Neural Network and Spatial Model to Estimate Sustainable Transport Demand in an Extensive Metropolitan Area

Design and Implementation of a Prosthesis System Controlled by Electromyographic Signals Means, Characterized with Artificial Neural Networks

Around the world many people loss a body member for many reasons, where advances of technology may be useful to help these people to improve the quality of their lives. Then, designing a technologically advanced prosthesis with natural movements is worthy for scientific, commercial, and social reasons. Thus, research of manufacturing, designing, and signal processing may lead up to a low-cost affordable prosthesis. This manuscript presents a low-cost design proposal for an electromyographic electronic system, which is characterized by a neural network based process. Moreover, a hand-type prosthesis is presented and controlled by using the processed electromyographic signals for a required particular use. For this purpose, the user performs several movements by using the healthy-hand to get some electromyographic signals. After that, the obtained signals are processed in a neural network based controller. Once an usable behavior is obtained, an exact replica of controlled motions are adapted for the other hand by using the designed prosthesis. The characterization process of bioelectrical signals was performed by training twenty characteristics obtained from the original raw signal in contrast with other papers in which seven characteristics have been tested on average. The proposed model reached a 95.2% computer test accuracy and 93% accuracy in a real environment experiment. The platform was tested via online and offline, where the best response was obtained in the online execution time.

Spatial Models and Neural Network for Identifying Sustainable Transportation Projects with Study Case in Querétaro, an Intermediate Mexican City

The construction of urban and transport indicators aims for a better diagnosis that enables technical and precise decision-making for the public administration or private investment. Therefore, it is common to make comparisons and observe which has better diagnosis results in a diversity of indexes and models. The present study made a comparative analysis of spatial models using artificial intelligence to estimate transport demand. To achieve this goal, the audit field was recollected in specific urban corridors to measure the indicators. A study case in Querétaro, an emergent city in the Mexican region known as El Bajío, is conducted. Two similar urban avenues in width and length and close to each other were selected to apply a group of spatial models, evaluating the avenues by segments and predicting the public transport demand. The resulting database was analyzed using Artificial Neural Networks. It displays specific indicators that have around 80% of correlations. The results facilitate the localization of the avenue segments with the most volume of activity, supporting interventions in urban renewal and sustainable transportation projects.