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Mixed layer depth (MLD) is an important oceanographic parameter. However, the lack of direct observations of MLD hampers both specification and investigation of its spatial and temporal variability. An important alternative to direct observation would be the ability to estimate MLD from surface parameters easily available from satellites. In this study, we demonstrate estimation of MLD using Artificial Neural Network methods and surface meteorology from a surface mooring in the Arabian Sea. The estimated MLD had a root mean square error of 7.36 m and a coefficient of determination (R 2 ) of 0.94. About 67% (91%) of the estimates lie within Ϯ 5 m (Ϯ10 m) of the MLD determined from temperature sensors on the mooring.

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Figure 1: Specially constructed objects like Chazelle polyhedra (left) realize a quadratic number of intersecting pairs of polygons in the worst case. Our novel algorithm supports inter-and intra-object collision detection for real-world polygon soups, including topology changes of fracturing objects (middle) and deformable objects (right), in parallel constant time. AbstractWe define a novel geometric predicate and a class of objects that enables us to prove a linear bound on the number of intersecting polygon pairs for colliding 3D objects in that class. Our predicate is relevant both in theory and in practice: it is easy to check and it needs to consider only the geometric properties of the individual objects -it does not depend on the configuration of a given pair of objects. In addition, it characterizes a practically relevant class of objects: we checked our predicate on a large database of real-world 3D objects and the results show that it holds for all but the most pathological ones. Our proof is constructive in that it is the basis for a novel collision detection algorithm that realizes this linear complexity also in practice. Additionally, we present a parallelization of this algorithm with a worst-case running time that is independent of the number of polygons. Our algorithm is very well suited not only for rigid but also for deformable and even topology-changing objects, because it does not require any complex data structures or pre-processing. We have implemented our algorithm on the GPU and the results show that it is able to find in real-time all colliding polygons for pairs of deformable objects consisting of more than 200k triangles, including self-collisions.

Abstract-We present a novel geometric data structure for approximate collision detection at haptic rates between rigid objects. Our data structure, which we call inner sphere trees, supports different kinds of queries, namely, proximity queries and a new method for interpenetration computation, the penetration volume, which is related to the water displacement of the overlapping region and, thus, corresponds to a physically motivated force.The main idea is to bound objects from the inside with a set of non-overlapping spheres. Based on such sphere packings, a "inner bounding volume hierarchy" can be constructed. In order to do so, we propose to use an AI clustering algorithm, which we extend and adapt here.The results show performance at haptic rates both for proximity and penetration volume queries for models consisting of hundreds of thousands of polygons.

We present novel algorithms for updating bounding volume hierarchies of objects undergoing arbitrary deformations. Therefore, we introduce two new data structures, the kinetic AABB tree and the kinetic BoxTree.The event-based approach of the kinetic data structures framework enables us to show that our algorithms are optimal in the number of updates. Moreover, we show a lower bound for the total number of BV updates, which is independent of the number of frames.We used our kinetic bounding volume hierarchies for collision detection and performed a comparison with the classical bottomup update method. The results show that our algorithms perform up to ten times faster in practically relevant scenarios.

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