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José M. Barceló Ordinas

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José M. Barceló Ordinas


Titular d'Universitat (Associate Professor, acredited as Full Professor)
Departament d'Arquitectura de Computadors (DAC)
Statistical Analysis of Networks and Systems (SANS) Research Group included in CDNS Research Group
Universitat Politècnica de Catalunya (UPC)
Campus Nord, Mòdul C6, despatx 214
Jordi Girona, 1-3
08034 BARCELONA, Spain
e-mail-1: jose dot maria dot barcelo at upc dot edu
e-mail-2 (alias of the previous one): joseb at ac dot upc dot edu
Tel: +34 93 40 54051
Fax: +34 93 40 17055

Biography


I am graduated in Telecommunication Engineering (equivalent to BSc and MSc) at Universitat Politècnica de Catalunya (UPC) and got the PhD degree in Telecommunication Engineering at UPC in 1998. I am also graduated in Mathematics (equivalent to BSc and MSc) at UNED.

I now am Associate Professor, and I am acredited as Full Professor, at the Computer Architecture Department at UPC. I joined the Statistical Analysis of Networks and Systems (SANS) Research Group at the Computer Architecture Departament in 1993. The SANS research group, previously called CompNet , is a subgroup of the CNDS research group. From that year I have participated in several European projects such as EXPLOIT, BAF, EXPERT, NETPERF and MOEBIUS, WIDENS (FP6) and Networks of Excellence (NoE) EuroNGI (FP6), EuroNFI (FP6) and EuroNF (FP7) and H2020 CAPTOR.

In past years, I researched in Broadband Networks (my PhD was on ATM traffic control), TCP/IP protocol design, Internet Topology (BGPv4 and Internet taxonomy). I also worked in several areas related to ad hoc networks, such as ad hoc routing protocols, Wireless Sensor Networks (WSN), Smart Cities, Mobile Wireless Networks, Vehicular Networks and Delay/Disruptive Tolerant Networks. I have also worked on how to extract "human mobility patterns" (Smart Data) from mobility traces, e.g. from smartphone devices, IoT devices, etc., and on digital tracing contact protocols.

In recent years, I have been working on how to ensure the quality of data obtained in real air quality monitoring sensor networks, e.g. projects H2020 CAPTOR, SEMIOTIC (TIN2016-78473-C3-1-R), or IMAQ (PID2019-107910RB-I00). The problem arises when current low-cost sensors (LCSs) do not provide accurate data, due to the lack of calibration of low-cost air pollution sensors, or because measurements from LCSs have outliers, gaps (need for imputation of missing data), etc. LCS measurements have to follow a pipeline process to obtain good quality estimates. These sensors have to be calibrated using machine learning techniques. In addition, the sensors are quite sensitive to environmental conditions, so recalibration methods are needed. Some data are also lost or missing and signal reconstruction techniques need to be applied to predict gaps in the data, e.g., using the whole network and applying graphical signal processing (GSP) techniques. Other challenges in air quality monitoring sensor networks are the detection of outliers or the creation of proxies and virtual sensors. The techniques we use to ensure data quality are machine learning and deep learning techniques. Some of these tasks can be performed locally (in what we call the estimation layer) and others using neighbouring sensor nodes (in what we call the network-wide processing layer).

I am also collaborating with REPSOL S.A (January/2023-June/2025) in the project "Quantum Cognitive Digital Industry" (CDTI 2022 missions) in which we analyse the quality of data in IoT platforms using quantum techniques.

If you are interested in following a PhD in our research group, please, send me an email (jose dot maria dot barcelo at upc dot edu). We regularly have access to PhD grants.

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Research topics

Publications

International Projects

National Projects

Service

Teaching