ensure continuity of the magnetic field across the entire length of the coil. The entire field is then periodically extended as a half-range cosine Fourier series about either end of the coil. The resultant Fourier coefficients are then substituted into the Fourier-series expressions for the internal and external magnetic fields, and current densities and stream functions on both the primary coil and shield. A contour plot of the stream function directly gives the required coil winding patterns. Spherical harmonic analysis and shielding analysis on field calculations from a ZX shim coil indicate that example designs and theory are well matched.

A novel target-field method for magnetic resonance shim coils: III. Shielded zonal and tesseral coils

This paper continues the development of a new approach for the design of shim and gradient coils, used in magnetic resonance imaging (MRI) applications. A cylindrical primary coil of radius a and length 2L is placed inside a co-axial shield cylinder of radius b. An active shielding strategy is used to create a desired target field at an arbitrarily specified (cylindrical) location within the primary coil, and to annul the field at a certain radius outside the shield. The form of the interior target field may be chosen arbitrarily by the designer, although zonal and tesseral harmonics are typically used in MRI applications. The method presented here designs coil windings on both the primary and shielding cylinders, to produce fields that conform to the specified interior target field and the annulled field exterior to the shield. An additional feature of the method presented here is that the target field inside the primary coil is matched at two different radii, to improve overall accuracy. The method is illustrated by designing several shielded shim coils, for creating higher order tesseral fields located asymmetrically within the coil. The simpler case of pure zonal fields is discussed separately and applied to the design of some higher order shielded coils.

Decomposition of ground biomass of secondary forest and yield of annual crops in no tillage

ABSTRACT The objective of this work was to evaluate the dynamics of decomposition process of chopped secondary forest system, previously enriched with legumes Inga velutina Willd. and Stryphnodendron pulcherrimum (Willd.) Hochr. and the contribution of this process to the nutrient input to the cultivation of corn and bean under no-tillage. The experimental design was a randomized block, split plot with four replications. The plots were two species (I. velutina and S. pulcherrimum) and the subplots were seven times of evaluation (0, 7, 28, 63, 189, 252, 294 days after experiment installation). There was no difference (p ≥ 0.05) between the secondary forest systems enriched and no interaction with times for biomass waste, decomposition constant and half-life time. The waste of S. pulcherrimum trees had higher (p < 0.05) C/N ratio than that I. velutina. However, this one was higher (p < 0.05) in lignin content. Nevertheless, the dynamics of residue decomposition was similar. The corn yield was higher (p < 0.05) in cultivation under I.velutina waste. Meanwhile, the beans planted after corn, shows similar (p > 0.05) yield in both areas, regardless of the waste origin.

Optimal methodology for distribution systems reconfiguration based on OPF and solved by decomposition technique

This paper presents a new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach. The objective minimizes power losses, balancing load among feeders and subject to constraints: capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages and radial optimal operation of networks. The Generalized Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages; the first one is the Master problem and is formulated as a mixed integer non-linear programming problem. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a non-linear programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in GAMS. The effectiveness of the proposal is demonstrated through two examples extracted from the literature.

Directional Moving Target Indication for Civil Traffic Monitoring Using Single Channel SAR

This paper presents a novel moving target indicator which is selective with respect to a direction of interest. Preliminary results indicate that the obtained selectivity may have high interest in civil traffic monitoring using single channel SAR data.

A trophic analysis of target species of macrobenthos in a subtropical coast community: a taxa relationship essay

Studies on the feeding habits of aquatic organisms are a requirement for the management and sustainable use of marine ecosystems. The aim of the present research was to analyze the habits and trophic similarities of decapods, starfish and fish in order to propose trophic relationships between taxa, using Hennigian methods of phylogenetic systematics. This new grouping hypothesis, based on shared and exclusive food items and food types, corresponds to the broad taxonomic groups used in the analysis. Our results indicate that algae, Mollusca, Polychaeta, Crustacea, Echinodermata and Actinopterygii are the most exploited common resources among the species studied. Starfish were differentiated from other organisms for being stenophagic, and were grouped for feeding on bivalve mollusks. A larger group of fish and crustaceans shares algae and mainly crustaceans as food items. A third group united all eight species of Actinopterygii. This largest subgroup of fish is typically carnivorous, feeding on Anthozoa and a great quantity of Crustacea. Synodus foetens has a special position among fishes, due to its unique feeding on nematodes. A Euclidean distance dendrogram obtained in a previous publication grouped S. foetens with starfish. That result was based on a few non-exclusive shared similarities in feeding modes, as well as on shared absences of items, which are not an adequate grouping factor. Starfish are stenophagic, eating bivalves almost exclusively. Synodus foetens and Isopisthus parvipinnis have restricted food items, and are thus intermediary in relation to starfish, decapods, and other fish, which are euryphagous. The trophic cladogram displays details of food items, whether or not shared by all species. The resulting trophic analysis is consistent with known historical relationships.

Hyperspectral subspace identification

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

Subacute effects of the thiodicarb pesticide on target organs of male wistar rats: biochemical, histological, and flow cytometry studies

Thiodicarb, a carbamate pesticide widely used on crops, may pose several environmental and health concerns. This study aimed to explore its toxicological profile on male rats using hematological, biochemical, histopathological, and flow cytometry markers. Exposed animals were dosed daily at 10, 20, or 40 mg/kg/body weight (group A, B, and C, respectively) during 30 d. No significant changes were observed in hematological parameters among all groups. After 10 d, a decrease of total cholesterol levels was noted in rats exposed to 40 mg/kg. Aspartate aminotransferase (AST) activity increased (group A at 20 d; groups A and B at 30 d) and alkaline phosphatase (ALP) (group B at 30 d) activity significantly reduced. At 30 d a decrease of some of the other evaluated parameters was observed with total cholesterol and urea levels in group A as well as total protein and creatinine levels in groups A and B. Histological results demonstrated multi-organ dose-related damage in thiodicarb-exposed animals, evidenced as hemorrhagic and diffuse vacuolation in hepatic tissue; renal histology showed disorganized glomeruli and tubular cell degeneration; spleen was ruptured with white pulp and clusters of iron deposits within red pulp; significant cellular loss was noted at the cortex of thymus; and degenerative changes were observed within testis. The histopathologic alterations were most prominent in the high-dose group. Concerning flow cytometry studies, an increase of lymphocyte number, especially T lymphocytes, was seen in blood samples from animals exposed to the highest dose. Taken together, these results indicate marked systemic organ toxicity in rats after subacute exposure to thiodicarb.

Implementação em hardware reconfigurável de método de separação de dados hiperespetrais

Relatório do Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Local fractional variational iteration and decomposition methods for wave equation on cantor sets within local fractional operators

We perform a comparison between the fractional iteration and decomposition methods applied to the wave equation on Cantor set. The operators are taken in the local sense. The results illustrate the significant features of the two methods which are both very effective and straightforward for solving the differential equations with local fractional derivative.

Setting target rotation time in PROFIBUS based real-time distributed applications

In this paper, we analyse the ability of Profibus fieldbus to cope with the real-time requirements of a Distributed Computer Control System (DCCS), where messages associated to discrete events must be made available within a maximum bound time. Our methodology is based on the knowledge of real-time traffic characteristics, setting the network parameters in order to cope with timing requirements. Since non-real-time traffic characteristics are usually unknown at the design stage, we consider an operational profile where, constraining non-real-time traffic at the application level, we assure that realtime requirements are met.

analytical model and measurements of the target erosion depth profile of balanced and unbalanced planar magnetron cathodes

The erosion depth profile of planar targets in balanced and unbalanced magnetron cathodes with cylindrical symmetry is measured along the target radius. The magnetic fields have rotational symmetry. The horizontal and vertical components of the magnetic field B are measured at points above the cathode target with z = 2 x 10(-3) m. The experimental data reveal that the target erosion depth profile is a function of the angle. made by B with a horizontal line defined by z = 2 x 10(-3) m. To explain this dependence a simplified model of the discharge is developed. In the scope of the model, the pathway lengths of the secondary electrons in the pre-sheath region are calculated by analytical integration of the Lorentz differential equations. Weighting these lengths by using the distribution law of the mean free path of the secondary electrons, we estimate the densities of the ionizing events over the cathode and the relative flux of the sputtered atoms. The expression so deduced correlates for the first time the erosion depth profile of the target with the angle theta. The model shows reasonably good fittings to the experimental target erosion depth profiles confirming that ionization occurs mainly in the pre-sheath zone.

Laser-induced diffusion decomposition in Fe-V thin-film alloys

We investigate the origin of ferromagnetism induced in thin-film (similar to 20 nm) Fe-V alloys by their irradiation with subpicosecond laser pulses. We find with Rutherford backscattering that the magnetic modifications follow a thermally stimulated process of diffusion decomposition, with formation of a-few-nm-thick Fe enriched layer inside the film. Surprisingly, similar transformations in the samples were also found after their long-time (similar to 10(3) s) thermal annealing. However, the laser action provides much higher diffusion coefficients (similar to 4 orders of magnitude) than those obtained under standard heat treatments. We get a hint that this ultrafast diffusion decomposition occurs in the metallic glassy state achievable in laser-quenched samples. This vitrification is thought to be a prerequisite for the laser-induced onset of ferromagnetism that we observe. 2014 Elsevier B.V. All rights reserved.

Petri net model decomposition - a model based approach supporting distributed execution

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica, Especialidade de Sistemas Digitais, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

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.