This paper presents surface temperature responses of various tissue phantoms and in vitro and in vivo biological materials in air to non-ablative pulsed CO2 laser irradiation, measured with a thermocamera. We studied cooling off behavior of the materials after a laser pulse, to come to an understanding of heat accumulation and related thermal damage during (super) pulsed CO2 laser irradiation. The experiments show a very slow decay of temperatures in the longer time regime. This behavior is well predicted by a simple model for one-dimensional heat flow that considers the CO2 laser radiation as producing a heat flux on the material surface. The critical pulse repetition frequency for which temperature accumulation is sufficiently low is estimated at about 5 Hz. Although we have not investigated the ablative situation, our results suggest that very low pulse frequencies in microsurgical procedures may be recommended.
In passive infrared localization (PIL) humans are located based on their thermal radiation. Thus, an active tag is not required and privacy is guaranteed due to non-identifying sensors. However, in case of multi-target tracking, the nonidentifying sensors result in missing associations between targets and measurements. As additionally the number of humans in the surveillance region is unknown and varying, conventional localization approaches like the JPDA or MHT filter cannot be applied since they require a fixed number of targets or are not tractable.The Probability Hypothesis Density (PHD) filter, on the other hand, that propagates only the first order moment of the multitarget probability distribution offers an efficient and elegant way to handle the aspects of missing association and varying target number. Furthermore, false measurements as well as missed detection are considered implicitly.In this paper we present an implementation of this filter for PIL that allows an accurate and reliable tracking of several humans. The accuracy and reliability are evaluated under the influence of noise. Thus, several simulations and real measurements are carried out that reveal a mean error of less than 30 cm in case of three humans. Moreover, efficiency tests show that on standard PCs update rates of more than 50 Hz are achievable.
Exploiting the natural thermal infrared radiation of humans is a promising approach for an accurate, comfortable and inexpensive indoor localization system. However, different sources of disturbance make the development challenging. In order to provide valid sensor data for various scenarios an adequate simulation environment is needed. In this paper we present a real-time scene simulator that allows the simulation of dynamic indoor environments and the resulting output signals of infrared sensors. The composition of such environments is simplified by using an object and sensor database. In order to enable real-time processing, OpenGL and hardware acceleration is applied. Evaluations show that the accuracy of the chosen approach is sufficient to develop algorithms for a Thermal Infrared Localization System (ThILo). Furthermore, it can be shown that real-time processing is possible for a complete location system in typical indoor environments.
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