Protocolo para la zonificación intraparcelaria de la viña para vendimia selectiva a partir de imágenes multiespectratrales

En este trabajo se presenta un protocolo para la zonificación intraparcelaria de la viña con la finalidad de vendimia selectiva. Se basa en la adquisición de una imagen multiespectral detallada en el momento del envero, a partir de la cual se obtiene el índice de vegetación de la diferencia normalizada (NDVI). Este índice se clasifica en áreas de vigor alto y bajo mediante un proceso de clasificación no supervisada (algoritmo ISODATA). Las zonas resultantes se generalizan y se transfieren al monitor de cosecha de una máquina vendimiadora para realizar la recolección selectiva. La uva recolectada según este protocolo en parcelas control ha mostrado diferenciación en cuanto a parámetros de calidad como el pH, la acidez total, el contenido de polifenoles y el color. La imagen multiespectral utilizada fue adquirida por el satélite Quickbird-2. Los datos de calidad de la uva fueron muestreados según una malla regular de 5 filas por 10 cepas, procediendo a un test estadístico de rangos múltiples para analizar la separación de medias de las variables analizadas en cada zona de NDVI.

Huulisuihkun homogeenisuuden vaikutus paperin rakenneominaisuuksiin

Työssä verrattiin perälaatikoilla 1 ja 2 valmistettujen papereiden rakenteellisia ominaisuuksia. Paperin rakenteessa formaatio oli tärkein ominaisuus ja sen jälkeen kuituorientaatio. Näytteiden valintaperusteena pidettiin sitä, että vertailtavilla näytteillä formaatio (pohja) oli paras kyseisellä perälaatikolla ja vetolujuussuhteet olivat samoja. Toinen valintatapa oli verrata samoissa virtausolosuhteissa valmistettuja papereita keskenään. Näytteiden formaatio mitattiin betaradiografialla. Fosforikuvalevystä skannattu kuva analysoitiin kuva-analyysillä. Mittauksen etuna oli suuri erottelukyky, joka mahdollisti monipuolisen tunnuslukujen laskennan. Näistä esimerkkeinä olivat keskihajonta, vinous ja huipukkuus. Lisäksi määritettiin flokkikokojakaumat sekä kone- että poikkisuuntaan. Kuituorientaation määrityksessä paperinäyte revittiin kerroksiin, kerrokset skannattiin ja kuvat analysoitiin kuvankäsittelyohjelmilla. Juova- ja kuituorientaatioanalyysissä määritettiin orientaatiokulma, max/min-arvo ja anisotropia. Virtaviiva-analyysin tunnusluku oli pyörrekoko. Käytettäessä tunnuslukuna variaatiokerrointa formaatio oli parempi perälaatikolla 1 ali- ja yliperällä. Tasaperän läheisyydessä formaatio oli huonompi. Keskihajonta oli pienempi perälaatikolla 1, mutta erot perälaatikoiden välillä tasaantuivat lähellä tasaperää. Flokkikoko oli koko s/v-alueella hieman suurempi perälaatikolla 1. Virtaviiva-analyysin avulla saatiin selville, että perälaatikolla 1 valmistettujen papereiden paikallinen orientaatiovaihtelu ja pintojen toispuoleisuus oli lievempää kuin perälaatikolla 2.

RNA enrichment method for quantitative transcriptional analysis of pathogens in vivo applied to the fungus Candida albicans.

UNLABELLED: In vivo transcriptional analyses of microbial pathogens are often hampered by low proportions of pathogen biomass in host organs, hindering the coverage of full pathogen transcriptome. We aimed to address the transcriptome profiles of Candida albicans, the most prevalent fungal pathogen in systemically infected immunocompromised patients, during systemic infection in different hosts. We developed a strategy for high-resolution quantitative analysis of the C. albicans transcriptome directly from early and late stages of systemic infection in two different host models, mouse and the insect Galleria mellonella. Our results show that transcriptome sequencing (RNA-seq) libraries were enriched for fungal transcripts up to 1,600-fold using biotinylated bait probes to capture C. albicans sequences. This enrichment biased the read counts of only ~3% of the genes, which can be identified and removed based on a priori criteria. This allowed an unprecedented resolution of C. albicans transcriptome in vivo, with detection of over 86% of its genes. The transcriptional response of the fungus was surprisingly similar during infection of the two hosts and at the two time points, although some host- and time point-specific genes could be identified. Genes that were highly induced during infection were involved, for instance, in stress response, adhesion, iron acquisition, and biofilm formation. Of the in vivo-regulated genes, 10% are still of unknown function, and their future study will be of great interest. The fungal RNA enrichment procedure used here will help a better characterization of the C. albicans response in infected hosts and may be applied to other microbial pathogens. IMPORTANCE: Understanding the mechanisms utilized by pathogens to infect and cause disease in their hosts is crucial for rational drug development. Transcriptomic studies may help investigations of these mechanisms by determining which genes are expressed specifically during infection. This task has been difficult so far, since the proportion of microbial biomass in infected tissues is often extremely low, thus limiting the depth of sequencing and comprehensive transcriptome analysis. Here, we adapted a technology to capture and enrich C. albicans RNA, which was next used for deep RNA sequencing directly from infected tissues from two different host organisms. The high-resolution transcriptome revealed a large number of genes that were so far unknown to participate in infection, which will likely constitute a focus of study in the future. More importantly, this method may be adapted to perform transcript profiling of any other microbes during host infection or colonization.

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

Color ad spectral image assessment using novel quality and fidelity techniques

The ongoing development of the digital media has brought a new set of challenges with it. As images containing more than three wavelength bands, often called spectral images, are becoming a more integral part of everyday life, problems in the quality of the RGB reproduction from the spectral images have turned into an important area of research. The notion of image quality is often thought to comprise two distinctive areas – image quality itself and image fidelity, both dealing with similar questions, image quality being the degree of excellence of the image, and image fidelity the measure of the match of the image under study to the original. In this thesis, both image fidelity and image quality are considered, with an emphasis on the influence of color and spectral image features on both. There are very few works dedicated to the quality and fidelity of spectral images. Several novel image fidelity measures were developed in this study, which include kernel similarity measures and 3D-SSIM (structural similarity index). The kernel measures incorporate the polynomial, Gaussian radial basis function (RBF) and sigmoid kernels. The 3D-SSIM is an extension of a traditional gray-scale SSIM measure developed to incorporate spectral data. The novel image quality model presented in this study is based on the assumption that the statistical parameters of the spectra of an image influence the overall appearance. The spectral image quality model comprises three parameters of quality: colorfulness, vividness and naturalness. The quality prediction is done by modeling the preference function expressed in JNDs (just noticeable difference). Both image fidelity measures and the image quality model have proven to be effective in the respective experiments.

Resolução multivariada de curvas com mínimos quadrados alternantes: descrição, funcionamento e aplicações

Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) is a resolution method that has been efficiently applied in many different fields, such as process analysis, environmental data and, more recently, hyperspectral image analysis. When applied to second order data (or to three-way data) arrays, recovery of the underlying basis vectors in both measurement orders (i.e. signal and concentration orders) from the data matrix can be achieved without ambiguities if the trilinear model constraint is considered during the ALS optimization. This work summarizes different protocols of MCR-ALS application, presenting a case study: near-infrared image spectroscopy.

Holocene Varved Lake Sediments as Records of Palaeoenvironmental Change and Geomagnetic Palaeosecular Variation in Eastern Finland

This dissertation discusses Holocene palaeoenvironmental and palaeomagnetic secular variation (PSV) records reconstructed from sediments preserved in Lake Lehmilampi (63º37´N, 29º06´E) and Lake Kortejärvi (63º37´N, 28º56´E) in eastern Finland. Several piston and freeze cores were obtained from both lakes for varve and magnetic analyses. Sediment samples were impregnated in low-viscosity epoxy and physical parameters of varves, including varve thickness and relative grey-scale values, were recorded using x-ray densitometry combined with semiautomatic digital image analysis. On average, varve records of Lehmilampi and Kortejärvi cover 5122 and 3902 years, respectively. Past solar activity, as estimated by residual ₁₄C data, compares favourably with varve thicknesses from Lehmilampi during the last 2000 years. This indicates the potential of clastic-organic varves to record sensitively climatic variations. Bulk magnetic parameters, including magnetic susceptibility together with natural, anhysteretic and isothermal remanent magnetizations, were measured to describe mineral magnetic properties and geomagnetic palaeosecular variation recorded in the sediments. Main stages in the development of the investigated lakes are reflected in the variations in the mineral magnetic records, sediment lithology and composition. Similar variations in magnetic parameters and sediment organic matter suggest contribution of bacterial magnetite in the magnetic assemblages of Lehmilampi. Inclination and relative declination records yielded largely consistent results, attesting to the great potential of these sediments to preserve directional palaeosecular variation in high resolution. The PSV data from Lehmilampi and Kortejärvi were stacked into North Karelian PSV stack, which may be used for dating homogenous lake sediments in the same regional context. Reconstructed millennial variations in relative palaeointensity results are approximately in agreement with those seen in the absolute palaeointensity data from Europe. Centennial variations in the relative palaeointensity, however, are influenced by environmental changes. Caution is recommended when using varved lake sediments in reconstructing relative palaeointensity.

Analysis of trophic responses in lesioned brain: focus on basic fibroblast growth factor mechanisms

The actions of fibroblast growth factors (FGFs), particularly the basic form (bFGF), have been described in a large number of cells and include mitogenicity, angiogenicity and wound repair. The present review discusses the presence of the bFGF protein and messenger RNA as well as the presence of the FGF receptor messenger RNA in the rodent brain by means of semiquantitative radioactive in situ hybridization in combination with immunohistochemistry. Chemical and mechanical injuries to the brain trigger a reduction in neurotransmitter synthesis and neuronal death which are accompanied by astroglial reaction. The altered synthesis of bFGF following brain lesions or stimulation was analyzed. Lesions of the central nervous system trigger bFGF gene expression by neurons and/or activated astrocytes, depending on the type of lesion and time post-manipulation. The changes in bFGF messenger RNA are frequently accompanied by a subsequent increase of bFGF immunoreactivity in astrocytes in the lesioned pathway. The reactive astrocytes and injured neurons synthesize increased amount of bFGF, which may act as a paracrine/autocrine factor, protecting neurons from death and also stimulating neuronal plasticity and tissue repair

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

Automatic image-based identification of Saimaa ringed seals

The Saimaa ringed seal is one of the most endangered seals in the world. It is a symbol of Lake Saimaa and a lot of effort have been applied to save it. Traditional methods of seal monitoring include capturing the animals and installing sensors on their bodies. These invasive methods for identifying can be painful and affect the behavior of the animals. Automatic identification of seals using computer vision provides a more humane method for the monitoring. This Master's thesis focuses on automatic image-based identification of the Saimaa ringed seals. This consists of detection and segmentation of a seal in an image, analysis of its ring patterns, and identification of the detected seal based on the features of the ring patterns. The proposed algorithm is evaluated with a dataset of 131 individual seals. Based on the experiments with 363 images, 81\% of the images were successfully segmented automatically. Furthermore, a new approach for interactive identification of Saimaa ringed seals is proposed. The results of this research are a starting point for future research in the topic of seal photo-identification.

Radiographic analysis in castor bean seeds (Ricinus communis L.)

Castor bean cropping has great social and economic value, but its production has been affected by factors such as low quality seeds used for sowing. The quick and precise evaluation of seed quality by x-ray test is known as an effective method to evaluate seed lots, but little is known about the interpretation between of the type of radiographic image and the seed quality correlation. The potential of x-ray analysis as a marker of seed physiological quality and as an initial process for the implementation of the use of computer-assisted image analysis was investigated using castor bean seeds of the different cultivars. The seeds were classified according to internal morphology visualized in the radiography and subjected to the germination test, emergency and seedling growth rate. It was possible to identify the different types of internal tissues, morphological and physical damage in castor bean seeds using the x-ray test. Tissues generating translucent images, embryo deformation, or tissues with less than 50% of endosperm reserves or spotted, negatively affected the physiological potential of the seed lots. Radiographic analysis is effective as an instrument to improve castor bean seed lot quality. This non destructive analysis allows the prediction of seedling performance and enabled the selection of high-quality seeds under the standards of a sustainable and precision agriculture

X-Ray analysis to assess mechanical damage in sweet corn seeds

The X-ray test is a precise, fast and non-destructive method to detect mechanical damage in seeds. In the present study, the efficiency of X-ray analysis in identifying the extent of mechanical damage in sweet corn seeds and its relationship with germination and vigor was evaluated. Hybrid 'SWB 551' (sh2) seeds with round (R) and flat (F) shapes were classified as large (L), medium (M1, M2 and M3) and small (S), using sieves with round and oblong screens. After artificial exposure to different levels of damage (0, 1, 3, 5 and 7 impacts), seeds were X-rayed (15 kV, 5 min) and submitted to germination (25 °C/5 days) and cold (10 °C/7 days) tests. Digital images of normal and abnormal seedlings and ungerminated seeds from germination and cold tests were jointly analyzed with the seed X-ray images. Results showed that damage affecting the embryonic axis resulted in abnormal seedlings or dead seeds in the germination and cold tests. The X-ray analysis is efficient for identifying mechanical damage in sweet corn seeds, allowing damage severity to be associated with losses in germination and vigor.

Computerized imaging analysis of seedlings for assessment of physiological potential of wheat seeds

Nowadays, image analysis is one of the most modern tools in evaluating physiological potential of seeds. This study aimed at verifying the efficiency of the seedling imaging analysis to assess physiological potential of wheat seeds. The seeds of wheat, cultivars IAC 370 and IAC 380, each of which represented by five different lots, were stored during four months under natural environmental conditions of temperature (T) and relative humidity (RH), in municipality of Piracicaba, Stated of São Paulo, Brazil. For this, bimonthly assessments were performed to quantify moisture content and physiological potential of seeds by means of tests of: germination, first count, accelerated aging, electrical conductivity, seedling emergence, and computerized analysis of seedlings, using the Seed Vigor Imaging System (SVIS®). It has been concluded that the computerized analyses of seedling through growth indexes and vigor, using the SVIS®, is efficient to assess physiological potential of wheat seeds.

Real-time monitoring of laser scribing process utilizing high-speed camera

Currently, laser scribing is growing material processing method in the industry. Benefits of laser scribing technology are studied for example for improving an efficiency of solar cells. Due high-quality requirement of the fast scribing process, it is important to monitor the process in real time for detecting possible defects during the process. However, there is a lack of studies of laser scribing real time monitoring. Commonly used monitoring methods developed for other laser processes such a laser welding, are sufficient slow and existed applications cannot be implemented in fast laser scribing monitoring. The aim of this thesis is to find a method for laser scribing monitoring with a high-speed camera and evaluate reliability and performance of the developed monitoring system with experiments. The laser used in experiments is an IPG ytterbium pulsed fiber laser with 20 W maximum average power and Scan head optics used in the laser is Scanlab’s Hurryscan 14 II with an f100 tele-centric lens. The camera was connected to laser scanner using camera adapter to follow the laser process. A powerful fully programmable industrial computer was chosen for executing image processing and analysis. Algorithms for defect analysis, which are based on particle analysis, were developed using LabVIEW system design software. The performance of the algorithms was analyzed by analyzing a non-moving image from the scribing line with resolution 960x20 pixel. As a result, the maximum analysis speed was 560 frames per second. Reliability of the algorithm was evaluated by imaging scribing path with a variable number of defects 2000 mm/s when the laser was turned off and image analysis speed was 430 frames per second. The experiment was successful and as a result, the algorithms detected all defects from the scribing path. The final monitoring experiment was performed during a laser process. However, it was challenging to get active laser illumination work with the laser scanner due physical dimensions of the laser lens and the scanner. For reliable error detection, the illumination system is needed to be replaced.

Generating Aesthetically Pleasing Images in a Virtual Environment using Particle Swarm Optimization

This research focuses on generating aesthetically pleasing images in virtual environments using the particle swarm optimization (PSO) algorithm. The PSO is a stochastic population based search algorithm that is inspired by the flocking behavior of birds. In this research, we implement swarms of cameras flying through a virtual world in search of an image that is aesthetically pleasing. Virtual world exploration using particle swarm optimization is considered to be a new research area and is of interest to both the scientific and artistic communities. Aesthetic rules such as rule of thirds, subject matter, colour similarity and horizon line are all analyzed together as a multi-objective problem to analyze and solve with rendered images. A new multi-objective PSO algorithm, the sum of ranks PSO, is introduced. It is empirically compared to other single-objective and multi-objective swarm algorithms. An advantage of the sum of ranks PSO is that it is useful for solving high-dimensional problems within the context of this research. Throughout many experiments, we show that our approach is capable of automatically producing images satisfying a variety of supplied aesthetic criteria.