I currently work as Technical Lead at ScioSense, where I am responsible for the development of the latest generation of chemical sensors for automotive applications, home automation, and consumer electronics. I did my PhD and postdoc research at the Signal and Information Processing for Sensing Systems group of the Institute for BioEngineering of Catalonia. I also worked as associate professor at the University of Barcelona.
PhD in Engineering and Applied Sciences, 2019
University of Barcelona
MSc in Data Science and Big Data, 2015
University of Barcelona
MSc in Computer Science, 2013
University of Southern California
BSc in Telecommunication Engineering, 2010
University Autonoma of Madrid
MATLAB, Python, C, C++, Java, LabVIEW
SQL, Oracle, DBMS
Leadership, Team work, Communication
Resilience, Commitment, Creativity
I taught the following courses of the MSc in Biomedical and Telecommunication Engineering:
A nano-drone with olfaction capabilities for gas source localization and mapping.
Reduction of power consumption in MOX sensors by pulsed temperature operation (PTO) and signal processing.
A drone with olfaction capabilities to quantify malodours produced by wastewater treatment plants (WWTPs).
SomnoAlert is an advanced driver-assistance system (ADAS) that identifies inadequate driving states related to driving quality.
Smartplane project intends to create an aircraft that isn’t just fun to fly, but one that will make personal flight a viable mode of transportation.
Geographic information system (GIS) for locating fire hydrants and buildings on fire on a university campus.
Recent advances in miniaturization of chemical instrumentation and in low-cost small drones are catalyzing exponential growth in the use of such platforms for environmental chemical sensing applications. The versatility of chemically sensitive drones is reflected by their rapid adoption in scientific, industrial, and regulatory domains, such as in atmospheric research studies, industrial emission monitoring, and in enforcement of environmental regulations. As a result of this interdisciplinarity, progress to date has been reported across a broad spread of scientific and non-scientific databases, including scientific journals, press releases, company websites, and field reports. The aimof this paper is to assemble all of these pieces of information into a comprehensive, structured and updated reviewof the field of chemical sensing using small drones.We exhaustively review current and emerging applications of this technology, as well as sensing platforms and algorithms developed by research groups and companies for tasks such as gas concentration mapping, source localization, and flux estimation. We conclude with a discussion of the most pressing technological and regulatory limitations in current practice, and how these could be addressed by future research.
This paper describes the design of a low-pass differentiator filter with linear phase and finite impulse response (FIR) for extracting transient features of gas sensor signals (the so-called bouts) which are relevant for accurately estimating the source-receptor distance in a turbulent plume. Our current proposal addresses the shortcomings of previous bout estimation methods, namely: (i) they were based in non-causal digital filters precluding real time operation, (ii) they used non-linear phase filters leading to waveform distortions and (iii) the smoothing action was achieved by two filters in cascade, precluding an easy tuning of filter performance. The presented filter preserves the signal waveform in the bandpass region for maximum reliability concerning both bout detection and amplitude estimation. Thanks to its FIR design, the filter can be implemented with nonrecursive structures, thus being inherently stable and allowing an easy algorithmic implementation and optimization. As a case study, we apply the proposed filter to predict the source-receptor distance from recordings obtained with a metal oxide (MOX) gas sensor in a wind tunnel. We demonstrate that proper tuning of the proposed filter can reduce the prediction error to 8 cm (in a distance range of 1.45 m) improving previously reported performances in the same dataset by a factor of 2.5. The performance of bout-based features are also benchmarked against traditional source-receptor distance estimators such as the mean, variance and maximum of the response. We also study how the length of the measurement window affects the performance of different signal features and how to tune the filter parameters to make the predictive models insensitive to wind speed. A MATLAB implementation of the proposed filter and all analysis code used in this study is provided.