Abstract: The present invention is directed to a system and method which allows managers to identify anomalies in organizations, brands or projects at an early stage. Advantage is taken of the fact that a large percentage of the elements of all organizational (project) problems are promptly captured in some form on an electronic notation. Portions of these notations are abstracted into categories. Each categorized abstract is displayed in an appropriate partition for review by one or more managers. As a category becomes populated with more and more abstracts, the visual color of the representations of the abstracts of that category can change to reflect their individual ages or other attributes of that category's abstracts. For example, the color of each abstract-representing graphical “dot” can fade over time. A group of new “dots” within a category, or blinking dots representing content that is particularly well matched to the category definition can graphically signify extra concern.

Abstract: Secure commerce systems and methods are disclosed. Commercial security is first provided by identity verification via voice authentication using voice similarity analysis, and by a set of random, continually-changing passwords that can be encrypted and uniquely identified with a fictitious and anonymous user name. Each anonymous user name is uniquely associated with a consumer's real identity that is protected by a set of security measures of a Knowledge Manager (KM), for use with an Anonymous Payment Card (APC) and an Anonymous Internet-email Account. The KM issues a fictitious, “one-time” (e.g. never re-used) APC number and user name to a participating consumer who wishes to attain anonymity, along with a mailing address at a “Ghost Shipping” location, such that packages sent to the consumer can be re-packaged and re-shipped to the consumer without the seller or the shipper knowing the consumer's real identity.

Abstract: Image features are generated by performing wavelet transformations at sample points on images stored in electronic form. Multiple wavelet transformations at a point are combined to form an image feature vector. A prototypical set of feature vectors, or atoms, is derived from the set of feature vectors to form an “atomic vocabulary.” The prototypical feature vectors are derived using a vector quantization method, e.g., using neural network self-organization techniques, in which a vector quantization network is also generated. The atomic vocabulary is used to define new images. Meaning is established between atoms in the atomic vocabulary. High-dimensional context vectors are assigned to each atom. The context vectors are then trained as a function of the proximity and co-occurrence of each atom to other atoms in the image. After training, the context vectors associated with the atoms that comprise an image are combined to form a summary vector for the image.

Abstract: A "fuzzy" associative memory system, and a related method for its use, the system having a large data vector memory for the storage of a large number of vectors having multiple data fields, to be compared with a number of comparison windows defined by upper and lower limits for each data field of the vectors. Output results in the form of match indications are accumulated in an output buffer for output to a host computer, which also initiates the searches. The large data vector memory facilitates window searching and searching for the closest neighbors to a given vector. It also provides a technique for searching large numbers of windows, and for classifying unknown patterns even when pattern classes are irregular in shape.

Abstract: A method of data reduction in non-coherent SLAR systems enhances the usefulness of such systems in moving target environments. Digitized MT data is processed to identify and classify target footprints. At least some of the footprints are replaced by an indicator of the centroid thereof and its motion. Clutter is rejected. Other footprints are replaced with truncated versions thereof. The technique is suitable for real time application.

Abstract: The signal to be classified is sampled and the samples are multiplied by weighting functions prior to performing discrete Fourier transforms, power calculations, and normalization thereon so that the preprocessor is essentially a plurality of channel bandpass filter stages, the k-th one of which has a frequency response approximating an ideal filter defined by ##EQU1## where k=O, .alpha., 2.alpha., 3.alpha., . . . , r.alpha..alpha.=constant, 1.ltoreq..alpha..ltoreq.N/2r+1=greatest integer in N/.alpha.This preprocessor transforms blocks of one dimensional data into spectra in such a way that data blocks coming from similar sources will have spectra that are close to one another in spectrum space. The discrete Fourier transform is modified to remove or reduce the phase dependency problem and to enhance the clustering of similar spectra by the weighting function above.