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The present work addresses the influence of the support on the catalytic behavior of Co3O4-based catalysts in the combustion of lean methane present in the exhaust gases from natural gas vehicular engines. Three different supports were selected, namely γ-alumina, magnesia and ceria and the corresponding catalysts were loaded with a nominal cobalt content of 30 wt. %. The samples were characterized by N2 physisorption, wavelength dispersive X-ray fluorescence (WDXRF), X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS) and temperature-programmed reduction with hydrogen and methane. The performance was negatively influenced by a strong cobalt-support interaction, which in turn reduced the amount of active cobalt species as Co3O4. Hence, when alumina or magnesia supports were employed, the formation of CoAl2O4 or Co-Mg mixed oxides, respectively, with a low reducibility was evident, while ceria showed a lower affinity for deposited cobalt and this remained essentially as Co3O4. Furthermore, the observed partial insertion of Ce into the Co3O4 lattice played a beneficial role in promoting the oxygen mobility at low temperatures and consequently the catalytic activity. This catalyst also exhibited a good thermal stability while the presence of water vapor in the feedstream induced a partial inhibition, which was found to be completely reversible.

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Over the last decades, wearable systems have gained interest for monitoring of physiological variables, promoting health, and improving exercise adherence in different populations ranging from elite athletes to patients. In this paper, we present a wearable system for the continuous real-time monitoring of respiratory frequency (fR), heart rate (HR), and movement cadence during physical activity. The system has been experimentally tested in the laboratory (by simulating the breathing pattern with a mechanical ventilator) and by collecting data from one healthy volunteer. Results show the feasibility of the proposed device for real-time continuous monitoring of fR, HR, and movement cadence both in resting condition and during activity. Finally, different synchronization techniques have been investigated to enable simultaneous data collection from different wearable modules.

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Over the past few years, Intelligent Spaces (ISs) have received the attention of many Wireless Sensor Network researchers. Recently, several studies have been devoted to identify their common capacities and to set up ISs over these networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks for the purpose of implementing ISs. This work presents a distributed architecture proposal for collaborative Fuzzy Rule-Based Systems embedded in Wireless Sensor Networks, which has been designed to optimize the implementation of ISs. This architecture includes the following: (a) an optimized design for the inference engine; (b) a visual interface; (c) a module to reduce the redundancy and complexity of the knowledge bases; (d) a module to evaluate the accuracy of the new knowledge base; (e) a module to adapt the format of the rules to the structure used by the inference engine; and (f) a communications protocol. As a real-world application of this architecture and the proposed methodologies, we show an application to the problem of modeling two plagues of the olive tree: prays (olive moth, Prays oleae Bern.) and repilo (caused by the fungus Spilocaea oleagina). The results show that the architecture presented in this paper significantly decreases the consumption of resources (memory, CPU and battery) without a substantial decrease in the accuracy of the inferred values.

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This work presents a new approach for collaboration among sensors in Wireless Sensor Networks. These networks are composed of a large number of sensor nodes with constrained resources: limited computational capability, memory, power sources, etc. Nowadays, there is a growing interest in the integration of Soft Computing technologies into Wireless Sensor Networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks. The objective of this work is to design a collaborative knowledge-based network, in which each sensor executes an adapted Fuzzy Rule-Based System, which presents significant advantages such as: experts can define interpretable knowledge with uncertainty and imprecision, collaborative knowledge can be separated from control or modeling knowledge and the collaborative approach may support neighbor sensor failures and communication errors. As a real-world application of this approach, we demonstrate a collaborative modeling system for pests, in which an alarm about the development of olive tree fly is inferred. The results show that knowledge-based sensors are suitable for a wide range of applications and that the behavior of a knowledge-based sensor may be modified by inferences and knowledge of neighbor sensors in order to obtain a more accurate and reliable output.

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