Software & Algorithms

software and algorithms

SpecMapper Mapping Data (RFT-525)

SpecMapper™ is a new labor and money saving computerized software that maps Assembly Codes used in building information modeling (BIM) to the MasterSpec specification sections.  Architectural firms will be able to save time and labor in developing proposals for building project submissions.  SpecMapper™ was co-developed by North Dakota State University investigators and RLE Holding, Inc. (RLE) architects to enable architects to more efficiently develop submissions for building projects using the computer rather than the tedious manual labor that is normally required.  SpecMapper™ is co-owned by the NDSU Research Foundation and RLE Holding, Inc. and is being offered to other architectural firms in three formats depending on your preference.

Asynchronous Cellular Automaton Provides Benefits Over Field-Programmable Gate Arrays (RFT-256)

North Dakota State University scientists have created a unique asynchronous cellular automaton which is believed to have several distinct advantages over currently available field-programmable gate arrays (FPGAs). These cellular automata are easily scaled from small circuits to large computing arrays.

Parameter Optimized, Vertical, Nearest-Neighbor-Vote and Boundary-Based Classification RFT-203

This invention involves a Computer Aided Detection (CAD) model that is designed to diagnose Pulmonary Embolism (PE) from CT image information data sheet. This high performance, classification system, includes a Nearest Neighbor Vote based classification and a Local Decision Boundary based classification combined with an evolutionary algorithm for parameter optimization and a vertical data structure for efficient processing. The invention was developed as a solution for the ACM KDD Cup Competition 2006, and won task 3 of that competition.

Vertical Set Inner Product Technology (VSIP) with Predicate Trees RFT-159

Vertical Set Inner Product Technology (VSIP) provides an order of magnitude speed-up to clustering numeric data (both full-space clustering and subspace clustering onto any subspace—including oblique subspaces) by providing a horizontal calculation across vertical predicate-tree datasets. The ability to scale the computations to very large data sets is the significant improvement over traditional methods. Can be used in conjunction with P-Tree (Peano count) models and associated algorithms for datamining applications.

Peano Count Model (PCM) RFT-75

Standard data mining techniques have been sufficient for some areas of information analysis where the datasets are small enough that analysis can be performed relatively quickly and efficiently. However, these standard data mining techniques, such as association rule mining (ARM), have not been as successful in areas such as bioinformatics, nanotechnology, VLSI design, and spatial data, which each have extremely large data sets and where mining implicit relationships among the data can be prohibitively time-consuming. These NDSU-developed data mining technologies are designed specifically for organizing extremely large datasets into an efficiently usable form. The organizational format of the data takes into account the fact that different bits of data can have different degrees of contribution to value. For example, in some applications, high-order bits alone may provide the necessary information for data mining, making the retention of all data unnecessary. The organizational format used also recognizes the need to facilitate the representation of a precision hierarchy. That is, a band may be well represented by a single bit or may require eight bits to be appropriately represented.