Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged, blossomed, and have produced the-state-of-the-art in many domains. Due to the capability in feature extraction and mapping, it is very helpful to predict the high-frequency details lost in the low-resolution image. In this paper, we give an overview to recent advances on deep learning based models and methods that have been applied for single image super-resolution task. We also summarize, compare and discuss various models from the past, present for comprehensive understanding and finally provide open problems and the possible directions for future research.

In order to improve the distance teaching of minimally invasive surgery techniques, an integrated system has been developed. It comprises a telecommunications system, a server, a workstation, some medical peripherals and several computer applications developed in the Minimally Invasive Surgery Centre. The latest peripherals, such as robotized teleoperating systems for telesurgery and virtual reality peripherals, have been added. The visualization of the zone to be treated, along with the teacher's explanations, enables the student to understand the procedures of the operation much better.

This paper present a method for image segmentation, which is an adaptation of the classical active contours algorithm, also called "snakes", using a new internal energy approach. The classical model computes the energy function based on changes in gradient values, thus determining the detection of the object's edges. In the proposed model, the active contour moves attracted or repelled by its mass center, thus keeping the inertia towards shape compression or expansion. This represents an intuitive, simple and efficient scheme that constitutes an alternative to classical segmentation methods.

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