International Business Machines Corporation - Armonk NY
International Classification:
G11B 20/18
US Classification:
714 47, 360 53, 369 53, 714704
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.
Wireless Security Access Management For A Portable Data Storage Cartridge
International Business Machines Corporation - Armonk NY
International Classification:
H04K 1/00 G03F 7/00
US Classification:
713182, 707 9, 707100
Abstract:
A portable security system mounted in a portable data storage cartridge for managing access by users to the cartridge. A programmable computer processor mounted in the cartridge is powered by and transfers data to a data storage drive via a wireless RF interface, when mounted in the drive. A user table has a unique user identifier for each authorized user and lists permitted activities of the user for the cartridge. The user identifier comprises a user symbol and a user decrypting sender public key. An authentication message from the authorized user is encrypted by a sender private key and a receiver public key. The cartridge processor decrypts the message employing a receiver private key and the sender public key, whereby the user authentication message is known to have come from the user and grants access to the user for the listed activities for the cartridge.
Identifying A State Of A Data Storage Drive Using An Artificial Neural Network Generated Model
International Business Machines Corporation - Armonk NY
International Classification:
G06N 5/00
US Classification:
706 15, 73599, 436172
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.
Identifying A State Of A System Using An Artificial Neural Network Generated Model
International Business Machines Corporation - Armonk NY
International Classification:
G06F 15/00
US Classification:
702189, 702197
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. Thus, a high similarity between the trial vector and an exemplar vector which represent a “good” system is likely to have come from a “good” system.
Method And Apparatus For Providing Error Correction Capability To Longitudinal Position Data
Roy D. Cideciyan - Rueschlikon, CH Evangelos S. Eleftheriou - Zurich, CH Glen A. Jaquette - Tucson AZ, US Paul J. Seger - Tucson AZ, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G11C 29/00
US Classification:
714771, 714785
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.
Roy D. Cideciyan - Rueschlikon, CH Paul J. Seger - Tucson AZ, US Keisuke Tanaka - Tokyo-to, JP
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
H03M 7/00
US Classification:
341 59, 341 50
Abstract:
Method and apparatus are provided for encoding and decoding rate-(s(K+1)/s(K+1)+1) TCMTR(j,k,t,a) codes, where s is the ECC symbol size in bits and K is the number of unencoded symbols that are interleaved with an (s+1)-bit encoded block at the output of a rate-s/(s+1) encoder that encodes the r-th s-bit symbol. K=m/s−1 where m=s(K+1) is the total number of bits to be encoded. Error propagation is reduced, thus allowing the ECC code to correct errors efficiently. Header error-rate is also reduced by eliminating occurrence of likely error events at the detector output. Although initially an RLL code may be designed for an ECC symbol size of s bits, the RLL encoding of the present invention may be used in conjunction with ECC schemes that utilize symbol sizes other than s bits.
Roy D. Cideciyan - Rueschlikon, CH Evangelos S. Eleftheriou - Zurich, CH Thomas Mittelholzer - Zurich, CH Paul J. Seger - Tucson AZ, US Keisuke Tanaka - Tokyo-to, JP
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
H03M 5/00
US Classification:
341 58, 360 48, 341 59
Abstract:
An unencoded m-bit data input sequence is divided into a block of n bits and a block of m−n bits. The block of n bits is divided into a first set of n+1 encoded bits, wherein at least one of P subblocks of the first set satisfies a G, M and I constraints. The first set of n+1 encoded bits is mapped into a second set of n+1 encoded bits wherein at least one of P subblocks of the second set gives rise to at least Q transitions after 1/(1+D) precoding. A second set of n+1 encoded bits is divided into P encoded subblocks and the P encoded subblocks are interleaved among (m−n)/s unencoded symbols so as to form a (m+1)-bit output sequence codeword which is then stored on a data storage medium.
Modeling Error Correction Capability With Estimation Of Defect Parameters
International Business Machines Corporation - Armonk NY
International Classification:
H03M 13/00
US Classification:
714755, 714762, 714786, 714788, 703 13
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. If desired, a histogram may be generated which includes the rate at which errors occurred and subsequently used to estimate the probability of an error event.
Name / Title
Company / Classification
Phones & Addresses
Paul Seger Branch Manager
Veterans of Foreign Wars of The United States Civic and Social Associations
320 Patricia Ln, Hanover Park, IL 60103 (630)2132266
Cooper and Associats via Aspen Technical Chicago, IL Jan 2014 to Aug 2014 Instrument and Controls EngineerAmbitech Engineering Corporation Downers Grove, IL Apr 2012 to Aug 2013 Senior EngineerIllinois River Energy River, IL 2006 to 2012 Instrumentation Automation and Controls EngineerGas Technology Institute
1998 to 2006 Principal EngineerDT Merrill
1994 to 1998 Systems EngineerBHS Systems
1992 to 1994 Systems EngineerPyramid Controls
1986 to 1992 Application EngineerAuto-Tech
1985 to 1986 Development EngineerBelden Wire
1981 to 1985 Equipment Development EngineerAES Technology
1972 to 1981 Electrical Engineer and Department Supervisor
Education:
College of DuPage 2002 Instrumentation and Controls EngineeringFree State College of Engineering MD BSEE
Skills:
Labview 2010 to 2010 2010, Word, Excel, Outlook MySql RsLogix 500 MS Access 2010 Labview RsLogix 5000 CadSoft Schematic and PCB Design Wonderware Unified Modeling Language (UML) Fisher Specification Manage Formation Programming Languages Working Knowledge of AutoCAD Masoneilan ValSpeQ Java, Perl, C and Forth
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