Abstract: The state or condition of a system may be evaluated by comparing a set of selected parameter values, converted into a trial vector, with a number of model or exemplar vectors, each of which was represents a particular state or condition of a sample system. Examples of such conditions may include “good”, “marginal”, “unacceptable”, “worn”, “defective”, or other general or specific conditions. Sets of parameter values from the system are converted into input vectors. Unprocessed vectors are then processed against the input vectors in an artificial neural network to generate the exemplar vectors. The exemplar vectors are stored in a memory of an operational system. During operation of the system, the trial vector is compared with the exemplar vectors. The exemplar vector which is closest to the trial vector represents a state which most closely represents the current state of the system.

Abstract: A method, system and program product accurately model the error characteristics of a communications system, such as a tape storage system. Input parameters are entered which describe defect rates and sizes, Codeword Data Structure bytes, and any interleaving factor. Bit defects from simulated defect sources are generated, defined by the starting and ending bits of each defect within a codeword. Any codewords which are defect-free are filtered out and not processed further, thereby increasing the processing speed of the model. Within the defect streams, overlapping defects are merged, redefining defect regions by starting and ending bits. Because only the definitions are processed, not the entire length of the codewords or defects, processing efficiency is further enhanced. The number of defects that occur in each codeword is determined and the probability of the occurrence of N bytes in error per processed codeword may be computed.

Abstract: A method and apparatus for providing error correction capability to longitudinal position data are disclosed. Initially, data are encoded via a set of even LPOS words and a set of odd LPOS words. The encoded data are then decoded by generating a set of syndrome bits for each of the LPOS words. A determination is then made as to whether or not there is an error within one of the LPOS words based on its corresponding syndrome bits.

Abstract: The state or condition of a system may be evaluated by comparing a set of selected parameter values, converted into a trial vector, with a number of model or exemplar vectors, each of which was represents a particular state or condition of a sample system. Examples of such conditions may include “good”, “marginal”, “unacceptable”, “worn”, “defective”, or other general or specific conditions. Sets of parameter values from the system are converted into input vectors. Unprocessed vectors are then processed against the input vectors in an artificial neural network to generate the exemplar vectors. The exemplar vectors are stored in a memory of an operational system. During operation of the system, the trial vector is compared with the exemplar vectors. The exemplar vector which is closest to the trial vector represents a state which most closely represents the current state of the system.

Abstract: The state or condition of a data storage drive, or a subsystem within a drive, may be evaluated by comparing a set of selected parameter values, converted into a trial vector, with a number of model or exemplar vectors, each of which was represents a particular state or condition of a sample drive. Examples of such conditions may include “good”, “marginal”, “unacceptable”, “worn”, “defective”, or other general or specific conditions. Sets of parameter values from the drive are converted into input vectors. Unprocessed vectors are then processed against the input vectors in an artificial neural network to generate the exemplar vectors. The exemplar vectors are stored in a memory of an operational drive. During operation of the drive, the trial vector is compared with the exemplar vectors. The exemplar vector which is closest to the trial vector represents a state which most closely represents the current state of the drive.

Abstract: The present invention differentiates between systematic and non-systematic conditions by observing a figure of merit over a series of many observation events. In a data storage recording environment, the particular figure of merit used is the number of data segments that must be re-written (due to errors) to a recording medium in order to assure that an entire data set is correctly written. A larger number of re-written segments is indicative of a significant error condition. After each data set is completely and correctly written, the number of re-written segments for the data set is reported as an “event.” A running history of the classified events (or the events themselves) is maintained. Then, at a predetermined time, the history is analyzed and a decision made as to whether any observed events meets predetermined criteria for a systematic condition.