Combination of optical and on-destructivemechanicaltechniques for the measurement of maturity in peach

Increasing consumer dissatisfaction related with lack of ripeness in peach has been repeatedly reported since 1990 to the present day. There is thus, a great interest in improving the assessment of peach maturity, currently based on Magness Taylor firmness (destructive, highly variable, and time consuming) and colour (not reliable for highly coloured varieties). The present research studies as an alternative several non-destructive (ND) measurements, based on multispectral imaging, visible spectra, and low mass impact response. Their relationship with maturity, as well as the potential of their combination was studied. As a result, two rather independent (R2 = 0.3) groups of non-destructive measurements, chlorophyll related optical indexes and low mass impact (LMI) measurements, were identified. Optical measurements showed the best behaviour for assessing maturity at harvest, while LMI measurements reflected handling incidences, showing a promising potential to be used to control transport and postharvest handling.

Series combination of a switched dc-dc converter and a linear regulator for high frequency RF envelope amplifier

Classical linear amplifiers such as A, AB and B offer very good linearity suitable for RF power amplifiers. However, its inherent low efficiency limits its use especially in base-stations that manage tens or hundreds of Watts. The use of linearization techniques such as Envelope Elimination and Restoration (EER) allow an increase of efficiency keeping good linearity. This technique requires a very fast dc-dc power converter to provide variable voltage supply to the power amplifier. In this paper, several alternatives are analyzed to implement the envelope amplifier based on a cascade association of a switched dc-dc converter and a linear regulator. A simplified version of this approach is also suitable to operate with Envelope Tracking technique.

PSO and neural networks: Optimal combination to solve non linear complex problems

Swarm colonies reproduce social habits. Working together in a group to reach a predefined goal is a social behaviour occurring in nature. Linear optimization problems have been approached by different techniques based on natural models. In particular, Particles Swarm optimization is a meta-heuristic search technique that has proven to be effective when dealing with complex optimization problems. This paper presents and develops a new method based on different penalties strategies to solve complex problems. It focuses on the training process of the neural networks, the constraints and the election of the parameters to ensure successful results and to avoid the most common obstacles when searching optimal solutions.

Fundamentación del conjunto tractor-apero para la labor de surcado y su influencia en el consumo energético en cultivos de raíces y tubérculos

El cumplimiento de los objetivos agrotécnicos de los cultivos de raíces y tubérculos exige que la labor de surcado configure un caballón que permita el trabajo de los posteriores aperos que participan en las demás labores de cultivo. En la agricultura cubana se emplean diversos tipos de surcadores que difieren en las características de los elementos aporcadores. Este hecho afecta a la capacidad de trabajo del conjunto tractor-apero y por extensión en el consumo de combustible y el rendimiento del cultivo. El objetivo de este trabajo ha sido fundamentar el empleo del apero más adecuado para la labor de surcado en cultivos de raíces y tubérculos (patata, boniato, yuca) en Fluvisoles, teniendo en cuenta el comportamiento de las dimensiones y forma del caballón, cualidades tecnológicas explotativas del conjunto tractor-apero, consumo energético del proceso productivo y el rendimiento agrícola. Los tratamientos considerados estuvieron determinados por la utilización del surcador fertilizador IIMA-BALDAN 4.500 (T1), y los surcadores aporcadores SA-5 (T2) y SA-3 (T3). En los tres tratamientos se alcanzó un perfil de la sección transversal del caballón de configuración cóncavo-convexo. El surcador BALDAN logró el mejor comportamiento de los indicadores tecnológico-explotativos lo que se tradujo en la mayor capacidad de trabajo de 1,36 ha h-1 y el menor consumo de combustible de 5,38 L ha-1. Este mismo tratamiento proporcionó el mayor rendimiento medio de los cultivos superando en un 43,2% al del surcador SA-5 y en un 57,9% al del surcador SA-3. Desde el punto de vista energético T1, obtuvo una mejor eficiencia energética superior en un 43,5% a la del tratamiento T2, y en un 57,1% a la del tratamiento T3. ABSTRACT The achievement of agrotechnical objectives in the cultivation of roots and tubers demands the plow through activity creates a ridge to allow the work of subsequent farming tools participating in other cultivation works. In Cuban Agriculture various kinds of furrowers are used which differ in the characteristics of their earth up elements. This fact affects work capacity of the tractor-farming tool combination and by extension to fuel consumption and cultivation yield. The aim of this work has been to support the use of the most accurate farming tool in the plow through activity for the cultivation of roots and tubers (potato, sweet potato, cassava) in Fluvisoils, taking into account the behaviour of the ridge´s dimensions and shape, technologic-explotative qualities of the tractor-farming tool combination, energy consumption of the productive process and agricultural yield. The treatments considered were determined by the use of fertlizing furrower IIMA-BALDAN 4.500 (T1), and the earth up furrowers SA-5 (T2) and SA-3 (T3). During the three treatments a transverse section profile of the ridge under concave-convex configuration was obtained. The furrower BALDAN achieved the best behaviour of the technologic-explotative indicators, which was translated into the largest work capacity of 1,36 ha h-1 and the smallest fuel consumption of 5,38 L ha-1. This treatment also provided the biggest average yield of the cultivation, overcoming in a 43,2% the furrower SA-5 and furrower SA-3 in a 57,9%. From the energetic point of view T1 gained better energetic efficiency than treatment T2 in a 43,5% and treatment T3 in a 57,1%.

The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.

Brain structural and functional recovery following initiation of combination antiretroviral therapy

NeuroAIDS persists in the era of combination antiretroviral therapies. We describe here the recovery of brain structure and function following 6 months of therapy in a treatment-naive patient presenting with HIV-associated dementia. The patient’s neuropsychological test performance improved and his total brain volume increased by more than 5 %. Neuronal functional connectivity measured by magnetoencephalography changed from a pattern identical to that observed in other HIV-infected individuals to one that was indistinguishable from that of uninfected control subjects. These data suggest that at least some of the effects of HIV on the brain can be fully reversed with treatment.

Efficient algorithms for dynamic probabilistic safety assessment. An application to convex damage domain.

The design of nuclear power plant has to follow a number of regulations aimed at limiting the risks inherent in this type of installation. The goal is to prevent and to limit the consequences of any possible incident that might threaten the public or the environment. To verify that the safety requirements are met a safety assessment process is followed. Safety analysis is as key component of a safety assessment, which incorporates both probabilistic and deterministic approaches. The deterministic approach attempts to ensure that the various situations, and in particular accidents, that are considered to be plausible, have been taken into account, and that the monitoring systems and engineered safety and safeguard systems will be capable of ensuring the safety goals. On the other hand, probabilistic safety analysis tries to demonstrate that the safety requirements are met for potential accidents both within and beyond the design basis, thus identifying vulnerabilities not necessarily accessible through deterministic safety analysis alone. Probabilistic safety assessment (PSA) methodology is widely used in the nuclear industry and is especially effective in comprehensive assessment of the measures needed to prevent accidents with small probability but severe consequences. Still, the trend towards a risk informed regulation (RIR) demanded a more extended use of risk assessment techniques with a significant need to further extend PSA’s scope and quality. Here is where the theory of stimulated dynamics (TSD) intervenes, as it is the mathematical foundation of the integrated safety assessment (ISA) methodology developed by the CSN(Consejo de Seguridad Nuclear) branch of Modelling and Simulation (MOSI). Such methodology attempts to extend classical PSA including accident dynamic analysis, an assessment of the damage associated to the transients and a computation of the damage frequency. The application of this ISA methodology requires a computational framework called SCAIS (Simulation Code System for Integrated Safety Assessment). SCAIS provides accident dynamic analysis support through simulation of nuclear accident sequences and operating procedures. Furthermore, it includes probabilistic quantification of fault trees and sequences; and integration and statistic treatment of risk metrics. SCAIS comprehensively implies an intensive use of code coupling techniques to join typical thermal hydraulic analysis, severe accident and probability calculation codes. The integration of accident simulation in the risk assessment process and thus requiring the use of complex nuclear plant models is what makes it so powerful, yet at the cost of an enormous increase in complexity. As the complexity of the process is primarily focused on such accident simulation codes, the question of whether it is possible to reduce the number of required simulation arises, which will be the focus of the present work. This document presents the work done on the investigation of more efficient techniques applied to the process of risk assessment inside the mentioned ISA methodology. Therefore such techniques will have the primary goal of decreasing the number of simulation needed for an adequate estimation of the damage probability. As the methodology and tools are relatively recent, there is not much work done inside this line of investigation, making it a quite difficult but necessary task, and because of time limitations the scope of the work had to be reduced. Therefore, some assumptions were made to work in simplified scenarios best suited for an initial approximation to the problem. The following section tries to explain in detail the process followed to design and test the developed techniques. Then, the next section introduces the general concepts and formulae of the TSD theory which are at the core of the risk assessment process. Afterwards a description of the simulation framework requirements and design is given. Followed by an introduction to the developed techniques, giving full detail of its mathematical background and its procedures. Later, the test case used is described and result from the application of the techniques is shown. Finally the conclusions are presented and future lines of work are exposed.

Phytostabilization of metals in mine soils using Brassica juncea in combination with organic amendments

Background and aims The high metal bioavailability and the poor conditions of mine soils yield a low plant biomass, limiting the application of phytoremediation techniques. A greenhouse experiment was performed to evaluate the effects of organic amendments on metal stabilization and the potential of Brassica juncea L. for phytostabilization in mine soils. Methods Plants were grown in pots filled with soils collected from two mine sites located in Central Spain mixed with 0, 30 and 60 tha?1 of pine bark compost and horse- and sheep-manure compost. Plant biomass and metal concentrations in roots and shoots were measured. Metal bioavailability was assessed using a rhizosphere-based method (rhizo), which consists of a mixture of low-molecular-weight organic acids to simulate root exudates. Results Manure reduced metal concentrations in shoots (10?50 % reduction of Cu and 40?80 % of Zn in comparison with non-amended soils), bioconcentration factor (10?50 % of Cu and 40?80 % of Zn) and metal bioavailability in soil (40?50 % of Cu and 10?30 % of Zn) due to the high pH and the contribution of organic matter. Manure improved soil fertility and was also able to increase plant biomass (5?20 times in shoots and 3?30 times in roots), which resulted in a greater amount of metals removed from soil and accumulated in roots (increase of 2?7 times of Cu and Zn). Plants grown in pine bark treatments and in non-amended soils showed a limited biomass and high metal concentrations in shoots. Conclusions The addition of manure could be effective for the stabilization of metals and for enhancing the phytostabilization ability of B. juncea in mine soils. In this study, this species resulted to be a potential candidate for phytostabilization in combination with manure, differing from previous results, in which B. juncea had been recognized as a phytoextraction plant.

Refined facial disparity maps for automatic creation of 3D avatars

We propose a new method to automatically refine a facial disparity map obtained with standard cameras and under conventional illumination conditions by using a smart combination of traditional computer vision and 3D graphics techniques. Our system inputs two stereo images acquired with standard (calibrated) cameras and uses dense disparity estimation strategies to obtain a coarse initial disparity map, and SIFT to detect and match several feature points in the subjects face. We then use these points as anchors to modify the disparity in the facial area by building a Delaunay triangulation of their convex hull and interpolating their disparity values inside each triangle. We thus obtain a refined disparity map providing a much more accurate representation of the the subjects facial features. This refined facial disparity map may be easily transformed, through the camera calibration parameters, into a depth map to be used, also automatically, to improve the facial mesh of a 3D avatar to match the subjects real human features.

Planar point sets with large minimum convex decompositions

We show the existence of sets with n points (n ? 4) for which every convex decomposition contains more than (35/32)n?(3/2) polygons,which refutes the conjecture that for every set of n points there is a convex decomposition with at most n+C polygons. For sets having exactly three extreme pointswe show that more than n+sqr(2(n ? 3))?4 polygons may be necessary to form a convex decomposition.

A Schwarz lemma for Kahler affine metrics and the canonical potential of a proper convex cone

This is an account of some aspects of the geometry of Kahler affine metrics based on considering them as smooth metric measure spaces and applying the comparison geometry of Bakry-Emery Ricci tensors. Such techniques yield a version for Kahler affine metrics of Yau s Schwarz lemma for volume forms. By a theorem of Cheng and Yau, there is a canonical Kahler affine Einstein metric on a proper convex domain, and the Schwarz lemma gives a direct proof of its uniqueness up to homothety. The potential for this metric is a function canonically associated to the cone, characterized by the property that its level sets are hyperbolic affine spheres foliating the cone. It is shown that for an n -dimensional cone, a rescaling of the canonical potential is an n -normal barrier function in the sense of interior point methods for conic programming. It is explained also how to construct from the canonical potential Monge-Ampère metrics of both Riemannian and Lorentzian signatures, and a mean curvature zero conical Lagrangian submanifold of the flat para-Kahler space.

Enhancing regression models for complex systems using evolutionary techniques for feature engineering

This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimizes error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98%. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.

Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction

An innovative background modeling technique that is able to accurately segment foreground regions in RGB-D imagery (RGB plus depth) has been presented in this paper. The technique is based on a Bayesian framework that efficiently fuses different sources of information to segment the foreground. In particular, the final segmentation is obtained by considering a prediction of the foreground regions, carried out by a novel Bayesian Network with a depth-based dynamic model, and, by considering two independent depth and color-based mixture of Gaussians background models. The efficient Bayesian combination of all these data reduces the noise and uncertainties introduced by the color and depth features and the corresponding models. As a result, more compact segmentations, and refined foreground object silhouettes are obtained. Experimental results with different databases suggest that the proposed technique outperforms existing state-of-the-art algorithms.

Scalable UWB photonic generator based on the combination of doublet pulses

We propose and experimentally demonstrate a scalable and reconfigurable optical scheme to generate high order UWB pulses. Firstly, various ultra wideband doublets are created through a process of phase-tointensity conversion by means of a phase modulation and a dispersive media. In a second stage, doublets are combined in an optical processing unit that allows the reconfiguration of UWB high order pulses. Experimental results both in time and frequency domains are presented showing good performance related to the fractional bandwidth and spectral efficiency parameters.

A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.