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synthesis and Characterization of Organic Bio-Absorbents Coming from sugarcane Bagasse

Journal of surface Engineered Materials and Advanced Technology 2016年第3期6卷 125-133页

作者： Israel Hernández Romero Juan Rodrigo Laguna Camacho Raúl enrique Contreras Bermúdez Francisca sandoval Reyes Erika Gaona santiago Celia Marí a Calderón Ramón Lizeth Rí os Velasco jesús enrique escalante martí nez Facultad de Ciencias Quí micas de la Universidad Veracruzana Tuxpan M&eacute xico Facultad de Ingenierí a Mecánica de la Universidad Veracruzana Tuxpan M&eacute xico

In this work, a modification of the sugarcane bagasse is performed, in order to obtain organic catalysts. The bagasse analysis is performed using X-ray diffraction (XRD) and Fourier Transformer Infrared spectroscopy (FTIR), which indicated that characteristic peaks determined its chemical compounds. In addition, scanning Electron Microscopy (sEM) is used to know the morphology. Finally, a discoloration test is conducted on an azo compound (methylene blue) in an aqueous medium, obtaining an efficiency of 98.6%.

关键词： Organic Bio-Absorbents Natural and Modified sugarcane Bagasse Methylene Blue sugarcane Bagasse Characterization

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Compound Hidden Markov Model for Activity Labelling

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International Journal of Intelligence science 2015年第5期5卷 177-195页

作者： Jose Israel Figueroa-Angulo jesus savage Ernesto Bribiesca Boris escalante Luis enrique sucar Biorobotics Laboratory Universidad Nacional Autonoma de Mexico Mexico City Mexico Computer science Department Universidad Nacional Autonoma de Mexico Mexico City Mexico Electrical Engineer Department Universidad Nacional Autonoma de Mexico Mexico City Mexico Computer science Department Instituto Nacional de Astrofisica Optica y Electronica Puebla Mexico

This research presents a novel way of labelling human activities from the skeleton output computed from RGB-D data from vision-based motion capture systems. The activities are labelled by means of a Compound Hidden Markov Model. The linkage of several Linear Hidden Markov Models to common states, makes a Compound Hidden Markov Model. Each separate Linear Hidden Markov Model has motion information of a human activity. The sequence of most likely states, from a sequence of observations, indicates which activities are performed by a person in an interval of time. The purpose of this research is to provide a service robot with the capability of human activity awareness, which can be used for action planning with implicit and indirect Human-Robot Interaction. The proposed Compound Hidden Markov Model, made of Linear Hidden Markov Models per activity, labels activities from unknown subjects with an average accuracy of 59.37%, which is higher than the average labelling accuracy for activities of unknown subjects of an Ergodic Hidden Markov Model (6.25%), and a Compound Hidden Markov Model with activities modelled by a single state (18.75%).

关键词： Hidden Markov Model Compound Hidden Markov Model Activity Recognition Human Activity Human Motion Motion Capture skeleton Computer Vision Machine Learning Motion Analysis

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