@article {772,
	title = {Characterisation of Received Signal Strength Perturbations using Allan Variance},
	journal = {IEEE Transactions on Aerospace and Electronic Systems},
	volume = {54},
	year = {2018},
	pages = {873-889},
	chapter = {873},
	author = {Luo, Chunbo and Pablo Casaseca-de-la-Higuera and McClean, Sally and Parr, Gerard and Ren, Peng}
}
@conference {800,
	title = {Compressed UAV sensing for flood monitoring by solving the continuous travelling salesman problem over hyperspectral maps},
	booktitle = {Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018},
	year = {2018},
	publisher = {International Society for Optics and Photonics},
	organization = {International Society for Optics and Photonics},
	author = {Pablo Casaseca-de-la-Higuera and Antonio Trist{\'a}n-Vega and Hoyos-Barcel{\'o}, Carlos and S. Merino-Caviedes and Wang, Qi and Luo, Chunbo and Wang, Xinheng and Wang, Zhi}
}
@conference {martin2012cardiovascular,
	title = {Cardiovascular signal reconstruction based on shape modelling and non-stationary temporal modelling},
	booktitle = {Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European},
	year = {2012},
	pages = {1826{\textendash}1830},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Diego Mart{\'\i}n-Mart{\'\i}nez and Pablo Casaseca-de-la-Higuera and Marcos Martin-Fernandez and Carlos Alberola-Lopez}
}
@proceedings {515,
	title = {Characterization of activity epochs in actimetric registries for infantile colic diagnosis: Identification and feature extraction based on wavelets and symbolic dynamics},
	volume = {32},
	year = {2010},
	pages = {2383-2386},
	publisher = {IEEE},
	author = {Diego Mart{\'\i}n-Mart{\'\i}nez and Pablo Casaseca-de-la-Higuera and Gonzalo Vegas-S{\'a}nchez-Ferrero and Lucilio Cordero-Grande and Jesus Maria Andres-de-Llano and Jose Ramon Garmendia-Leiza and Julio Ardura-Fernández}
}
@proceedings {casaseca2006comparative,
	title = {A comparative study on microcalcification detection methods with posterior probability estimation based on Gaussian mixture models},
	year = {2005},
	pages = {49{\textendash}54},
	publisher = {IEEE},
	abstract = {Automatic detection of microcalcifications in mammograms constitutes a helpful tool in breast cancer diagnosis. Radiologist{\textquoteright}s confidence level on microcalcification detection would be improved if a probability estimate of its presence could be obtained from computer-aided diagnosis. In this paper we explore detection performance of a simple Bayesian classifier based on Gaussian mixture probability density functions (pdf). Posterior probability of microcalcification presence may be estimated from the probabilistic model. Two model selection algorithms have been tested, one based on the minimum message length criterion and the other on discriminative criteria obtained from the classifier performance. In addition, we propose a complementing model selection algorithm in order to improve the initial system performance obtained with these methods. Simulation results show that our model gets a good compromise between classification performance and probability estimation accuracy},
	doi = {https://doi.org/10.1109/IEMBS.2005.1616339},
	url = {https://ieeexplore.ieee.org/abstract/document/1616339},
	author = {Pablo Casaseca-de-la-Higuera and J I Arribas and Emma Mu{\~n}oz-Moreno and Carlos Alberola L{\'o}pez}
}
