A DSP-BASED active contour model

In this paper a DSP-based active contour model for tracking of the endocardium in a sequence of echocardiographic images is presented. If a contour is available in the first frame of a sequence, the contours in the subsequent frames are segmented. Deformable active contours is a technique that combine geometry, physics and approximation theory in order to solve problems of fundamental importance to medical image analysis; such as segmentation, representation and matching of shapes, and the tracking of objects in movement. The procedure has been developed on a DSP processor using its hardware features. The results are illustrated using a sequence of four-chambers apical echocardiographic images

Reduction of the speckle noise in echocardiographic images by a cubic spline filter

One of the main problems to resolve in the processing of biomedical images is the reduction of noise. The problem is specially important if the noise has a multiplicative nature (speckle noise), for instance if the object of analysis is an ultrasonic image. In this report we carry out a review of techniques which can be used to reduce this type of noise on four-chamber view B-mode echocardiographic images in an appropriated way. Different ways of nonlinear filtering, adaptive techniques based on the statistical ordering and a cubic spline interpolation will be shown as suitable techniques for this objective but regarding quantitative and qualitative results we have obtained, we can confirm that a cubic spline filter is the most suitable filter that we have reviewed.This work has been supported by Fundación Séneca of Región de Murcia and Ministerio de Ciencia y Tecnología of Spain, under grants PB/63/FS/02 and TIC2003-09400- C04-02, respectively

Distribution and breeding performance of ahigh-density Eagle Owl Bubo bubo population in southeast Spain

CapsuleDespite very high breeding density, no density-dependent effects on reproductive parameterswere detected.AimsTo describe the distribution, abundance and breeding performance of Eagle Owls and to analysedensity-dependent effects on breeding parameters.MethodsWe censused a high-density population of Eagle Owls in southeast Spain between 2003 and2010. To census the population we employed acoustic signals and searched for field signs. Breedingperformance was determined by nest monitoring.ResultsThe population’s density, productivity and fledgling rate were the highest recorded for this species.We detected a negative relationship between the laying date and productivity. Despite breeding pairs’ highdensity, no density-dependent effects on reproductive parameters were detected.ConclusionsOur results suggest that resources in the study area (mainly the availability of RabbitsOryc-tolagus cuniculus) and adult turnover might be responsible for this population’s high density and breedingsuccess

An automatic welding defects classifier system

Radiographic inspection is a well-established testing method to detect weld defects. However, interpretation of radiographic films is a difficult task. The reliability of such interpretation and the expense of training suitable experts have allowed that the efforts being made towards automation in this field. In this paper, we describe an automatic detection system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features was proposed and extracted between defect candidates. In a third stage, an artificial neural network for weld defect classification was used under three regularisation process with different architectures. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used in the aim to give better performance for defect classification in both cases

Deep learning technology for weld defects classification based on transfer learning and activation features

Weld defects detection using X-ray images is an effective method of nondestructive testing. Conventionally, this work is based on qualified human experts, although it requires their personal intervention for the extraction and classification of heterogeneity. Many approaches have been done using machine learning (ML) and image processing tools to solve those tasks. Although the detection and classification have been enhanced with regard to the problems of low contrast and poor quality, their result is still unsatisfying. Unlike the previous research based on ML, this paper proposes a novel classification method based on deep learning network. In this work, an original approach based on the use of the pretrained network AlexNet architecture aims at the classification of the shortcomings of welds and the increase of the correct recognition in our dataset. Transfer learning is used as methodology with the pretrained AlexNet model. For deep learning applications, a large amount of X-ray images is required, but there are few datasets of pipeline welding defects. For this, we have enhanced our dataset focusing on two types of defects and augmented using data augmentation (random image transformations over data such as translation and reflection). Finally, a fine-tuning technique is applied to classify the welding images and is compared to the deep convolutional activation features (DCFA) and several pretrained DCNN models, namely, VGG-16, VGG-19, ResNet50, ResNet101, and GoogLeNet. The main objective of this work is to explore the capacity of AlexNet and different pretrained architecture with transfer learning for the classification of X-ray images. The accuracy achieved with our model is thoroughly presented. The experimental results obtained on the weld dataset with our proposed model are validated using GDXray database. The results obtained also in the validation test set are compared to the others offered by DCNN models, which show a best performance in less time. This can be seen as evidence of the strength of our proposed classification model.This work has been partially funded by the Spanish Government through Project RTI2018-097088-B-C33 (MINECO/FEDER, UE)

Conserving outside protected areas: edge effects and avian electrocutions on the periphery of Special Protection Areas

Electrocution on power lines is one of the principal problems facing raptors and other mediumand large-sized birds at the global scale. The recent European-based Spanish state legislation on avian electrocutions has focused on Special Protection Areas (SPA). Here we evaluate whether this policy has been successful, using the Community of Valencia, Spain, as a regional model. We compiled a database of 400 electrocution events from information on electrocuted birds taken into Wildlife Recovery Centres and incidents registered by the main local power company during the last decade. A small proportion (c.18%) of electrocution casualties occurred within SPA boundaries but the 5 km wide belt immediately surrounding the SPAs produced more than three times the number of avian electrocutions (c.60% of the total recorded). This was probably caused by higher densities of both power lines and susceptible birds, and higher use of the pylons for perching and roosting in the areas surrounding the SPAs. We therefore conclude that the focus on preventative measures being applied within SPAs is inefficient and that action should be targeted in these peripheral areas. Our results illustrate a classic problem of an edge effect associated with a protected area, where external human influences directly affect the persistence of protected species within reserves. Equally, they support the idea that management strategies within parks cannot be independent of the human activities surrounding them

Scavenger guild and consumption patterns of an invasive alien fish species in a Mediterranean wetland

Invasive Alien Species (IAS) alter ecosystems, disrupting ecological processes and driving the loss of ecosystem services. The common carp Cyprinus carpio is a hazardous and widespread IAS, becoming the most abundant species in many aquatic ecosystems. This species transforms ecosystems by accumulating biomass to the detriment of other species, thus altering food webs. However, some terrestrial species, such as vertebrate scavengers, may benefit from dead carps, by incorporating part of the carp biomass into the terrestrial environment. This study describes the terrestrial vertebrate scavenger assemblage that benefits from carp carcasses in a Mediterranean wetland. We also evaluate the seasonal differences in the scavenger assemblage composition and carrion consumption patterns. Eighty carp carcasses (20 per season) were placed in El Hondo Natural Park, a seminatural mesohaline wetland in south-eastern Spain, and we monitored their consumption using camera traps. We recorded 14 scavenger species (10 birds and four mammals) consuming carp carcasses, including globally threatened species. Vertebrates consumed 73% of the carrion biomass and appeared consuming at 82% of the carcasses. Of these carcasses consumed, 75% were completely consumed and the mean consumption time of carcasses completely consumed by vertebrates was 44.4 h (SD = 42.1 h). We recorded differences in species richness, abundance, and assemblage composition among seasons, but we did not find seasonal differences in consumption patterns throughout the year. Our study recorded a rich and efficient terrestrial vertebrate scavenger assemblage benefitting from carp carcasses. We detected a seasonal replacement on the scavenger species, but a maintenance of the ecological function of carrion removal, as the most efficient carrion consumers were present throughout the year. The results highlight the importance of vertebrate scavengers in wetlands, removing possible infectious focus, and moving nutrients between aquatic and terrestrial environments.JMPG was supported by Spanish Ministry of Science, Innovation and Universities contracts (IJC-2019-038968). ESG received the grants PID 2021-124744NA-I00 and RYC2019-027216-I funded by MCIN/AEI/ https://doi.org/10.13039/501100011033 and by ESF Investing in your future

Using deep learning for defect classification on a small weld X-ray image dataset

This document provides a comparative evaluation of the performance of a deep learning network for different combinations of parameters and hyper-parameters. Although there are numerous studies that report on performance in deep learning networks for ordinary data sets, their performance on small data sets is much less evaluated. The objective of this work is to demonstrate that such a challenging small data set, such as a welding X-ray image data set, can be trained and evaluated obtaining high precision and that it is possible thanks to data augmentation. In fact, this article shows that data augmentation, also a typical technique in any learning process on a large data set, plus that two image channels, such as channels B (blue) and G (green), both are replaced by the Canny edge map and a binary image provided by an adaptive Gaussian threshold, respectively, gives to the network a 3% increase in accuracy, approximately. In summary, the objective of this work is to present the methodology used and the results obtained to estimate the classification accuracy of three main classes of welding defects obtained on a small set of welding X-ray image data.The authors wants to acknowledge the work of the rest of the participants in this project, namely: J.A. López-Alcantud, P. Rubio-Ibañez, Universidad Politécnica de Cartagena, J.A. Díaz-Madrid, Centro Universitario de la Defensa - UPCT and T.J. Kazmierski, University of Southampton. This work has been partially funded by Spanish government through project numbered RTI2018-097088-B-C33 (MINECO/FEDER,UE)

Dielectric constant tunability at microwave frequencies and pyroelectric behavior of lead-free submicron-structured (Bi0.5Na0.5)1-xBaxTiO3 ferroelectric ceramics

In this article we show that the dielectric constant of lead-free ferroelectric ceramics based on the solid solution (1-x)(Bi0.5Na0.5)TiO3-xBaTiO3, with compositions at or near the morphotropic phase boundary (MPB), can be tuned by a local applied electric field. Two compositions have been studied, one at the MPB, with x=0.06 (BNBT6), and another one towards the BNT side of the phase diagram, with x=0.04 (BNBT4). The tunability of the dielectric constant is measured at microwave frequencies between 100 MHz and 3 GHz by a non-resonant method and simultaneously applying a DC electric field. As expected, the tunability is higher for the composition at the MPB (BNBT6), reaching a maximum value of 60 % for an electric field of 900 V/cm, compared with the composition below this boundary (BNBT4), which saturates at 40 % for an electric field of 640 V/cm. The high tunability in both cases is attributed to the fine grain and high density of the samples, which have a submicron homogeneous grain structure with grain size of the order of a few hundred nanometers. Such properties make these ceramics attractive for microwave tunable devices. Finally, we have tested these ceramics for their application as infrared pyroelectric detectors and we have found that the pyroelectric figure of merit is comparable to traditional lead containing pyroelectrics.This work was supported by Ministerio de Ciencia e Innovación of Spain (TIN2009-14372-C03-02), Fundación Séneca (15303/PI/10), and CSIC (PIE 201060E069)

FPGA synthesis of an stereo image matching architecture for autonomous mobile robots

This paper describes a hardware proposal to speed up the process of image matching in stereo vision systems like those employed by autonomous mobile robots. This proposal combines a classical window-based matching approach with a previous stage, where key points are selected from each image of the stereo pair. In this first step the key point extraction method is based on the SIFT algorithm. Thus, in the second step, the window-based matching is only applied to the set of selected key points, instead of to the whole images. For images with a 1% of key points, this method speeds up the matching four orders of magnitude. This proposal is, on the one hand, a better parallelizable architecture than the original SIFT, and on the other, a faster technique than a full image windows matching approach. The architecture has been implemented on a lower power Virtex 6 FPGA and it achieves a image matching speed above 30 fps.This work has been funded by Spanish government project TEC2015-66878-C3-2-R (MINECO/FEDER, UE)