Dataset for detecting motorcyclists in pedestrian areas.

This paper presents a semi-automated, scalable, and homologous methodology towards IoT implemented in Python for extracting and integrating images in pedestrian and motorcyclist areas on the road for constructing a multiclass object classifier. It consists of two stages. The first stage deals with creating a non-debugged data set by acquiring images related to the semantic context previously mentioned, using an embedded device connected 24/7 via Wi-Fi to a free and public CCTV service in Medellin, Colombia. Through artificial vision techniques, and automatically performs a comparative chronological analysis to download the images observed by 80 cameras that report data asynchronously. The second stage proposes two algorithms focused on debugging the previously obtained data set. The first one facilitates the user in labeling the data set not debugged through Regions of Interest (ROI) and hotkeys. It decomposes the information in the nth image of the data set in the same dictionary to store it in a binary Pickle file. The second one is nothing more than an observer of the classification performed by the user through the first algorithm to allow the user to verify if the information contained in the Pickle file built is correct.

Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction.

Diabetes Mellitus is a serious metabolic condition for global health associations. Recently, the number of adults, adolescents and children who have developed Type 1 Diabetes Mellitus (T1DM) has increased as well as the mortality statistics related to this disease. For this reason, the scientific community has directed research in developing technologies to reduce T1DM complications. This contribution is related to a feedback control strategy for blood glucose management in population samples of ten virtual adult subjects, adolescents and children. This scheme focuses on the development of an inverse optimal control (IOC) proposal which is integrated by neural identification, a multi-step prediction (MSP) strategy, and Takagi-Sugeno (T-S) fuzzy inference to shape the convenient insulin infusion in the treatment of T1DM patients. The MSP makes it possible to estimate the glucose dynamics 15 min in advance; therefore, this estimation allows the Neuro-Fuzzy-IOC (NF-IOC) controller to react in advance to prevent hypoglycemic and hyperglycemic events. The T-S fuzzy membership functions are defined in such a way that the respective inferences change basal infusion rates for each patient's condition. The results achieved for scenarios simulated in Uva/Padova virtual software illustrate that this proposal is suitable to maintain blood glucose levels within normoglycemic values (70-115 mg/dL); furthermore, this level remains less than 250 mg/dL during the postprandial event. A comparison between a simple neural IOC (NIOC) and the proposed NF-IOC is carried out using the analysis for control variability named CVGA chart included in the Uva/Padova software. This analysis highlights the improvement of the NF-IOC treatment, proposed in this article, on the NIOC approach because each subject is located inside safe zones for the entire duration of the simulation.