Abstract: A computer-implemented method and computer system for supervised machine learning and classification comprises applying, by a dimensional visualization module incorporating a global objective function, an unsupervised dimension reduction algorithm to a dataset including a plurality of data points capable of visual representation to produce a dimensionally reduced dataset and parsing, by the dimensional visualization module, the dimensionally reduced dataset to a user interface module for visual display of the dimensionally reduced dataset.

Abstract: An example embodiment of the invention provides a computer-implemented method of clustering data. The method comprises performing, by a clustering module, an nth pass of clustering on data to produce a plurality of nth clusters. A cluster analysis module calculates a plurality of components of each of the nth clusters and de-weights a largest component of each of the nth clusters to produce a plurality of de-weighted nth clusters. The clustering module performs an n+1th pass of clustering on the plurality of de-weighted nth clusters to produce a plurality of n+1th clusters. The method may be reiterated.

Abstract: A method 30 and system 10 for facilitating safe handling of a physical medium of financial exchange, such as cash notes is provided to prevent cash-in-transit heists. The method 30 includes physically marking 33, using a first processing device 8, cash with a unique owner identifier which is associated with the owner of the cash, and physically invalidating 34 the cash through cutting, staining or stamping it. Once invalidated, the worthless cash can be transported risk-free to a desired cash processing facility. Upon arrival, the method includes verifying 44, using an authentication module 24, authenticity of the unique owner identifier and cash notes themselves. If verification fails, law enforcement is notified. If passed, the cash is destroyed 38, using a second processing device 9 which digitizes, using a digitizer 22, the cash notes and transfers a digital equivalent value, which is credited to the owner's account, to an electronic record keeping system 12.

Abstract: A computer-implemented method and computer system for supervised machine learning and classification comprises applying, by a dimensional visualization module incorporating a global objective function, an unsupervised dimension reduction algorithm to a dataset including a plurality of data points capable of visual representation to produce a dimensionally reduced dataset and parsing, by the dimensional visualization module, the dimensionally reduced dataset to a user interface module for visual display of the dimensionally reduced dataset.

Abstract: A computer-implemented method to identify RPI (Radio Frequency Interference) in a spectrogram includes dividing, by a matrix module of a computer processor, the spectrogram into a plurality of blocks, each block comprising a matrix of data points. The method includes applying, by a masking module of the computer processor, a plurality of matrix pooling functions to each block, the plurality of matrix pooling functions configured to produce a plurality of summarized pools. The method includes creating, by a masking module of the computer processor, a line mask for each of the pools and creating, by the masking module, an RPI mask based at least on the line masks, the RPI mask being indicative of RPI.

Abstract: An example embodiment of the invention provides a computer-implemented method of clustering data. The method comprises performing, by a clustering module, an nth pass of clustering on data to produce a plurality of nth clusters. A cluster analysis module calculates a plurality of components of each of the nth clusters and de-weights a largest component of each of the nth clusters to produce a plurality of de-weighted nth clusters. The clustering module performs an n+1th pass of clustering on the plurality of de-weighted nth clusters to produce a plurality of n+1th clusters. The method may be reiterated.

Abstract: A computer-implemented method to identify RPI (Radio Frequency Interference) in a spectrogram includes dividing, by a matrix module of a computer processor, the spectrogram into a plurality of blocks, each block comprising a matrix of data points. The method includes applying, by a masking module of the computer processor, a plurality of matrix pooling functions to each block, the plurality of matrix pooling functions configured to produce a plurality of summarized pools. The method includes creating, by a masking module of the computer processor, a line mask for each of the pools and creating, by the masking module, an RPI mask based at least on the line masks, the RPI mask being indicative of RPI.