Non-cooperative identification of civil aircraft using a generalised mutual subspace method

The subspace-based methods are effectively applied to classify sets of feature vectors by modelling them as subspaces. However, their application to the field of non-cooperative target identification of flying aircraft is barely seen in the literature. In these methods, setting the subspace dimensionality is always an issue. Here, it is demonstrated that a modified mutual subspace method, which uses softweights to set the importance of each subspace basis, is a promising classifier for identifying sets of range profiles coming from real in-flight targets with no need to set the subspace dimensionality in advance. The assembly of a recognition database is also a challenging task. In this study, this database comprises predicted range profiles coming from electromagnetic simulations. Even though the predicted and actual profiles differ, the high recognition rates achieved reveal that the algorithm might be a good candidate for its application in an operational target recognition system

Behavioural patterns in aggregated demand response developments for communities targeting renewables

Encouraging consumers to embrace renewable energies and energy-efficient technologies is at stake, and so the energy players such as utilities and policy-makers are opening up a range of new value propositions towards more sustainable communities. For instance, developments of turn-key demand response aggregation and optimisation of distributed loads are rapidly emerging across the globe in a variety of business models focused on maximising the inherent flexibility and diversity of the behind-the-meter assets. However, even though these developments" added value is understood and of wide interest, measurement of the desired levels of consumer engagement is still on demonstration stages and assessment of technology readiness. In this paper, we analyse the characteristics of the loads, the behaviour of parameters, and in a final extent, the behaviour of each kind of consumer participating in aggregated demand scheduling. We apply both non-automatic and machine learning methods to extract the relevant factors and to recognise the potential consumer behaviour on a series of scenarios that are drawn using both synthetic data and living labs datasets. Our experimentation showcases a number of three patterns in which factors like the community"s demand volume and the consumer"s flexibility dominate and impact the performance of the tested development. The experimentation also makes current limitations arise within the existing electricity consumption datasets and their potential for inference and forecasting demand flexibility analytics.Comunidad de Madri

Reinforced Feedback in Virtual Environment for Plantar Flexor Poststroke Spasticity Reduction and Gait Function Improvement

Background. Ankle spasticity is a frequent phenomenon that limits functionality in poststroke patients. Objectives. Our aim was to determine if there was decreased spasticity in the ankle plantar flex (PF) muscles in the plegic lower extremity (LE) and improvement of gait function in stroke patients after traditional rehabilitation (TR) in combination with virtual reality with reinforced feedback, which is termed "reinforced feedback virtual environment" (RFVE). Methods. The evaluation, before and after treatment, of 10 hemiparetic patients was performed using the Modified Ashworth Scale (MAS), Functional Ambulatory Category (FAC), and Functional Independence Measure (FIM). The intervention consisted of 1 hour/day of TR plus 1 hour/day of RFVE (5 days/week for 3 weeks; 15 sessions in total). Results. The MAS and FAC reached statistical significance (P<0.05). The changes in the FIM did not reach statistical significance (P=0.066). The analysis between the ischemic and haemorrhagic patients showed significant differences in favour of the haemorrhagic group in the FIM scale. A significant correlation between the FAC and the months after the stroke was established (P=-0.711). Indeed, patients who most increased their score on the FAC at the end of treatment were those who started the treatment earliest after stroke. Conclusions. The combined treatment of TR and RFVE showed encouraging results regarding the reduction of spasticity and improvement of gait function. An early commencement of the treatment seems to be ideal, and future research should increase the sample size and assessment tools

Towards sustainable energy-efficient communities based on a scheduling algorithm

The Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR strategies, we propose a DR model that promotes consumers reaching coordinated behaviour towards more sustainable (and green) communities. A cooperative DR system is designed not only to bolster energy efficiency management at both home and district levels, but also to integrate the renewable energy resource information into the community's energy management. Initially conceived in a centralised way, a data collector called the "aggregator" will handle the operation scheduling requirements given the consumers' time preferences and the available electricity supply from renewables. Evaluation on the algorithm implementation shows feasible computational cost (CC) in different scenarios of households, communities and consumer behaviour. Number of appliances and timeframe flexibility have the greatest impact on the reallocation cost. A discussion on the communication, security and hardware platforms is included prior to future pilot deployment.Comunidad de Madri

Non-cooperative target identification based on singular value decomposition

Non-Cooperative Target Identification based on High Resolution Range Profiles is a key research domain in the Defense industry. In this paper a method based on the application of Singular Value Decomposition to a matrix of range profiles is defined. The decomposition is applied to reduce dimensionality and to accomplish recognition in the transformed domain. So as to confirm the feasibility of the methodology, identification experiments of profiles coming from electromagnetic simulations are conducted, revealing promising results

A wearable closed-loop insulin delivery system based on low-power SoCs

The number of patients living with diabetes has increased significantly in recent years due to several factors. Many of these patients are choosing to use insulin pumps for their treatment, artificial systems that administer their insulin and consist of a glucometer and an automatic insulin supply working in an open loop. Currently, only a few closed-loop insulin delivery devices are commercially available. The most widespread systems among patients are what have been called the “Do-It-Yourself Hybrid Closed-Loop systems.” These systems require the use of platforms with high computing power. In this paper, we will present a novel wearable system for insulin delivery that reduces the energy and computing consumption of the platform without affecting the computation requirements. Patients’ information is obtained from a commercial continuous glucose sensor and a commercial insulin pump operating in a conventional manner. An ad-hoc embedded system will connect with the pump and the sensor to collect the glucose data and process it. That connection is accomplished through a radiofrequency channel that provides a suitable system for the patient. Thus, this system does not require to be connected to any other processor, which increases the overall stability. Using parameters configured by the patient, the control system will make automatic adjustments in the basal insulin infusion thereby bringing the patient’s glycaemia to the target set by a doctor’s prescription. The results obtained will be satisfactory as long as the configured parameters faithfully match the specific characteristics of the patient. Results from the simulation of 30 virtual patients (10 adolescents, 10 adults, and 10 children), using a python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator and a python implementation of the proposed algorithm, are presented

A new framework for deep learning video based Human Action Recognition on the edge

Nowadays, video surveillance systems are commonly found in most public and private spaces. These systems typically consist of a network of cameras that feed into a central node. However, the processing aspect is evolving towards distributed approaches, leveraging edge-computing. These distributed systems are capable of effectively addressing the detection of people or events at each individual node. Most of these systems, rely on the use of deep-learning and segmentation algorithms which enable them to achieve high performance, but usually with a significant computational cost, hindering real-time execution. This paper presents an approach for people detection and action recognition in the wild, optimized for running on the edge, and that is able to work in real-time, in an embedded platform. Human Action Recognition (HAR) is performed by using a Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM). The input to the LSTM is an ad-hoc, lightweight feature vector obtained from the bounding box of each detected person in the video surveillance image. The resulting system is highly portable and easily scalable, providing a powerful tool for real-world video surveillance applications (in the wild and real-time action recognition). The proposal has been exhaustively evaluated and compared against other state-of-the-art (SOTA) proposals in five datasets, including four widely used (KTH, WEIZMAN, WVU, IXMAX) and a novel one (GBA) recorded in the wild, that includes several people performing different actions simultaneously. The obtained results validate the proposal, since it achieves SOTA accuracy within a much more complicated video surveillance real scenario, and using a lightweight embedded hardware.European CommissionAgencia Estatal de InvestigaciónUniversidad de Alcal

Alimentación y actividad física como factores determinantes del estado nutricional de los escolares de seis a nueve años

Tesis (Profesor de Educación Física para la Enseñanza Básica, Licenciado en Educación)En el siguiente documento se detalla una investigación realizada por un grupo de estudiantes de la Universidad Andrés Bello para optar al título de Profesor de Educación Física para la Enseñanza General Básica. La investigación está orientada a determinar cómo los factores de alimentación y actividad física influyen en el estado nutricional de niños entre seis a nueve años de edad. Para esto, se interviene el Colegio Saint Andrew ubicado en la comuna de Las Condes de la Región Metropolitana y el Colegio San Lorenzo ubicado en el pueblo de Coya en la comuna de Machalí de la Región del Libertador General Bernardo O´Higgins de Chile. En dichos establecimientos se seleccionan al azar y con previo consentimiento un total de 64 sujetos, los que serán sometidos a distintas pruebas y mediciones. Estas pruebas son el test de caminata en seis minutos, encuesta alimenticia, dinamometría de brazo y mediciones antropométricas de pliegues subcutáneos. A través de los resultados que van a entregar estas pruebas y mediciones, se espera obtener valores que determinen en qué nivel afectan los factores de alimentación y sedentarismo en el nivel nutricional de un sujeto.The following document details an investigation carried out by a group of students of the Andrés Bello University to qualify for the title of Professor of Physical Education for Basic General Education. The research is aimed at determining how the factors of food and physical activity influence the nutritional status of children between six to nine years of age. For this, Saint Andrew College is located in the Las Condes district of the Metropolitan Region and the San Lorenzo School located in the town of Coya in the Machalí district of the Region of Libertador General Bernardo O'Higgins of Chile. In these establishments, a total of 64 subjects are selected at random and with prior consent, who will be subjected to different tests and measurements. These tests are the six-minute walk test, food survey, arm dynamometry and anthropometric measurements of subcutaneous folds. Through the results that are going to deliver these tests and measurements, it is expected to obtain values that determine in which level they affect the factors of feeding and sedentarism in the nutritional level of a subjec

Accuracy and precision of agents orientation in an indoor positioning system using multiple infrastructure lighting spotlights and a PSD sensor

In indoor localization there are applications in which the orientation of the agent to be located is as important as knowing the position. In this paper we present the results of the orientation estimation from a local positioning system based on position-sensitive device (PSD) sensors and the visible light emitted from the illumination of the room in which it is located. The orientation estimation will require that the PSD sensor receives signal from either 2 or 4 light sources simultaneously. As will be shown in the article, the error determining the rotation angle of the agent with the on-board sensor is less than 0.2 degrees for two emitters. On the other hand, by using 4 light sources the three Euler rotation angles are determined, with mean errors in the measurements smaller than 0.35◦ for the x- and y-axis and 0.16◦ for the z-axis. The accuracy of the measurement has been evaluated experimentally in a 2.5 m-high ceiling room over an area of 2.2 m2 using geodetic measurement tools to establish the reference ground truth values.Junta de Comunidades de Castilla-La Manch

Analysis of Multiple-Access Discrimination Techniques for the Development of a PSD-Based VLP System

There are several technologies and techniques available when developing indoor positioning systems (IPS). Recently, the development of positioning systems based on optical signals has aroused great interest, mainly those using visible light from the lighting infrastructure. In this work, we analyze which techniques give better results to lay the foundations for the development of a Visible Light Positioning system (VLP). Working only with a receiver, it is analyzed what the result of determining the position of different emitters is when they emit simultaneously and without any synchronism. The results obtained by Frequency Division Multiple Access (FDMA) (with digital bandpass filters, I/Q demodulation, and FFT) and Code Division Multiple Access (CDMA) are compared. The interference between signals when emitted simultaneously from multiple emitters is analyzed as well as the errors they cause and how these effects can be mitigated. As a result of the research, the advantages and disadvantages using different multiple-access determination techniques are determined. In addition, advantages and disadvantages of using FDMA and CDMA techniques as well as hardware requirements that make one more feasible than the other are presented. The system behavior, in terms of errors, is established using FDMA and different configurations such as: I/Q, RMS, or FFT. The work also determines the error rates that can be obtained with the different FDMA and CDMA configurations, considering different error scenarios and integration time. Synthetic emulations and empirical tests were performed, which concluded that IPS systems based on optical signals and PSD sensors can achieve very high measurement accuracies and a high measurement rate. Obtained positioning errors in a room of 3 m height are less than 1 cm when working in noisy environments.Agencia Estatal de Investigació