Detection of Pedestrians in Reverse Camera Using Multimodal Convolutional Neural Networks.

In recent years, the application of artificial intelligence (AI) in the automotive industry has led to the development of intelligent systems focused on road safety, aiming to improve protection for drivers and pedestrians worldwide to reduce the number of accidents yearly. One of the most critical functions of these systems is pedestrian detection, as it is crucial for the safety of everyone involved in road traffic. However, pedestrian detection goes beyond the front of the vehicle; it is also essential to consider the vehicle's rear since pedestrian collisions occur when the car is in reverse drive.

Machine Learning Model Based on Lipidomic Profile Information to Predict Sudden Infant Death Syndrome.

Sudden infant death syndrome (SIDS) represents the leading cause of death in under one year of age in developing countries. Even in our century, its etiology is not clear, and there is no biomarker that is discriminative enough to predict the risk of suffering from it. Therefore, in this work, taking a public dataset on the lipidomic profile of babies who died from this syndrome compared to a control group, a univariate analysis was performed using the Mann-Whitney test, with the aim of identifying the characteristics that enable discriminating between both groups.

FPGA embedded multichannel analyzer.

An multichannel analyzer has been designed, and its performance has been evaluated. The multichannel analyzer is embedded into a Field programmable gate array. The design incudes the virtual instrument in order to hand and to visualize the pulse height spectrum.