About the research project «Compressive Sampling Based Acquisition Models For Secure Transmission and Reliable Data Recovery in Smart Grids»
Research line: Energy efficiency and renewable energy
Mentor: Prof. José Ignacio Martínez-Torre (URJC)
Smart grids belong to a class of energy management systems that allow the monitoring and control of the electrical power grid among the generation, transmission, distribution, up to the consumer meter. In order to offer efficient energy management, smart grids require high data transmission rates with nodes collecting a wide variety of information from heterogeneous and distributed network of sensors. Furthermore, this energy management modality should provide secure data transmission against no authorized operations. However, the large sizes of the power systems, as well as the diversity of distributed sensors, leads to high-dimensional data that challenge the storage and processing capabilities of the control stations. In this sense, compressive sampling has emerged as an acquisition paradigm that senses and compresses simultaneously the relevant information of the system under observation. In this context, the compressed data can be securely transmitted by continually updating the structure of the sensing matrix. Therefore, the development of a compressive sampling-based acquisition model is proposed which enables the reduction of the number of sensors in smart grids.
About Juan Marcos Ramírez Rondón
Juan Marcos Ramírez Rondón received the B.S. diploma in electrical engineering at the Universidad de Los Andes (ULA), Mérida, Venezuela, in 2002. In 2004, He joined as a teaching and research staff of the Electrical Engineering Department at ULA. He received the M.S. in biomedical engineering and the Doctor’s degree in applied sciences at ULA, in 2007 and 2017, respectively. His research experience is focused on the development of sparse signal representation algorithms based on maximum likelihood estimators. Furthermore, he worked in the proposal of recursive structures based on the weighted myriad filter.
Additionally, he worked as a postdoctoral intern at the High Dimensional Signal Processing (HDSP) Group, Bucaramanga, Colombia (2017-2018). In 2019, he worked at the HDSP group as a consultant for the UIS-ECOPETROL scientific and technological agreement. Under the advising of Dr. Henry Arguello Fuentes, the research focused on spectral image classification from compressive spectral imaging measurements.
Currently, he is working «Compressive Sampling Based Acquisition Models For Secure Transmission and Reliable Data Recovery in Smart Grids» project as a GET Cofund Fellow at the Universidad Rey Juan Carlos, together with the Hardware-Software Design (GDHwSw) Research group.
ResearchGate Profile: https://www.researchgate.net/profile/Juan_Ramirez35
Most recent publications
Ramirez, Juan Marcos, and Henry Arguello. «Spectral Image Classification From Multi-Sensor Compressive Measurements.» IEEE Transactions on Geoscience and Remote Sensing (2019).
Ramirez, Juan Marcos, and Henry Arguello. «Multiresolution Compressive Feature Fusion for Spectral Image Classification.» IEEE Transactions on Geoscience and Remote Sensing (2019).
Vargas, Hector, Juan Ramirez, and Henry Arguello. «ADMM-Based ℓ1− ℓ1 Optimization Algorithm For Robust Sparse Channel Estimation In OFDM Systems.» Signal Processing (2019): 107296.
Hinojosa, Carlos, Juan Marcos Ramirez, and Henry Arguello. «Spectral-Spatial Classification from Multi-Sensor Compressive Measurements Using Superpixels.» 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019.