Juan Antonio Alvarez García

Reconfigurable distributed network control system for industrial plant automation

Use of advanced communication technologies, highly integrated control, and programming platforms drastically increases the performance of industrial control systems. That is the case of Motronic, where the synergistic collaboration between industry and academia has led to an advanced distributed network control system. To be commercially successful, it needs to have a low cost and to be robust, even if this requirement implies that it is a custom design and not based on previously existing commercial solutions. Use of standards and off-the-shelf products lower development costs, but usually raise production costs. In this paper, we show that, in certain applications, design of a new system from scratch is more advantageous. This system comprises a set of dynamically reconfigurable local controller nodes, a graphical programming environment, a remote supervision and control system, and a fault-tolerant fiber optical network. TCP/IP connectivity is provided by the use of a local gateway. Motronic is currently being applied in the integrated control of large production plants and in energy and power management industries.

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Abstract Currently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required.

Advertising Liking Recognition Technique Applied to Neuromarketing by Using Low-Cost EEG Headset

In this paper a new neuroscience technique is applied into Marketing, which is becoming commonly known as the field of Neuromarketing. The aim of this paper is to recognize how brain responds during the visualization of short advertising movies. Using low cost electroencephalography (EEG), brain regions used during the presentation have been studied. We may wonder about how useful it is to use neuroscience knowledge in marketing, what can neuroscience add to marketing, or why use this specific technique. By using discrete techniques over EEG frequency bands of a generated labeled dataset, C4.5 and ANN learning methods have been applied to obtain the score assigned to each ads by the user. This techniques allows to reach more than 82% of accuracy, which is an excellent result taking into account the kind of low-cost EEG sensors used.

Discrete Fluid Samples Microextractor

A discrete fluid samples microextractor is presented in this paper. The main advantage of this device is that the extraction is controlled, not only in time but also in fluid volume. A one shot valve is in charge of extracting a certain quantity of fluid in the fixed moment. It is designed to be integrated in array, so the final design is composed by several modules which are activated sequentially to extract fluid samples throughout the time. It is manufactured in polymer to reduce its cost Experimental results that prove feasibility of design are commented.

Photovoltaic and fuel cells in power microelectromechanical systems for smart energy management

Abstract Photovoltaic mini-generators and fuel cells are the most feasible alternatives for applications where a high-density micropower supply is needed. This chapter will focus on photovoltaic and fuel cell technologies in powering microelectromechanical systems for smart energy management, such as bioimplantable devices or portable electronic devices. Sun sensors are devices that are being newly introduced into photovoltaic systems to improve their performance. The chapter ends with a discussion of the operating principle of these sensors and their role within the photovoltaic system is described.