No abstract

Foraminifera, or forams for short, are ubiquitous in the world ocean (Sengupta, 1999). Along with their abundance, their biodiversity and extent of geologic record make their fossils of particular interest to paleontologists and paleoclimatologists (Schmiedl, 2019). The sand-sized fraction of deep sea sediments is often dominated by planktic foraminifera, of which there are about 50 extant species (Schiebel & Hemleben, 2017). Although modern benthic foraminiferal species number in the thousands, they are typically only abundant in shallow (shelf) environments and in poorly preserved abyssal sediments. For this work, we focus on deep-sea sediment and as such do not include analysis or commentary on benthic foraminifera. Due to the small size and great abundance of planktic foraminifera, hundreds or possibly thousands can often be picked from a single cubic centimeter of ocean floor mud. Foraminifera samples are normally sorted by species before they are used for either academic or

X-ray diffraction imaging (XRDI) offers the potential for reduced false alarm rates, increased throughputs, and more sensitive explosives detection performance in aviation security applications. The deployment of computed tomography (CT) systems across carry-on and checked baggage screening lanes has both reinforced the need for orthogonal detection technologies and created an exciting new opportunity for the implementation of XRDI. Our team at Quadridox built a novel XRDI system that, when combined with a CT system, realizes full-tunnel assessment of checked bags at a belt speed of 20 cm/s. We integrated our XRDI system with a Smiths CTX 5800 explosives detection system (EDS) and collected bag data containing both benign and threat objects. We describe the XRDI system, show examples of the resulting hybrid CT and XRD dataset, and present performance results for the hybrid system.

Abstract-We propose a method to both quickly and robustly extract geometric information from trajectory data. While point density may be of interest in some applications, trajectories provide different guarantees about our data such as path densities as opposed to location densities provided by points. We aim to utilize the concise nature of quadtrees in two dimensions to reduce run time complexity of counting trajectories in a neighborhood. We compare the accuracy of our methodology to a common current practice for subsampling a structure. Our results show that the proposed method is able to capture the geometric structure. We find an improvement in performance over the current practice in that our method is able to extract only the salient data and ignore trajectory outliers.

No abstract