37results about How to "Consumes less memory" patented technology

Electronic television program guide information is downloaded, stored and searched by a local device. A plurality of linked lists are used to remove redundancy, increase search speed, and allow dynamic categories. When a new category is encountered a new head node for a link list data structure is created. When a first program guide object containing information related to that category is downloaded, a link is made (i.e., a pointer is stored) from the head node to the guide object. When subsequent program guide objects containing information related to that category are downloaded, links are added to the end of the list or inserted in alphabetical order. As guide objects expire the links are removed. When no links remain in a particular category the head node for that category is deleted.

The invention relates to a map linear symbol drawing method and a map linear symbol drawing system based on a graphics processing unit (GPU). According to the method, under a three-dimensional rendering programmable assembly line system, a shader language is used for sending a function relation between map linear symbol coloring and linear parameters to the GPU for calculation, so that map linear symbols can be drawn with high efficiency. The method comprises the following steps of establishing a vortex array caching object by taking half-linear width as a buffering region according to a linear center line, recording xyz of the position of a vortex and transverse and longitudinal coordinate relation parameters uv of the vortex on the whole buffering zone; writing a vortex shader, sending the uv parameters into a fragment shader, and performing rasterization interpolation by using the GPU; and building a function of a fragment color and the uv in the fragment shader, and calculating pixel coloring. In an actual drawing process, the vortex caching objects are bound to be property parameters for sending; global parameters required in a shader file are set; and therefore, the linear symbols can be drawn.

The invention discloses a multi-modal dynamic gesture recognition method based on a lightweight 3D residual network and a TCN. Firstly, original videos in a data set are sampled and sorted and storedaccording to a time sequence; then, the lightweight 3D residual network is pre-trained by using a large public gesture recognition data set, and thus storing a weight file of the model; then, the RGB-D image sequence is used as input, the lightweight 3D residual network and the time convolution network are used as basic models to extract long-term and short-term spatial and temporal features, andan attention mechanism is used for weighted fusion of multi-modal information, wherein the RGB sequence and the Depth sequence are respectively input into the same network structure; finally, a full connection layer is used for classification, a cross entropy loss function is used for calculating a loss value, and accuracy and F1Score are used as evaluation indexes of the network model. The invention not only can achieve high classification accuracy, but also has the advantage of low parameter quantity.

The invention discloses a model identification method based on a local characteristic aggregation descriptor. First of all, SIFT characteristics of vehicle images in a model database are extracted; then Kmeans clustering is performed on the SIFT characteristics of all the vehicle images so as to form K cluster centers and obtain a dictionary with K visual words; then, each SIFT characteristic is endowed with a closest visual word for each model image; a residual error cumulant between SIFT characteristic vectors around each visual word and current visual words is counted, and the local characteristic agglomeration descriptor of a current vehicle picture is obtained; finally, an indexable coding image database of n model types is obtained through quantification coding from local characteristic agglomeration descriptors of n model images of a training module; and for a test vehicle image, the local characteristic agglomeration descriptor of the test vehicle image is extracted in the same way as a query vector for introduction into an image database for indexing, and the model of a test vehicle is identified through matching by use of an approximation nearest neighbor search method.

The present invention provides a storage method of executable code under an ARMv7m architecture. The method involves five logic units including a compilation and burning program, a startup program, an FPB association program, an execution program, and a bus exception handling program. According to the method, the FPB association program associates a RAM space to an address that is not associated with a physical flash, and uses the RAM space as a storage space where the executable code is stored, and a CPU kernel uses the storage space to acquire and execute a machine instruction. In addition, the RAM space may be dynamically updated to a new value, and may be re-reset and re-associated by the FPB to a new flash address. With the method according to the present invention, a small on-chip flash is used to store FPB management code. As such, a small RAM space and a cheap off-chip flash are used to expand the storage space of the executable code to 512 MB.

The invention provides a mononucleotide variation detecting method based on blood circulation tumor DNA, a device and a storage medium. The method comprises the steps of acquiring mutation data of each site of the blood circulation tumor DNA of a testing sample, wherein the mutation data comprises a site mutation frequency; acquiring a confidence range of each site background mutation frequency ofthe training sample, wherein the confidence range is obtained through performing learning modeling on all three-base mutation frequency and site mutation frequency in each training sample and training the model by means of an in-place updated list; comparing the site mutation frequency of each site of the testing sample with the confidence range of the background mutation frequency of each site in the model, and outputting the mononucleotide variation wherein the site mutation frequency of the testing sample is not in the confidence range. The mononucleotide variation detecting method, the device and the storage medium have advantages of greatly optimizing a calculation resource requirement and a detecting speed, improving sensitivity and accuracy in detecting ctDNA mononucleotide mutation, and satisfying a requirement for ctDNA mononucleotide mutation detecting reliability in clinical tumor detection.

The invention discloses a semantic segmentation-based surface feature recognition and classification method and device. The method comprises the following steps: 1) acquiring multi-source remote sensing data of each region, and combining the data of the same region into a sample to obtain a sample set; 2) establishing a semantic segmentation model; training the model by using the sample set, wherein the semantic segmentation model is formed by connecting an encoder, a central module and a decoder in series; the encoder is formed by connecting N encoding modules in series, the decoder is formedby connecting N decoding modules and a point convolution module in series, and the center module is formed by connecting a convolution module C1 and a convolution module C2 in series, and each encoding module El is formed by connecting a convolution module El1, a convolution module El2 and a down-sampling module DSl in series, and each decoding module D1 is formed by connecting an up-sampling module USl, a convolution module Dl2, a convolution module Dl1 and a convolution module Dl0 in series; and 3) processing the remote sensing data to be identified by using the trained model to obtain an identification result of the ground object type.

A method and system for finding a longest prefix match for a key in a computer network is disclosed. The method and system include providing a main engine and providing an auxiliary engine. The main engine is for storing a first plurality of addresses and for searching the first plurality of addresses for the longest prefix match for the key. None of the first plurality of addresses is a prefix for another address of the first plurality of addresses. The auxiliary engine is for storing and searching a second plurality of addresses. A first address of the second plurality of addresses is capable of including the prefix for a second address of the first plurality of addresses or for a third address for the second plurality of addresses. None of the first plurality of addresses is the prefix for any of the second plurality of addresses. Each of the second plurality of addresses is distinct from each of the first plurality of addresses.

The invention provides a memory optimization method for video animation picture storage. The memory optimization method comprises the steps of extracting video animation pictures, and acquiring a PNG picture of each frame in the video animation pictures based on the video animation pictures; based on the PNG picture of each frame, converting the pictures into JPG pictures; and based on the JPG pictures, converting the pictures into video animation pictures for reproduction. The memory optimization method for the video animation picture storage according to the invention can reduce the memory consumption of the video animation picture processing by extracting the video animation pictures, converting the PNG pictures into the JPG pictures through alpha channel data and then deleting the alpha channel data, so as to achieve the purpose of memory optimization and reduce the memory of run-time pictures.

The invention relates to an LTE-A (LTE-Advanced) control channel resource demapping method and belongs to the technical field of communication. According to the method, data extraction of integral multiples is carried out on system bandwidths through utilization of FIR (Finite Impulse Response) filters and the bandwidths are normalized into 1.4M, so resource consumption for resource demapping is reduced, and the problem that the computing quantity is complex, the operation speed is slow and the source consumption is high due to high data quantity under high bandwidths is solved. According to the method, through decoding of PBCHs (Physical Broadcast Channels), the system bandwidths are acquired, all system bandwidths are normalized, the computing quantity of a terminal is reduced, the memory consumption is reduced, and the operation speed of the terminal is improved.

The invention relates to a bridge monitoring method and system based on binocular vision, and the method comprises: calibrating a first camera and a second camera, obtaining the relative pose relationbetween the two cameras, and determining the real-time three-dimensional coordinates of a central point on a calibration plate under a first camera coordinate system; calculating a coordinate conversion relationship between the first camera coordinate system and the calibration plate coordinate system according to the calibration information, and converting a three-dimensional coordinate of the central point under the first camera coordinate system into a real-time three-dimensional coordinate under the calibration plate coordinate system; and determining the displacement information of the bridge by combining the three-dimensional coordinates of the central point at the initial moment under the calibration plate coordinate system. By calibrating the relative pose relationship between thetwo cameras, the three-dimensional coordinate of the center point of the calibration plate under the first camera coordinate system is converted to be under the coordinate system of the calibration plate, and the displacement information of the bridge is determined by combining the coordinates of the center point of the initial moment under the coordinate system of the calibration plate; and manual measurement is replaced, and the method is high in precision, high in efficiency and low in price.

The invention discloses an image semantic segmentation method based on multi-receptive-field context semantic information. The method comprises the steps of 1, converting an input image into a pixel matrix through convolution operation; 2, converting the same pixel matrix into a plurality of feature maps with multi-receptive-field context semantic information by adopting expansion convolution with different expansion rates; 3, performing feature extraction and down-sampling processing on the feature map with the multi-receptive-field context semantic information through converter encoders in different subnets to obtain a plurality of down-sampling feature maps with different receptive fields; 4, carrying out step-by-step up-sampling processing on the down-sampling feature map through a decoder to obtain feature maps with the same size and dimension, and generating a final feature fusion map; and 5, the feature fusion image completes image segmentation prediction through a convolutional neural network. The method can be effectively applied to image semantic segmentation, does not lose deep low-resolution features and fine-grained features, consumes a small memory, is remarkable in effect, and is convenient to popularize.

The invention discloses an audio feature extraction method and device based on a re-parameterized decoupling mode. The method comprises the following steps: acquiring a to-be-detected voice sample of a target speaker; preprocessing the to-be-detected voice sample; extracting acoustic features of the preprocessed to-be-detected voice sample; and inputting the acoustic features into a network reasoning module to obtain voiceprint feature vectors, wherein the network reasoning module is a network model of a single-path structure converted by a trained multi-layer network training module through re-parameterization. According to the method, the multi-branch structure is used in the training stage to achieve a better convergence effect, the multi-branch structure is re-parameterized into the single-path structure in the reasoning stage to obtain a better effect than the multi-branch structure with equivalent parameter quantity, the speed is higher, and the memory consumption is lower.

The invention discloses a navigation method for correcting an inertia angle of a robot based on a visual angle. Pure inertia navigation is completed just through the data of a gyroscope in a short-time walking process of a mobile robot, low-pass filtering processing is carried out on a difference value between the angle of the gyroscope and the angle of a machine body acquired by a camera so as toobtain a filtering angle correction value which is kept in a stable state for a long time, and after the mobile robot takes a relatively long time to traverse a preset planning path in a corresponding room area, a filtering angle correction value obtained at a corresponding latest sampling moment is fed back to a visual initial navigation angle acquired and calculated on a preset planned path ina non-traversed room area so as to realize compensation of accumulated errors, so that the problem of offset errors accumulated by long-time inertial navigation can be solved; therefore, the navigation angle of the robot is kept high in precision for a long time, and the mobile robot is prevented from deviating in a short-time walking path and a long-time walking path. And the real-time performance of robot navigation is improved.

The invention discloses a construction method of a super-resolution convolutional neural network model based on a portable system. The method comprises the steps of 1, constructing a convolutional neural network model; 2, training the convolutional neural network model in the step 1; and 3, deploying the convolutional neural network model trained in the step 2 to realize a super-resolution function of the image. According to the method, an up-sampling algorithm of the sub-pixel convolution layer of the ESPCN is adopted, a network structure before the sub-pixel convolution layer is modified, and a residual block is introduced to construct a network.

The invention discloses an IMEI number search method and device and a mobile terminal. The IMEI number search method includes the steps that an input IMEI number to be searched for is received; the IMEI number to be searched for is segmented into a first digital section and a second digital section; whether a file path corresponding to the IMEI number to be searched for exists or not is judged according to the first digital section; if the file path exists, a pre-generated file on the file path is searched; if a digital section consistent with the second digital section is found in the pre-generated file, it is determined that search succeeds. According to the IMEI number search method and device and the mobile terminal, the IMEI number search efficiency can be effectively improved, and the memory consumption is low.