Nowadays spectrometers are useful in many applications such as bio-medical technology among other industrial fields. State of the art low-cost spectrometers are usually equipped with linear photoelectric array detectors (line detectors). This thesis covers part of the software development for a low-cost multichannel spectrometer using a matrix detector instead of a linear array detector. The image acquisition including an automatic integration time optimizer was implemented. Furthermore, algorithms for data extraction and calibration were developed. Finally, the multi-channel-system with the new software was compared with a high-resolution spectrometer and the results were discussed.

Nowadays spectrometers are useful in many applications such as bio-medical technology among other industrial fields. State of the art low-cost spectrometers are usually equipped with linear photoelectric array detectors (line detectors). This thesis covers part of the software development for a low-cost multichannel spectrometer using a matrix detector instead of a linear array detector. The image acquisition including an automatic integration time optimizer was implemented. Furthermore, algorithms for data extraction and calibration were developed. Finally, the multi-channel-system with the new software was compared with a high-resolution spectrometer and the results were discussed.

Nowadays spectrometers are useful in many applications such as bio-medical technology among other industrial fields. State of the art low-cost spectrometers are usually equipped with linear photoelectric array detectors (line detectors). This thesis covers part of the software development for a low-cost multichannel spectrometer using a matrix detector instead of a linear array detector. The image acquisition including an automatic integration time optimizer was implemented. Furthermore, algorithms for data extraction and calibration were developed. Finally, the multi-channel-system with the new software was compared with a high-resolution spectrometer and the results were discussed.

METODOLOGÍA MARCO TEÓRICO SISTEMAS DE VISIÓN Y HARDWARE: VISIÓN ARTIFICIAL PROPIEDADES BÁSICAS DE LAS IMÁGENES DIGITALES INTRODUCCIÓN A LOS SENSORES DE IMAGEN CMOS ESTRATEGIAS PARA EL PROCESAMIENTO DE IMÁGENES DIGITALES SOFTWARE DEDICADO PARA EL PROCESAMIENTO INTELIGENCIA ARTIFICIAL REDES NEURONALES ALGORITMO K-NEAREST NEIGHBORS ELECCIÓN DEL K POSIBLES VARIANTES DEL ALGORITMO BÁSICO CONTROLADORES INDUSTRIALES SERVO POSICIONAMIENTO INDUSTRIAL MOTOR SÍNCRONO DE IMÁN PERMANENTE DISEÑO E IMPLEMENTACIÓN DISEÑO DE SISTEMA IMPLEMENTACIÓN DEL SISTEMA PROGRAMACIÓN DEL SISTEMA DIAGRAMA DE FLUJO DEL FUNCIONAMIENTO PROCESO DE APRENDIZAJE CLASIFICACIÓN DE PIEZAS SEPARAR LAS PIEZAS PRUEBAS Y RESULTADOS