Lab

B105 Electronic Systems Lab

About the lab

The B105 Electronic Systems Lab is part of the Electronic Engineering Department at UPM,
and focus their activities on Embedded System design, Wireless Sensor Networks and Cognitive Networks

Featured research (59)

Herein, we describe the Virtually Enhanced Senses (VES) system, a novel and highly configurable wireless sensor-actuator network conceived as a development and test-bench platform of navigation systems adapted for blind and visually impaired people. It allows to immerse its users into “walkable” purely virtual or mixed environments with simulated sensors and validate navigation system designs prior to prototype development. The haptic, acoustic, and proprioceptive feedback supports state-of-art sensory substitution devices (SSD). In this regard, three SSD were integrated in VES as examples, including the well-known “The vOICe”. Additionally, the data throughput, latency and packet loss of the wireless communication can be controlled to observe its impact in the provided spatial knowledge and resulting mobility and orientation performance. Finally, the system has been validated by testing a combination of two previous visual-acoustic and visual-haptic sensory substitution schemas with 23 normal-sighted subjects. The recorded data includes the output of a “gaze-tracking” utility adapted for SSD.
In this work, we present autonomous active tags. The power sources of these active tags employ energy harvesting techniques, specifically, solar and mechanical techniques. The integration of these techniques, and the storage of the energy obtained with a supercapacitor, converts the active tag into an autonomous device. These tags work in a low power mode in which they dynamically adjust their radio communication capabilities. Such a configuration depends on the application. We tested the tags in a real environment with testing parameters to check the modules, meaning more wake-ups over a longer time. Under these conditions, the tags gather enough energy to autonomously maintain standby operation on a sunny day for ten hours. In conclusion, this autonomous active tag is a demonstration that the integration of energy harvesting techniques, supercapacitor storage and the management of low power modes for transceivers, microcontrollers, and memories creates a device without energy dependencies that only depends on the requirements, and can be used in many applications related to, for instance, smart homes, smart cities, smart cars, and connected forests.
The continuous increase in the number of mobile and Internet of Things (IoT) devices, as well as in the wireless data traffic they generate, represents an essential challenge in terms of spectral coexistence. As a result, these devices are now expected to make efficient and dynamic use of the spectrum by employing Cognitive Radio (CR) techniques. In this work, we focus on the Automatic Modulation Classification (AMC). AMC is essential to carry out multiple CR techniques, such as dynamic spectrum access, link adaptation and interference detection, aimed at improving communications throughput and reliability and, in turn, spectral efficiency. In recent years, multiple Deep Learning (DL) techniques have been proposed to address the AMC problem. These DL techniques have demonstrated better generalization, scalability and robustness capabilities compared to previous solutions. However, most of these techniques require high processing and storage capabilities that limit their applicability to energy- and computation-constrained end-devices. In this work, we propose a new gated recurrent unit neural network solution for AMC that has been specifically designed for resource-constrained IoT devices. We trained and tested our solution with over-the-air measurements of real radio signals. Our results show that the proposed solution has a memory footprint of 73.5 kBytes, 51.74% less than the reference model, and achieves a classification accuracy of 92.4%.
In this paper, the Virtually Enhanced Senses (VES) System is described. It is an ARCore-based, mixed-reality system meant to assist blind and visually impaired people's navigation. VES operates in indoor and outdoor environments without any previous in-situ installation. It provides users with specific, runtime-configurable stimuli according to their pose, i.e., position and orientation, and the information of the environment recorded in a virtual replica. It implements three output data modalities: wall-tracking assistance, acoustic compass, and a novel sensory substitution algorithm, Geometry-based Virtual Acoustic Space (GbVAS). The multimodal output of this algorithm takes advantage of natural human perception encoding of spatial data. Preliminary experiments of GbVAS have been conducted with sixteen subjects in three different scenarios, demonstrating basic orientation and mobility skills after six minutes training.
The increase in the number of mobile and Internet of Things (IoT) devices, along with the demands of new applications and services, represents an important challenge in terms of spectral coexistence. As a result, these devices are now expected to make an efficient and dynamic use of the spectrum, and to provide processed information instead of simple raw sensor measurements. These communication and processing requirements have direct implications on the architecture of the systems. In this work, we present MIGOU, a wireless experimental platform that has been designed to address these challenges from the perspective of resource-constrained devices, such as wireless sensor nodes or IoT end-devices. At the radio level, the platform can operate both as a software-defined radio and as a traditional highly integrated radio transceiver, which demands less node resources. For the processing tasks, it relies on a system-on-a-chip that integrates an ARM Cortex-M3 processor, and a flash-based FPGA fabric, where high-speed processing tasks can be offloaded. The power consumption of the platform has been measured in the different modes of operation. In addition, these hardware features and power measurements have been compared with those of other representative platforms. The results obtained confirm that a state-of-the-art tradeoff between hardware flexibility and energy efficiency has been achieved. These characteristics will allow for the development of appropriate solutions to current end-devices’ challenges and to test them in real scenarios.

Lab head

Alvaro Araujo
Department
  • Departmento de Ingeniería Electrónica

Members (9)

J.M. Rabaey
  • University of California, Berkeley
Alba Rozas
  • Universidad Politécnica de Madrid
Roberto Rodriguez-Zurrunero
  • Universidad Politécnica de Madrid
Francisco Tirado-Andrés
  • Universidad Politécnica de Madrid
Ramiro Utrilla
  • Universidad Politécnica de Madrid
Octavio Nieto-Taladriz
  • Universidad Politécnica de Madrid
Santiago Real Valdés
  • Universidad Politécnica de Madrid
Jose Martin
  • Universidad Politécnica de Madrid
Jan M. Rabaey
Jan M. Rabaey
  • Not confirmed yet
Arno Thielens
Arno Thielens
  • Not confirmed yet
Gabriel Mujica
Gabriel Mujica
  • Not confirmed yet
Ana Belén Rodríguez González
Ana Belén Rodríguez González
  • Not confirmed yet
Ali Moin
Ali Moin
  • Not confirmed yet
Francisco Tirado-Andrés
Francisco Tirado-Andrés
  • Not confirmed yet
Elena Romero
Elena Romero
  • Not confirmed yet
Rosa Pita
Rosa Pita
  • Not confirmed yet

Alumni (4)

Elena Romero
  • Universidad Politécnica de Madrid
Javier Blesa
  • Universidad Politécnica de Madrid
Guillermo Jara
Guillermo Jara
gisela mur
gisela mur