Design and validation of a preliminary instrument to contextualize interactions through information technologies of health professionals.

The communication of e-Health has been transformed with the advancement of information technologies, therefore it is feasible to carry out studies in the context of health professionals' interactions. Objective: This study aimed to design and validate a preliminary questionnaire to investigate the context of the communications of health professionals through information technologies considering three significant dimensions. Method: The stages provided by Hernández Sampieri guided the building, validation through Cronbach's alpha and factorial analysis. The questionnaire was applied to 43 participants who simulated health professionals. Results: We obtained an instrument that includes a demographic data section and 20 items distributed into three factors. Internal consistency reliability with Cronbach's alpha values generally of 0.848 and higher than 0.811 was obtained in each dimension. Kaiser-Meyer-Olkin's measure of sampling adequacy was regular, with 0.781, and Bartlett's test of sphericity was significant (p < 0.001). Conclusion: It is necessary to apply in real-world environments to reaffirm the results obtained.

EEG Feature Extraction Using Evolutionary Algorithms for Brain-Computer Interface Development.

Brain-computer interfaces are systems capable of mapping brain activity to specific commands, which enables to remotely automate different types of processes in hardware devices or software applications. However, the development of brain-computer interfaces has been limited by several factors that affect their performance, such as the characterization of events in brain signals and the excessive processing load generated by the high volume of data. In this paper, we propose a method based on computational intelligence techniques to handle these problems, turning them into a single optimization problem. An artificial neural network is used as a classifier for event detection, along with an evolutionary algorithm to find the optimal subset of electrodes and data points that better represents the target event. The obtained results indicate our approach is a competitive and viable alternative for feature extraction in electroencephalograms, leading to high accuracy values and allowing the reduction of a significant amount of data.

Implementation and integration of image processing blocks in a real-time bottle classification system.

A practical solution to the problems caused by the water, air, and soil pollution produced by the large volumes of waste is recycling. Plastic and glass bottle recycling is a practical solution but sometimes unfeasible in underdeveloped countries. In this paper, we propose a high-performance real-time hardware architecture for bottle classification, that process input image bottles to generate a bottle color as output. The proposed architecture was implemented on a Spartan-6 Field Programmable Gate Array, using a Hardware Description Language. The proposed system was tested for several input resolutions up to 1080 p, but it is flexible enough to support input video resolutions up to 8 K. There is no evidence of a high-performance bottle classification system in the state-of-the-art. The main contribution of this paper is the implementation and integration of a set of dedicated image processing blocks in a high-performance real-time bottle classification system. These hardware modules were integrated into a compact and tunable architecture, and was tested in a simulated environment. Concerning the image processing algorithm implemented in the FPGA, the maximum processing rate is 60 frames per second. In practice, the maximum number of bottles that can be processed would be limited by the mechanical aspects of the bottle transportation system.

On-Device Learning of Indoor Location for WiFi Fingerprint Approach.

Indoor positioning is a recent technology that has gained interest in industry and academia thanks to the promising results of locating objects, people or robots accurately in indoor environments. One of the utilized technologies is based on algorithms that process the Received Signal Strength Indicator (RSSI) in order to infer location information without previous knowledge of the distribution of the Access Points (APs) in the area of interest. This paper presents the design and implementation of an indoor positioning mobile application, which allows users to capture and build their own RSSI maps by off-line training of a set of selected classifiers and using the models generated to obtain the current indoor location of the target device. In an early experimental and design stage, 59 classifiers were evaluated, using data from proposed indoor scenarios. Then, from the tested classifiers in the early stage, only the top-five classifiers were integrated with the proposed mobile indoor positioning, based on the accuracy obtained for the test scenarios. The proposed indoor application achieves high classification rates, above 89%, for at least 10 different locations in indoor environments, where each location has a minimum separation of 0.5 m.

An FPGA implementation to detect selective cationic antibacterial peptides.

Exhaustive prediction of physicochemical properties of peptide sequences is used in different areas of biological research. One example is the identification of selective cationic antibacterial peptides (SCAPs), which may be used in the treatment of different diseases. Due to the discrete nature of peptide sequences, the physicochemical properties calculation is considered a high-performance computing problem. A competitive solution for this class of problems is to embed algorithms into dedicated hardware. In the present work we present the adaptation, design and implementation of an algorithm for SCAPs prediction into a Field Programmable Gate Array (FPGA) platform. Four physicochemical properties codes useful in the identification of peptide sequences with potential selective antibacterial activity were implemented into an FPGA board. The speed-up gained in a single-copy implementation was up to 108 times compared with a single Intel processor cycle for cycle. The inherent scalability of our design allows for replication of this code into multiple FPGA cards and consequently improvements in speed are possible. Our results show the first embedded SCAPs prediction solution described and constitutes the grounds to efficiently perform the exhaustive analysis of the sequence-physicochemical properties relationship of peptides.