Hospital automation system RFID-based: technology embedded in smart devices (cards, tags and bracelets).

RFID is a technology being adopted in many business fields, especially in the medical field. This work has the objective to present a system for automation of a hospital clinical analysis laboratory. This system initially uses contactless smart cards to store patient's data and for authentication of hospital employees in the system. The proposed system also uses RFID tags stuck to containers containing patient's collected samples for the correct identification of the patient who gave away the samples. This work depicts a hospital laboratory workflow, presents the system modeling and deals with security matters related to information stored in the smart cards.

MP-HA: multicycles protocol for hospital automation over multicast addressing.

This paper presents a Multicycles Protocol for Hospital Automation (MP-HA) that works over multicast addressing and uses a Master-Slave architecture. The protocol creates a segmented logical network based on multicast addressing associated to hospital beds. The objective of MP-HA is to ensure the determinism on network through medium access control mechanism increasing the transmission throughput. Thus, it creates a periodical environment making use of parallel cycles which is called multicycles.

An extended model for a spiking neuron class.

This paper proposes an extension to the model of a spiking neuron for information processing in artificial neural networks, developing a new approach for the dynamic threshold of the integrate-and-fire neuron. This new approach invokes characteristics of biological neurons such as the behavior of chemical synapses and the receptor field. We demonstrate how such a digital model of spiking neurons can solve complex nonlinear classification with a single neuron, performing experiments for the classical XOR problem. Compared with rate-coded networks and the classical integrate-and-fire model, the trained network demonstrated faster information processing, requiring fewer neurons and shorter learning periods. The extended model validates all the logic functions of biological neurons when such functions are necessary for the proper flow of binary codes through a neural network.