Low energy compressive spectral imaging sensors
Mentor: Javier Castillo Villar
Email: javier.castillo@urjc.es
Phone: (+34) 916647496
University: Universidad Rey Juan Carlos
Partner Host Institution: N.A.
Keywords: Smart energy management systems, compressive sampling, Smart Grid, Security, Big data, High Performance Computing

Low energy compressive spectral imaging sensors

Specific Topic: Energy monitoring and control system
The systems based in spectral images has been proved as a very powerful tool in many different applications such as biotechnology, high precision agriculture, remote sensing, satellite image systems, artificial vision systems, among others (otras (Ref: https://resonon.com/applications) (Ref2: https://grindgis.com/remote-sensing/10-important-applications-of-hyperspectral-image).
However, these systems have the disadvantage of the requirement of a great amount of data during the sensing of the different spectral bands. This implies a great energy consumption and has driven in recent years the development of smart sensors that can sense the least possible data whilst obtaining good results.
This research line is devoted to the study and development of systems for sensing spectral images based on smart sensors. The main goal is to design sensing systems appropriate for each application but with the reduction of the energy consumption in the sensing process in mind and with computing systems that can analyze the data in all spectral bands efficiently

Departament: Computing Science, Computer Architecture, Programming Languages and Systems and Statistics and Operative Investigation
Research Group: GDHwSw
More Information:
Relevants projects on the area: Project name: Context & Inteligence, Sponsor: Ministerio de economía y competitividad, Ref: IPT-2012-0912-4300000, Duration: 01/05/2012 – 31/12/2014, Main Researcher: David Ríos
Relevants publications on the area: 1.- Autor: Jeison Marín Alfonso, Jose Ignacio Martínez Torre, Henry Arguello Fuentes, Leonardo Betancur Agudelo, Título: Compressive Multispectral Spectrum Sensing for Spectrum Cartography, Ref.: Sensors, Num , Vol: 18, Pag: 387-407, Fecha: 2018, ISSN: 1424-8220, DOI: 10.3390/s18020387, JCR 2017: 2,67
2.- Autor: Jeison Marín A. Jose I. Martinez T. Leonardo Betancur. Herny Arguello. Titulo: Compressive Multispectral Model for Spectrum Sensing in Cognitive Radio Networks. Ref: 25th European Signal Processing Conference (EUSIPCO). Pag: pp. 2575-2571. Fecha: 2017, ISSN: 2076-1465, ISBN: 978-0-9928626-7-1, doi: 10.23919/EUSIPCO.2017.8081675, Google Scholar index: 26, CORE 2017 Rating: B