135 results about "Data binning" patented technology

A process and system for profiling code executing on a data processing system is provided. Event-based trace data is recorded in response to selected events, and the event-based trace data includes an indication which code is being interrupted. The trace data may be processed to identify a thread or method that was executing during the event. A periodically occurring event is also detected, and a call stack associated with the profiled code is identified in response to detection of the periodically occurring event, such as a timer interrupt. The call stack is examined to identify each routine that is currently executing during the periodically occurring event, and the trace data is recorded with the call stack information. The trace data from the recorded events and the trace data from the call stacks are processed to generate a tree structure in which the nodes indicate the call structure of the routine information from both the trace events and the call stacks.

A user interface may be designed to receive conflicting data from multiple data sources during a merger of legacy computer systems. The interface automatically validates the conflicting data against each other to generate a final data set for review. Summary information for each data set can be displayed in a single interface such that the summary data sets are individually selectable and provide an overview of the data merger process for each data set. By drilling down through the summary data, the user interface can display individual data sets where data conflicts can be resolved, missing data can be provided, and duplicate data can be eliminated. When the merger process is complete, the interface can translate the data set into a standardized format that can be exported to form generation modules to generate, for example, a welcome letter based on the translated data set.

A computer-implemented method for use with an extensible markup language (XML) document includes inputting a high-level mapping specification for a schema mapping; and generating a target XML document based on the mapping. The method may perform schema mapping-based XML transformation as a three-phase process comprising tuple extraction, XML-fragment generation, and data merging. The tuple extraction phase may be adapted to handle streamed XML data (as well as stored/indexed XML data). The data merging phase may use a hybrid method that can dynamically switch between main memory-based and disk-based algorithms based on the size of the XML data to be merged.

The invention relates to a dataset construction method and device, a mobile terminal and a computer readable storage medium. The method comprises the steps that a first dataset which contains a firstpreset amount of data and carries annotation information is acquired according to a learning task; a classification model is trained on the first dataset, and precision information of the classification model is assessed; when the precision information reaches a preset value, non-annotated data is screened based on the trained classification model, and the screened data is merged into the first dataset to form a second dataset; and the data in the second dataset is classified and cleaned based on the trained classification model to form a target dataset with a target amount of data. In this way, semi-automatic data collection, screening and annotation can be realized, a large amount of high-quantity data for classification model training can be acquired on the basis of consuming little manpower, manpower cost is greatly saved, and meanwhile dataset construction efficiency is improved.

Embodiments provide consumers browsing and shopping online for durable goods such as new vehicles with pricing information for user-specified vehicle configurations based at least in part on dynamic, user-centric local regions that can be any shape. A local region may initially center around or start from user-provided geo-specific information and be refined using criteria such as distance, demographics, buying behaviors, etc. The system can leverage data from the finest level of granularity available and dynamically determine multiple, potentially overlapping, local regions which can be user-specific, product-specific, or both. A pricing model may incorporate the data within a dynamic local region thus determined. The parameters of the pricing model may be weighted utilizing a weighting function that can be dynamically adapted to individual users as well as specific vehicles. The pricing model may provide for pricing information that is not limited or constrained by standard administrative or political boundaries.

Cluster- and descriptor-based recommender systems are disclosed which can, for example, scale to voluminous data. The data is generally organized into records and items. In one embodiment, a method first consolidates the data into groups, such as clusters or descriptors. The method determines a predicted vote for a particular record and a particular item, using a similarity scoring approach, such as a likelihood similarity approach, or a correlation similarity approach, based on the groups. The predicted vote can then be output.

Inconsistencies between configuration rules represent a significant concern when modeling a product using configuration rules. The consistency checking system approaches a configuration model from the perspective of a sets of features and families. The configuration space of a model represents the entire set of all combinations of selections within a configuration model. The consistency checking system operates on subsets of the configuration space by consolidating data within the configuration space into minimized subsets that represent a portion of the configuration space where a particular consistency error can occur. Thus, the contents of each subset vary depending upon which consistency error is being checked, and consistency checking is performed on reduced subsets determined on an error by error basis rather than on the configuration space as a whole.

The invention relates to a mask image defection detection method and a detection system thereof. The method comprises the following steps: step of obtaining a mask image-obtaining a mask image to be detected and a normal mask image; step of feature extraction and format conversion-extracting edge characteristic and converted it as geometric figure format to be stored; step of filling-filling the blank area of the rectangular edge contour of the geometric figure; step of data merging-carrying out figure superposition; step of contour simulation-carrying out the contour simulation of wafer; step of critical dimensions analysis of the simulated contour-measuring the critical dimensions and calculating the difference of critical dimensions; and step of judge-judging whether the mask defection restoration is succeed or not through the difference of critical dimensions. The invention further relates to a mask image defection detection system. The method and system in the invention is simple and practicable, and has a substantial effect, thus the lithography mask production cost of 45 nm or higher nanometer level lithography technology is effectively reduced.

The invention discloses a method and system for processing an incremental snapshot. The method comprises the following steps of establishing a snapshot volume with preset logical block particle size corresponding to a source volume, and remapping new data blocks in an assigned source volume or an assigned snapshot volume to a target snapshot volume through remapping operation when data merging and updating need to be carried out on data blocks corresponding to an assigned logic block in the target snapshot volume with the preset logical block particle size and the new data block corresponding to the assigned logic block in the assigned source volume or the assigned source snapshot volume. According to the method and system for processing the incremental snapshot, the data merging and the updating are carried out on the logic block needing to be updated in an existing snapshot volume through the remapping operation, updating according to needs on an identical snapshot volume especially on non-writable snapshot data is achieved, flexibility of data storage and updating is improved, and data merging and updating efficiency is improved.

The invention provides a machine vision inspection technology, related to the improvement about the device of Guass template convolution. It includes image sensor 1, the register group of the list convolution coefficient 2 and the register group of the line convolution coefficient 3. Its character lies in that it consists of address generator 4, line register group 5, list data combination circuit 6, list convolution circuit 7, serial shift register group 8, line data combination circuit 9 and line convolution circuit 10. The present invention reduce the memory capacity of the template and the use of logical resource; fulfill the big template Guass convolution at real-time by the parallel and pipelined approach; greatly improve the velocity of Guass template convolution.

The invention discloses an adaptive balanced filter device in optical fiber communication. The device comprises a data grouping unit, a time-frequency transformation unit, a balanced filter unit, an inverse Fourier transformation unit and a data merging unit, wherein the data grouping unit is used for grouping the received time-domain signals according to sample point indexes to obtain grouped time-domain signals; the time-frequency transformation unit is used for transforming the grouped time-domain signals into grouped frequency-domain signals; the balanced filter unit is used for performing balanced filtration on the grouped frequency-domain signals according to a frequency-domain balanced filter coefficient; the inverse Fourier transformation unit is used for performing inverse Fourier transformation on the grouped frequency-domain signals after balanced filtration to obtain grouped time-domain signals after balanced filtration; and the data merging unit is used for merging the grouped time-domain signals after balanced filtration to obtain complete time-domain signals after balanced filtration. The invention further simultaneously discloses an adaptive balanced filter method in optical fiber communication.

The invention discloses a dual-line center-of-mass following imaging method. When in treatment of a whole spot image, gray values of the current two lines of pixels are simultaneously read at each time, the gray values of the two lines of the pixels are simultaneously compared with a preset threshold, when at least one of the two lines of the pixels is larger than the threshold, the dual-line data labeling, the dual-line data merging and the dual-line data accumulation processes are implemented till the completion of the treatment of the whole spot image, and the obtained coordinates of the center-of-mass of the spot image are output. The invention further simultaneously discloses a dual-line center-of-mass following imaging device, and the use of the method and the device can improve the parallel treatment ability of data and the treatment speed of the data.

A correct error correction code can be generated even if a RAM error occurs before writing store data in cache memory (RAM) after confirming that cache line data for storage includes no errors. Before writing the store data, cache line data for storage is stored in a register, the store data is written to the cache memory, the stored contents of the register are merged with the store data, and an error correction code is generated for a result of the merger.

Disclosed herein is a method of analyzing three-dimensional data obtained from a chromatography/spectrometry process, in particular an LC/MS process using a two-dimensional multi-variant statistical analysis. The LC portion permits separation of analytes within a sample. The information obtained from such a procedure typically depends on retention time (R_t). As the analytes progress through the system, they enter the MS region of the LC/MS system. There they are ionized and a mass detector then detects these ionized species. The information procured from this procedure is generally reported as intensity for a corresponding m/z value. Therefore, an LC/MS system provides at least three pieces of information. Principle component analysis (PCA) is a robust method of multi-variant analysis of this type of data between different samples. However, typically, PCA analysis is performed using only two-dimensional data. Therefore, it is expected that at least one parameter of data obtained from a LC/MS operation is lost, however, the methods disclosed herein preserves all three mathematical dimensions.

The invention discloses a method and a system for searching images by images applied to medical image auxiliary diagnosis analysis. The method comprises the following steps: S1, extracting focus anatomical position, form description and quantitative analysis information in a medical image and organizing the information into formatted data; S2, combining the data obtained at the step S1 with formatted and standardized medical history, examination, pathology, treatment and other data of the same patient to form a feature label of a case image; S3, matching the feature tags with tags in a case database by adopting a matching algorithm, and outputting a plurality of pieces of case information with higher matching degrees and diagnosis suggestions of the case information. According to the method, the iconography characteristic data is taken as a core, and other case feature data are used as assistance, so the structured data are organized to serve as feature tags of case images, reference ideas and prompt suggestions of case diagnosis and analysis are provided for image analysts by searching for closest cases, and the image analysts can obtain more comprehensive, objective and accurateconclusions.

Disclosed is a methodology for the collection, processing and distribution of traffic data and delivering more accurate routing in GPS-based Personal Navigation Assistants (PNAs). The methodology includes a) Data Collection Methodology (DCM), b) a Data Incorporation Methodology (DIM), and c) a Routing Methodology (RM). The DCM regards the collection of information related to the traffic flow in each road. The DIM includes a procedure executed at a central system (CS), outside the PNA, performing the incorporation of the “trace data” (i.e. user-submitted route-related information, including a list of periodically collected, time-stamped GPS samples, augmented with other parameter values defining the vehicle-type, weather-conditions, road-type, holiday-type), collected by the DCM. The RM determines, by means of suitable PNA software, the shortest route from a starting point to a final destination, through the combining the static data from existing maps residing inside the PNA, with the dynamic (time-variant) data derived by the DIM.

A method and system for performing write-through store operations of valid data of varying sizes in a data processing system, where the data processing system includes multiple processors that are coupled to an interconnect through a memory hierarchy, where the memory hierarchy includes multiple levels of cache, where at least one lower level of cache of the multiple of levels of cache requires store operations of all valid data of at least a predetermined size. First, it is determined whether or not a write-through store operation is a cache hit in a higher level of cache of the multiple levels of cache. In response to a determination that cache hit has occurred in the higher level of cache, the write-through store operation is merged with data read from the higher level of cache to provide a merged write-through operation of all valid data of at least the predetermined size to a lower level of cache. The merged write-through operation is performed in the lower level of cache, such that write-through operations of varying sizes to a lower level of cache which requires write operations of all valid data of at least a predetermined size are performed with data merged from a higher level of cache.

An improved forward error correction (FEC) technique for coding speech data provides an encoder module which primary-encodes an input speech signal using a primary synthesis model to produce primary-encoded data, and redundant-encodes the input speech signal using a redundant synthesis model to produce redundant-encoded data. A packetizer combines the primary-encoded data and the redundant-encoded data into a series of packets and transmits the packets over a packet-based network, such as an Internet Protocol (IP) network. A decoding module primary-decodes the packets using the primary synthesis model, and redundant-decodes the packets using the redundant synthesis model. The technique provides interaction between the primary synthesis model and the redundant synthesis model during and after decoding to improve the quality of a synthesized output speech signal. Such ''interaction'', for instance, may take the form of updating states in one model using the other model.

The invention discloses a machine tool cutter residual life prediction method based on LSTM + CNN, and the method comprises the steps: carrying out the judgment of the signal features of uploaded training data, and distinguishing a continuous signal and a discrete signal; performing data merging on the real-time data of different frequencies sampled by the sensor; checking whether missing values or abnormal values exist in the training data and the real-time data or not; if the missing values or the abnormal values exist, using a moving average method to supplement the missing values or replacing the abnormal values, so as to enable the data to be complete and effective, and removing outliers; carrying out selection and dimension reduction on the training data and the real-time data according to data characteristics so as to facilitate model fitting and prevent an over-fitting phenomenon; and training and testing the LSTM + CNN model, and adjusting training parameters and model parameters according to the error, so as to reduce the error to a reasonable range. According to the method, the precision of the prediction result is improved by adopting a grouping mode and a dimension reduction mode, deterministic factors and uncertain factors are comprehensively considered, and the precision of the prediction result can be effectively improved.

The invention discloses a multiple-dimension data management method and device. The method includes the steps that corresponding necessary attributes are determined according to the preset management function, and at least one necessary attribute is selected from the necessary attributes to serve as a main key attribute; according to all pieces of original data, corresponding newly-increased data is generated; the number of the pieces of the newly-increased data is determined according to the number of the main key attributes, the value of the at least one main key attribute in each piece of newly-increased data serves as a preset character, and the values of the other attributes are same as the corresponding original data; the original data and the newly-increased data are subjected to merging treatment according to the main key attributes, and target data is obtained. Compared with the original data, in the target data, unnecessary attributes are reduced, the multiple pieces of data with one or more same necessary attribute values in the original data are merged into one piece, and the problem that due to the enormous data volume, the data querying performance is reduced can be further solved.