Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data

In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94 ≤ R2 ≤ 0.98, 5.37 ≤ RMSE ≤ 7.9 and 20 ≤ MAE ≤ 33.This research was funded in part by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22, and in part by the University of Alicante (ref. VIGROB-114)

Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy

In this letter, a new approach for crop phenology estimation with remote sensing is presented. The proposed methodology is aimed to exploit tools from a dynamical system context. From a temporal sequence of images, a geometrical model is derived, which allows us to translate this temporal domain into the estimation problem. The evolution model in state space is obtained through dimensional reduction by a principal component analysis, defining the state variables, of the observations. Then, estimation is achieved by combining the generated model with actual samples in an optimal way using a Kalman filter. As a proof of concept, an example with results obtained with this approach over rice fields by exploiting stacks of TerraSAR-X dual polarization images is shown.This project was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) and in part by EU FEDER under Project TEC2011-28201-C02-02

Dynamical Approach for Real-Time Monitoring of Agricultural Crops

In this paper, a novel approach for exploiting multitemporal remote sensing data focused on real-time monitoring of agricultural crops is presented. The methodology is defined in a dynamical system context using state-space techniques, which enables the possibility of merging past temporal information with an update for each new acquisition. The dynamic system context allows us to exploit classical tools in this domain to perform the estimation of relevant variables. A general methodology is proposed, and a particular instance is defined in this study based on polarimetric radar data to track the phenological stages of a set of crops. A model generation from empirical data through principal component analysis is presented, and an extended Kalman filter is adapted to perform phenological stage estimation. Results employing quad-pol Radarsat-2 data over three different cereals are analyzed. The potential of this methodology to retrieve vegetation variables in real time is shown.This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Project TEC2011-28201-C02-02 and in part by the Generalitat Valenciana under Project ACOMP/2014/136

Elective Recurrent Inguinal Hernia Repair: Value of an Abdominal Wall Surgery Unit

Inguinal hernia; Abdominal wall surgeryHernia inguinal; Cirugía de la pared abdominalHèrnia inguinal; Cirurgia de la paret abdominalBackground The aim of this study was to analyze the impact of an abdominal wall surgery unit on postoperative complications (within 90 days postoperatively), hernia recurrence and chronic postoperative inguinal pain after elective recurrent inguinal hernia repair. Methods We conducted a retrospective cohort study of all adult patients who underwent elective recurrent inguinal hernia repair between January 2010 and October 2021. Short- and long-term outcomes were compared between the group of patients operated on in the abdominal wall surgery unit and the group of patients operated on by other units not specialized in abdominal wall surgery. A logistic regression model was performed for hernia recurrence. Results A total of 250 patients underwent elective surgery for recurrent inguinal hernia during the study period. The patients in the abdominal wall surgery group were younger (P ≤ 0.001) and had fewer comorbidities (P ≤ 0.001). There were no differences between the groups in terms of complications. The patients in the abdominal wall surgery group presented fewer recurrences (15% vs. 3%; P = 0.001). Surgery performed by the abdominal wall surgery unit was related to fewer recurrences in the multivariate analysis (HR = 0.123; 95% CI = 0.21–0.725; P = 0.021). Conclusions Specialization in abdominal wall surgery seems to have a positive impact in terms of recurrence in recurrent inguinal hernia repair. The influence of comorbidities or type of surgery (i.e., outpatient surgery) require further study.Open Access Funding provided by Universitat Autonoma de Barcelona. This work did not receive external funding from any source other than the authors’ institution

On the extraction of cellulose nanowhiskers from food by-products and their comparative reinforcing effect on a polyhydroxybutyrate-co-valerate polymer

The present work reports on the characterization of cellulose nanowhiskers (CNW) extracted from three different food by-products, i.e., wheat straw (WSCNW), Brewers spent grains (BGCNW) and olive pomace (OPCNW), by using an optimized hydrolysis method similar to that developed to extract bacterial cellulose nanowhiskers (BCNW). WSCNW and BGCNW were seen to present optimal properties, with aspect ratio, crystallinity and thermal stability values comparable to those of BCNW. Additionally, the optimized hydrolysis treatment led to extraction yields higher than those previously reported for food by-products. The CNW were subsequently incorporated into a commercial polyhydroxybutyrate-co-valerate polymer (PHBV) by solution casting, and the produced nanocomposites were characterized. Although the addition of BGCNW and WSCNW was advantageous in terms of mechanical performance in comparison with OPCNW, no significant enhancement of the pure PHBV mechanical properties was reported because of the low nanofiller loadings used and the inherent difficulty of achieving a high degree of dispersion by the casting method. Interestingly, BGCNW and WSCNW presented reduced moisture sensitivity as compared with BCNW, leading to greater barrier performance and resulting in oxygen permeability reductions up to 26 % with WSCNW and 44 % with BGCNW.Noelle Peutat, on leave from the University of Grenoble in France, is acknowledged for her great dedication and support in the experimental work. M. Martinez-Sanz would like to thank the Spanish Ministry of Education for FPU Grant 1484. The authors acknowledge financial support from the EU FP7 ECOBIOCAP Project. The Electronic Microscopy Department in the SCIE from the University of Valencia is acknowledged for the support with SEM and TEM analyses. The Portuguese authors also acknowledge support from the FCT (Portuguese Foundation for Science and Technology) through strategic project PEst-OE/EQB/LA0023/2013

Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach

Information of crop phenology is essential for evaluating crop productivity. In a previous work, we determined phenological stages with remote sensing data using a dynamic system framework and an extended Kalman filter (EKF) approach. In this paper, we demonstrate that the particle filter is a more reliable method to infer any phenological stage compared to the EKF. The improvements achieved with this approach are discussed. In addition, this methodology enables the estimation of key cultivation dates, thus providing a practical product for many applications. The dates of some important stages, as the sowing date and the day when the crop reaches the panicle initiation stage, have been chosen to show the potential of this technique.This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Project TEC2011-28201-C02-02, and in part by the Generalitat Valenciana under Project ACOMP/2014/136. All SAR images have been provided by DLR in the framework of projects LAN0021 and LAN0234 of the prelaunch AO of TerraSAR-X

Particle Filter Approach for Real-Time Estimation of Crop Phenological States Using Time Series of NDVI Images

Knowing the current phenological state of an agricultural crop is a powerful tool for precision farming applications. In the past, it has been estimated with remote sensing data by exploiting time series of Normalised Difference Vegetation Index (NDVI), but always at the end of the campaign and only providing results for some key states. In this work, a new dynamical framework is proposed to provide real-time estimates in a continuous range of states, for which NDVI images are combined with a prediction model in an optimal way using a particle filter. The methodology is tested over a set of 8 to 13 rice parcels during 2008–2013, achieving a high determination factor R2=0.93 ( n=379 ) for the complete phenological range. This method is also used to predict the end of season date, obtaining a high accuracy with an anticipation of around 40–60 days. Among the key advantages of this approach, phenology is estimated each time a new observation is available, hence enabling the potential detection of anomalies in real-time during the cultivation. In addition, the estimation procedure is robust in the case of noisy observations, and it is not limited to a few phenological stages.This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Projects TEC2011-28201-C02-02 and TIN2014-55413-C2-2-P

Firefighter and victims protecting solution based on wireless body area network nodes

Interconnectivity between Web systems and sensor networks is used to provide smart services for the Internet of Things. These services are based on data collection and processing to obtain useful information about the supervised environment. With this information it is possible to provide smart services, but some of them must be considered as protected by the legislation regarding privacy of personal data. In order to face this issue, security and privacy mechanisms must be used. So as to deal with the limited resources in sensor networks, these mechanisms must be as lightweight as possible to preserve the enough Quality of Service. However, these mechanisms must fulfill security and privacy requirements defined by the regulations. This paper describes a Wireless Body Area Network application providing services to protect firefighter work in hazardous environments. The firefighter wears a special shirt with sensors embedded. These sensors are able to monitor not only the firefighter health status, but also they can be connected to external sensors in order to monitor the health status of the victims. These external sensors are part of the equipment carried by the firefighter to face the emergencies and save lives. Thus, they are able to obtain external medical aid

A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time

[EN] This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms. Ten real breasts were used for this work. Their deformation due to the displacement of two compression plates was simulated off-line using the finite element (FE) method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict in real-time the deformation of the breast tissues during the compression. The models were a decision tree and two tree-based ensemble methods (extremely randomized trees and random forest). Two different experimental setups were designed to validate and study the performance of these models under different conditions. The mean 3D Euclidean distance between nodes predicted by the models and those extracted from the FE simulations was calculated to assess the performance of the models in the validation set. The experiments proved that extremely randomized trees performed better than the other two models. The mean error committed by the three models in the prediction of the nodal displacements was under 2 man, a threshold usually set for clinical applications. The time needed for breast compression prediction is sufficiently short to allow its use in real-time (< 0.2 s).This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through research projects TIN2014-52033-R and DPI2013-40859-R with the support of European FEDER funds.Martínez Martínez, F.; Rupérez Moreno, MJ.; Martínez-Sober, M.; Solves Llorens, JA.; Lorente, D.; Serrano-Lopez, A.; Martinez-Sanchis, S.... (2017). A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time. Computers in Biology and Medicine. 90:116-124. https://doi.org/10.1016/j.compbiomed.2017.09.019S1161249