Abstract: Methods and apparatus are provided for determining whether a built-in-test fault code (BITFC) data sequence generated by a built-in-test (BIT) of a particular module of a complex system is indicative of an actual fault condition. A regression function is generated for the particular module based on stored BITFC data sequences generated by the BIT and stored repair data for that module from a fault history database. Later, during operation of the particular module, the BIT generates a new BITFC data sequence. A processor can then load the new BITFC data sequence and execute the regression function with respect to the new BITFC data sequence to determine whether the new BITFC data sequence is indicative of an actual fault condition at the particular module or is indicative of a false fault condition at the particular module.

Abstract: A system and method of generating a generic binary classifier for the presence of one or more toxins in water is provided. Features are extracted from a plurality of normalized a priori data sets that include one or more control data sets that are representative of an electric cell-substrate impedance sensor (ECIS) response to water with no toxins therein, and a plurality of treatment data sets that are representative of an ECIS response to water with a toxin therein. A plurality of classifier algorithms are trained using the extracted features, and a plurality of classification models are generated from each of the trained classifier algorithms. Each of the classification models is evaluated and, based on the evaluation of each classification model, a subset thereof is selected. The selected subset of the classification models is supplied as the generic binary classifier.

Abstract: Methods and apparatus are provided for determining the fault diagnosability of a health monitoring software application for a complex system. The method includes extracting data from the software application containing a relationship between one or more failure modes of the complex system and one or more evidence items of the complex system, the a priori probabilities of each failure mode occurring, and the a priori probability of each evidence item occurring. The method also includes creating one or more matrices relating the one or more FMs to the one or more evidence items. The method further includes analyzing the one or more matrices and the a priori probabilities to determine the diagnosability of each FM.

Abstract: Methods and apparatus are provided for determining whether a built-in-test fault code (BITFC) data sequence generated by a built-in-test (BIT) of a particular module of a complex system is indicative of an actual fault condition. A regression function is generated for the particular module based on stored BITFC data sequences generated by the BIT and stored repair data for that module from a fault history database. Later, during operation of the particular module, the BIT generates a new BITFC data sequence. A processor can then load the new BITFC data sequence and execute the regression function with respect to the new BITFC data sequence to determine whether the new BITFC data sequence is indicative of an actual fault condition at the particular module or is indicative of a false fault condition at the particular module.

Abstract: In a method and apparatus for performing commercial and other activities using one or more two- or three-dimensional representational images, superimposing a two- or three-dimensional representational image, containing an image of the workpiece in an advanced state of assembly, with a surface or onto the actual workpiece, and utilizing the superimposed representational image to assist in the assembly of the workpiece. One or more two- or three-dimensional representational images are created, e.g., using standard photography, holography or computer imaging, and are placed in a frame integrated within the assembly line of the manufacturing facility. The workpiece is positioned in relation to location of the representational image, such that the representational image may be superimposed onto the workpiece. An assembly operation is then performed using the superimposed representational image as a template.

Abstract: In a system for authenticating information, a holographic element, including a first layer of optical information in a first coded pattern and a second layer of optical information in a second coded pattern is inserted into a reader. The reader includes an opening into which the holographic element may be positioned, a light source, a first detector positioned at a first predetermined distance from the location of the holographic element when the holographic element is positioned in the opening, and a second detector positioned at a second predetermined distance from the location of the holographic element when the holographic element is positioned in the opening.

Abstract: In an object verifier having a housing and an object holder, an object may be placed in the object holder for observation by an operator. The object is illuminated using a collimated beam of white light that is generated by a light generator. The collimated beam of white light is passed through a beam splitter with the two portions of the collimated beam of white light presented to the object at a 90 degree angle one from the other. The interior of the housing includes a reflective surface for maximal illumination of the object. The observer may view the illuminated object through a viewing window and/or through a magnification window. The magnification window provides for the viewing of the object in greater detail.

Abstract: The invention is a teachable system and method for predicting the interactions of proteins with other proteins, nucleic acids and small molecules. A database containing protein sequences and information regarding protein interactions is used to “teach” the machine. Proteins with unknown interactions are compared by the machine to proteins in the database. Homologs of proteins known to interact in the database are predicted to interact.

Abstract: The invention is a teachable system and method for predicting the interactions of proteins with other proteins, nucleic acids and small molecules. A database containing protein sequences and information regarding protein interactions is used to “teach” the machine. Proteins with unknown interactions are compared by the machine to proteins in the database. Homologs of proteins known to interact in the database are predicted to interact.