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This article describes the design, development and implementation of a set of microservices based on an architecture that enables detection and assisted clinical diagnosis within the field of infectious diseases of elderly patients, via a telemonitoring system. The proposed system is designed to continuously update a medical database fed with vital signs from biosensor kits applied by nurses to elderly people on a daily basis. The database is hosted in the cloud and is managed by a flexible microservices software architecture. The computational paradigms of the edge and the cloud were used in the implementation of a hybrid cloud architecture in order to support versatile high-performance applications under the microservices pattern for the pre-diagnosis of infectious diseases in elderly patients. The results of an analysis of the usability of the equipment, the performance of the architecture and the service concept show that the proposed e-health system is feasible and innovative. The system components are also selected to give a cost-effective implementation for people living in disadvantaged areas. The proposed e-health system is also suitable for distributed computing, big data and NoSQL structures, thus allowing the immediate application of machine learning and AI algorithms to discover knowledge patterns from the overall population., European Commission

One of main contributions to the consumption of energy in buildings is the heating, ventilation and air condition (HVAC) system. In fact, it consumes more than half of the energy. Multi-HVAC systems can be seen as an intermediate level of modeling, composed by different HVAC subsystems (e.g. boilers, chillers, heat pumps, among others), that define their operations and enabling the simulation of their behavior at a given time. The main purpose is to develop effective control systems with multi-objective optimization to improve the energy efficiency and the reduction of greenhouse gas (GHG) emissions in the cities. This article proposes the simulation of the behavior and operations of different HVAC subsystems in a particular building making use of physical models. The objective is achieved with different approaches, taking the functional specifications provided by the vendors of the HVAC subsystems.

This article proposes a control strategy based on the constrained generalized predictive control (GPC) in combination with a geometric attitude controller for trajectory tracking of the quadcopter vehicle. The generalized predictive control approach allows tracking time varying trajectories using optimal control actions to meet constraints on the desired trajectory. The geometric attitude controller tracks the reference attitude defined by the generalized predictive controller. The proposed controller is tested experimentally in a low- cost quadcopter instrumented with an attitude and heading reference system and an infrared camera based positioning system.

This article addresses the problem of quadrotors following a path geometry under the action of a constant external disturbance and with partial state measurement. To solve these issues, under the assumption of time scale separation between the translational dynamics and the closed-loop rotational dynamics, we propose an integral output robust maneuvering controller in combination with a translational velocity observer and an external disturbance estimator. Experimental results demonstrate the performance of the proposed control algorithm.

A new nonlinear passivity based controller for a diesel internal combustion engine with variable geometry turbocharger system is presented. The controller design uses three input-output pairs. An input-output pair is used to stabilize the zero dynamics of the system and for the other two pairs controllers are designed such that a two-inputs two-outputs passive systems that allows output tracking is obtained. The proposed controller is applied to the nonlinear multivariate model diesel engine and its performance is verified through simulations.

A new power flow split strategy for hybrid vehicles based on generalized predictive control (GPC) is presented. A velocity tracking problem, together with the minimization of fuel consumption and constraints in batteries state of charge and power sources limits, is solved without requiring any a priori knowledge of power use. To allow real-time computation, the proposed strategy uses simplified models of a diesel-electric hybrid vehicle. Simulation results are presented that confirm good performance of the strategy for three different driving cycles.

A controller for a turbocharged diesel engine is designed by feedback linearization and constrained generalized predictive control (GPC). The complete diesel engine-turbocharger model is posed in such a way that GPC is applicable to control engine speed, air-fuel ratio (AFR) and exhaust gas recirculated fraction (EGR). The designed controller is simulated over a driving cycle obtaining good tracking performance.

This work introduces a new technique of robust compression on signals and images, known as Compressive Sensing (CS). It is a new and advanced technique which can reconstruct sparse signals from a few random acquired samples, achieved to avoid the Nyquist's criteria. Reconstruction of a signal with just few data is a hard task converted in a lineal optimization process with various ways to find out the solution. Widely-used for research at present, CS is a useful tool of sampling/compression that only works with sparse signals, moreover we were able to implement CS in some kind of signals and images, but it is necessary to "rewrite it" according to a few meaningful terms, which can be obtained using properties time/frequency/energy, etc. The derived, cosine discrete transform (DCT), Fourier and Wavelet analysis are some of the tools to sparse convertion of signals and images.

In silico modeling of Gene Regulatory Networks has gained a lot of attention recently as it gives a very powerful tool to experimental biologists to gather the knowledge gained from different biological experiments and understand the dynamics of the overall system. One of the key dynamics that is often interesting is the steady states of the networks which biologically corresponds to the cellular states. In our previous paper, we gave an efficient method called GenYsis to compute these steady states in Boolean representation of Gene Regulatory Network. It has been observed that protein may be expressed at more then two level of expression. This may result in different cellular outcomes. To address this issue, we present here a multiple-level modeling methodology that allows us to be more accurate. In this paper we extend our software GenYsis to model gene regulatory networks where each node in the network may take multiple values.