NeuroPod: a real-time neuromorphic spiking CPG applied to robotics

Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being designed to take a step further, by performing more complex tasks that have been carried out by smart animals or humans up to date. To this end, inspiration needs to be taken from biological examples. For instance, insects are able to optimally solve complex environment navigation problems, and many researchers have started to mimic how these insects behave. Recent interest in neuromorphic engineering has motivated us to present a real-time, neuromorphic, spike-based Central Pattern Generator of application in neurorobotics, using an arthropod-like robot. A Spiking Neural Network was designed and implemented on SpiNNaker. The network models a complex, online-change capable Central Pattern Generator which generates three gaits for a hexapod robot locomotion. Recon gurable hardware was used to manage both the motors of the robot and the real-time communication interface with the Spiking Neural Networks. Real-time measurements con rm the simulation results, and locomotion tests show that NeuroPod can perform the gaits without any balance loss or added delay.Ministerio de Economía y Competitividad TEC2016-77785-

Live Demonstration: neuromorphic robotics, from audio to locomotion through spiking CPG on SpiNNaker.

This live demonstration presents an audio-guided neuromorphic robot: from a Neuromorphic Auditory Sensor (NAS) to locomotion using Spiking Central Pattern Generators (sCPGs). Several gaits are generated by sCPGs implemented on a SpiNNaker board. The output of these sCPGs is sent in a real-time manner to an Field Programmable Gate Array (FPGA) board using an AER-to-SpiNN interface. The control of the hexapod robot joints is performed by the FPGA board. The robot behavior can be changed in real-time by means of the NAS. The audio information is sent to the SpiNNaker board which classifies it using a Spiking Neural Network (SNN). Thus, the input sound will activate a specific gait pattern which will eventually modify the behavior of the robot.Ministerio de Economía y Competitividad TEC2016-77785-

Gaussian Markov Random fields and totally positive matrices

The present paper focuses on the study of the conditions under which the covariance matrix of a multivariate Gaussian distribution is totally positive, paying particular attention to multivariate Gaussian distributions that are Gaussian Markov Random Fields. More specifically, it is proven that, if the graph over which the Gaussian Markov Random Field is defined consists of path graphs and the covariances between adjacent variables on the graph are non-negative, then there always exists a reordering of the variables that renders the resulting covariance matrix totally positive. Moreover, this reordering is identified and some cases for which the conditions for the covariance matrix of a multivariate Gaussian distribution to be totally positive are necessary and sufficient are provided

Bio-plausible digital implementation of a reward modulated STDP synapse

Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) is a learning method for Spiking Neural Network (SNN) that makes use of an external learning signal to modulate the synaptic plasticity produced by Spike-Timing-Dependent Plasticity (STDP). Combining the advantages of reinforcement learning and the biological plausibility of STDP, online learning on SNN in real-world scenarios can be applied. This paper presents a fully digital architecture, implemented on an Field-Programmable Gate Array (FPGA), including the R-STDP learning mechanism in a SNN. The hardware results obtained are comparable to the software simulations results using the Brian2 simulator. The maximum error is of 0.083 when a 14-bits fix-point precision is used in realtime. The presented architecture shows an accuracy of 95% when tested in an obstacle avoidance problem on mobile robotics with a minimum use of resources

Anatomía y función de la articulación coxofemoral. Anatomía artroscópica de la cadera

ResumenObjetivoLa anatomía de la cadera presenta una serie de peculiaridades que condicionan el tratamiento artroscópico de su patología. El objetivo de la presente publicación es describir los hallazgos anatómicos y biomecánicos más destacados para la aplicación clínica y terapéutica.MétodoDividiremos el capítulo en biomecánica de la cadera con aplicación clínica y las estructuras anatómicas según estén en el compartimento central o en periférico.ResultadosLa necesidad de tracción para poder acceder a la articulación y la dificultad de movilidad dentro de la misma, nos obliga a conocer la anatomía normal y sus variantes. En el compartimento central describiremos estructuras como el labrum, cartílago acetabular, ligamento redondo, fosita semilunar y cartílago de carga de la cabeza femoral. En el compartimento periférico se observará el cartílago de la cabeza, cara no articular del labrum, cápsula y diferentes plicas sinoviales.ConclusionesConocer la anatomía artroscópica y sus variantes, junto con nociones básicas de biomecánica de la cadera, nos permiten mejorar nuestra orientación en una articulación de difícil acceso.Relevancia clínicaEl conocimiento de la anatomía artroscópica y la biomecánica aplicada de la cadera nos permite acortar nuestra curva de aprendizaje quirúrgico en artroscopia de cadera.Nivel de evidenciaOpinión de expertos Nivel IV.AbstractObjectiveHip joint anatomy has a number of peculiarities that determine the arthroscopic treatment. The aim of this article is to describe the most significant anatomical and biomechanical findings for clinical and therapeutic applications.MethodWe divide the chapter into hip biomechanics with clinical application and anatomical structures of the central or peripheral compartment.ResultsAccess and mobility into the hip joint is difficult, and requires understanding the normal anatomy and its variants. In the central compartment, we describe important structures such as the labrum, acetabular cartilage, round ligament, acetabular cartilage, and cartilage of the femoral head. In the peripheral compartment, femoral head cartilage, non-articular labrum, capsule and synovial folds are described.ConclusionsUnderstanding hip arthroscopic anatomy and its variants, along with the basics of hip biomechanics, allow us to improve our orientation in a joint with a difficult access.Clinical relevanceThe knowledge of applied anatomy and arthroscopic hip biomechanics allows us to reduce our surgical learning curve in hip arthroscopy technique.Level of evidenceLevel IV Expert opinion

Real-time detection of uncalibrated sensors using neural networks

Nowadays, sensors play a major role in several fields, such as science, industry and everyday technology. Therefore, the information received from the sensors must be reliable. If the sensors present any anomalies, serious problems can arise, such as publishing wrong theories in scientific papers, or causing production delays in industry. One of the most common anomalies are uncalibrations. An uncalibration occurs 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 is presented. This development integrates an artificial neural network as the main component which learns from the behavior of the sensors under calibrated conditions. Then, after being trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed system is able to detect the 100% of the presented uncalibration events, although the time response in the detection depends on the resolution of the model for the specific location, i.e., the minimum statistically significant variation in the sensor behavior that the system is able to detect. This architecture can be adapted to different contexts by applying transfer learning, such as adding new sensors or having different environments by re-training the model with minimum amount of dataEuropean Union (UE). H2020 VIMS Grant ID: 878757Ministerio de Ciencia, Innovación y Universidades PID2019-105556GB-C33 (MIND-ROB)European Union H2020 CHIST-ERA SMALL (PCI2019-111841-2

Environmental predictors of filarial infection in Amazonian primates : Ecological factors and primate filarial infection

Altres ajuts: acord transformatiu CRUE-CSICUTP en procés de revisióAltres ajuts: ERANet17/HLH-0271, and by the National Council for Scientific and Technological Development (CNPq) (grant number 201,475/2017-0).Filarial nematode infections are common in primates, but have received little attention in the Neotropics. Epidemiological data on filarial infections in primates are still too sparse to fully understand the complex of this parasitism, especially because of the difficulty in studying the ecology and epidemiology of wild primates. We describe natural infections by Dipetalonema parasitizing 211 primates belonging to eight free-living primate genera in Amazonia, and assess the relationships between parasitic indicators and climatic (rainfall and river level), ecological (fruiting periods of plants) and biological (sex, species' body mass, group size and density) factors. The overall prevalence was 64.4% (95% CI: 64.0 - 64.9); parasitic mean abundance (N filariae per individual) and parasitic mean intensity (N filariae per infected host) of infection were 11.9 (95% CI: 8.3 - 15.6) and 18.4 (95% CI: 13.4 - 23.4) filariae/individual, respectively. Although we observed differences in parasitic parameters among primate genera, there was no correlation between parasitic parameters with density, body mass or group size. Sapajus, Cebus and Lagothrix had the highest prevalence and parasitic mean intensity. Using Lagothrix lagotricha poeppigii, the most sampled species (n = 92), as a model, we found that the number of filariae per infected host was associated with fruit production in swamp forests during the dry season, the time of food scarcity. The long periods of food shortage may cause environmental stress on primates, impairing their immune defenses and leading to increased parasite load but not affecting infection prevalence. However, the lack of information on vector ecology, key to understand risk factors associated to infection rate, prevents confirming the existence of an infection pattern dependent on food availability

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

Geología de la cueva

Peer reviewe