2026-06-29T20:30:07Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/73522023-03-22T11:12:24Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Diez Pastor, José Francisco Latorre Carmona, Pedro Garrido Labrador, José Luis Ramírez Sanz, José Miguel Rodríguez Diez, Juan José Radar signal Feature extraction Classification Stacking Tangible user interfaces Radar technology has evolved considerably in the last few decades. There are many areas where radar systems are applied, including air traffic control in airports, ocean surveillance, and research systems, to cite a few. Other types of sensors have recently appeared, which allow tracking sub-millimeter motion with high speed and accuracy rates. These millimeter-wave radars are giving rise to myriad new applications, from the recognition of the material close objects are made, to the recognition of hand gestures. They have also been recently used to identify how a person interacts with digital devices through the physical environment (Tangible User Interfaces, TUIs). In this case, the radar is used to detect the orientation, movement, or distance from the objects to the user’s hands or the digital device. This paper presents a thoughtful comparative analysis of different feature extraction techniques and classification strategies applied on a series of datasets that cover problems such as the identification of materials, element counting, or determining the orientation and distance of objects to the sensor. The results outperform previous works using these datasets, especially when the accuracy was lowest, showing the benefits feature extraction techniques have on classification performance. 2023-01-31 2023-01-31 2021-07 info:eu-repo/semantics/article http://hdl.handle.net/10259/7352 10.3390/app11156745 2076-3417 eng Applied sciences. 2021, V. 11, n. 15, 6745 https://doi.org/10.3390/app11156745 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119894GB-I00/ES/APRENDIZAJE AUTOMATICO CON DATOS ESCASAMENTE ETIQUETADOS PARA LA INDUSTRIA 4.0/ info:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20//Métodos y Aplicaciones Industriales del Aprendizaje Semisupervisado/ http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional MDPI