Performance Bounds for Finite Moving Average Change Detection: Application to Global Navigation Satellite Systems

Due to the widespread deployment of Global Navigation Satellite Systems (GNSSs) for critical road or urban applications, one of the major challenges to be solved is the provision of integrity to terrestrial environments, so that GNSS may be safety used in these applications. To do so, the integrity of the received GNSS signal must be analyzed in order to detect some local effect disturbing the received signal. This is desirable because the presence of some local effect may cause large position errors, and hence compromise the signal integrity. Moreover, the detection of such disturbing effects must be done before some pre-established delay. This kind of detection lies within the field of transient change detection. In this work, a finite moving average stopping time is proposed in order to approach the signal integrity problem with a transient change detection framework. The statistical performance of this stopping time is investigated and compared, in the context of multipath detection, to other different methods available in the literature. Numerical results are presented in order to assess their performance.Comment: 12 pages, 2 figures, transaction paper, IEEE Transaction on Signal Processing, 201

Object-Based Image Classification of Summer Crops with Machine Learning Methods

The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification task

Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery

This paper approaches the problem of weed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sun ower and maize crops. Precision agriculture referred to weed control is mainly based on the design of early post-emergence site-speci c control treatments according to weed coverage, where one of the most important challenges is the spectral similarity of crop and weed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work rstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole eld data spectrum for the classi cation method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of di erent nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of di erent statistical, spatial and texture metrics represents a new avenue with huge potential for between and within crop-row weed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great in uence for weed mapping in both sun ower and maize crop

European Hernia Society guidelines on management of rectus diastasis

Rectus diastasis; Hernia; GuidelinesDiástasis del recto; Hernia; PautasDiàstasi del recte; Hèrnia; PautesBackground The definition, classification and management of rectus diastasis (RD) are controversial in the literature and a variety of different surgical treatments have been described. This article reports on the European Hernia Society (EHS) Clinical Practice Guideline for RD. Method The Guideline group consisted of eight surgeons. The Grading of Recommendation, Assessment, Development and Evaluation (GRADE) approach and the Appraisal of Guidelines for Research and Evaluation (AGREE) instrument were used. A systematic literature search was done in November 2018 and updated in November 2019 and October 2020. Nine Key Questions (KQs) were formulated. Results Literature reporting on the definition, classification, symptoms, outcomes and treatments was limited in quality, leading to weak recommendations for the majority of the KQs. The main recommendation is to define RD as a separation between rectus muscles wider than 2 cm. A new classification system is suggested based on the width of muscle separation, postpregnancy status and whether or not there is a concomitant hernia. Impaired body image and core instability appear to be the most relevant symptoms. Physiotherapy may be considered before surgical management. It is suggested to use linea alba plication in patients without concomitant hernia and a mesh-based repair of RD with concomitant midline hernias. Conclusion RD should be defined as a separation of rectus muscles wider than 2 cm and a new classification system is suggested

Weed mapping in early-season sunflower fields using images from an unmanned aerial vehicle (UAV)

Revista oficial de la Asociación Española de Teledetección[EN] Weed mapping in early season requires of very high spatial resolution images (pixels <5 cm). Currently only Unmanned Aerial Vehicles (UAV) can take such images. The aim of this work was to evaluate the optimal flight altitude for mapping weeds in an early season sunflower field using a low-cost camera that took images in the visible spectrum at several flight altitudes (40, 60, 80 and 100 m). The object based image analysis procedure used for weed mapping was divided in two main phases: 1) crop-row identification, and 2) crop, weed and bare soil classification. The algorithm identified the crop rows with 100% accuracy at every flight altitude (phase 1) and it detected weed-free zones with 100% accuracy in the images captured at 40 and 60 m flight altitude. In weed-infested zones, the classification algorithm obtained the best results in the images captured at low altitude (40 m), reporting 71% of correctly classified sampling frames (phase 2). Most of errors committed (incorrectly classified frames) were produced by non-detection of weeds (negative false). Subsequent studies would consist in a multi-temporal study aiming to detect weeds are at a more advance growth stage. It could reduce the percentage of negative false in the classification.[ES] La discriminación de malas hierbas en fase temprana con técnicas de teledetección requiere imágenes re-motas de muy elevada resolución espacial (píxeles <5 cm). Actualmente, sólo los vehículos aéreos no tripulados (UAV) pueden generar este tipo de imágenes. El objetivo de este trabajo fue evaluar imágenes UAV tomadas con una cámara visible a diferentes alturas de vuelo (40, 60, 80 y 100 m) y cuantificar la influencia de la resolución espacial en la discrimi-nación de malas hierbas en fase temprana en un cultivo de girasol. Se aplicó un algoritmo de clasificación de imágenes basado en objetos, el cual se divide en dos fases principales: 1) detección de líneas de cultivo y 2) clasificación de cultivo, malas hierbas y suelo desnudo. El algoritmo resultó 100% eficaz en la detección de las líneas de cultivo en todos los ca-sos (fase 1), así como en la detección de zonas libres de mala hierba en las imágenes tomadas a 40 y 60 m de altura. En las zonas con presencia de malas hierbas, los mejores resultados se obtuvieron en las imágenes tomadas a baja altura (40 m), con un 71% de marcos de muestreo clasificados correctamente (fase 2). La mayoría de los fallos de clasificación cometidos en todas las imágenes fueron falsos negativos, es decir, malas hierbas no detectadas debido a su pequeño tamaño en el momento de la captura de las imágenes. Por tanto, el siguiente paso sería desarrollar un estudio multi-temporal para estudiar la detección de las malas hierbas en estados fenológicos más avanzados. Esto podría facilitar su discriminación en las imágenes y, por tanto, disminuir el porcentaje de falsos negativos en las clasificacionesEste trabajo fue financiado por el proyecto Recupera 2020 (Ministerio de Economía y Competitividad y Fondos FEDER de la Unión Europea). La investigación de Jorge Torres Sánchez fue financiada por el programa FPI (CSIC y fondos FEDER).Peña, J.; Torres-Sánchez, J.; Serrano-Pérez, A.; López-Granados, F. (2014). Detección de malas hierbas en girasol en fase temprana mediante imágenes tomadas con un vehículo aéreo no tripulado (UAV). Revista de Teledetección. (42):39-48. doi:10.4995/raet.2014.3148SWORD39484

Plasmonic nanodevice with magnetic funcionalities: fabrication and characterization

We have designed and fabricated a nanodevice exhibiting simultaneously ferromagnetic properties of nanostructures with plasmonic properties of continuous films. Our device consists of an array of nanomagnets on top of a continuous plasmonic film. The patterned nanomagnets magnetic state is single domain and well-defined shape anisotropy. Despite the presence of the patterned media on top of the Au film, the system exhibits surface plasmon resonance characteristics of a continuous film, i.e., propagating surface plasmon-polaritons

Topologically protected superconducting ratchet effect generated by spin-ice nanomagnets

We have designed, fabricated and tested a robust superconducting ratchet device based on topologically frustrated spin ice nanomagnets. The device is made of a magnetic Co honeycomb array embedded in a superconducting Nb film. This device is based on three simple mechanisms: (i) the topology of the Co honeycomb array frustrates in-plane magnetic configurations in the array yielding a distribution of magnetic charges which can be ordered or disordered with in-plane magnetic fields, following spin ice rules; (ii) the local vertex magnetization, which consists of a magnetic half vortex with two charged magnetic Neel walls; (iii) the interaction between superconducting vortices and the asymmetric potentials provided by the Neel walls. The combination of these elements leads to a superconducting ratchet effect. Thus, superconducting vortices driven by alternating forces and moving on magnetic half vortices generate a unidirectional net vortex flow. This ratchet effect is independent of the distribution of magnetic charges in the array