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Pacific Palisades, CA, United States

Grant
Agency: Department of Defense | Branch: Air Force | Program: STTR | Phase: Phase I | Award Amount: 99.96K | Year: 2009

Compressive Sensing (CS) is an emerging field based on the realization that a small collection of nonadaptive linear projections of a compressible signal contain enough information for reconstruction and processing. Expanding on this emerging technology, this project will make significant contributions in the following ways to the problem of anamoly detection in hyperspectral imagery: (i) increase our capacity for hyperspectral image acquisition developing new imaging and spectroscopic systems; (ii) expand the frontiers of CS from signal recovery to new applications including knowledge learning including anamoly detection; and (iii) strengthen our ability for extracting knowledge from huge data sets such as hyperspectral images beyong current limits imposed by existing computational resources and methods. BENEFIT: Our results have the potential to be economically revolutionary given the growing importance of digital imaging in many endeavors.)


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase I | Award Amount: 79.99K | Year: 2009

We shall develop state-of-the-art algorithms and software to rapidly restore degraded videos and images where the nature of the degradation is not well detailed. We shall use revolutionary new mathematical techniques involving total variation and nonlocal total variation blind restoration and oversampling, compressive sensing, Bregman iteration, graph cuts and fast nonlocal filtering.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 749.99K | Year: 2001

Phase I work has resulted in the invention of a new, revolutionary multiscale technique that is very well suited for compressing DTED. This technique includes(1)accurate reconstruction of terrain features via simple add-on to any compressionroutine,(2)feature based compression, (3) curve and surface compression, (4) fast, feature preserving data retrieval. Phase II general objectives involve (1) further basic algorithm development, (2) validating the data structure with optimalfeature/geometry based metrics, (3) addition of state-of-art multiresolution/PDE based image processing tasks,(4) developing a prototype suitable for installation and evaluation at a Navy site, (5) integrating DTED with other sensor data. Phase IIIcommercialization plans involve possible joint ventures with any of several large image compression companies.


A method and apparatus for compressing three-dimensional point cloud data is disclosed. In one aspect, a method for compressing three dimensional point cloud includes steps of retrieving three-dimensional point cloud data; providing one or more grids to the three-dimensional point cloud data; assigning one binary digit to each three-dimensional grid voxel containing said point cloud data and assigning the other binary digit to each three-dimensional grid voxel that does not have said point cloud data; converting the three-dimensional grid into two-dimensional tiles; and storing information of a plurality of binary strings in said two-dimensional tiles. In one embodiment, the step of storing information of a plurality of binary strings in said two-dimensional tiles includes a step of storing the number of repeating times of each binary digit in the binary strings. The method can significantly reduce memory space, as well as preserving small details of the point cloud.


Grant
Agency: Department of Defense | Branch: Defense Advanced Research Projects Agency | Program: SBIR | Phase: Phase I | Award Amount: 98.97K | Year: 2003

The proposed innovation consists of a new analytic and computational method for rarefied gas dynamics (RGD). The prevalent computational method for RGD is the Direct Simulation Monte Carlo (DSMC) method. DSMC becomes computationally intractable in thenear-continuum regime, which is a significant limitation on its capability for materials processing applications. The new method is an interpolated fluid/Monte Carlo (IFMC) method that will accelerate DSMC in the near-continuum regime, removing thislimitation. The IFMC method is an improvement over existing acceleration methods for RGD in that it is a single uniform method, valid for the full range of Knudsen numbers, with the correct asymptotic behavior in the continuum and near-continuum regimes. This innovation will provide a greatly accelerated and more robust computational tool for simulation of materials processing and micro-electro-mechanical systems (MEMS), and will be of significant commercial value for the electronics and aerospaceindustries.

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