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Dynamic light scattering (DLS) is widely used for particle size determination in the sub-micrometer range. The utility of combining simultaneous multiangle DLS and static light scattering (SLS) in obtaining more robust and reproducible particle size distributions has recently been demonstrated (Langmuir 1995(Langmuir , 11, 2480(Langmuir -2485. This approach yields excellent results but is tedious and demands high-quality SLS data which, in turn, require a sophisticated and high-quality photometer system. In this paper we investigate ways to improve further the attractiveness of this approach by examining two possible schemes to reduce the experimental overhead in these measurements. We consider combining SLS measurements with only a few angles of DLS data and also recording only DLS data at many angles and iteratively reconstructing the SLS data during the data analysis. We show that in the latter case it is possible to obtain particle size distribution results as good as those obtained with simultaneous multiangle DLS and SLS, by doing DLS at 10 scattering angles and perhaps as few as 5 angles. This approach has a number of advantages including shorter measurement times and the ability to use lower quality photometer systems which may not be capable of precision SLS measurements.

Ground penetrating radar (GPR) uses electromagnetic waves to image, locate, and identify changes in electric and magnetic properties in the ground. The received signal comprises not only the target echoes but also strong reflections from the rough, uneven ground surface, which impair subsurface inspections and visualization of buried objects. In this paper, a background clutter mitigation and target detection method using low-rank and sparse priors is proposed for GPR data. The radar signal is decomposed into the sum of a low-rank component and a sparse component, plus noise. The low-rank component captures the ground surface reflections and background clutter, whereas the sparse component contains the target reflections. The effectiveness of the proposed method is evaluated on real radar signals collected from buried landmines and improvised explosive devices. The experimental results show that the proposed method successfully removes the background clutter and estimates the target signals.

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