Análisis de componentes principales utilizando python para identificar clúster asociados a muestras de cacao seco sano e infectado con monilia en Norte de Santander

The result of the research project is associated with data from the implementation of a multisensory electronic system orcommonly called electronic smell. Through the use of a data acquisition system and LabView software, the volatile concentration data associatedwith samples of healthy cocoa and infected with monilia, applied in the drying phase or stage. Once the data is acquired, the Python softwareused for preprocessing and processing data. Allowing the user to identify the cluster associated with each class, healthy cocoa or with monilia.As a method for learning unsupervised automatic, PCA principal component analysis is implemented for the respective processing. The resultsobtained vary according to the method of data preprocessing, a robust climber and preprocessed Euclidean was implemented, which presentsbetter results of grouping samples by class.El resultado del proyecto de investigación está asociado a data proveniente de la implementación de un sistema electrónicomultisensorial o común mente denominado olfato electrónico. Mediante el uso de un sistema de adquisición de datos y software LabView sealmacena la data de la concentración de volátiles asociados a muestras de cacao sano e infectado con monilia, aplicado en la fase o etapa desecado. Una vez adquirido los datos se procede a implementar en el software Python el pre-procesamiento y procesamiento de data, permitiendoal usuario por medio de un gráfico identificar el clúster asociados a cada clase, cacao sano o con monilia. Como método para aprendizaje deautomático no supervisado, se implementa análisis de componentes principales PCA para el respectivo procesamiento. Los resultados obtenidosvarían de acuerdo al método de preprocesado de datos. Para el desarrollo se implementó un escalador robusto y preprocesado euclidiano, el cualpresenta mejores resultados de agrupamiento de muestras por clas

Smart Sensor Network Based High Quality Air Pollution Monitoring System Using Labview

A ZigBee based wireless sensor network is implemented in this paper which is of low-cost solar-powered air quality monitoring system. The main objective of the proposed architecture is to interfacing various sensors to measure the sensor analog data and displayed in LabVIEW on the monitor using the graphical user interface (GUI).  The real time ambient air quality monitoring in smart cities is of greater significance for the health of people. The wireless network sensor nodes are placed at different traffic signals in the smart cities which collect and report real-time data on different gases which are present in the environment such as carbon monoxide (CO), nitrogen dioxide (NO2), methane (CH4) and humidity. The proposed system allows smart cities to monitor air quality conditions on a desktop/laptop computer through an application designed using graphical programming based LabVIEW software and provides an alert if the air quality characteristics exceed acceptable levels. The sensor network was successfully tested on the campus of the institute of aeronautical engineering, Hyderabad. The sensor data are indicated by different indicators on the front panel of LabVIEW and also different charts are plotted with respect to time and amplitude which explains the severity of polluted areas