IMPORTANCE OF PHYSICAL ACTIVITY AND ITS IMPACT ON THE CARDIOVASCULAR PART

A documentary review was carried out on the production and publication of research papers related to the study of the variables Radio Media and Social Networks. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2017-2022. The information provided by this platform was organized through graphs and figures categorizing the information by the Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors on the proposed theme is referenced through a qualitative analysis. Among the main findings made through this research, it is found that India with 15 publications, was the country with the highest scientific production registered on behalf of authors affiliated with institutions of that nation. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material referring to the study of impact headlines in the Radio Media and their Social Networks was Computer Science with 32 published documents, and the Type of Publication that was most used during the period indicated above was the Journal Article that represents 55% of the total scientific production

SMART TRAFFIC SIGNAL CONTROL SYSTEM FOR TWO INTER-DEPENDENT INTERSECTIONS IN AKURE, NIGERIA

The increasing growth in urban population and vehicular volume coupled with inefficient traffic management results in traffic congestion on road networks. In this work, a smart/intelligent traffic signal control system was developed for two inter-dependent intersections in Akure, Nigeria. The system developed in this work uses deep learning and computer vision techniques to estimate the density of traffic and uses this information to adaptively switch traffic signals based on the traffic density estimated. Simulation results show that in 30 minutes of simulation, 32 signal cycles can be achieved and 967 vehicles can move at these two inter-dependent intersection

SMART TRAFFIC SIGNAL CONTROL SYSTEM FOR TWO INTER-DEPENDENT INTERSECTIONS IN AKURE, NIGERIA

The increasing growth in urban population and vehicular volume coupled with inefficient traffic management results in traffic congestion on road networks. In this work, a smart/intelligent traffic signal control system was developed for two inter-dependent intersections in Akure, Nigeria. The system developed in this work uses deep learning and computer vision techniques to estimate the density of traffic and uses this information to adaptively switch traffic signals based on the traffic density estimated. Simulation results show that in 30 minutes of simulation, 32 signal cycles can be achieved and 967 vehicles can move at these two inter-dependent intersection

An Unsupervised Learning Model for Deformable Medical Image Registration

We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for each pair of images, which can be time-consuming for large data. We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest. Given a new pair of scans, we can quickly compute a registration field by directly evaluating the function using the learned parameters. We model this function using a convolutional neural network (CNN), and use a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field. The proposed method does not require supervised information such as ground truth registration fields or anatomical landmarks. We demonstrate registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice. Our method promises to significantly speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is available at https://github.com/balakg/voxelmorph .Comment: 9 pages, in CVPR 201