GPU CUDA Accelerated Image Inpainting using Fourth Order PDE Equation

This paper describes the technique to accelerate inpainting process using fourth order PDE equation using GPU CUDA. Inpainting is the process of filling in missing parts of damaged images based on information gleaned from surrounding areas. It uses the GPU computation advantage to process PDE equation into parallel process. Fourth order PDE will be solved using parallel computation in GPU. This method can speed up the computation time up to 36x using NVDIA GEFORCE GTX 67

The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch

Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely because of the size in bytes of the data sets, but also because of the complexity of modern data sets. Mathematical limitations of familiar algorithms and techniques in dealing with such data sets create a critical need for new paradigms for the representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multiresolution data across application domains. Some of the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based geosciences. Unfortunately, valuable results pertaining to these problems are mostly to be found only in publications outside of astronomy. Here we offer brief overviews of a number of concepts, techniques and developments, some "old" and some new. These are generally unknown to most of the astronomical community, but are vital to the analysis and visualization of complex datasets and images. In order for astronomers to take advantage of the richness and complexity of the new era of data, and to be able to identify, adopt, and apply new solutions, the astronomical community needs a certain degree of awareness and understanding of the new concepts. One of the goals of this paper is to help bridge the gap between applied mathematics, artificial intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in Astronomy, special issue "Robotic Astronomy

Fine Sediment Dynamics in Dredge Plumes

The research presented in this study is motivated by the need to improve predictions of transport and fate of cohesive sediments suspended during dredging operations. Two techniques are presented to quantify vertical sediment flux within dredge plumes. A mass-balance approach using an Acoustic Doppler Current Profiler (ADCP) is described and demonstrated to accurately estimate vertical mass flux and settling velocity for a suspension of fine sand from a dredged material placement operation. A new digital video settling column for simultaneous measurement of particle size and settling velocity is described and evaluated. The Particle Imaging Camera System (PICS) is a single-chambered, digital video settling column, which permits rapid acquisition (within 2--3 minutes) of image sequences within dredge plumes. Image analysis methods are presented, which provide improved estimates of particle size, settling velocity, and inferred particle density. A combination of Particle Tracking Velocimetry (PTV) and Particle Image Velocimetry (PIV) techniques is described, which permits general automation of image analysis collected from video settling columns. In the fixed image plane, large particle velocities are determined by PTV and small particle velocities are tracked by PIV and treated as surrogates for fluid velocities. The large-particle settling velocity (relative to the suspending fluid) is determined by the vector difference of the large and small particle settling velocities. The combined PTV/PIV image analysis approach is demonstrated for video settling column data collected within a mechanical dredge plume in Boston Harbor. The automated PTV/PIV approach significantly reduces uncertainties in measured settling velocity and inferred floc density. Size, settling velocities, and density of suspended sediments were measured with PICS within a trailing suction hopper dredge plume in San Francisco Bay. Results indicated that suspended sediments within the plume were predominantly in the clay and fine silt size classes, as aggregates with d\u3e30 microm. Suspended bed aggregates (defined by densities of 1200 to 1800 kg m-3) represented 0.2--0.5 of total suspended mass, and size and settling velocity of this class were time invariant. Flocs (densities\u3c1200 kg m-3) represented 0.5 to 0.8 of total suspended mass, and size and settling velocity of flocs was seen to increase with time. The peak diameter of bed aggregates and flocs occurred near 90 microm and 200 microm, respectively, corresponding to peak settling velocities of about 1 mm s-1 in each case. Floc settling velocities increased with particle size d1.1, while bed aggregate settling velocity increased like d1.3. Numerical modeling approaches to representing settling velocities for hopper dredge plumes are discussed in light of the experimental findings. Size-dependant settling velocities were well-described by a fractal-based relationship when the suspension was treated with discrete classes for each of the aggregate states. Time-dependent increases in floc size and settling velocity confirm that flocculation is a first-order process which should be included in numerical plume models. Correlations between settling velocity and suspended sediment concentration were weak and statistically insignificant, implying that commonly applied empirical relationships are inappropriate for dredge plumes