@article {2975,
	title = {Brain Computer Interface Development Based on Recurrent Neural Networks and ANFIS Systems.},
	journal = {Soft Computing Applications in Optimization, Control and Recognition},
	volume = {294},
	year = {2013},
	chapter = {215},
	doi = {10.1007/978-3-642-35323-9_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-642-35323-9_9},
	author = {Emmanuel Morales-Flores and Ramirez-Cortes, J. and Gomez-Gil, P. and Alarcon-Aquino, V.}
}
@article {2977,
	title = {FPGA-based educational platform for real-time image processing experiments.},
	journal = {Computer Applications in Engineering Education},
	volume = {21},
	year = {2013},
	month = {03/2013},
	chapter = {193},
	abstract = {In this paper, an implementation of an educational platform for real-time linear and morphological image filtering using a FPGA NexysII, Xilinx{\textregistered}, Spartan 3E, is described. The system is connected to a USB port of a personal computer, which in that way form a powerful and low-cost design station for educational purposes. The FPGA-based system is accessed through a MATLAB graphical user interface, which handles the communication setup and data transfer. The system allows the students to perform comparisons between results obtained from MATLAB simulations and FPGA-based real-time processing. Concluding remarks derived from course evaluations and lab reports are presented.},
	keywords = {education, filtering, hardware, image, processing},
	doi = {10.1002/cae.20461},
	url = {http://www.researchgate.net/publication/227651194_FPGAbased_educational_platform_for_realtime_image_processing_experiments},
	author = {Ramirez-Cortes, J. and Martinez-Carballido and Alarcon-Aquino, V. and Gomez-Gil, P. and Emmanuel Morales-Flores}
}
@article {2976,
	title = {Mental Tasks Temporal Classification Using an Architecture Based on ANFIS and Recurrent Neural Networks.},
	journal = {Recent Advances on Hybrid Intelligent Systems},
	volume = {451},
	year = {2013},
	month = {2013},
	chapter = {135},
	abstract = {In this paper, an architecture based on adaptive neuro-fuzzy inference systems (ANFIS) assembled to recurrent neural networks, applied to the problem of mental tasks temporal classification, is proposed. The electroencephalographic signals (EEG) are pre-processed through band-pass filtering in order to separate the set of energy signals in alpha and beta bands. The energy in each band is represented by fuzzy sets obtained through an ANFIS system, and the temporal sequence corresponding to the combination to be detected, associated to the specific mental task, is entered into a recurrent neural networks. This experiment has been carried out in the context of brain-computer-interface (BCI) systems development. Experimentation using EEG signals corresponding to mental tasks exercises, obtained from a database available to the international community for research purposes, is reported. Two recurrent neural networks are used for comparison purposes: Elman network and a fully connected recurrent neural network (FCRNN) trained by RTRL-EKF (real time recurrent learning {\textendash} extended Kalman filter). A classification rate of 88.12\% in average was obtained through the FCRNN during the generalization stage.},
	isbn = {978-3-642-33020-9},
	doi = {10.1007/978-3-642-33021-6_11},
	url = {http://link.springer.com/chapter/10.1007\%2F978-3-642-33021-6_11},
	author = {Emmanuel Morales-Flores and Ramirez-Cortes, J. and Gomez-Gil, P. and Alarcon-Aquino, V.}
}
