Abstract: A user's location may be estimated by applying a probability function to raw user location data taken from various telephony or wireless systems. The probability function may estimate a user's location based on a training dataset that may be generated a priori to the analysis. A training dataset may be generated or updated by analyzing queries made with global positioning system (GPS) data to extract a device's GPS location. The probability function may be generated in part from physical maps. The estimated location may improve location accuracy, especially when attempting to map a user's location with accuracies that may be much smaller than a cell of a cellular telephony system.

Abstract: A user's location may be estimated by applying a probability function to raw user location data taken from various telephony or wireless systems. The probability function may estimate a user's location based on a training dataset that may be generated a priori to the analysis. A training dataset may be generated or updated by analyzing queries made with global positioning system (GPS) data to extract a device's GPS location. The probability function may be generated in part from physical maps. The estimated location may improve location accuracy, especially when attempting to map a user's location with accuracies that may be much smaller than a cell of a cellular telephony system.

Abstract: There is provided a method of storing data in a cloud storage system, the method including: generating a file identifier for a data file desired to be stored in the cloud storage system; encrypting the file identifier of the data file using a homomorphic encryption technique to produce an encrypted file identifier of the data file; and transmitting the encrypted file identifier to the cloud storage system for performing data deduplication in relation to the cloud storage system with respect to the data file. There is also provided an associated client device and cloud storage system.

Abstract: An adaptation technique for decoding low-density parity-check (LDPC) codes for hard disk drive (HDDs) systems is disclosed. The method includes tuning the normalization factor for LDPC decoding for each data zone and read head during the test stage of manufacturing. The LDPC decoder can be either a sum-product algorithm (SPA) decoder or a Min-Sum decoder. The channel detector can be any soft-output detector, such as a soft-output Viterbit detector (SOVA), a BCJR detector, a pattern-dependent noise-predictive (PDNP) detector, or a bi-directional pattern-dependent noise-predictive (BiPDNP) detector. The adaptation technique can optimize the LDPC decoding performance for each data zone and read head, thereby relaxing the acceptance criteria for hard disk drive read/write heads and disk media, enabling acceptance and use of a much broader range of head and media for hard disk drives.

Abstract: There is provided a method of predicting a noise component associated with a readback signal in a channel from a dedicated servo medium including a data recording layer and a dedicated servo layer. The method including: providing at least one noise prediction module for each of a plurality of types of servo patterns of the servo layer, and predicting the noise component by, for each of a plurality of segments of the readback signal of the data recording layer, using a selected one of the noise prediction modules. In particular, the selected one of the noise prediction module for a segment of the readback signal is selected based on the type of the servo pattern associated with the segment of the readback signal. There is also provided a corresponding noise predictor, a method of detecting data bits from the readback signal, and a hard disk drive system.

Abstract: There is provided a method of predicting a noise component associated with a readback signal in a channel from a dedicated servo medium including a data recording layer and a dedicated servo layer. The method including: providing at least one noise prediction module for each of a plurality of types of servo patterns of the servo layer, and predicting the noise component by, for each of a plurality of segments of the readback signal of the data recording layer, using a selected one of the noise prediction modules. In particular, the selected one of the noise prediction module for a segment of the readback signal is selected based on the type of the servo pattern associated with the segment of the readback signal. There is also provided a corresponding noise predictor, a method of detecting data bits from the readback signal, and a hard disk drive system.

Abstract: An adaptation technique for decoding low-density parity-check (LDPC) codes for hard disk drive (HDDs) systems is disclosed. The method includes tuning the normalization factor for LDPC decoding for each data zone and read head during the test stage of manufacturing. The LDPC decoder can be either a sum-product algorithm (SPA) decoder or a Min-Sum decoder. The channel detector can be any soft-output detector, such as a soft-output Viterbit detector (SOVA), a BCJR detector, a pattern-dependent noise-predictive (PDNP) detector, or a bi-directional pattern-dependent noise-predictive (BiPDNP) detector. The adaptation technique can optimize the LDPC decoding performance for each data zone and read head, thereby relaxing the acceptance criteria for hard disk drive read/write heads and disk media, enabling acceptance and use of a much broader range of head and media for hard disk drives.

Abstract: A method performed by a disk drive, comprising: receiving a plurality of signal samples over a channel in the disk drive; executing a forward pattern-dependent noise prediction (PDNP) operation on the plurality of the signal samples; generating, based on execution of the forward PDNP operation, a first detection of recorded data bits in the plurality of received signal samples; executing a backward PDNP operation on the plurality of the received signal samples; generating, based on execution of the backward PDNP operation, a second detection of recorded data bits in the plurality of received signal samples; comparing the first detection to the second detection; identifying, based on comparing, one or more erasures in the received plurality of signal samples; and generating one or more sequences of bits that promote correction of the one or more erasures.

Abstract: A method performed by a disk drive, comprising: receiving a plurality of signal samples over a channel in the disk drive; executing a forward pattern-dependent noise prediction (PDNP) operation on the plurality of the signal samples; generating, based on execution of the forward PDNP operation, a first detection of recorded data bits in the plurality of received signal samples; executing a backward PDNP operation on the plurality of the received signal samples; generating, based on execution of the backward PDNP operation, a second detection of recorded data bits in the plurality of received signal samples; comparing the first detection to the second detection; identifying, based on comparing, one or more erasures in the received plurality of signal samples; and generating one or more sequences of bits that promote correction of the one or more erasures.