Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

New self-assembled supramolecular hydrogels based on dehydropeptides

Supramolecular hydrogels rely on small molecules that self-assemble in water as a result of the cooperative effect of several relatively weak intermolecular interactions. Peptide-based low molecular weight hydrogelators have attracted enormous interest owing to the simplicity of small molecules combined with the versatility and biocompatibility of peptides. In this work, naproxen, a well known non-steroidal anti-inflammatory drug, was N-conjugated with various dehydrodipeptides to give aromatic peptide amphiphiles that resist proteolysis. Molecular dynamics simulations were used to obtain insight into the underlying molecular mechanism of self-assembly and to rationalize the design of this type of hydrogelators. The results obtained were in excellent agreement with the experimental observations. Only dehydrodipeptides having at least one aromatic amino acid gave hydrogels. The characterization of the hydrogels was carried out using transmission electron microscopy (TEM), circular dichroism (CD), fluorescence spectroscopy and also rheological assays.

Experimental validation of a proposed single-phase five-level active rectifier operating with model predictive current control

This paper presents a model predictive current control applied to a proposed single-phase five-level active rectifier (FLAR). This current control strategy uses the discrete-time nature of the active rectifier to define its state in each sampling interval. Although the switching frequency is not constant, this current control strategy allows to follow the reference with low total harmonic distortion (THDF). The implementation of the active rectifier that was used to obtain the experimental results is described in detail along the paper, presenting the circuit topology, the principle of operation, the power theory, and the current control strategy. The experimental results confirm the robustness and good performance (with low current THDF and controlled output voltage) of the proposed single-phase FLAR operating with model predictive current control.