Expecting dirt but saying dart: The creation of a blend memory

During a naming task, time pressure and a manipulation of the proportion of related prime-target pairs were used to induce subjects to generate an expectation to the prime. On some trials, the presented target was orthographically and generally phonologically similar to the expected tal-get. The expectancy manipulation was barely detectable in the priming data but was clearly evident on a final recognition test. In addition, the recognition data showed that the nearly simultaneous activation of an expectation and sensory information derived from the orthographically and phonologically similar target produced a false memory. It is argued that this represents a blend memory.

Imaging and Characterisation of Dirt Particles in Pulp and Paper

Dirt counting and dirt particle characterisation of pulp samples is an important part of quality control in pulp and paper production. The need for an automatic image analysis system to consider dirt particle characterisation in various pulp samples is also very critical. However, existent image analysis systems utilise a single threshold to segment the dirt particles in different pulp samples. This limits their precision. Based on evidence, designing an automatic image analysis system that could overcome this deficiency is very useful. In this study, the developed Niblack thresholding method is proposed. The method defines the threshold based on the number of segmented particles. In addition, the Kittler thresholding is utilised. Both of these thresholding methods can determine the dirt count of the different pulp samples accurately as compared to visual inspection and the Digital Optical Measuring and Analysis System (DOMAS). In addition, the minimum resolution needed for acquiring a scanner image is defined. By considering the variation in dirt particle features, the curl shows acceptable difference to discriminate the bark and the fibre bundles in different pulp samples. Three classifiers, called k-Nearest Neighbour, Linear Discriminant Analysis and Multi-layer Perceptron are utilised to categorize the dirt particles. Linear Discriminant Analysis and Multi-layer Perceptron are the most accurate in classifying the segmented dirt particles by the Kittler thresholding with morphological processing. The result shows that the dirt particles are successfully categorized for bark and for fibre bundles.

Evaluation of dirt films, lamps and procedures for headlamp washer tests. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

The old dirt dobber's garden book,

Illustrated lining-papers.