Implementación de una Support Vector Machine en RVC – CAL para imágenes hiperespectrales

El análisis de imágenes hiperespectrales permite obtener información con una gran resolución espectral: cientos de bandas repartidas desde el espectro infrarrojo hasta el ultravioleta. El uso de dichas imágenes está teniendo un gran impacto en el campo de la medicina y, en concreto, destaca su utilización en la detección de distintos tipos de cáncer. Dentro de este campo, uno de los principales problemas que existen actualmente es el análisis de dichas imágenes en tiempo real ya que, debido al gran volumen de datos que componen estas imágenes, la capacidad de cómputo requerida es muy elevada. Una de las principales líneas de investigación acerca de la reducción de dicho tiempo de procesado se basa en la idea de repartir su análisis en diversos núcleos trabajando en paralelo. En relación a esta línea de investigación, en el presente trabajo se desarrolla una librería para el lenguaje RVC – CAL – lenguaje que está especialmente pensado para aplicaciones multimedia y que permite realizar la paralelización de una manera intuitiva – donde se recogen las funciones necesarias para implementar el clasificador conocido como Support Vector Machine – SVM. Cabe mencionar que este trabajo complementa el realizado en [1] y [2] donde se desarrollaron las funciones necesarias para implementar una cadena de procesado que utiliza el método unmixing para procesar la imagen hiperespectral. En concreto, este trabajo se encuentra dividido en varias partes. La primera de ellas expone razonadamente los motivos que han llevado a comenzar este Trabajo de Investigación y los objetivos que se pretenden conseguir con él. Tras esto, se hace un amplio estudio del estado del arte actual y, en él, se explican tanto las imágenes hiperespectrales como sus métodos de procesado y, en concreto, se detallará el método que utiliza el clasificador SVM. Una vez expuesta la base teórica, nos centraremos en la explicación del método seguido para convertir una versión en Matlab del clasificador SVM optimizado para analizar imágenes hiperespectrales; un punto importante en este apartado es que se desarrolla la versión secuencial del algoritmo y se asientan las bases para una futura paralelización del clasificador. Tras explicar el método utilizado, se exponen los resultados obtenidos primero comparando ambas versiones y, posteriormente, analizando por etapas la versión adaptada al lenguaje RVC – CAL. Por último, se aportan una serie de conclusiones obtenidas tras analizar las dos versiones del clasificador SVM en cuanto a bondad de resultados y tiempos de procesado y se proponen una serie de posibles líneas de actuación futuras relacionadas con dichos resultados. ABSTRACT. Hyperspectral imaging allows us to collect high resolution spectral information: hundred of bands covering from infrared to ultraviolet spectrum. These images have had strong repercussions in the medical field; in particular, we must highlight its use in cancer detection. In this field, the main problem we have to deal with is the real time analysis, because these images have a great data volume and they require a high computational power. One of the main research lines that deals with this problem is related with the analysis of these images using several cores working at the same time. According to this investigation line, this document describes the development of a RVC – CAL library – this language has been widely used for working with multimedia applications and allows an optimized system parallelization –, which joins all the functions needed to implement the Support Vector Machine – SVM - classifier. This research complements the research conducted in [1] and [2] where the necessary functions to implement the unmixing method to analyze hyperspectral images were developed. The document is divided in several chapters. The first of them introduces the motivation of the Master Thesis and the main objectives to achieve. After that, we study the state of the art of some technologies related with this work, like hyperspectral images, their processing methods and, concretely, the SVM classifier. Once we have exposed the theoretical bases, we will explain the followed methodology to translate a Matlab version of the SVM classifier optimized to process an hyperspectral image to RVC – CAL language; one of the most important issues in this chapter is that a sequential implementation is developed and the bases of a future parallelization of the SVM classifier are set. At this point, we will expose the results obtained in the comparative between versions and then, the results of the different steps that compose the SVM in its RVC – CAL version. Finally, we will extract some conclusions related with algorithm behavior and time processing. In the same way, we propose some future research lines according to the results obtained in this document.

Effects of a bifenthrin-treated net on the natural enemies Aphidius colemani (Haliday) and Adalia bipunctata L. in a cucumber crop in Central Spain. Semi-field experiments

Pest management practices that rely on pesticides are growing increasingly less effective and environmentally inappropriate in many cases and the search of alternatives is under focus nowadays. Exclusion of pests from the crop by means of pesticide-treated screens can be an eco-friendly method to protect crops, especially if pests are vectors of important diseases. The mesh size of nets is crucial to determine if insects can eventually cross the barrier or exclude them because there is a great variation in insect size depending on the species. Long-lasting insecticide-treated (LLITN) nets, factory pre-treated, have been used since years to fight against mosquitoes vector of malaria and are able to retain their biological efficacy under field for 3 years. In agriculture, treated nets with different insecticides have shown efficacy in controlling some insects and mites, so they seem to be a good tool in helping to solve some pest problems. However, treated nets must be carefully evaluated because can diminish air flow, increase temperature and humidity and decrease light transmission, which may affect plant growth, pests and natural enemies. As biological control is considered a key factor in IPM nowadays, the potential negative effects of treated nets on natural enemies need to be studied carefully. In this work, the effects of a bifentrhin-treated net (3 g/Kg) (supplied by the company Intelligent Insect Control, IIC) on natural enemies of aphids were tested on a cucumber crop in Central Spain in autumn 2011. The crop was sown in 8x6.5 m tunnels divided in 2 sealed compartments with control or treated nets, which were simple yellow netting with 25 mesh (10 x 10 threads/cm2; 1 x 1 mm hole size). Pieces of 2 m high of the treated-net were placed along the lateral sides of one of the two tunnel compartments in each of the 3 available tunnels (replicates); the rest was covered by a commercial untreated net of a similar mesh. The pest, Aphis gossypii Glover (Aphidae), the parasitoid Aphidius colemani (Haliday) (Braconidae) and the predator Adalia bipunctata L. (Coccinellidae) were artificially introduced in the crop. Weekly sampling was done determining the presence or absence of the pest and the natural enemies (NE) in the 42 plants/compartment as well as the number of insects in 11 marked plants. Environmental conditions (temperature, relative humidity, UV and PAR radiation) were recorded. Results show that when aphids were artificially released inside the tunnels, neither its number/plant nor their distribution was affected by the treated net. A lack of negative effect of the insecticide-treated net on natural enemies was also observed. Adalia bipunctata did not establish in the crop and only a short term control of aphids was observed one week after release. On the other hand, A. colemani did establish in the crop and a more long-term effect on the numbers of aphids/plant was detected irrespective of the type of net. KEY WORDS: bifenthrin-treated net, Adalia bipunctata, Aphidius colemani, Aphis gossypii, semi-field

A source reconstruction technique for planar arrays of wide slots

A method to reconstruct the excitation coefficients of wide-slot arrays from near-field data is presented. The plane wave spectrum (PWS) is used for reconstruction, and the shape of the field distribution on a wide slot is considered in the calculation of the PWS. The proposed algorithm is validated through the reconstruction of the excitation coefficients of a wide-slot array with element failures from the simulated nearfield data. The element failures are clearly located by the proposed algorithm

Stability of nonrelativistic quantum mechanical matter coupled to the (ultraviolet cutoff) radiation field

We announce a proof of H-stability for the quantized radiation field, with ultraviolet cutoff, coupled to arbitrarily many non-relativistic quantized electrons and static nuclei. Our result holds for arbitrary atomic numbers and fine structure constant. We also announce bounds for the energy of many electrons and nuclei in a classical vector potential and for the eigenvalue sum of a one-electron Pauli Hamiltonian with magnetic field.

Protective immune responses induced by secretion of a chimeric soluble protein from a recombinant Mycobacterium bovis bacillus Calmette-Guérin vector candidate vaccine for human immunodeficiency virus type 1 in small animals.

A recombinant Mycobacterium bovis bacillus Calmette-Guérin (BCG) vector-based vaccine that secretes the V3 principal neutralizing epitope of human immunodeficiency virus (HIV) could induce immune response to the epitope and prevent the viral infection. By using the Japanese consensus sequence of HIV-1, we successfully constructed chimeric protein secretion vectors by selecting an appropriate insertion site of a carrier protein and established the principal neutralizing determinant (PND)-peptide secretion system in BCG. The recombinant BCG (rBCG)-inoculated guinea pigs were initially screened by delayed-type hypersensitivity (DTH) skin reactions to the PND peptide, followed by passive transfer of the DTH by the systemic route. Further, immunization of mice with the rBCG resulted in induction of cytotoxic T lymphocytes. The guinea pig immune antisera showed elevated titers to the PND peptide and neutralized HIVMN, and administration of serum IgG from the vaccinated guinea pigs was effective in completely blocking the HIV infection in thymus/liver transplanted severe combined immunodeficiency (SCID)/hu or SCID/PBL mice. In addition, the immune serum IgG was shown to neutralize primary field isolates of HIV that match the neutralizing sequence motif by a peripheral blood mononuclear cell-based virus neutralization assay. The data support the idea that the antigen-secreting rBCG system can be used as a tool for development of HIV vaccines.

Three-dimensional inverse modelling of magnetic anomaly sources based on a genetic algorithm.

We present a modelling method to estimate the 3-D geometry and location of homogeneously magnetized sources from magnetic anomaly data. As input information, the procedure needs the parameters defining the magnetization vector (intensity, inclination and declination) and the Earth's magnetic field direction. When these two vectors are expected to be different in direction, we propose to estimate the magnetization direction from the magnetic map. Then, using this information, we apply an inversion approach based on a genetic algorithm which finds the geometry of the sources by seeking the optimum solution from an initial population of models in successive iterations through an evolutionary process. The evolution consists of three genetic operators (selection, crossover and mutation), which act on each generation, and a smoothing operator, which looks for the best fit to the observed data and a solution consisting of plausible compact sources. The method allows the use of non-gridded, non-planar and inaccurate anomaly data and non-regular subsurface partitions. In addition, neither constraints for the depth to the top of the sources nor an initial model are necessary, although previous models can be incorporated into the process. We show the results of a test using two complex synthetic anomalies to demonstrate the efficiency of our inversion method. The application to real data is illustrated with aeromagnetic data of the volcanic island of Gran Canaria (Canary Islands).

Theory of ferromagnetism in planar heterostructures of (Mn,III)-V semiconductors

A density-functional theory of ferromagnetism in heterostructures of compound semiconductors doped with magnetic impurities is presented. The variable functions in the density-functional theory are the charge and spin densities of the itinerant carriers and the charge and localized spins of the impurities. The theory is applied to study the Curie temperature of planar heterostructures of III-V semiconductors doped with manganese atoms. The mean-field, virtual-crystal and effective-mass approximations are adopted to calculate the electronic structure, including the spin-orbit interaction, and the magnetic susceptibilities, leading to the Curie temperature. By means of these results, we attempt to understand the observed dependence of the Curie temperature of planar δ-doped ferromagnetic structures on variation of their properties. We predict a large increase of the Curie temperature by additional confinement of the holes in a δ-doped layer of Mn by a quantum well.

Coherent state construction of representations of osp(2|2) and primary fields of osp(2|2) conformal field theory

Representations of the superalgebra osp(2/2)(k)((1)) and current superalgebra. osp(2/2)k in the standard basis are investigated. All finite-dimensional typical and atypical representations of osp(2/2) are constructed by the vector coherent state method. Primary fields of the non-unitary conformal field theory associated with osp(2/2)(k)((1)) in the standard basis are obtained for arbitrary level k. (C) 2004 Elsevier B.V. All rights reserved.

The design of planar gradient coils. Part ll: A weighted superposition method

In this work a superposition technique for designing gradient coils for the purpose of magnetic resonance imaging is outlined, which uses an optimized weight function superimposed upon an initial winding similar to that obtained from the target field method to generate the final wire winding. This work builds on the preliminary work performed in Part I on designing planar insertable gradient coils for high resolution imaging. The proposed superposition method for designing gradient coils results in coil patterns with relatively low inductances and the gradient coils can be used as inserts into existing magnetic resonance imaging hardware. The new scheme has the capacity to obtain images faster with more detail due to the deliver of greater magnetic held gradients. The proposed method for designing gradient coils is compared with a variant of the state-of-the-art target field method for planar gradient coils designs, and it is shown that the weighted superposition approach outperforms the well-known the classical method.

Multigradient field-active contour model for multilayer boundary detection of ultrasound rectal wall image

Extraction and reconstruction of rectal wall structures from an ultrasound image is helpful for surgeons in rectal clinical diagnosis and 3-D reconstruction of rectal structures from ultrasound images. The primary task is to extract the boundary of the muscular layers on the rectal wall. However, due to the low SNR from ultrasound imaging and the thin muscular layer structure of the rectum, this boundary detection task remains a challenge. An active contour model is an effective high-level model, which has been used successfully to aid the tasks of object representation and recognition in many image-processing applications. We present a novel multigradient field active contour algorithm with an extended ability for multiple-object detection, which overcomes some limitations of ordinary active contour models—"snakes." The core part in the algorithm is the proposal of multigradient vector fields, which are used to replace image forces in kinetic function for alternative constraints on the deformation of active contour, thereby partially solving the initialization limitation of active contour for rectal wall boundary detection. An adaptive expanding force is also added to the model to help the active contour go through the homogenous region in the image. The efficacy of the model is explained and tested on the boundary detection of a ring-shaped image, a synthetic image, and an ultrasound image. The experimental results show that the proposed multigradient field-active contour is feasible for multilayer boundary detection of rectal wall

A planar microwave imaging system with step-frequency synthesized pulse using different calibration methods

An experimental study of a planar microwave imaging system with step-frequency synthesized pulse for possible use in medical applications is described. Simple phantoms, consisting of a cylindrical plastic container with air or oil imitating fatty tissues and small highly reflective objects emulating tumors, are scanned with a probe antenna over a planar surface in the X-band. Different calibration schemes are considered for successful detection of these objects. (c) 2006 Wiley Periodicals, Inc.

Optical force field mapping in microdevices

We present a method for characterizing microscopic optical force fields. Two dimensional vector force maps are generated by measuring the optical force applied to a probe particle for a grid of particle positions. The method is used to map Out the force field created by the beam from a lensed fiber inside a liquid filled microdevice. We find transverse gradient forces and axial scattering forces on the order of 2 pN per 10 mW laser power which are constant over a considerable axial range (> 35 mu m). These findings suggest Future useful applications of lensed fibers for particle guiding/sorting. The propulsion of a small particle at a constant velocity of 200 mu m s(-1) is shown.

Non-zero mean Gaussian process prior wind field models

This report outlines the derivation and application of a non-zero mean, polynomial-exponential covariance function based Gaussian process which forms the prior wind field model used in 'autonomous' disambiguation. It is principally used since the non-zero mean permits the computation of realistic local wind vector prior probabilities which are required when applying the scaled-likelihood trick, as the marginals of the full wind field prior. As the full prior is multi-variate normal, these marginals are very simple to compute.

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.