Jesús Antonio Álvarez-Cedillo

DC Motor Speed Control via a DC to DC Buck Power Converter

This paper presents a hierarchical control system designed for permanent DC motor powered by a DC/DC Buck converter. Hierarchical controller based on differential flatness property of the plant system is meant to control the angular speed for tracking trajectories applications. This controller provides the voltage profiles that have to be tracked by the Buck converter and consequently a low hierarchy controller is designed to ensure the aforementioned. The low hierarchy controller goal is accomplished by using another control law based on differential flatness that controls the Buck converter. The contribution is validated through numerical simulations.

Implementation Strategy of NDVI Algorithm with Nvidia Thrust

The calculation of Normalized Difference Vegetation Index (NDVI) has been studied in literature by multiple authors inside the remote sensing field and image processing field, however its application in large image files as satellite images restricts its use or need preprocessed phases to compensate for the large amount of resources needed or the processing time. This paper shown the implementation strategy to calculates NDVI for satellite images in RAW format, using the benefits of economic Supercomputing that were obtained by the video cards or Graphics Processing Units (GPU). Our algorithm outperforms other works developed in NVIDIA CUDA, the images used were provided by NASA and taken by Landsat 71 located on the Mexican coast, Ciudad del Carmen, Campeche.

Circular object recognition from satellite images

Pattern recognition has been the object of interest for many researchers in different fields. In remote sensing, geo-spatial patterns such as circular structures can be observed from satellite imagery and aerial photographs. Recognizing circular structures is difficult in remote sensing imagery. In this article, a detection method of circular forms in satellite image is investigated. Based on Adaboost and Haar features, the method is a machine learning technique. Training data sets are extracted from GoogleEarth. Experiments and results are provided.

Adaboost classifier by artificial immune system model

An algorithm combining Artificial Immune System and AdaBoost called Imaboost is proposed to improve the feature selection and classification performance. Adaboost is a machine learning technique, which generates a strong classifier as a combination of simple classifiers. In Adaboost, through learning, the search for the best simple classifiers is replaced by the clonal selection algorithm. Haar features extracted from face database are chosen as a case study. A comparison between Adaboost and Imaboost is provided.