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2681results about How to "Reduce time complexity" patented technology

Method and system for joint resource allocation and computing unloading in software defined vehicle-mounted edge network

ActiveCN110035410AGood decisionMinimize the total processing delay of on-board tasksResource allocationParticular environment based servicesData informationMathematical model
The invention discloses a method and a system for joint resource allocation and computing unloading in a software defined vehicle-mounted edge network. The method comprises the following steps: establishing a mathematical model of a wireless communication theory according to data information, and modeling a joint resource allocation and calculation unloading problem as a mixed integer nonlinear programming problem (MINLP) according to the mathematical model; decomposing an original problem into a resource allocation sub-problem and a calculation unloading sub-problem through a Tammer decomposition method, and adopting function monotonicity definition, Lagrangian duality and KKT (Karush-Kahn-Tucker) to obtain optimal resource distribution, and obtaining an optimal unloading strategy by adopting a multi-stage low-complexity heuristic algorithm; wherein the server can allocate computing resources to the vehicle according to the scheme of the controller, and the vehicle selects the serverto unload the computing task according to the strategy of the controller. According to the invention, an optimal unloading strategy and an optimal resource allocation scheme are provided for vehicles,and the total processing delay of vehicle-mounted tasks in a system range is reduced.
Owner:CENT SOUTH UNIV

Method for detecting and classifying all-network flow abnormity on line

The invention discloses a method for detecting and classifying all-network flow abnormity on line. The method comprises the following steps of: (I) acquiring network flow (NetFlow), namely, receiving a NetFlow data packet transmitted from a border router by adopting a NetFlow collector, resolving the data packet and aggregating data streams to form data suitable for statistical analysis, and transmitting the data to a central control board through network to store in a database; (II) building a flow matrix taking the entropy of flow characteristics as measure; (III) detecting the flow abnormity on line by adopting a main increment component analyzing method; and (IV) constructing sample points in four-dimensional space by utilizing residual vector acquired through on-line detection and classifying the flow abnormity on line by adopting an increment k-mean value clustering method. The method has the advantages of detecting the flow abnormity on line, classifying the flow abnormity on line in real time, meeting the requirement on the real-time detection and classification of the flow abnormity better and laying the technical foundation for subsequently defending against network attack, along with lower time complexity and storage expenditure.
Owner:中国人民解放军陆军炮兵防空兵学院

Human face video processing-based heart rate detection method

The invention discloses a human face video processing-based heart rate detection method. The method is implemented by the steps of (1) controlling a camera to acquire a video that the camera is over against a human face for 10s; (2) performing human face characteristic point detection and tracking on the acquired video, and performing tilt correction to obtain a stable human face video; (3) performing color space conversion on the human face video; (4) extracting a clean BVP (Blood Volume Pulse) signal from the human face video by utilizing video color amplification; and (5) performing iterative frequency domain analysis on a BVP and computing the frequency of the BVP signal to estimate a heart rate. According to the method, the interferences of a background and a rigid head motion are eliminated by utilizing human face characteristic point detection and tracking, the clean BVP signal is extracted through the video color amplification, and the iterative heart rate estimation is carried out, so that the accuracy of heart rate detection is improved and very good robustness is ensured; and the method can be used for non-contact heart rate detection in a real scene under cooperative and non-cooperative conditions.
Owner:HEFEI UNIV OF TECH

Face recognition method of deep convolutional neural network

The invention discloses a face recognition method of a deep convolutional neural network, which reduces the time complexity, and enables a weight in the network to still have a high classification capacity under the condition of reducing the number of training samples. The face recognition method comprises a training stage and a classification stage. The training stage comprises the steps of (1) randomly generating a weight wj between an input unit and a hidden unit and an offset bj of the hidden unit, wherein j equals to 1,...,L and represents the number of the weight and the offset, and the total number is L; (2) inputting a training image Y and a label thereof, by using a forward conduction formula hw, b(x)=f(W<T>x), wherein hw, b(x) is an output value, x is input, and an output value hw, b(x<(i)>) of each layer is calculated; (3) calculating the offset of the last layer according to a label value and an output value of the last layer; (4) calculating the offset of each layer according to the offset of the last layer, and acquiring the gradient direction; and (5) updating the weight. The classification stage comprises the steps of (a) keeping all parameters in the network to be unchanged, and recording a category vector outputted by the network of each training sample; (b) calculating a residual error delta, wherein delta=||hw, b(x<(i)>)-y<(i)>||<2>; and (c) classifying a tested image according to the minimum residual error.
Owner:BEIJING UNIV OF TECH

An image super-resolution reconstruction method based on sparse representation and deep learning

The invention discloses an image super-resolution reconstruction method based on sparse representation and deep learning, and solves the problems that the image super-resolution process is complex incalculation and the quality of a reconstructed image is poor. The method comprises the following implementation steps: collecting and extracting training data blocks and a chromaticity and brightnessdictionary for combined optimization training; independently reconstructing a high-resolution image block; Carrying out high-resolution image reconstruction of sparse representation; Training a residual error network based on deep learning to optimize high-frequency details; Image super-resolution reconstruction. In order to prevent an edge effect and a fuzzy effect, chroma and brightness data aredistinguished and independently reconstructed; in order to optimize high-frequency detail information of a sparse representation output high-resolution image, the high-resolution image based on sparse representation reconstruction is input into a residual network, and a high-frequency residual image is output through four times of convolution feature extraction and feature fusion and input bitwise addition to reconstruct a super-resolution image. The method is low in calculation complexity, high in image reconstruction quality and widely applied to the fields of remote sensing monitoring, criminal investigation, traffic management and the like.
Owner:XIDIAN UNIV

Method for remapping resource demand dynamic change based on virtual network

The invention discloses a method for remapping resource demand dynamic change based on a virtual network, relating to the internet technical field. The method comprises the following steps of: establishing a physical network model, a virtual network model and a physical network available resource model; according to the physical network model and the virtual network model, establishing the original virtual network mapping model; according to the physical network model and the original virtual network mapping model, determining available physical network resources; for the resource demand dynamic change of the virtual network, clearly determining a virtual node and a virtual link to be remapped; determining an order for joining the remapping process of the virtual node to be remapped; according to the order, completing the node mapping and the relative link mapping to the virtual node to be remapped; and updating the available physical network resources. The method disclosed by the invention aims to achieve an optimization purpose of minimum resource consumption, and therefore, the resource cost of each virtual network in the remapping process can be minimized so as to accord with the actual situation.
Owner:TSINGHUA UNIV

Multidimensional weighted 3D recognition method for dynamic gestures

The invention discloses a multidimensional weighted 3D recognition method for dynamic gestures. At the training stage, firstly, standard gestures are segmented to obtain a feature vector of the standard gestures; secondly, coordinate system transformation, normalization processing, smoothing processing, downsampling and differential processing are performed to obtain a feature vector set of the standard gestures, weight values of all joint points and weight values of all dimensions of elements in the feature vector set, and in this way, a standard gesture sample library is constructed. At the recognition stage, by the adoption of a multidimensional weighted dynamic time warping algorithm, the dynamic warping distances between the feature vector set Ftest of the gestures to be recognized and feature vector sets Fc =1,2,...,C of all standard gestures in the standard gesture sample library are calculated; when the (m, n)th element S(m, n) of a cost matrix C is calculated, consideration is given to the weight values of all the joint points and the weight values of all the dimensions of the elements, the joint points and coordinate dimensions making no contribution to gesture recognition are removed, in this way, the interference on the gesture recognition by joint jittering and false operation of the human body is effectively removed, the anti-interference capacity of the algorithm is enhanced, and finally the accuracy and real-time performance of the gesture recognition are improved.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA

Polarimetric SAR classification method based on semi-supervised convolutional neural network

The invention discloses a polarimetric SAR classification method based on a semi-supervised convolutional neural network. According to the method, first, image data is input, and super pixel segmentation is performed; training samples and test samples are extracted; a neighborhood preserving and sparse filtering deep learning network (NDSFN) is adopted to perform unsupervised pre-training on parameters of the convolutional neural network, and a needed filter set is obtained; a feature map is obtained through convolution operation; fuzzy processing is performed on the feature map through downsampling; parameter settings of the NDSFN are adjusted according to the filter size in a second convolution layer to obtain a new feature map; a Softmax classifier is utilized to perform image classification on the training samples; and a small quantity of marked samples are adopted for slight adjustment to obtain a polarimetric SAR terrain classification. Through the method, by constructing a novelsemi-supervised convolutional neural network (SNCNN) model, the problem that a traditional supervised convolutional neural network has a high requirement for marked samples is effectively solved.
Owner:XIDIAN UNIV

Unmanned aerial vehicle routing inspection image retrieval system and method based on electric transmission line and GIS

The invention discloses an unmanned aerial vehicle routing inspection image retrieval system and method based on an electric transmission line and a GIS. The unmanned aerial vehicle routing inspection image retrieval system comprises an image processing server, an external signal input device, a result display device, an unmanned aerial vehicle and a ground monitor station. The external signal input device inputs external signals or instructions to the image processing server, the result display device outputs a processing result of the image processing server, the ground monitor station reads the flight state information and routing inspection result data of the unmanned aerial vehicle and sends the read data to the image processing server, and the ground monitor station is in wireless communication with the image processing server. By means of the unmanned aerial vehicle routing inspection image retrieval system and method based on the electric transmission line and the GIS, the data retrieving and processing efficiency of the routing inspection result can be improved, the image processing cost in routing inspection is lowered, the image retrieving and processing speed of the server is increased, and the processing time cost of the routing inspection result is lowered.
Owner:STATE GRID INTELLIGENCE TECH CO LTD

Weighted association rule mining method based on data source partition matrix

InactiveCN106126577AQuick calculation of supportReduce I/O timeSpecial data processing applicationsRule miningRound complexity
The invention relates to a weighted association rule mining method based on a data source partition matrix, and belongs to the technical field of manual intelligence, machine learning and data mining. By using a manual intelligence method, implied valuable expert knowledge can be mined from massive and complex industrial text data and expressed in an association rule manner, and a support can be provided for establishment of a knowledge base in an expert system. According to the method, a classic association rule mining algorithm is analyzed; a method for rapidly computing a support degree based on the data source partition matrix is provided aiming at the problem that an Apriori algorithm is low in efficiency as a data set is scanned repeatedly; and a weighted calibration method based on probability is used for weighting data item sets during an association rule mining process aiming at the problem that the Apriori algorithm treats data items in the transaction data set equally. Experiments verify that the provided method can reduce space complexity and time complexity of traditional association rule mining.
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY

Infrared image-based weak and small moving target detecting method

The invention discloses an infrared image-based weak and small moving target detecting method. The method disclosed by the invention comprises the steps of median background difference image generation, accumulated difference image generation, difference image segmentation, periodical motion area elimination and false target elimination. Concretely, the method comprises: establishing a grayscale histogram for each pixel to count the frequency of the appearance of the pixel on each grayscale in the latest time period, calculating a median to obtain median background, and calculating the absolute frame difference between the median background and a detected frame to obtain a median background difference image; calculating the frame difference between each two adjacent frames continuously, and accumulatively adding difference images to obtain an accumulated difference image; eliminating interference from periodical motion areas, and highlighting a real moving target; and finding a reasonable target track by using motion and grayscale consistency of the target. The method disclosed by the invention can accurately and continuously detect the weak and small moving target day and night at a remote distance, has high real-time performance and high robustness, and is easy to promote in application field of military robot battle reconnaissance, video monitoring and the like.
Owner:NANJING UNIV OF SCI & TECH

Method and apparatus to detect lesions of diabetic retinopathy in fundus images

InactiveUS20140314288A1Minimal run-time complexityFastImage enhancementImage analysisCotton wool patchesHard exudates
The present invention relates to the design and implementation of a three stage computer-aided screening system that analyzes fundus images with varying illumination and fields of view, and generates a severity grade for diabetic retinopathy (DR) using machine learning. In the first stage, bright and red regions are extracted from the fundus image. An optic disc has similar structural appearance as bright lesions, and the blood vessel regions have similar pixel intensity properties as the red lesions. Hence, the region corresponding to the optic disc is removed from the bright regions and the regions corresponding to the blood vessels are removed from the red regions. This leads to an image containing bright candidate regions and another image containing red candidate regions. In the second stage, the bright and red candidate regions are subjected to two-step hierarchical classification. In the first step, bright and red lesion regions are separated from non-lesion regions. In the second step, the classified bright lesion regions are further classified as hard exudates or cotton-wool spots, while the classified red lesion regions are further classified as hemorrhages and micro-aneurysms. In the third stage, the numbers of bright and red lesions per image are combined to generate a DR severity grade. Such a system will help in reducing the number of patients requiring manual assessment, and will be critical in prioritizing eye-care delivery measures for patients with highest DR severity.
Owner:PARHI KESHAB K +1
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