A weed monitoring system using UAV-imagery and the Hough transform

Usually, crops require the use of herbicides as a useful manner of controlling the quality and quantity of crop production. Although there are weed-free areas, the most common approach is to broadcast herbicides entirely over crop fields, resulting in a reduction of profits and increase in environmental risks. Recently, patch spraying has allowed the use of site-specific weed management, allowing precise and timely weed maps at very early phenological stage, either by ground sampling or remote analysis. Remote imagery from piloted planes and satellites are not suitable for this purpose given their low spatial and temporal resolutions, however, unmanned aerial vehicles (UAV) represent an excellent alternative. This paper presents a new classification framework for weed monitoring via UAV showing promising results and accurate generalisation in different scenariosLos cultivos precisan del uso de herbicidas para controlar la calidad y cantidad de producción. A pesar de que las malas hierbas se distribuyen en rodales, la práctica más extendida es la fumigación de herbicidas en todo el cultivo, resultando en un aumento del coste y de riesgos mediambientales. La pulvericación por parches ha dado lugar al auge de otras técnicas de manejo de malas hierbas, permitiendo su tratamiento en un estado fenológico temprano. Las imágenes remotas de aviones pilotados o satélites no son útiles en este caso debido a su baja resolución espacial y temporal. Sin embargo, este no es el caso de los vehículos aéreos no tripulados. Este artículo presenta un nuevo método para monitorización de malas hierbas usando este tipo de vehículos, mostrando resultados prometedore

Semi-supervised Learning for Ordinal Kernel Discriminant Analysis

Ordinal classication considers those classication problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or di_cult to obtain in this type of problems because, in many cases, ordinal labels are given by an user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classi_cation where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classi_cation, which is combined with our developed classi_cation strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classication in a battery of 30 datasets, showing 1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and 2) the advantage of computing distances in the feature space induced by the kernel function

On the use of evolutionary time series analysis for segmenting paleoclimate data

Recent studies propose that different dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TPs). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the development of a time series segmentation algorithm for paleoclimate data in order to find segments sharing common statistical patterns. The proposed algorithm uses a clustering-based approach for evaluating the solutions and six statistical features, most of which have been previously considered in the detection of early warning signals in paleoclimate TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series, together with a method to compare the segmentations. The final segments provided by the algorithm are used to construct a prediction model, whose promising results show the importance of segmentation for improving the understanding of a time series

Probing Ion-Ion and Electron-Ion Correlations in Liquid Metals within the Quantum Hypernetted Chain Approximation

We use the Quantum Hypernetted Chain Approximation (QHNC) to calculate the ion-ion and electron-ion correlations for liquid metallic Li, Be, Na, Mg, Al, K, Ca, and Ga. We discuss trends in electron-ion structure factors and radial distribution functions, and also calculate the free-atom and metallic-atom form-factors, focusing on how bonding effects affect the interpretation of X-ray scattering experiments, especially experimental measurements of the ion-ion structure factor in the liquid metallic phase.Comment: RevTeX, 19 pages, 7 figure

A mitochondrial half-size ABC transporter is involved in cadmium tolerance in Chlamydomonas reinhardtii

Five cadmium-sensitive insertional mutants, all affected at the CDS1 ('cadmium-sensitive 1') locus, have been previously isolated in the unicellular green alga Chlamydomonas reinhardtii. We here describe the cloning of the Cds1 gene (8314 bp with 26 introns) and the corresponding cDNA. The Cds1 gene, strongly induced by cadmium, encodes a putative protein (CrCds1) of 1062 amino acid residues that belongs to the ATM/HMT subfamily of half-size ABC transporters. This subfamily includes both vacuolar HMT-type proteins transporting phytochelatin-cadmium complexes from the cytoplasm to the vacuole and mitochondrial ATM-type proteins involved in the maturation of cytosolic Fe/S proteins. Unlike the Delta sphmt1 cadmium-sensitive mutant of Schizosaccharomyces pombe that lacks a vacuolar HMT-type transporter, the cds1 mutant accumulates a high amount of phytochelatin-cadmium complexes. By epitope tagging, the CrCds1 protein was localized in the mitochondria. Even though mitochondria of cds1 do not accumulate important amounts of 'free' iron, the mutant cells are hypersensitive to high iron concentrations. Our data show for the first time that a mitochondrial ATM-like transporter plays a major role in tolerance to cadmium.Peer reviewe

Density functional theories and self-energy approaches

A purpose-designed microarray platform (Stressgenes, Phase 1) was utilised to investigate the changes in gene expression within the liver of rainbow trout during exposure to a prolonged period of confinement. Tissue and blood samples were collected from trout at intervals up to 648 h after transfer to a standardised confinement stressor, together with matched samples from undisturbed control fish. Plasma ACTH, cortisol, glucose and lactate were analysed to confirm that the neuroendocrine response to confinement was consistent with previous findings and to provide a phenotypic context to assist interpretation of gene expression data. Liver samples for suppression subtractive hybridisation (SSH) library construction were selected from within the experimental groups comprising “early” stress (2–48 h) and “late” stress (96–504 h). In order to reduce redundancy within the four SSH libraries and yield a higher number of unique clones an additional subtraction was carried out. After printing of the arrays a series of 55 hybridisations were executed to cover 6 time points. At 2 h, 6 h, 24 h, 168 h and 504 h 5 individual confined fish and 5 individual control fish were used with control fish only at 0 h. A preliminary list of 314 clones considered differentially regulated over the complete time course was generated by a combination of data analysis approaches and the most significant gene expression changes were found to occur during the 24 h to 168 h time period with a general approach to control levels by 504 h. Few changes in expression were apparent over the first 6 h. The list of genes whose expression was significantly altered comprised predominantly genes belonging to the biological process category (response to stimulus) and one cellular component category (extracellular region) and were dominated by so-called acute phase proteins. Analysis of the gene expression profile in liver tissue during confinement revealed a number of significant clusters. The major patterns comprised genes that were up-regulated at 24 h and beyond, the primary examples being haptoglobin, β-fibrinogen and EST10729. Two representative genes from each of the six k-means clusters were validated by qPCR. Correlations between microarray and qPCR expression patterns were significant for most of the genes tested. qPCR analysis revealed that haptoglobin expression was up-regulated approximately 8-fold at 24 h and over 13-fold by 168 h.This project was part funded by the European Commission (Q5RS-2001-02211), Enterprise Ireland and the Natural Environment Research Council of the United Kingdom

Active Brownian Particles. From Individual to Collective Stochastic Dynamics

We review theoretical models of individual motility as well as collective dynamics and pattern formation of active particles. We focus on simple models of active dynamics with a particular emphasis on nonlinear and stochastic dynamics of such self-propelled entities in the framework of statistical mechanics. Examples of such active units in complex physico-chemical and biological systems are chemically powered nano-rods, localized patterns in reaction-diffusion system, motile cells or macroscopic animals. Based on the description of individual motion of point-like active particles by stochastic differential equations, we discuss different velocity-dependent friction functions, the impact of various types of fluctuations and calculate characteristic observables such as stationary velocity distributions or diffusion coefficients. Finally, we consider not only the free and confined individual active dynamics but also different types of interaction between active particles. The resulting collective dynamical behavior of large assemblies and aggregates of active units is discussed and an overview over some recent results on spatiotemporal pattern formation in such systems is given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte

Strange particle production in proton-proton collisions at s=0.9\sqrt{s}=0.9 TeV with ALICE at the LHC

The production of mesons containing strange quarks (K$^0_s$, $\phi$) and both singly and doubly strange baryons ($\Lambda$, Anti-$\Lambda$, and $\Xi$+Anti-$\Xi$) are measured at central rapidity in pp collisions at $\sqrt{s}$ = 0.9 TeV with the ALICE experiment at the LHC. The results are obtained from the analysis of about 250 k minimum bias events recorded in 2009. Measurements of yields (dN/dy) and transverse momentum spectra at central rapidities for inelastic pp collisions are presented. For mesons, we report yields () of 0.184 $\pm$ 0.002 stat. $\pm$ 0.006 syst. for K$^0_s$ and 0.021 $\pm$ 0.004 stat. $\pm$ 0.003 syst. for $\phi$. For baryons, we find = 0.048 $\pm$ 0.001 stat. $\pm$ 0.004 syst. for $\Lambda$, 0.047 $\pm$ 0.002 stat. $\pm$ 0.005 syst. for Anti-$\Lambda$ and 0.0101 $\pm$ 0.0020 stat. $\pm$ 0.0009 syst. for $\Xi$+Anti-$\Xi$. The results are also compared with predictions for identified particle spectra from QCD-inspired models and provide a baseline for comparisons with both future pp measurements at higher energies and heavy-ion collisions.Comment: 33 pages, 21 captioned figures, 10 tables, authors from page 28, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/387