Discrimination of real and virtual surfaces with sinusoidal and triangular gratings using the fingertip and stylus.

Two-interval two-alternative forced-choice discrimination experiments were conducted separately for sinusoidal and triangular textured surface gratings from which amplitude (i.e., height) discrimination thresholds were estimated.

Laser optical sensor, a label-free on-plate Salmonella enterica colony detection tool.

Unlabelled: We investigated the application capabilities of a laser optical sensor, BARDOT (bacterial rapid detection using optical scatter technology) to generate differentiating scatter patterns for the 20 most frequently reported serovars of Salmonella enterica. Initially, the study tested the classification ability of BARDOT by using six Salmonella serovars grown on brain heart infusion, brilliant green, xylose lysine deoxycholate, and xylose lysine tergitol 4 (XLT4) agar plates. Highly accurate discrimination (95.

Development of an integrated optical analyzer for characterization of growth dynamics of bacterial colonies.

In order to understand the biophysics behind collective behavior of a bacterial colony, a confocal displacement meter was used to measure the profiles of the bacterial colonies, together with a custom built optical density circuits. The system delivered essential information related to the quantitative growth dynamics (height, diameter, aspect ratio, optical density) of the bacterial colony. For example, the aspect ratio of S.

Determine the quality of human embryonic stem colonies with laser light scattering patterns.

Background: With the prompt developments of regenerative medicine, the potential clinical applications of human embryonic stem cells have attracted intense attention. However, the labor-intensive and complex manual cell selection processes required during embryonic stem cell culturing have seriously limited large-scale production and broad applications. Thus, availability of a label-free, non-invasive platform to replace the current cumbersome manual selection has become a critical need.

Classification of bacterial contamination using image processing and distributed computing.

Disease outbreaks due to contaminated food are a major concern not only for the food-processing industry but also for the public at large. Techniques for automated detection and classification of microorganisms can be a great help in preventing outbreaks and maintaining the safety of the nations food supply. Identification and classification of foodborne pathogens using colony scatter patterns is a promising new label-free technique that utilizes image-analysis and machine-learning tools.