Improving Radar Human Activity Classification Using Synthetic Data with Image Transformation

Machine Learning (ML) methods have become state of the art in radar signal processing, particularly for classification tasks (e.g., of different human activities). Radar classification can be tedious to implement, though, due to the limited size and diversity of the source dataset, i.e., the data measured once for initial training of the Machine Learning algorithms. In this work, we introduce the algorithm Radar Activity Classification with Perceptual Image Transformation (RACPIT), which increases the accuracy of human activity classification while lowering the dependency on limited source data. In doing so, we focus on the augmentation of the dataset by synthetic data. We use a human radar reflection model based on the captured motion of the test subjects performing activities in the source dataset, which we recorded with a video camera. As the synthetic data generated by this model still deviates too much from the original radar data, we implement an image transformation network to bring real data close to their synthetic counterpart. We leverage these artificially generated data to train a Convolutional Neural Network for activity classification. We found that by using our approach, the classification accuracy could be increased by up to 20%, without the need of collecting more real data

Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the onboarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.Comment: 5 pages, 6 figures. Accepted for presentation at IEEE conference VTC2023-Spring. Available dataset at https://ieee-dataport.org/open-access/berlin-v2

Migración de la intranet y extranet de un grupo de investigación para el uso de nuevas tecnologías Web

El portal preexistente del grupo de investigación OEG (www.oeg-upm.net) está construído sobre ODESeW, que es una plataforma para el desarrollo de aplicaciones web semánticas, principalmente portales semánticos, que utiliza WebODE como base de conocimiento. WebODE es un sistema software que da soporte al desarrollo y mantenimiento de ontologías, es decir, edición, visualización, aprendizaje, mezcla, alineamiento, traducción, evaluación y gestión de la configuración, evolución y documentación de la ontología. También implementa una capa de servicios ODESeW sigue un modelo MVC (Model-View-Controller), y por tanto presenta los siguientes módulos representativos: Modelo de Datos Contiene las ontologías necesarias para la puesta en marcha y el mantenimiento de los portales semánticos basados en ODESeW. Los diferentes modelos están coordinados por el gestor de modelo de datos, encargado de recibir y manejar las peticiones que provienen del controlador para mostrar las visualizaciones de los términos. Vistas Las vistas son utilizadas para mostrar información del modelo de datos, tanto en formatos centrados en la generación de documentos HTML, como en la generación de documentos RDF(S) u OWL. Controlador : El controlador es el encargado de recibir las peticiones del usuario y completar estas peticiones con el modelo de datos. Una vez realizada esta tarea, llama al modelo de navegación y al modelo de composición, devolviendo al usuario la vista generada a su petición o Modelo de navegación El modelo de navegación representa la forma en la que el usuario se mueve entre las visualizaciones. o Modelo de composición El modelo de composición es el encargado de incluir unas visualizaciones dentro de otras. A continuación se describen las ventajas e inconvenientes principales de la utilización de esta plataforma para el desarrollo del portal de un grupo de investigación, frente a la utilización de un sistema de gestión de contenidos (CMS, Content Management System por sus siglas en inglés) tradicional

Strategic management in family business. The missing concept of the familiness learning mechanism

Producción CientíficaPurpose –The purpose of this article is to provide a comprehensive understanding of the roots of family firms’ competitive advantages by defining and testing the familiness learning mechanisms that emerge from the interaction between family and firm. Because family members are economically, emotionally and socially attached to the firm, family firms are expected to be able to develop unique and difficult to imitate learning mechanisms related to family firm value creation. Design/methodology/approach – This study operationalizes and tests the concept of the familiness learning mechanism using a sample of nonlisted Spanish family firms. The sample is analyzed using the structural equation modeling method. Findings – Results show that family firms’ ability to accumulate internal and external knowledge, integrate social knowledge, as well as create and retain socioemotional knowledge forms the concept of the familiness learning mechanism, and the authors show what implications it might have for family firm value creation. Originality/value – By using the dynamic capabilities approach, this article highlights the importance of the knowledge and learning derived from family involvement in the firm. The creation of learning mechanisms occurs because of the close relationships between family members and their simultaneous participation in the family and in the company systems, which creates a unique context wherein knowledge and learning emerge in an idiosyncratic mannerMinisterio de ciencia e Innovación español (Programa Estatal de Fomento de la Investigación científica y técnica de excelencia) (ECO2016-78128-P

Improving Radar Human Activity Classification Using Synthetic Data with Image Transformation

Machine Learning (ML) methods have become state of the art in radar signal processing, particularly for classification tasks (e.g., of different human activities). Radar classification can be tedious to implement, though, due to the limited size and diversity of the source dataset, i.e., the data measured once for initial training of the Machine Learning algorithms. In this work, we introduce the algorithm Radar Activity Classification with Perceptual Image Transformation (RACPIT), which increases the accuracy of human activity classification while lowering the dependency on limited source data. In doing so, we focus on the augmentation of the dataset by synthetic data. We use a human radar reflection model based on the captured motion of the test subjects performing activities in the source dataset, which we recorded with a video camera. As the synthetic data generated by this model still deviates too much from the original radar data, we implement an image transformation network to bring real data close to their synthetic counterpart. We leverage these artificially generated data to train a Convolutional Neural Network for activity classification. We found that by using our approach, the classification accuracy could be increased by up to 20%, without the need of collecting more real data