A new fiber optic sensing technology for measuring in-cylinder pressure in automotive engines was investigated. The optic sensing element consists of two mirrors in an in-line single mode fiber that are separated by some distance. To withstand the harsh conditions inside an engine, the Fiber Fabry-Perot Interferometer (FFPI) element was coated with gold and copper. The metal-protected fiber sensor was embedded into a small cut in the metal casing of the spark plug. At first, the sensing element was dipped in liquid gold and cured. Then the gold-coated fiber sensor was electroplated with copper. Finally, the metal-coated fiber sensor was embedded in the spark plug. The spark-plug-embedded FFPI sensor was monitored using a signal conditioning unit. Field tests were carried out in a 3-cylinder automotive engine with a piezoelectric pressure sensor as a reference transducer up to about 3500 rpm. The fiber optic sensor data generally matched those measured by the piezoelectric reference sensor. The use of a Vertical Cavity Surface Emitting Laser (VCSEL) diode as a light source in an FFPI optic sensor system was investigated. Reflected light from the FFPI sensing element was used to measure the optical path difference. With a 1550nm VCSEL as the light source in a 12mm cavity length Fiber Fabry-Perot Interferometer, spectral characteristics were examined to determine the proper combination of dc bias current, modulation current amplitude and modulation frequency. Single VCSEL operation and regular fringe patterns were achieved. The laser tuning was -41.2 GHz/mA and was determined from measurements of the shift in the spectral peak of the VCSEL diode output as a function of dc bias current. By testing the fringe movement as the FFPI sensor was heated, the temperature tuning coefficient for the optical length was determined to be 11 x 10-6 ºC. The results of these experiments indicate that the use of VCSEL diode as a light source for the FFPI sensor offers a viable alternative to the use of Distributed Feedback (DFB) laser diodes for monitoring at a lower bias current and modulating current amplitude.

nÃo hà / Imagens de fundo de olho constituem um valioso recurso para o diagnÃstico mÃdico, pois muitas vezes apresentam indicaÃÃes de doenÃas oftÃlmicas como as da retina e atà mesmo doenÃas sistÃmicas como diabetes, hipertensÃo e arteriosclerose. Esta tese trata de algoritmos de detecÃÃo de estruturas como a fÃvea, mÃcula, exsudatos e disco Ãptico (DO) em imagens de retina. Em se tratando de algoritmos de detecÃÃo da fÃvea em imagens coloridas de retina, propomos um algoritmo assim como conjunto de regras para avaliaÃÃo dos mesmos. A detecÃÃo automÃtica desta estrutura anatÃmica à um prÃ-requisito para o diagnÃstico auxiliado por computador de vÃrias doenÃas da retina, como a degeneraÃÃo macular. Entretanto, as pequenas dimensÃes e baixo contraste da fÃvea dificultam a execuÃÃo desta tarefa de detecÃÃo. O algoritmo proposto determina a regiÃo de interesse levando em consideraÃÃo as coordenadas do DO e o fato da fÃvea ser uma Ãrea escura, homogÃnea e sem presenÃa de vasos sanguÃneos. Em seguida, o mÃtodo realiza a etapa de segmentaÃÃo dos vasos e pesquisa pela janela com menor mÃdia de intensidade de cor na imagem resultante da fusÃo entre os canais vermelho e verde. Os testes do algoritmo de detecÃÃo da fÃvea foram realizados em trÃs bases de imagens pÃblicas de referÃncia ARIA, DRIVE e MESSIDOR. Neste trabalho, propomos ainda um algoritmo de detecÃÃo de exsudatos em imagens de retina. A metodologia proposta combina agrupamento nebuloso e tÃcnicas de morfologia matemÃtica. Os resultados confirmam a melhoria no desempenho do mÃtodo de detecÃÃo quando comparado aos mÃtodos disponÃveis na literatura. Portanto, comparamos os resultados de seis algoritmos automÃticos de detecÃÃo do DO disponÃveis na literatura, utilizando dados de referÃncia das bases pÃblicas ARIA, STARE, DRIVE e MESSIDOR. O objetivo era determinar a robustez dos mesmos em detectar o DO em imagens de retina saudÃveis e com a presenÃa de patologias. Observamos que em geral os mÃtodos de detecÃÃo de DO que apresentam melhor desempenho o fazem em bases menos desafiadoras como as duas Ãltimas, ou seja, eles alcanÃam as maiores taxas de acerto. / Fundus images are valuable resource in diagnosis because they often present indications about retinal, ophthalmic, and even systemic diseases such as diabetes, hypertension, and arteriosclerosis. This thesis focuses on algorithms to detect fovea, exudates and optic disk (OD) in retina images. Regarding fovea detection algorithms in colored retina images, we propose an algorithm and furthermore a set of rules to assess them. Automatic detection of this anatomical structure is a prerequisite for computer-aided diagnosis of several retinal diseases, such as macular degeneration. However, the small dimension and weak contrast of the fovea area on retina images make difficult this task detection, directly. The proposed algorithm determines a region of interest taking into account OD coordinates and the fact that the fovea is a homogeneous dark area without blood vessels. Then, the method performs the vessel segmentation step and searches for the lowest mean color intensity window in the image that results from the fusion between the red and green channels. Tests were carried out on three public benchmark databases. In addition, this thesis proposes an algorithm for exudate detection in retina images. The proposed methodology combines fuzzy clustering and mathematical morphology techniques. The results confirm the performance improvement provided by the proposed methodology, when comparing it to other methods available in the literature. In this work, we compare the results of six different automatic algorithms for OD detection, using the public benchmark image database named ARIA, STARE, DRIVE and MESSIDOR. We aimed to test the robustness of the algorithms in detecting the OD in healthy and pathological retina images. In general, we observed that these methods performed better in less challenging databases as the two last ones, i.e. they achieved the highest success rates in DRIVE and MESSIDOR.