The adaptability and parallel computing capabilities of neural networks make them specially adequate for error correcting tasks. Feed forward neural networks for soft-decision decoding of block codes in channels with additive white gaussian noise are presented. When the noise is not white, we deduce the optimal set of weights for the connections of the network. These weights are also approximately obtain by an error back propagation algorithm. A practical realization for a BCH (7,4) code is presented, and exhaustive numerical simulations are performed on it.

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Genomic information is key for the implementation of real personalized medicine. Nevertheless, access to this kind of information must be controlled because of its high privacy and security requirements. Several genomic information formats exist, although we have started from MPEG-G as it includes metadata and protection mechanisms since its inception and provides a hierarchical structure to organize the information contained. The proposed GIPAMS modular architecture provides a secure and controlled access to genomic information, which may help on improving personalized medicine as described in this paper.

Many music works produced in the past still exist only as original manuscripts or as photocopies. Preserving them entails their digitalization and consequent accessibility in a digital format easy-to-manage. The manual process to carry out this task is very time consuming and error prone. Optical music recognition (OMR) is a form of structured document image analysis where music symbols are isolated and identified so that the music can be conveniently processed. While OMR systems perform well on printed scores, current methods for reading handwritten musical scores by computers remain far from ideal. One of the fundamental stages of this process is the staff line detection. In this paper a new method for the automatic detection of music stave lines based on a shortest path approach is presented. Lines with some curvature, discontinuities, and inclination are robustly detected. The proposed algorithm behaves favourably when compared experimentally with wellestablished algorithms.

To handle genomic information while supporting FAIR principles, we present GIPAMS, a modular architecture. GIPAMS provides security and privacy to manage genomic information by means of several independent services and modules that interact among them in an orchestrated way. The paper analyzes how some security and privacy aspects of the FAIRification process are covered by the GIPAMS platform.