I am a senior post-doctoral researcher specializing in digital signal analysis and processing. My background is in electronic engineering, my expertise areas include experimental data acquisition, hardware/software interfacing, embedded systems design, neural network analysis, programming in Matlab, C/C++, Handel-C and assembler.
Currently, I work at the Institute of Biomedical Engineering (IBME), at the Department of Engineering Science, Oxford University. My project is focused on an implantable sensor for patients with Chronic Cardiac Failure (CHF). Previously, I worked for more than 10 years at the Invensys UTC for Advanced Instrumentation, which is also part of the Department of Engineering Science, where I carried out research and software development for the design of self-validating instruments. I was in charge of the instruments’ hardware/software interface, and dealt with signal acquisition and processing issues in embedded designs based on a PC processor and one or two field-programmable gate arrays (FPGA). The group collectively won the IET Measurement prize in 2007, for the ‘Digital Coriolis Mass Flow Metering’. I have been involved in the department’s teaching activities, usually as laboratory demonstrator in control and maths, but also as a college tutor in electronics and control for engineering students.
I completed a DPhil at Oxford University in 2001, under the supervision of Prof Lionel Tarassenko, head of the Biomedical Signal Processing group, at the Department of Engineering Science. During my DPhil, my research focused on signal processing, autoregressive modelling and neural network analysis of the electroencephalographic signal (EEG). My project was related with the detection of micro-arousals in sleep-EEG of patients with obstructive sleep apnoea syndrome (OSAS) and the estimation of their alertness level while performing a vigilance task. Before coming to Oxford, I worked as a university lecturer in the Engineering Faculty at Universidad Central de Venezuela, where my research involved topographic mapping of the brain based on 8-channel scalp EEG signals.
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