95 results about "Soft data" patented technology

Data stored in non-volatile storage is decoded using iterative probabilistic decoding. An error correcting code such as a low density parity check code may be used. In one approach, initial reliability metrics, such as logarithmic likelihood ratios, are used in decoding sensed states of a set of non-volatile storage element. The decoding attempts to converge by adjusting the reliability metrics for bits in code words which represent the sensed state. Soft data bits are read from the memory if the decoding fails to converge. Initial reliability metric values are provided after receiving the hard read results and at each phase of the soft bit operation(s). In one embodiment, a second soft bit is read from the memory using multiple subsets of soft bit compare levels. While reading at the second subset of compare levels, decoding can be performed based on the first subset data.

Data stored in non-volatile storage is decoded using iterative probabilistic decoding. An error correcting code such as a low density parity check code may be used. In one approach, initial reliability metrics, such as logarithmic likelihood ratios, are used in decoding sensed states of a set of non-volatile storage element. The decoding attempts to converge by adjusting the reliability metrics for bits in code words which represent the sensed state. Soft data bits are read from the memory if the decoding fails to converge. Initial reliability metric values are provided after receiving the hard read results and at each phase of the soft bit operation(s). In one embodiment, a second soft bit is read from the memory using multiple subsets of soft bit compare levels. While reading at the second subset of compare levels, decoding can be performed based on the first subset data.

Methods and apparatus are provided for soft data generation for memory devices based on a performance factor adjustment. At least one soft data value is generated for a memory device, by obtaining at least one read value; and generating the soft data value based on the obtained at least one read value and an adjustment based on one or more performance factors of the memory device. The read values may comprise, for example, data bits, voltage levels, current levels or resistance levels. The read values may be soft data or hard data. The possible performance factors include endurance, number of read cycles, retention time, temperature, process corner, inter-cell interference impact, location within the memory array and a pattern of aggressor cells. One or more pattern-dependent performance factors and/pr location-specific performance factors may also be considered. The generated soft data value may be a soft read value that is used to generate one or more log likelihood ratios, or may be the log likelihood ratios themselves.

A bitstream having a plurality of data sets is provided to an integrated circuit device such as an FPGA having test circuitry capable of routing data to the device's internal resources, with each data set including configuration information and a trigger signal. Successive data sets of the bitstream are sequentially processed by the test circuitry in response to the trigger signals to sequentially initialize the device's resources to various states. For some embodiments, each data set includes configuration data to configure one or more configurable elements of the device to implement a desired design and includes soft data for use by a processor embedded within the device. For one embodiment, control logic is provided to selectively wait for a predetermined time period before processing a next data set.

An integrated circuit such as an FPGA containing an embedded processor having test circuitry capable of controlling the processor's resources using JTAG commands includes a formatting circuit that formats soft data received from an external storage device into a JTAG-compatible bitstream that can be used by the processor's test circuitry to access and/or control the processor's resources at any time, thereby allowing the embedded processor's resources to be accessed and controlled during FPGA configuration operations before the processor has been initialized to an operational state without using an external configuration tool. For some embodiments, the formatting circuit is a state machine that formats soft data such as firmware code, software programs, processor commands, and the like received from the external storage device into a JTAG-compatible bitstream that can be loaded into and/or used to access the resources of the embedded processor via the processors' test circuitry.

Methods and apparatus are provided for computing soft data or log likelihood ratios for received values in communication or storage systems. Soft data values or log likelihood ratios are computed for received values in a communication system or a memory device by obtaining at least one received value; identifying a segment of a function corresponding to the received value, wherein the function is defined over a plurality of segments, wherein each of the segments has an associated set of parameters; and calculating the soft data value or log likelihood ratio using the set of parameters associated with the identified segment. The computed soft data values or log likelihood ratios are optionally provided to a decoder.

A receiver of COFDM digital television signals includes an inner decoder for iterative soft-decision decoding of concatenated convolutional coding (CCC) and an outer decoder for Reed-Solomon (RS) coding. The receiver generates error flags for identifying code symbols to be erased before the output symbols from the inner decoder are byte de-interleaved and supplied to the outer decoder. Generation of those flags depends on soft decoding results from the inner decoder. The method of locating errors ascribes to each byte supplied to the outer decoder for RS coding the highest lack-of-confidence level specified by the soft data bits associated with that byte. The method is described as being extended to locate byte errors in plural-dimension cross-interleaved Reed-Solomon codes (CIRC) apt to be employed in DTV broadcasting to mobile and handheld receivers.

Methods and apparatus are provided for approximating a probability density function or distribution for a received value in communication or storage systems. A target distribution is approximated for a received value in one or more of a communication system and a memory device, by substantially minimizing a squared error between the target distribution of the received values and a second distribution obtained by mapping a predefined distribution, such as a Gaussian distribution, through a mapping function, wherein the second distribution has an associated set of parameters. The mapping function can be, for example, a piecewise linear function. The second distribution has a plurality of segments and each of the segments has an associated set of parameters. The associated set of parameters can be used to compute probability values, soft data values or log likelihood ratios.

System and method for a hybrid-cache. Data received from a data source is cached within a static cache as stable data. The static cache is a cache having a fixed size. Portions of the stable data within the static cache are evicted to a dynamic cache when the static cache becomes full. The dynamic cache is a cache having a dynamic size. The evicted portions of the stable cache are enrolled into the dynamic cache as soft data.

The present disclosure describes a method that improves the long-range geobody continuity in Multiple Point Statistical methods, wherein the coarsest multi-grid level cells are simulated in a regular path, and the subsequent level cells are simulated in a random path as usual. The method is general and is applicable to different cases: such as hard data conditioning, soft data conditioning, non-stationarity modeling, 2 or more than 2 types of facies modeling, and 2D and 3D modeling. The method is particularly useful in reservoir modeling, especially for the channelized systems, but can be generally applied to other geological environments.

The present disclosure includes apparatuses and methods for monitoring error correction operations performed in memory. A number of embodiments include a memory and circuitry configured to determine a quantity of erroneous data corrected during an error correction operation performed on soft data associated with a sensed data state of a number of memory cells of the memory, determine a quality of soft information associated with the erroneous data corrected during the error correction operation performed on the soft data, and determine whether to take a corrective action on the sensed data based on the quantity of the erroneous data corrected during the error correction operation and the quality of the soft information associated with the erroneous data corrected during the error correction operation.

The present disclosure includes methods, devices, and systems for determining and using soft data in memory devices and systems. One or more embodiments include an array of memory cells and control circuitry coupled to the array. The control circuitry is configured to perform a number of sense operations on the memory cells using a number of sensing voltages to determine soft data associated with a target state of the memory cells, and adjust a sensing voltage used to determine the target state based, at least partially, on the determined soft data.

The invention relates to a Bayesian soil moisture estimation method based on multi-source remote sensing data. According to the Bayesian soil moisture estimation method, aiming at how to obtain high-precision soil moisture weighting probability soft data, 12 kinds of multi-source data are fused for the first time to calculate the weighting probability soft data, including downscaling FY3-B soil moisture products, and obtaining albedo (A), vegetation index NDVI (V) and surface temperature LST (T) by using MODIS products; elevation data is acquired by using an ASTER product; gradient, slope direction, plane curvature, profile curvature, surface roughness, humidity index and fluctuation amplitude data are obtained through calculation according to the elevation data obtained by the ASTER product; and weighted probability soft data is obtained by adopting two weight determination methods of multivariable correlation analysis and principal component analysis. The Bayesian soil moisture estimation method also analyzes precision analysis of different soft data quantities, proposes to guarantee sufficient soft data quantities, and plays an important role in obtaining higher-precision soil moisture spatial distribution.

The invention discloses a soil moisture prediction method based on Bayesian maximum entropy, which comprises the following steps: selecting an exemplary soil plot and setting a plurality of random sampling points on the soil plot; constructing a hard database and soft database of soil moisture content; using the hard and soft databases to deal with soil water content of random sampling points, including de-trend and exploratory analysis of soil water content, data normality test and spatio-temporal covariance estimation. Bayesian maximum entropy method and the hard database and soft database are combined to predict the soil water content after treatment. The invention integrates the hard data and the soft data together, predicts and estimates the posterior condition probability of the soilmoisture content, and improves the prediction accuracy of the soil moisture content.

A receiver is described for optical or electromagnetic signals comprising a digital equalizer and a Forward Error Correction, said digital equalizer and FEC combined together to obtain an iterative equalization and error-correction loop, plus a method is described for improving the exactness in relating binary data to a digitalized-data transmitting, analog electromagnetic or optical-electrical signal arriving at a receiver, comprising the steps a) transforming the arriving analog signals into analog signals in the form of digital soft data representing interim values, which indicate the value of the amplitude of the analog signal, b) feeding the soft data to a digital equalizer, c) sequential determination of the interim value representing the boundary at which the decision would have to be made whether a ‘0’ or a ‘1’ had to be related to the soft data when transformed to binary data in the digital equalizer, d) feeding the soft-data signal further processed in the way described to a soft-in/soft-out FEC, e) error correction of the soft data by means of the check bits contained in the signal, f) removal of the check bits, g) output of the error-corrected signal in the form of soft data, h) preprocessing the signal for reinjection into a digital equalizer, i) feeding back the signal to the digital equalizer placed in the signal path, and k) repeating steps c) to j) until no further improvement of the data quality can be detected or until a predefined number of iteration loops have been executed, and l) hard decision and transformation of the soft data preprocessed in the way described into binary data.

A method for channel estimation is provided. Firstly is to estimate channel impulse response (CIR) of training sequence. Next is to generate a soft data by means of an equalizer according to the CIR of the training sequence. Subsequently is to estimate CIRs of data sequences according to the interposed training sequence by correlation channel estimation. Next is to define a weight by means of auto-correlation of the estimated data sequence and then to cancel the interference of channel by the weight. Finally is to use the soft data and the non-interference CIR to find out the data stored in the data sequence.