Eureka-AI is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
Eureka AI

1864results about How to "Reduce computational complexity" patented technology

Method for detecting code similarity based on semantic analysis of program source code

The invention discloses a method for detecting code similarity based on semantic analysis of a program source code, which relates to computer program analyzing technology and a method for detecting complex codes of computer software. The method solves the prior problems of low similarity detection accuracy and high computing complexity on the codes of different syntactic representations and similar semantemes, and also solves the problem of incapability of realizing large-scale program code similarity detection. The method comprises the following steps: resolving two segments of source codes to be detected into two control dependence trees of a system dependence graph respectively and executing basic code standardization respectively; utilizing a measure method to extract candidate similar code control dependence trees of the control dependence trees which are subjected to the basic code standardization; executing an advanced code standardization operation on extracted candidate similar codes; and computing semantic similarity to obtain a similarity result so as to finish the code similarity detection. The method is applied to source code piracy detection, software component library query, software defect detection, program comprehension and the like.

Character extracting method in digital video based on character segmentation and color cluster

The invention relates to a character extracting method in a digital video based on character segmentation and color cluster, which comprises the following steps: (1) character segmentation: utilizing the characteristic differences of a character area and a character interval area to carry out vertical projection to segment images in the character area, namely, segmenting each row of area image containing a plurality of characters into a plurality of subarea images only containing a single character so as to reduce the post operating and treating difficulties and improve the identifying accuracy rate of OCR; and (2) character extraction: firstly, using the character color characteristic in the image to cluster colors, finding out an image layer containing maximum character information as a target image layer, and deleting the background area; and then, using the communicating characteristics of the characters to analyze a communicating area of the target image layer, and removing non-character areas to obtain such three results as single character images, an integral image of the character area and an integral image spliced by the single character images respectively, wherein all the three results are input to an OCR system to be identified, and the latter two results use the semantic processing function of the OCR and can accurately determine the characters with similar forms according to the context to improve the identifying effect.

Position-information-based device-to-device (D2D) clustering multicast method

The invention discloses a position-information-based device-to-device (D2D) clustering multicast method in a cellular network. The method comprises the following operation steps that: terminals upload own geographical position, transmission distance and residual energy information to a base station; the base station clusters the terminal by using a geographical-position-based clustering method according to the obtained information of all the terminals in a D2D area, and transmits clustering information to all the terminals; and after all the terminals receive the clustering information, two-hop transmission is realized by adopting a multicast transmission mode to ensure that all the terminals in the area can reliably receive data from a source terminal. According to the method, network layer topology and a multicast transmission technology are combined, so that the method is low in computational complexity, easy to implement and quite favorable for the realization of D2D communication in the cellular network, a D2D communication effect in a scenario with a plurality of terminals is greatly improved, the transmission energy consumption of the terminal is reduced to a certain extent, an energy utilization rate is increased, and terminal running time in the D2D communication is prolonged. The method has broad popularization and application prospect.

Method and apparatus for frame classification and rate determination in voice transcoders for telecommunications

A method and apparatus for frame classification and rate determination in voice transcoders. The apparatus includes a classifier input parameter preparation module that unpacks the bitstream from the source codec and selects the codec parameters to be used for classification, parameter buffers that store previous input and output parameters of previous frames, and a frame classification and rate decision module that uses the source codec parameters from the current frame and zero or more frames to determine the frame class, rate, and classification feature parameters for the destination codec. The classifier input parameter preparation module separates the bitstream code and unquantizes the sub-codes into the codec parameters. These codec parameters may include line spectral frequencies, pitch lag, pitch gains, fixed codebook gains, fixed codebook vectors, rate and frame energy. The frame classification and rate decision module comprises M sub-classifiers and a final decision module. The characteristics of the sub-classifiers are obtained by a classifier construction module, which comprises a training set generation module, a learning module and an evaluation module. The method includes preparing the classifier input parameters, constructing the frame and rate classifier and determining the frame class, rate decision and classification feature parameters for the destination codec using the intermediate parameters and bit rate of the source codec. Constructing the frame and rate classifier includes generating the training and test data and training and/or building the classifier.
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products