Assessing particle kinematics via template matching algorithms

Computation of canting errors in heliostats by flux map fitting: experimental assessment

In solar tower plants, thousands of heliostats reflect sunlight into a central receiver. Heliostats consist of a subset of mirrors called facets that must be perfectly oriented (i.e., canted) to concentrate as much solar radiation as possible. This study presents and validates the so-called flux map fitting technique to detect and correct canting errors. The computed distributions were matched to a series of images through an optimization algorithm. According to the sensitivity analysis, three images spread along a single day provide sufficient information for the algorithm to succeed. Using this methodology, four heliostats at the THEMIS research facility were recanted, thereby substantially increasing the optical quality in three of them. The procedure to infer the heliostat aimpoint was assessed.

Algorithms for Particle Detection in Complex Plasmas

In complex plasmas, the behavior of freely floating micrometer sized particles is studied. The particles can be directly visualized and recorded by digital video cameras. To analyze the dynamics of single particles, reliable algorithms are required to accurately determine their positions to sub-pixel accuracy from the recorded images. Typically, a straightforward algorithm such as the moment method is used for this task. Here, we combine different variations of the moment method with common techniques for image pre- and post-processing (e.g., noise reduction and fitting), and we investigate the impact of the choice of threshold parameters, including an automatic threshold detection, on synthetic data with known attributes. The results quantitatively show that each algorithm and method has its own advantage, often depending on the problem at hand. This knowledge is applicable not only to complex plasmas, but useful for any kind of comparable image-based particle tracking, e.g., in the field of colloids or granular matter.