Research and Development Projects

Enabling communication, coding and processing technologies for next-generation classical-quantum networks

Funded by: 
Ministerio Ciencia en Innovacion, AEI, Plan Nacional I+D+I (PID2022-137099NBC43)
Duration: 
2023-2026

The emerging 6G vision of seamless connectivity between the cyber and physical worlds will shape next-generation networks and services. Intended applications are, e.g., self-driving connected vehicles, health IoT-based services, and critical infrastructure surveillance, which integrate communication and sensing functionalities. Besides, MADDIE foresees that future networks will incorporate devices with quantum information processing capabilities, which may either boost network performance or act as adversaries that jeopardize network security. Therefore, for upcoming services and applications to become a reality, wireless and communication technologies must not only meet the demands of extremely high data throughput and low latency, but also ensure cybersecurity in hybrid classical-quantum networks.

Leveraging MADDIE participants’ know-how and expertise, this project will make use of classical and quantum coding, communications, and signal processing advanced techniques that will play a crucial role in answering the aforementioned challenges. Specifically, MADDIE will design coding and cryptographic algorithms for IoT-based applications in the context of classical-quantum networks. Furthermore, MADDIE will also explore innovative wireless technologies for the operation in the mmWave band and beyond, where future communication networks will be allocated to leverage massive bandwidth availability. Regarding co-existing sensing and communication networks, MADDIE will investigate distributed processing techniques that may possibly be combined with recent advances in graph signal processing to make efficient use of resources and increase performance. Finally, MADDIE will assess the impact of quantum devices in communication networks from both a computational and security perspective.

Non-Coherent Communications on the Grassmann Manifold

Funded by: 
HUAWEI TECHNOLOGIES SWEDEN AB
Duration: 
2021-2023

This project aims to develop efficient communication schemes over a non-coherent channel for the multi-user MIMO regime. Non-coherent MIMO communication is gaining renewed interest due to the advent of massive MIMO system. When the transmitter and/or receiver is equipped with a large antenna array, the overhead incurred by the channel estimation may significantly limit the effective throughput, and make it more difficult to acquire channel state information (CSI) for channels that vary rapidly.

Advances in coding and signal processing for the digital society (ADELE)

Funded by: 
Ministerio de Ciencia e Innovación (PID2019-104958RB-C43)
Duration: 
2020-2022

ADELE mission is twofold. First, it will develop advanced coding and signal processing techniques for wireless communication networks. Furthermore, leveraging on ADELE participants know-how and expertise, the project will address more ambitious long-term objectives and will contribute significantly to: (a) coding and information theory for quantum computing and communications, and (b) machine learning methods and the emerging field of graph signal processing for the analysis and operation of networks that handle massive amounts of data including, but not limited to, wireless communication networks.

Change-point Management: Active Sensing and Ensemble Learning (CAIMAN)

Funded by: 
Ministerio de Economia, Industria y Competitividad (TEC2017-86921-C2-1-R)
Duration: 
2018-2020

Online detection of abrupt changes in time series has received much attention in the past due to its importance in applications such as structural health monitoring or target detection and tracking. In particular, the signal processing community has established the basic theory of change-point detection, which more recently has also attracted the interest of the machine learning community.

Modelos no Lineales para la Predicción de Consumo Eléctrico y Consumo de Gas

Funded by: 
ALDRO
Duration: 
2018-2019

El proyecto PREDILECT tiene como objeto el desarrollo de modelos no lineales de predicción de series temporales, basados en distintos esquemas de aprendizaje-máquina e inteligencia artificial (ML-AI) para la predicción en distintas escalas temporales (horaria, diaria, semanal, mensual, anual), de los consumos de electricidad y gas para los clientes de la empresa Aldro, de tal forma que dicha empresa pueda estimar de manera más precisa que en la actualidad el volumen de electricidad/gas que debe comprar.

Coding and Signal Processing for Emerging Wireless Communication and Sensor Networks (CARMEN)

Funded by: 
Ministerio Economia y Competitividad (TEC2016-75067-C4-4-R)
Duration: 
2017-2019

CARMEN is a three-year (2017-2019) research project sponsored by Gobierno de España (TEC2016-75067-C4-1-R). CARMEN addresses two major trends in current wireless networks: radio interfaces with unprecedented high data rates and Wireless Sensor Networks (WSN).

Advances in Kernel methods for Structured Data (KERMES)

Funded by: 
Ministerio Economia y Competitividad (TEC2016-81900-REDT)
Duration: 
2017-2018

Kernel machines and related Bayesian approaches, such as Gaussian processes, have been widely and successfully used in practice for dealing with such data structures for regression and classification. Kernel methods allow to encode prior knowledge about the data characteristics, to learn the underlying latent functions explaining the data; and allow the combination of different data modalities. The long-term vision of KERMES is tied to open new frontiers and foster research towards new kernel algorithms, a stepping stone before the more ambitious far-end goal of machine reasoning.

COMONSENS Network (TEC2015-69648-REDC)

Funded by: 
Ministerio Economia y Competitividad
Duration: 
2016-2017

The COMONSENS Network aims at the fertilization of the research collaboration links initiated and developed during the COMONSENS project both among the 10 participating research groups in Spain and with external participants and networks. The COMONSENS project, www.comonsens.org, involved 135 researchers and surpassed all originally agreed performance objectives. The COMONSENS Network will continue the biannual COMONSENS workshops, which took place between 2009 and 2014.

Advanced Machine Learning Techniques for Pattern Recognition in Time Series (PRISMA)

Funded by: 
Ministerio de Economía y Competitividad (TEC2014-57402-JIN)
Duration: 
2015-2017

The goal of PRISMA is to advance the state of the art in machine learning theory and algorithms that exploit temporal information. PRISMA aims to develop a general probabilistic framework to deal with temporal dynamics in time series, based on Bayesian graphical models for temporal pattern recognition. Furthermore, PRISMA aims to extend the state-of-the-art in kernel methods and machine learning algorithms that deal explicitly with the temporal variable in pattern recognition problems. The developed theoretical framework will be evaluated in applications from several key areas of the modern digital society.

Distance sensing technical study and algorithm development

Funded by: 
SAYME wireless sensor network
Duration: 
2015-04 - 2016-04

This Project will provide technical and scientific support for the design and development of a distance measurement industrial system and for its integration in the SAYME wireless sensor networks system. Several sensor technologies will be considered and their compliance with the low cost, low power and small form factor specifications of the SAYME system will be evaluated. Moreover, signal processing algorithms will be developed to process sensor signals in order to provide reliable and accurate measurements.

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