A new formulation to assess the seismic demand of masonry structures by means of input energy

The main objective of this paper is to evaluate the elastic input energy of unreinforced masonry structures by means of the input energy spectrum. The energy is a novel approach which allows evaluating in a global and easily way the performance of the masonry structures. Structures modeled with non frame elements require of a great number of 2D or 3D elements, thereby making the calculation of the input energy a complicated issue. In this context, a new formulation that calculates the input energy using an input energy spectrum and the balance of energy is proposed. Two examples of application of unreinforced masonry structures were considered to evaluate the input energy and compare it with the proposed formula. The formula proposed shows interesting results that allowed identify the key features of the accelerograms that influence the input energy into structures

DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge Distillation

Deep learning and remote sensing techniques have significantly advanced water monitoring abilities; however, the need for annotated data remains a challenge. This is particularly problematic in wetland detection, where water extent varies over time and space, demanding multiple annotations for the same area. In this paper, we present DeepAqua, a self-supervised deep learning model that leverages knowledge distillation (a.k.a. teacher-student model) to eliminate the need for manual annotations during the training phase. We utilize the Normalized Difference Water Index (NDWI) as a teacher model to train a Convolutional Neural Network (CNN) for segmenting water from Synthetic Aperture Radar (SAR) images, and to train the student model, we exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. DeepAqua represents a significant advancement in computer vision techniques by effectively training semantic segmentation models without any manually annotated data. Experimental results show that DeepAqua outperforms other unsupervised methods by improving accuracy by 7%, Intersection Over Union by 27%, and F1 score by 14%. This approach offers a practical solution for monitoring wetland water extent changes without needing ground truth data, making it highly adaptable and scalable for wetland conservation efforts.Comment: 29 pages, 8 figures, 1 tabl

Frequency Analysis of a 64x64 Pixel Retinomorphic System with AER Output to Estimate the Limits to Apply onto Specific Mechanical Environment

The rods and cones of a human retina are constantly sensing and transmitting the light in the form of spikes to the cortex of the brain in order to reproduce an image in the brain. Delbruck’s lab has designed and manufactured several generations of spike based image sensors that mimic the human retina. In this paper we present an exhaustive timing analysis of the Address-Event- Representation (AER) output of a 64x64 pixels silicon retinomorphic system. Two different scenarios are presented in order to achieve the maximum frequency of light changes for a pixel sensor and the maximum frequency of requested directions on the output AER. Results obtained are 100 Hz and 1.66 MHz in each case respectively. We have tested the upper spin limit and found it to be approximately 6000rpm (revolutions per minute) and in some cases with high light contrast lost events do not exist.Ministerio de Ciencia e Innovación TEC2009-10639- C04-0

A Graph Approach to Observability in Physical Sparse Linear Systems

A sparse linear system constitutes a valid model for a broad range of physical systems, such as electric power networks, industrial processes, control systems or traffic models. The physical magnitudes in those systems may be directly measured by means of sensor networks that, in conjunction with data obtained from contextual and boundary constraints, allow the estimation of the state of the systems. The term observability refers to the capability of estimating the state variables of a system based on the available information. In the case of linear systems, diffierent graphical approaches were developed to address this issue. In this paper a new unified graph based technique is proposed in order to determine the observability of a sparse linear physical system or, at least, a system that can be linearized after a first order derivative, using a given sensor set. A network associated to a linear equation system is introduced, which allows addressing and solving three related problems: the characterization of those cases for which algebraic and topological observability analysis return contradictory results; the characterization of a necessary and sufficient condition for topological observability; the determination of the maximum observable subsystem in case of unobservability. Two examples illustrate the developed techniques

Performance Study of Software AER-Based Convolutions on a Parallel Supercomputer

This paper is based on the simulation of a convolution model for bioinspired neuromorphic systems using the Address-Event-Representation (AER) philosophy and implemented in the supercomputer CRS of the University of Cadiz (UCA). In this work we improve the runtime of the simulation, by dividing an image into smaller parts before AER convolution and running each operation in a node of the cluster. This research involves a test cases design in which the optimal parameters are set to run the AER convolution in parallel processors. These cases consist on running the convolution taking an image divided in different number of parts, applying to each part a Sobel filter for edge detection, and based on the AER-TOOL simulator. Execution times are compared for all cases and the optimal configuration of the system is discussed. In general, CRS obtain better performances when the image is divided than for the whole image.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

Real-time detection of uncalibrated sensors using Neural Networks

Nowadays, sensors play a major role in several contexts like science, industry and daily life which benefit of their use. However, the retrieved information must be reliable. Anomalies in the behavior of sensors can give rise to critical consequences such as ruining a scientific project or jeopardizing the quality of the production in industrial production lines. One of the more subtle kind of anomalies are uncalibrations. An uncalibration is said to take place when the sensor is not adjusted or standardized by calibration according to a ground truth value. In this work, an online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. This solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. Then, after trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively. This solution can be adapted to different contexts by means of transfer learning, whose application allows for the addition of new sensors, the deployment into new environments and the retraining of the model with minimum amounts of data

Applying active learning by contextualizing robotic applications to historical heritage

Optional university courses are designed to allow undergraduate students to specialize in relevant fields to enhance their skills and knowledge for their future careers. However, there are some cases in which students prioritize enrolling in courses that are easy to pass. This choice results in having students with low motivation and commitment, who mainly focus on doing just enough to pass the course, missing the opportunity to boost their skills. In this study, an eclectic approach is proposed, applying a mixture of active learning methods together with the theory of multiple intelligences to improve students' performance, motivation, and commitment throughout the course. The study was applied to the 56 students enrolled in the optional Micro-Robotics Application spring course in the year 2021 at the University of Cádiz (Spain). Results demonstrate that this combination of active learning methodologies increased students' motivation, prompting them to give their best in terms of commitment, performance, and creativity. Furthermore, they were convinced that during the course they not only learned relevant robotic knowledge but also acquired essential skills needed for their future. Finally, this study highlights the benefits and future directions for implementing active learning methodologies in science, technology, engineering, and mathematics courses

Novedades para la flora de Manzanera y su entorno (Sierra de Javalambre, Teruel)

Data are offered and commented on 14 taxa of new or little known vascular plants for the Sierra de Javalambre, province of Teruel, more specifically in the municipal boundaries of Manzanera and Riodeva. The contributions of Epipactis provincialis Aubenas & Robatsch for the first and Primula acaulis (L.) Hill for the second one stand out

Ionoluminescence on α-quartz: mechanisms and modeling

Ionoluminescence of α - quartz exhibits two dominant emission bands peaking at 1.9 eV. (NBOHCs) and 2.7 eV (STEs. The evolution of the red emission yield does not show a correlation with the concentrations of neither the NBOHC nor with that of other color centers. The blue emission yield closely follows the amorphization kinetics independently measured by RBS/C spectrometry. A simple theoretical model has been proposed; it assumes that the formation and recombination of STEs are the primary event and both, the light emissions and the lattice structural damage are a consequence this phenomenon. The model leads to several simple mathematical equations that can be used to simulate the IL yields and provide a reasonable fit to experimental kinetic data

Characterization and identification of field ectomycorrhizae of Boletus edulis and Cistus ladanifer

Field ectomycorrhizae sampled under Boletus edulis and Cistus ladanifer have been characterized and described in detail based on standard morphological and anatomical characters. The described ectomycorrhiza has traits typical of Boletales: whitish with three differentiated plectenchymatous layers in the mantle in plan view forming ring-like structures and rhizomorphs with highly differentiated hyphae. The inflated, smooth cystidia-like clavate end cells on the surface of the rhizomorphs and their slightly twisted external hyphae are additional characterizing features. The Hartig net occupies 1 1/2 rows of cortical cells, partly reaching the endodermis. Not all hyphae have clamps. The identification of the fungal symbiont as B. edulis was confirmed by ITS rDNA sequence comparison between mycorrhizas and sporocarps. The singularity of this symbiotic association, as well as its ecological and practical implications, are discussed