[en] In this work, the fluorescence properties of the fuel tracer 1-methylnaphthalene (1-MN) are investigated in a calibration flow cell at elevated temperatures and pressures. This fuel tracer is well suited to investigate mixture formation in diesel engine applications, as 1-MN is a natural diesel fuel component. A tracer-LIF (laser-induced fluorescence) concept is developed that enables temperature measurements using a two-color approach in oxygen-containing gas mixtures. Furthermore, the potential for simultaneous measurement of equivalence ratio (fuel–air ratio, FAR) is evaluated. First, a picosecond Nd:YLF-laser at 263 nm in combination with a spectrograph and a streak unit was used to investigate the spectral fluorescence emission and fluorescence lifetimes. In addition, a nanosecond Nd:YAG-laser at 266 nm was used for fluorescence calibration with regard to equivalence ratio and temperature. All measurements were performed in an oxygen-containing environment at different equivalence ratios, temperatures up to 800 K and pressures up to 2.5 MPa. The dependency of the fluorescence emission on equivalence ratio was studied for varied fuel amount and air concentrations. The calibration data form the basis for investigations of fuel distribution and temperature under realistic engine conditions in an oxygen-containing environment.

[en] The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a ' counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available