A Self-Adaptive Automatic Incident Detection System for Road Surveillance Based on Deep Learning.

We present an automatic road incident detector characterised by a low computational complexity for easy implementation in affordable devices, automatic adaptability to changes in scenery and road conditions, and automatic detection of the most common incidents (vehicles with abnormal speed, pedestrians or objects falling on the road, vehicles stopped on the shoulder, and detection of kamikaze vehicles). To achieve these goals, different tasks have been addressed: lane segmentation, identification of traffic directions, and elimination of unnecessary objects in the foreground. The proposed system has been tested on a collection of videos recorded in real scenarios with real traffic, including areas with different lighting.

Application of Mixed Reality to Ultrasound-guided Femoral Arterial Cannulation During Real-time Practice in Cardiac Interventions.

Mixed reality opens interesting possibilities as it allows physicians to interact with both, the real physical and the virtual computer-generated environment and objects, in a powerful way. A mixed reality system, based in the HoloLens 2 glasses, has been developed to assist cardiologists in a quite complex interventional procedure: the ultrasound-guided femoral arterial cannulations, during real-time practice in interventional cardiology. The system is divided into two modules, the transmitter module, responsible for sending medical images to HoloLens 2 glasses, and the receiver module, hosted in the HoloLens 2, which renders those medical images, allowing the practitioner to watch and manage them in a 3D environment.

A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties.

Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products.

An Insight of Deep Learning Based Demand Forecasting in Smart Grids.

Smart grids are able to forecast customers' consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand.