Monitoring of dry sliding wear using fractal analysis

Reliable online monitoring of wear remains a challenge to tribology research as well as to the industry. This\ud paper presents a new method for monitoring of dry sliding wear using digital imaging and fractal analysis.\ud Fractal values, namely fractal dimension and intercept, computed from the power spectrum of the images of a\ud wearing surface, are adopted as indicators of the dynamic wear process. Experimental results show that\ud progressive changes of fractal values might reveal the wear status of the surface

Time-of-flight estimation based on covariance models

We address the problem of estimating the time-of-flight (ToF) of a waveform that is disturbed heavily by additional reflections from nearby objects. These additional reflections cause interference patterns that are difficult to predict. The introduction of a model for the reflection in terms of a non-stationary auto-covariance function leads to a new estimator for the ToF of an acoustic tone burst. This estimator is a generalization of the well known matched filter. In many practical circumstances, for instance beacon-based position estimation in indoor situations, lack of knowledge of the additional reflections can lead to large estimation errors. Experiments show that the application of the new estimator can reduce these errors by a factor of about four. The cost of this improvement is an increase in computational complexity by a factor of about seven