University of Florida Research Foundation, Incorporated

Hitachi America, Ltd.

Ut-battelle, Inc.

Sanjay Ranka

Sartaj Kumar Sahni

Vineet Singh

Jonathan Gang Li

Srijit Kamath

Jatinder Palta

G D Ramkumar

Khaled Alsabti

Shalom Tsur

Anurag Srivastava

Yan Li

Eun-Sung Jung

Narayana Kamath

Jantinder Palta

Nageswara Sirikonda Venkata Rao

A connection-oriented network includes a control server. The control server receives requests to establish and utilize dedicated channels in the network. The control server utilizes various scheduling methods and algorithms to determine channels based on the request's requirements and resources of the network. For example, the control server may determine a channel based on: (i) a specified bandwidth in a specified time slot, (ii) highest available bandwidth in a specified time slot, (iii) earliest available time with a specified bandwidth and duration, and (iv) all available time slots with a specified bandwidth and duration.

Method and systems for bandwidth scheduling and path computation for connection-oriented networks

A method and associated systemfor delivering intensity-modulated radiation therapy (IMRT) uses variable feathering field splitting for intensity modulated fields of large size. A processor controls a beam-shaping device that splits the radiation beam into a plurality of radiation fields delivered to a patient. The processor in cooperation with the beam-shaping device implements a variable feathering method which includes providing an intensity matrix for the treatment of a patient, the intensity matrix having a plurality of rows and columns for spanning a prescribed radiation field including a prescribed field width. The prescribed width is compared to a maximum field width provided by the radiation treatment system. The intensity matrix is split into a plurality of spatially overlapping intensity submatrices by variably feathering the intensity matrix when the prescribed width exceeds the maximum field width, Radiotherapy is then provided to the patient using a leaf sequencing method to generate the submatrices.

Variable feathering field splitting for intensity modulated fields of large size

A method of delivering intensity modulated radiation therapy (IMRT) is disclosed. An intensity profile for the treatment of a patient is provided which spans a prescribed field width and includes a discrete profile having intensity values at each of a plurality of sample points bounded by the prescribed width. The prescribed width is compared to a maximum field width provided by the radiation treatment system. The intensity profile is split into a plurality of intensity profile portions, each having respective widths less than the maximum width if the prescribed width is greater than the maximum width. The prescribed field is also divided into a plurality of different profile portion split arrangements. A monitor unit (MU) efficiency is calculated for each of the arrangements. One of the arrangements is selected for delivery by the system using a leaf sequencing method.

Field splitting for intensity modulated fields of large size

A method of delivering radiation treatment using multi-leaf collimation includes the step of providing a radiation fluence map which includes an intensity profile. The fluence map is converted into a preliminary leaf sequence, wherein the preliminary leaf sequence minimizes machine on-time and is generated without leaf movement constraints. The leaf movement constraint is imposed on the preliminary leaf sequence. At least one constraint elimination algorithm is then applied, the algorithm adjusting the preliminary leaf sequence to minimize violations of the constraint while providing the desired fluence map and minimized radiation on-time. The method can be applied to SMLC and DLMC systems, and can include adjustment for the tongue-and-groove effect.

Leaf sequencing method and system

The present invention is an apparatus and method for classifying high-dimensional sparse datasets. A raw data training set is flattened by converting it from categorical representation to a boolean representation. The flattened data is then used to build a class model on which new data not in the training set may be classified. In one embodiment, the class model takes the form of a decision tree, and large itemsets and cluster information are used as attributes for classification. In another embodiment, the class model is based on the nearest neighbors of the data to be classified. An advantage of the invention is that, by flattening the data, classification accuracy is increased by eliminating artificial ordering induced on the attributes. Another advantage is that the use of large itemsets and clustering increases classification accuracy.

Method and apparatus for classification of high dimensional data

The present invention discloses a data mining method and apparatus that assigns weight values to items and/or transactions based on the value to the user, thereby resulting in association rules of greater importance. A conservative method, aggressive method, or a combination of the two can be used when generating supersets.

Method and apparatus for generating weighted association rules

Multidimensional similarity join finds pairs of multi-dimensional points that are within some small distance of each other. Databases in domains such as multimedia and time-series can require a high number of dimensions. The .epsilon.-k-d-B tree has been proposed as a data structure that scales better as number of dimensions increases compared to previous data structures such as the R-tree (and variations), grid-file, and k-d-B tree. We present a cost model of the .epsilon.-k-d-B tree and use it to optimize the leaf size. This new leaf size is shown to be better in most situations compared to previous work that used a constant leaf size. We present novel parallel procedures for the .epsilon.-k-d-B tree. A load-balancing strategy based on equi-depth histograms is shown to work well for uniform or low-skew situations, whereas another based on weighted, equi-depth histograms works far better for high-skew datasets. The latter strategy is only slightly slower than the former strategy for low skew datasets. The weights for the latter strategy are based on the same cost model that is used to determine optimal leaf sizes.

Structure and method for efficient parallel high-dimensional similarity

The present invention is directed to an improved data clustering method and apparatus for use in data mining operations. The present invention determines the pattern vectors of a k-d tree structure which are closest to a given prototype cluster by pruning prototypes through geometrical constraints, before a k-means process is applied to the prototypes. For each sub-branch in the k-d tree, a candidate set of prototypes is formed from the parent of a child node. The minimum and maximum distances from any point in the child node to any prototype in the candidate set is determined. The smallest of the maximum distances found is compared to the minimum distances of each prototype in the candidate set. Those prototypes with a minimum distance greater than the smallest of the maximum distances are pruned or eliminated. Pruning the number of remote prototypes reduces the number of distance calculations for the k-means process, significantly reducing the overall computation time.

Growing community of inventors

Gainesville, FL, United States of America

Sanjay Ranka