981 resultados para First-derivative spectrophotometry


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A water desaturation zone develops around a tunnel in water-saturated rock when the evaporative water loss at the rock surface is larger than the water flow from the surrounding saturated region of restricted permeability. We describe the methods with which such water desaturation processes in rock materials can be quantified. The water retention characteristic theta(psi) of crystalline rock samples was determined with a pressure membrane apparatus. The negative water potential, identical to the capillary pressure, psi, below the tensiometric range (psi < -0.1 MPa) can be measured with thermocouple psychrometers (TP), and the volumetric water contents, theta, by means of time domain reflectometry (TDR). These standard methods were adapted for measuring the water status in a macroscopically unfissured granodiorite with a total porosity of approximately 0.01. The measured water retention curve of granodiorite samples from the Grimsel test site (central Switzerland) exhibits a shape which is typical for bimodal pore size distributions. The measured bimodality is probably an artifact of a large surface ratio of solid/voids. The thermocouples were installed without a metallic screen using the cavity drilled into the granodiorite as a measuring chamber. The water potentials observed in a cylindrical granodiorite monolith ranged between -0.1 and -3.0 MPa; those near the wall in a ventilated tunnel between -0.1 and -2.2 MPa. Two types of three-rod TDR Probes were used, one as a depth probe inserted into the rock, the other as a surface probe using three copper stripes attached to the surface for detecting water content changes in the rock-to-air boundary. The TDR signal was smoothed with a low-pass filter, and the signal length determined based on the first derivative of the trace. Despite the low porosity of crystalline rock these standard methods are applicable to describe the unsaturated zone in solid rock and may also be used in other consolidated materials such as concrete.

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Background A recent method determines regional gas flow of the lung by electrical impedance tomography (EIT). The aim of this study is to show the applicability of this method in a porcine model of mechanical ventilation in healthy and diseased lungs. Our primary hypothesis is that global gas flow measured by EIT can be correlated with spirometry. Our secondary hypothesis is that regional analysis of respiratory gas flow delivers physiologically meaningful results. Methods In two sets of experiments n = 7 healthy pigs and n = 6 pigs before and after induction of lavage lung injury were investigated. EIT of the lung and spirometry were registered synchronously during ongoing mechanical ventilation. In-vivo aeration of the lung was analysed in four regions-of-interest (ROI) by EIT: 1) global, 2) ventral (non-dependent), 3) middle and 4) dorsal (dependent) ROI. Respiratory gas flow was calculated by the first derivative of the regional aeration curve. Four phases of the respiratory cycle were discriminated. They delivered peak and late inspiratory and expiratory gas flow (PIF, LIF, PEF, LEF) characterizing early or late inspiration or expiration. Results Linear regression analysis of EIT and spirometry in healthy pigs revealed a very good correlation measuring peak flow and a good correlation detecting late flow. PIFEIT = 0.702 · PIFspiro + 117.4, r2 = 0.809; PEFEIT = 0.690 · PEFspiro-124.2, r2 = 0.760; LIFEIT = 0.909 · LIFspiro + 27.32, r2 = 0.572 and LEFEIT = 0.858 · LEFspiro-10.94, r2 = 0.647. EIT derived absolute gas flow was generally smaller than data from spirometry. Regional gas flow was distributed heterogeneously during different phases of the respiratory cycle. But, the regional distribution of gas flow stayed stable during different ventilator settings. Moderate lung injury changed the regional pattern of gas flow. Conclusions We conclude that the presented method is able to determine global respiratory gas flow of the lung in different phases of the respiratory cycle. Additionally, it delivers meaningful insight into regional pulmonary characteristics, i.e. the regional ability of the lung to take up and to release air.

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We study the relativistic version of the Schrödinger equation for a point particle in one dimension with the potential of the first derivative of the delta function. The momentum cutoff regularization is used to study the bound state and scattering states. The initial calculations show that the reciprocal of the bare coupling constant is ultraviolet divergent, and the resultant expression cannot be renormalized in the usual sense, where the divergent terms can just be omitted. Therefore, a general procedure has been developed to derive different physical properties of the system. The procedure is used first in the nonrelativistic case for the purpose of clarification and comparisons. For the relativistic case, the results show that this system behaves exactly like the delta function potential, which means that this system also shares features with quantum filed theories, like being asymptotically free. In addition, in the massless limit, it undergoes dimensional transmutation, and it possesses an infrared conformal fixed point. The comparison of the solution with the relativistic delta function potential solution shows evidence of universality.

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A composite strontium isotopic seawater curve was constructed for the Miocene between 24 and 6 Ma by combining 87Sr/86Sr measurements of planktonic foraminifera from Deep Sea Drilling Project sites 289 and 588. Site 289, with its virtually continuous sedimentary record and high sedimentation rates (26 m/m.y.), was used for constructing the Oligocene to mid-Miocene part of the record, which included the calibration of 63 biostratigraphic datums to the Sr seawater curve using the timescale of Cande and Kent (1992 doi:10.1029/92JB01202). Across the Oligocene/Miocene boundary, a brief plateau occurred in the Sr seawater curve (87Sr/86Sr values averaged 0.70824) which is coincident with a carbon isotopic maximum (CM-O/M) from 24.3 to 22.6 Ma. During the early Miocene, the strontium isotopic curve was marked by a steep rise in 87Sr/86Sr that included a break in slope near 19 Ma. The rate of growth was about 60 ppm/m.y. between 22.5 and 19.0 Ma and increased to over 80 ppm/m.y. between 19.0 and 16 Ma. Beginning at ~16 Ma (between carbon isotopic maxima CM3 and CM4 of Woodruff and Savin (1991 doi:10.1029/91PA02561)), the rate of 87Sr/86Sr growth slowed and 87Sr/86Sr values were near constant from 15 to 13 Ma. After 13 Ma, growth in 87Sr/86Sr resumed and continued until ~9 Ma, when the rate of 87Sr/86Sr growth decreased to zero once again. The entire Miocene seawater curve can be described by a high-order function, and the first derivative (d87Sr/86Sr/dt) of this function reveals two periods of increased slope. The greatest rate of 87Sr/86Sr change occurred during the early Miocene between ~20 and 16 Ma, and a smaller, but distinct, period of increased slope also occurred during the late Miocene between ~12 and 9 Ma. These periods of steepened slope coincide with major phases of uplift and denudation of the Himalayan-Tibetan Plateau region, supporting previous interpretations that the primary control on seawater 87Sr/86Sr during the Miocene was related to the collision of India and Asia. The rapid increase in 87Sr/86Sr values during the early Miocene from 20 to 16 Ma imply high rates of chemical weathering and dissolved riverine fluxes to the oceans. In the absence of another source of CO2, these high rates of chemical weathering should have quickly resulted in a drawdown of atmospheric CO2 and climatic cooling through a reversed greenhouse effect. The paleoclimatic record, however, indicates a warming trend during the early Miocene, culminating in a climatic optimum between 17 and 14.5 Ma. We suggest that the high rates of chemical erosion and warm temperatures during the climatic optimum were caused by an increase in the contribution of volcanic CO2 from the eruption of the Columbia River Flood Basalts (CRFB) between 17 and 15 Ma. The decrease in the rate of CRFB eruptions at 15 Ma and the removal of atmospheric carbon dioxide by increased organic carbon burial in Monterey deposits eventually led to cooling and increased glaciation between ~14.5 and 13 Ma. The CRFB hypothesis helps to explain the significant time lag between the onset of increased rates of organic carbon burial in the Monterey at 17.5 Ma (as marked by increased delta13C values) and the climatic cooling and glaciation during the middle Miocene (as marked by the increase in delta18O values), which did not begin until ~14.5 Ma.

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Se presenta en esta comunicación el tratamiento de problemas de potencial en sistemas bidimensionales, haciendo uso de la discretización de su contorno o frontera mediante elementos parabólicos tanto en geometría como en las variables de campo. Se estudian las ventajas frente al uso de elementos isoparamétricos lineales dentro de la teoría del potencial. Se presenta también un estudio sobre las zonas singulares a que dan lugar los elementos parabólicos degenerados = This paper presents a B.I.E.M. for potential theory, using in the discretization a completely isoparametric parabolic formulation; that is, the field variable, its first derivative and the boundary domain are interpolated using second orden piecewise polinomic. Several results are presented and comparison is mode with other simpler formulations. Also treated is the posibility of modelling singular behavior by moving the midside mode of selected elements.

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The Project you are about to see it is based on the technologies used on object detection and recognition, especially on leaves and chromosomes. To do so, this document contains the typical parts of a scientific paper, as it is what it is. It is composed by an Abstract, an Introduction, points that have to do with the investigation area, future work, conclusions and references used for the elaboration of the document. The Abstract talks about what are we going to find in this paper, which is technologies employed on pattern detection and recognition for leaves and chromosomes and the jobs that are already made for cataloguing these objects. In the introduction detection and recognition meanings are explained. This is necessary as many papers get confused with these terms, specially the ones talking about chromosomes. Detecting an object is gathering the parts of the image that are useful and eliminating the useless parts. Summarizing, detection would be recognizing the objects borders. When talking about recognition, we are talking about the computers or the machines process, which says what kind of object we are handling. Afterwards we face a compilation of the most used technologies in object detection in general. There are two main groups on this category: Based on derivatives of images and based on ASIFT points. The ones that are based on derivatives of images have in common that convolving them with a previously created matrix does the treatment of them. This is done for detecting borders on the images, which are changes on the intensity of the pixels. Within these technologies we face two groups: Gradian based, which search for maximums and minimums on the pixels intensity as they only use the first derivative. The Laplacian based methods search for zeros on the pixels intensity as they use the second derivative. Depending on the level of details that we want to use on the final result, we will choose one option or the other, because, as its logic, if we used Gradian based methods, the computer will consume less resources and less time as there are less operations, but the quality will be worse. On the other hand, if we use the Laplacian based methods we will need more time and resources as they require more operations, but we will have a much better quality result. After explaining all the derivative based methods, we take a look on the different algorithms that are available for both groups. The other big group of technologies for object recognition is the one based on ASIFT points, which are based on 6 image parameters and compare them with another image taking under consideration these parameters. These methods disadvantage, for our future purposes, is that it is only valid for one single object. So if we are going to recognize two different leaves, even though if they refer to the same specie, we are not going to be able to recognize them with this method. It is important to mention these types of technologies as we are talking about recognition methods in general. At the end of the chapter we can see a comparison with pros and cons of all technologies that are employed. Firstly comparing them separately and then comparing them all together, based on our purposes. Recognition techniques, which are the next chapter, are not really vast as, even though there are general steps for doing object recognition, every single object that has to be recognized has its own method as the are different. This is why there is not a general method that we can specify on this chapter. We now move on into leaf detection techniques on computers. Now we will use the technique explained above based on the image derivatives. Next step will be to turn the leaf into several parameters. Depending on the document that you are referring to, there will be more or less parameters. Some papers recommend to divide the leaf into 3 main features (shape, dent and vein] and doing mathematical operations with them we can get up to 16 secondary features. Next proposition is dividing the leaf into 5 main features (Diameter, physiological length, physiological width, area and perimeter] and from those, extract 12 secondary features. This second alternative is the most used so it is the one that is going to be the reference. Following in to leaf recognition, we are based on a paper that provides a source code that, clicking on both leaf ends, it automatically tells to which specie belongs the leaf that we are trying to recognize. To do so, it only requires having a database. On the tests that have been made by the document, they assure us a 90.312% of accuracy over 320 total tests (32 plants on the database and 10 tests per specie]. Next chapter talks about chromosome detection, where we shall pass the metaphasis plate, where the chromosomes are disorganized, into the karyotype plate, which is the usual view of the 23 chromosomes ordered by number. There are two types of techniques to do this step: the skeletonization process and swiping angles. Skeletonization progress consists on suppressing the inside pixels of the chromosome to just stay with the silhouette. This method is really similar to the ones based on the derivatives of the image but the difference is that it doesnt detect the borders but the interior of the chromosome. Second technique consists of swiping angles from the beginning of the chromosome and, taking under consideration, that on a single chromosome we cannot have more than an X angle, it detects the various regions of the chromosomes. Once the karyotype plate is defined, we continue with chromosome recognition. To do so, there is a technique based on the banding that chromosomes have (grey scale bands] that make them unique. The program then detects the longitudinal axis of the chromosome and reconstructs the band profiles. Then the computer is able to recognize this chromosome. Concerning the future work, we generally have to independent techniques that dont reunite detection and recognition, so our main focus would be to prepare a program that gathers both techniques. On the leaf matter we have seen that, detection and recognition, have a link as both share the option of dividing the leaf into 5 main features. The work that would have to be done is to create an algorithm that linked both methods, as in the program, which recognizes leaves, it has to be clicked both leaf ends so it is not an automatic algorithm. On the chromosome side, we should create an algorithm that searches for the beginning of the chromosome and then start to swipe angles, to later give the parameters to the program that searches for the band profiles. Finally, on the summary, we explain why this type of investigation is needed, and that is because with global warming, lots of species (animals and plants] are beginning to extinguish. That is the reason why a big database, which gathers all the possible species, is needed. For recognizing animal species, we just only have to have the 23 chromosomes. While recognizing a plant, there are several ways of doing it, but the easiest way to input a computer is to scan the leaf of the plant. RESUMEN. El proyecto que se puede ver a continuación trata sobre las tecnologías empleadas en la detección y reconocimiento de objetos, especialmente de hojas y cromosomas. Para ello, este documento contiene las partes típicas de un paper de investigación, puesto que es de lo que se trata. Así, estará compuesto de Abstract, Introducción, diversos puntos que tengan que ver con el área a investigar, trabajo futuro, conclusiones y biografía utilizada para la realización del documento. Así, el Abstract nos cuenta qué vamos a poder encontrar en este paper, que no es ni más ni menos que las tecnologías empleadas en el reconocimiento y detección de patrones en hojas y cromosomas y qué trabajos hay existentes para catalogar a estos objetos. En la introducción se explican los conceptos de qué es la detección y qué es el reconocimiento. Esto es necesario ya que muchos papers científicos, especialmente los que hablan de cromosomas, confunden estos dos términos que no podían ser más sencillos. Por un lado tendríamos la detección del objeto, que sería simplemente coger las partes que nos interesasen de la imagen y eliminar aquellas partes que no nos fueran útiles para un futuro. Resumiendo, sería reconocer los bordes del objeto de estudio. Cuando hablamos de reconocimiento, estamos refiriéndonos al proceso que tiene el ordenador, o la máquina, para decir qué clase de objeto estamos tratando. Seguidamente nos encontramos con un recopilatorio de las tecnologías más utilizadas para la detección de objetos, en general. Aquí nos encontraríamos con dos grandes grupos de tecnologías: Las basadas en las derivadas de imágenes y las basadas en los puntos ASIFT. El grupo de tecnologías basadas en derivadas de imágenes tienen en común que hay que tratar a las imágenes mediante una convolución con una matriz creada previamente. Esto se hace para detectar bordes en las imágenes que son básicamente cambios en la intensidad de los píxeles. Dentro de estas tecnologías nos encontramos con dos grupos: Los basados en gradientes, los cuales buscan máximos y mínimos de intensidad en la imagen puesto que sólo utilizan la primera derivada; y los Laplacianos, los cuales buscan ceros en la intensidad de los píxeles puesto que estos utilizan la segunda derivada de la imagen. Dependiendo del nivel de detalles que queramos utilizar en el resultado final nos decantaremos por un método u otro puesto que, como es lógico, si utilizamos los basados en el gradiente habrá menos operaciones por lo que consumirá más tiempo y recursos pero por la contra tendremos menos calidad de imagen. Y al revés pasa con los Laplacianos, puesto que necesitan más operaciones y recursos pero tendrán un resultado final con mejor calidad. Después de explicar los tipos de operadores que hay, se hace un recorrido explicando los distintos tipos de algoritmos que hay en cada uno de los grupos. El otro gran grupo de tecnologías para el reconocimiento de objetos son los basados en puntos ASIFT, los cuales se basan en 6 parámetros de la imagen y la comparan con otra imagen teniendo en cuenta dichos parámetros. La desventaja de este método, para nuestros propósitos futuros, es que sólo es valido para un objeto en concreto. Por lo que si vamos a reconocer dos hojas diferentes, aunque sean de la misma especie, no vamos a poder reconocerlas mediante este método. Aún así es importante explicar este tipo de tecnologías puesto que estamos hablando de técnicas de reconocimiento en general. Al final del capítulo podremos ver una comparación con los pros y las contras de todas las tecnologías empleadas. Primeramente comparándolas de forma separada y, finalmente, compararemos todos los métodos existentes en base a nuestros propósitos. Las técnicas de reconocimiento, el siguiente apartado, no es muy extenso puesto que, aunque haya pasos generales para el reconocimiento de objetos, cada objeto a reconocer es distinto por lo que no hay un método específico que se pueda generalizar. Pasamos ahora a las técnicas de detección de hojas mediante ordenador. Aquí usaremos la técnica explicada previamente explicada basada en las derivadas de las imágenes. La continuación de este paso sería diseccionar la hoja en diversos parámetros. Dependiendo de la fuente a la que se consulte pueden haber más o menos parámetros. Unos documentos aconsejan dividir la morfología de la hoja en 3 parámetros principales (Forma, Dentina y ramificación] y derivando de dichos parámetros convertirlos a 16 parámetros secundarios. La otra propuesta es dividir la morfología de la hoja en 5 parámetros principales (Diámetro, longitud fisiológica, anchura fisiológica, área y perímetro] y de ahí extraer 12 parámetros secundarios. Esta segunda propuesta es la más utilizada de todas por lo que es la que se utilizará. Pasamos al reconocimiento de hojas, en la cual nos hemos basado en un documento que provee un código fuente que cucando en los dos extremos de la hoja automáticamente nos dice a qué especie pertenece la hoja que estamos intentando reconocer. Para ello sólo hay que formar una base de datos. En los test realizados por el citado documento, nos aseguran que tiene un índice de acierto del 90.312% en 320 test en total (32 plantas insertadas en la base de datos por 10 test que se han realizado por cada una de las especies]. El siguiente apartado trata de la detección de cromosomas, en el cual se debe de pasar de la célula metafásica, donde los cromosomas están desorganizados, al cariotipo, que es como solemos ver los 23 cromosomas de forma ordenada. Hay dos tipos de técnicas para realizar este paso: Por el proceso de esquelotonización y barriendo ángulos. El proceso de esqueletonización consiste en eliminar los píxeles del interior del cromosoma para quedarse con su silueta; Este proceso es similar a los métodos de derivación de los píxeles pero se diferencia en que no detecta bordes si no que detecta el interior de los cromosomas. La segunda técnica consiste en ir barriendo ángulos desde el principio del cromosoma y teniendo en cuenta que un cromosoma no puede doblarse más de X grados detecta las diversas regiones de los cromosomas. Una vez tengamos el cariotipo, se continua con el reconocimiento de cromosomas. Para ello existe una técnica basada en las bandas de blancos y negros que tienen los cromosomas y que son las que los hacen únicos. Para ello el programa detecta los ejes longitudinales del cromosoma y reconstruye los perfiles de las bandas que posee el cromosoma y que lo identifican como único. En cuanto al trabajo que se podría desempeñar en el futuro, tenemos por lo general dos técnicas independientes que no unen la detección con el reconocimiento por lo que se habría de preparar un programa que uniese estas dos técnicas. Respecto a las hojas hemos visto que ambos métodos, detección y reconocimiento, están vinculados debido a que ambos comparten la opinión de dividir las hojas en 5 parámetros principales. El trabajo que habría que realizar sería el de crear un algoritmo que conectase a ambos ya que en el programa de reconocimiento se debe clicar a los dos extremos de la hoja por lo que no es una tarea automática. En cuanto a los cromosomas, se debería de crear un algoritmo que busque el inicio del cromosoma y entonces empiece a barrer ángulos para después poder dárselo al programa que busca los perfiles de bandas de los cromosomas. Finalmente, en el resumen se explica el por qué hace falta este tipo de investigación, esto es que con el calentamiento global, muchas de las especies (tanto animales como plantas] se están empezando a extinguir. Es por ello que se necesitará una base de datos que contemple todas las posibles especies tanto del reino animal como del reino vegetal. Para reconocer a una especie animal, simplemente bastará con tener sus 23 cromosomas; mientras que para reconocer a una especie vegetal, existen diversas formas. Aunque la más sencilla de todas es contar con la hoja de la especie puesto que es el elemento más fácil de escanear e introducir en el ordenador.

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The first derivative of pressure over time (dP/dt) is a marker of left ventricular (LV) systolic function that can be assessed during cardiac catheterization and echocardiography. Radial artery dP/dt (Radial-dP/dt) has been proposed as a possible marker of LV systolic function (Nichols & O’Rourke, McDonald’s Blood Flow in Arteries) and we sought to test this hypothesis. Methods:We compared simultaneously recorded RadialdP/ dt (by high-fidelity tonometry) with LV-dP/dt (by highfidelity catheter and echocardiography parameters analogous to LV-dP/dt) in patients without aortic valve disease. In study 1, beat to beat Radial-dP/dt and LV-dP/dt were recorded at rest and during supine exercise in 12 males (aged 61±12 years) undergoing cardiac catheterization. In study 2, 2D-echocardiography and Radial-dP/dt were recorded in 59 patients (43 men; aged 64±10 years) at baseline and peak dobutamine-induced stress. Three measures at the basal septum were taken as being analogous to LV-dP/dt: (1) peak systolic strain rate, (2) strain rate (SR-dP/dt), and (3) tissue velocity during isovolumic contraction. Results: Study 1; there was a significant difference between resting LV-dP/dt (1461±383 mmHg/s) and Radial-dP/dt (1182±319 mmHg/s; P < 0.001), and a poor, but statistically significant, correlation between the variables (R2 = 0.006; P < 0.001) due to the high number of data points compared (n = 681). Similar results were observed during exercise. Study 2; there was a moderate association between baseline Radial-dP/dt and SRdP/ dt (R2 =−0.17; P < 0.01), but no significant relationship between Radial-dP/dt and all other echocardiographic measures analogous to LV-dP/dt at rest or peak stress (P > 0.05). Conclusion: The radial pressurewaveform is not a reliable marker of LV contractility.

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To make vision possible, the visual nervous system must represent the most informative features in the light pattern captured by the eye. Here we use Gaussian scale-space theory to derive a multiscale model for edge analysis and we test it in perceptual experiments. At all scales there are two stages of spatial filtering. An odd-symmetric, Gaussian first derivative filter provides the input to a Gaussian second derivative filter. Crucially, the output at each stage is half-wave rectified before feeding forward to the next. This creates nonlinear channels selectively responsive to one edge polarity while suppressing spurious or "phantom" edges. The two stages have properties analogous to simple and complex cells in the visual cortex. Edges are found as peaks in a scale-space response map that is the output of the second stage. The position and scale of the peak response identify the location and blur of the edge. The model predicts remarkably accurately our results on human perception of edge location and blur for a wide range of luminance profiles, including the surprising finding that blurred edges look sharper when their length is made shorter. The model enhances our understanding of early vision by integrating computational, physiological, and psychophysical approaches. © ARVO.

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We have shown previously that a template model for edge perception successfully predicts perceived blur for a variety of edge profiles (Georgeson, 2001 Journal of Vision 1 438a; Barbieri-Hesse and Georgeson, 2002 Perception 31 Supplement, 54). This study concerns the perceived contrast of edges. Our model spatially differentiates the luminance profile, half-wave rectifies this first derivative, and then differentiates again to create the edge's 'signature'. The spatial scale of the signature is evaluated by filtering it with a set of Gaussian derivative operators. This process finds the correlation between the signature and each operator kernel at each position. These kernels therefore act as templates, and the position and scale of the best-fitting template indicate the position and blur of the edge. Our previous finding, that reducing edge contrast reduces perceived blur, can be explained by replacing the half-wave rectifier with a smooth, biased rectifier function (May and Georgeson, 2003 Perception 32 388; May and Georgeson, 2003 Perception 32 Supplement, 46). With the half-wave rectifier, the peak template response R to a Gaussian edge with contrast C and scale s is given by: R=Cp-1/4s-3/2. Hence, edge contrast can be estimated from response magnitude and blur: C=Rp1/4s3/2. Use of this equation with the modified rectifier predicts that perceived contrast will decrease with increasing blur, particularly at low contrasts. Contrast-matching experiments supported this prediction. In addition, the model correctly predicts the perceived contrast of Gaussian edges modified either by spatial truncation or by the addition of a ramp.

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We describe a template model for perception of edge blur and identify a crucial early nonlinearity in this process. The main principle is to spatially filter the edge image to produce a 'signature', and then find which of a set of templates best fits that signature. Psychophysical blur-matching data strongly support the use of a second-derivative signature, coupled to Gaussian first-derivative templates. The spatial scale of the best-fitting template signals the edge blur. This model predicts blur-matching data accurately for a wide variety of Gaussian and non-Gaussian edges, but it suffers a bias when edges of opposite sign come close together in sine-wave gratings and other periodic images. This anomaly suggests a second general principle: the region of an image that 'belongs' to a given edge should have a consistent sign or direction of luminance gradient. Segmentation of the gradient profile into regions of common sign is achieved by implementing the second-derivative 'signature' operator as two first-derivative operators separated by a half-wave rectifier. This multiscale system of nonlinear filters predicts perceived blur accurately for periodic and aperiodic waveforms. We also outline its extension to 2-D images and infer the 2-D shape of the receptive fields.

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This work was developed with the objective of proposing a simple, fast and versatile methodological routine using near-infrared spectroscopy (NIR) combined with multivariate analysis for the determination of ash content, moisture, protein and total lipids present in the gray shrimp (Litopenaeus vannamei ) which is conventionally performed gravimetrically after ashing at 550 ° C gravimetrically after drying at 105 ° C for the determination of moisture gravimetrically after a Soxhlet extraction using volumetric and after digestion and distillation Kjedhal respectively. Was first collected the spectra of 63 samples processed boiled shrimp Litopenaeus vannamei species. Then, the determinations by conventional standard methods were carried out. The spectra centered average underwent multiplicative scattering correction of light, smoothing Saviztky-Golay 15 points and first derivative, eliminated the noisy region, the working range was from 1100,36 to 2502,37 nm. Thus, the PLS models for predicting ash showed R 0,9471; 0,1017 and RMSEP RMSEC 0,1548; Moisture R was 0,9241; 2,5483 and RMSEP RMSEC 4,1979; R protein to 0,9201; 1,9391 and RMSEP RMSEC 2,7066; for lipids R 0,8801; 0,2827 and RMSEP RMSEC 0,2329 So that the results showed that the relative errors found between the reference method and the NIR were small and satisfactory. These results are an excellent indication that you can use the NIR to these analyzes, which is quite advantageous, since conventional techniques are time consuming, they spend a lot of reagents and involve a number of professionals, which requires a reasonable runtime while after the validation of the methodology execution using NIR reduces all this time to a few minutes, saving reagents, time and without waste generation, and that this is a non-destructive technique.

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International audience

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Honey is rich in sugar content and dominated by fructose and glucose that make honey prone to crystallize during storage. Due to honey composition, the anhydrous glass transition temperature of honey is very low that makes honey difficult to dry alone and drying aid or filler is needed to dry honey. Maltodextrin is a common drying aid material used in drying of sugar-rich food. The present study aims to study the processing of honey powder by vacuum drying method and the impact of drying process and formulation on the stability of honey powder. To achieve the objectives, the series of experiments were done: investigating of maltodextrin DE 10 properties, studying the effect of drying temperature, total solid concentration, DE value, maltodextrin concentration and anti-caking agent on honey powder processing and stability. Maltodextrin provide stable glass compared to lower molecular weight sugars. Dynamic Dew Point Isotherm (DDI) data could be used to determine amorphous content of a system. The area under the first derivative curve from DDI curve is equal to the amount of water needed by amorphous material to crystallize. The drying temperature affected the amorphous content of vacuum-dried honey powder. The higher temperature seemed to result in honey powder with more amorphous component. The ratio of maltodextrin affected more significantly the stability of honey powder compared to the treatments of total solids concentration, DE value and drying temperature. The critical water activity of honey powder was lower than water activity of the equilibrium water content corresponding to BET monolayer water content. Addition of anti-caking agent increased stability and flow-ability of honey powder. Addition of Calcium stearate could inhibit collapse of the honey powder during storage.