Using a mixture of crystalline-Ho:ZrO2, precursor-Dy:Y2O3, and precursor-Eu:ZrO2 nanoparticles we develop thermal impulse sensors capable of measuring equivalent isothermal temperatures and durations during a heating event, with response times of <100 ms, and a temperature range of at least 673 K to 1173 K. In order to determine the temperature and duration from the sensors after the heating event we measure the sensors' fluorescence spectrum, which is then compared with lab based calibration data. By using two precursor materials with different reaction kinetics we are able to extract both temperature and duration. Based on blind sample testing we find that the sensors and calculation method are accurate for measuring temperature and duration, but currently suffer a lack of precision due to difficulties in producing homogeneously heated samples.
One of the main limitations of utilizing optimal wavefront shaping in imaging and authentication applications is the slow speed of the optimization algorithms currently being used. To address this problem we develop a microgenetic optimization algorithm (μGA) for optimal wavefront shaping. We test the abilities of the μGA and make comparisons to previous algorithms (iterative and simple-genetic) by using each algorithm to optimize transmission through an opaque medium. From our experiments we find that the μGA is faster than both the iterative and simple-genetic algorithms and that both genetic algorithms are more resistant to noise and sample decoherence than the iterative algorithm.
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