125results about How to "Small time complexity" patented technology

The invention provides a joint measurement method based on a laser radar and a binocular visible light camera to obtain accurate and dense three-dimensional information simply and efficiently. The joint measurement method comprises assuming that the laser radar is a camera device with a fixed internal reference; directly projecting three-dimensional point cloud data into a two-dimensional image, calculating rotation and translation relationships between the laser radar device and the binocular camera by using an image processing method and the matching between the two-dimensional images; creatively introducing an idea for obtaining a matrix norm and a matrix trace to solve a rotation matrix; finally fusing laser radar and binocular stereo vision point cloud data. The method not only can obtain an accurate position and attitude information, but also can reconstruct the special texture and feature information of a target surface, which has a high application value for spacecraft dockingand hostile satellite capture in the military field and workpiece measurement and unmanned driving in the civilian field.

The invention belongs to an infrared dim-small target detection method based on shear wave conversion in the infrared image processing technology. The method comprises the following steps: processing an original infrared image by employing nonsubsampled Laplace pyramid conversion and a Shearlet filter in succession to obtain high frequency information graphs of various directions under different scales, inhibiting background and noise interference information, enhancing target information and extracting a dim-small target. According to the invention, the nonsubsampled Laplace pyramid conversion and a Shearlet filter are employed to process the original infrared image, through same scale and different scales fusion processing of the obtained the high frequency information graphs, the interference information is inhibited, the target information is enhanced, and the high frequency information graphs are subjected to segmentation to obtain a clear dim-small target graph; thereby the method has the characteristics of a short detection processing flow, small data processing amount, short processing time, capability of effectively raising performance of detecting the infrared dim-small target and obviously distinguishing a target and a complex background in the image, a good effect and the like.

The invention relates to an image quality assessment method, in particular to a non-reference image quality assessment method based on information entropy characters, and belongs to the field of image analyzing. The method comprises the first step of carrying out Contourlet conversion on a distorted image to obtain N*M conversion sub-bands, the second step of dividing each conversion sub-band and the unconverted original distorted image, the third step of calculating null domain information entropy and frequency domain information entropy on each block coefficient matrix, and the fourth step of screening the blocking characters and calculating a mean value to obtain the quality character value of each conversion sub-band. The method of a support vector machine and the method of non-reference image quality assessment are utilized for testing on a test set, and quality prediction and assessment are carried out through quality character vectors corresponding to a disaggregated model, an evaluation model and the test set all of which are obtained through training. The non-reference image quality assessment method has the advantages of being high in subjective consistency, small in time complexity and good in university, can be embedded into application systems related to image quality, and has very high application value.

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method based on neighborhood similarity and mask enhancement, relating to the field of remote sensing image processing. The method mainly solves the problem that a difference map construction of SAR image change detection is seriously influenced by SAR image speckle noise and comprises the following steps of: (1) geometrically correcting and registering two SAR images I1 and I2 in the same scene at different times; (2) constructing a difference image map DI of the images I1 and I2 by using a neighborhood similarity operator; (3) performing mask enhancement on the difference image map DI to obtain a new difference image map NDI; and (4) clustering the new difference image map NDI to obtain a change detection result map CDI by using a K mean value clustering method determined by an initial point. The method has the characteristics of high operation efficiency, low algorithm complexity, low time complexity and good experimental effect, and can be used for the change detection of two time-phase SAR images.

The invention provides a facial image layer decomposition method based on an improved guide filter, and the method comprises the steps: carries out the improved guide filtering of a facial image brightness channel, obtaining a human face structure information layer, further carrying out differential operation, and obtaining a texture detail layer. According to the invention, the method carries out the regularization factor adaptive adjustment optimization of a guide filter and Gaussian blurring and corrosion processing of a parameter matrix image, and obtains different filtering effects of a facial image in different regions. The method is used for a digital makeup system, obtains an excellent layer decomposition effect, and remarkably reduces the time complexity of layer decomposition.

The invention provides a non-reference image quality evaluation method based on gradient relevance, and belongs to computer image analysis. The method comprises the following steps: firstly, determining three sub-characters, namely, an image gradient amplitude character, an image gradient direction change character and an image gradient amplitude change character, which are easily influenced by distortion in an image gradient; performing M*M image blocking on the three characters, and determining a statistic variance of the three characters in each image block, wherein the average number of the statistic variances of all the image blocks in the whole image is taken as an image characteristic; and finally, determining the image quality by combining a support vector with a two-step frame in image quality evaluation. The method provided by the invention has the advantages of being small in time complexity, high in subjective consistency, low in image feature dimension and good in universality; the method can be applied to applications related to small computer devices or image quality, and is high in practical value.

The invention relates to a Chinese variation text matching recognition method. The method comprises the following steps of: performing special encoding conversion on a target text and a mode character string to improve the similarity of texts; adding proper wildcard characters into the converted mode character string according to the structural characteristics of Chinese characters in the mode character string; and matching by using a precise character string matching algorithm (namely a CV-BM algorithm). The method solves the problems of the replacement of characters with similar shapes in the variation text and the matching difficulty based on the replacement of wrongly written characters with similar shapes. The problems of the replacement of the characters with the similar shapes in the variation text and the matching difficulty based on the replacement of wrongly written characters with the similar shapes can be solved. Compared with an image partitioning recognition method, the method has smaller time and space complexities, is more suitable for the quick matching of Chinese character information in a high-speed network data transmission environment, and can be widely applied to systems needing to match the Chinese character information, such as an intrusion prevention system, an information retrieval system and the like.

The invention discloses a power grid instantaneous frequency measuring and tracking method based on an orthogonal sub-band. The method comprises the steps that discrete sampling is carried out on an analog input signal; the signal after discrete sampling is filtered by a constructed orthogonal filter group, and is divided into two sine sub-band signals with the phase difference of 90 degrees; the mathematical relationship among three random and continuous sampling points in the sine sub-band signals is used to respectively calculate the instantaneous angular frequency of each sub-band; linear optimization integration based on a Gaussian noise model is carried out on the measurement results of two sub-bands, so as to acquire an instantaneous angular frequency measurement value with small error variance; Kalman filter estimation is carried out on the measurement value; and the instantaneous frequency of a power grid is tracked. According to the method provided by the invention, measuring and tracking accuracy is not affected by a non-periodic noise signal, power grid voltage/current amplitude variation and a harmonic signal; the method has the advantages of short data window and simple algorithm calculation; and the instantaneous frequency of a power grid fundamental wave can be accurately measured and tracked in real time.

The invention discloses a realization method for a wireless mesh network and nodes. The realization method for the wireless mesh network comprises steps: obtaining nodes and route information needing to be monitored in the network, according to the associated relationship between the nodes and the route, obtaining a monitoring acting candidate set in the network which covers maximal link and has minimum nodes, selecting a node set with larger residing weighted value of the nodes from the acting candidate set as a monitoring agency; adopting the selected agency to monitor the routing transmission of the wireless mesh network, wherein the anticipated transmission time SMETT of the route is measured in the transmission process, thus forming the route measuring result; and according to the result, controlling packet routing or call forwarding. The invention can increase network performance and reduce resource consumption.

The invention discloses an image encryption method which encrypts a clear text image through combination of chaotic mapping and DNA encoding. The image encryption method comprises the following steps:generating an initial key; generating a chaotic sequence and a scrambling vector by use of the initial key; performing scrambling encryption on the clear text image by use of the scrambling vector toobtain a scrambling image; performing DNA encoding on the chaotic sequence to obtain a first DNA sequence, and performing DNA encoding on the scrambling image to obtain a second DNA sequence; performing diffusion encryption on the DNA sequence by use of the first DNA sequence to obtain a third DNA sequence; and performing DNA decoding on the third DNA sequence to obtain a cipher text image.

The invention relates to a symmetric encryption method with an infinite key space, which adopts a group of public computer file sequences as a key; the plaintext of a file to be encrypted takes a fixed-length of m Bits as base unit in a physical layer in which the plaintext is stored; the plaintext is divided into a plurality of plaintext fragments in sequence; when the remaining length is less than m Bits, '0s' are added to cause the length to be m Bits; every file of the key divides a ciphertext into a plurality of key fragments in sequence with the same fixed-length of m Bits as the base unit in a physical layer and gives out an address coding of every key fragment in a form of 'file name/address offset'; if the remaining length of the last key fragment is less than m, '0s' are added to cause the length to be m Bits; the plaintext fragment is presented by any one address of the same key fragment. A code representation is carried out on all plaintext fragments in the plaintext by using the same key fragment so as to obtain the ciphertext.

The invention provides a method and device for path planning of mixed airspace heterogeneous aircrafts and belongs to the technical field of aircrafts. A path planning device includes an information acquisition module, a conflict detection module, a collision risk assessment module and a path planning module. The information acquisition module is used to obtain information of all aircrafts, the conflict detection module is used to determine whether a conflict occurs between the two aircrafts and whether the aircrafts pass through a dangerous area, and the collision risk assessment module is used to evaluate the safety of the aircrafts under certain uncertainties. The path planning module optimizes paths of each aircraft through a particle swarm optimization algorithm. The method for path planning includes the steps of first establishing a path planning model for the aircrafts in a mixed airspace; adopting a Monte Carlo method to measure the influence of the uncertainties on air safety,wherein obtained results are closer to practical flight and the planned path is more reasonable; and finally adopting the particle swarm optimization algorithm to optimize the path of each aircraft.The algorithm is small in time complexity, and the path planning is faster.

The invention discloses an image watermarking method based on phase singular value correction. The method comprises steps of: firstly embedding a watermark into a low-frequency sub-band transformed by a host image PDTDFB; secondly, extracting the high-frequency sub-band phase matrix of a to-be-detected image subjected to quaternion two-dimensional wavelet transform, subjecting the phase matrix to singular value decomposition (SVD), selecting the first four singular values in the main singular value as the eigenvector of a training sample image, and subjecting the sample to the training learning of geometric transformation parameters such as scaling, translation and rotation so as to obtain an LS-SVR training model; then calculating a mathematical model of the geometric correction of the watermarked to-be-detected image, and determining the model parameters by using the obtained LS-SVR training model to perform geometric correction; and finally, extracting the watermark information from the corrected watermarked image.

The invention discloses an image quality evaluation method based on information entropy. The method is used for solving the problem that an existing image quality evaluation method cannot effectivelyevaluate images of multiple distortion and deblurring distortion types. According to the method, an image library containing deblurring distortion is constructed, and then on the basis of the image library, a support vector machine is used for carrying out classified training according to extracted two-dimensional space entropy and spectrum entropy characteristics of images of the image library toobtain a distortion probability vector model; and then, score fitting is carried out on the characteristics of each type of images and the full reference quality evaluation index VSI of each type ofimages by utilizing support vector regression to obtain a specific distortion quality vector model. By means of the two models, a quality prediction score can be obtained for any image according to the information entropy characteristics of the image, and finally a final evaluation score is obtained by combining distortion detection and evaluation of ringing, residual blurring, noise and the likeof the deblurred image. Compared with the prior art, the method has the advantages of being more objective in evaluation and good in universality. In an image restoration experiment, rapid optimization of a plurality of parameters of the deblurring algorithm is realized by using the method, and the quality of the obtained restored image is obviously improved. The method can be embedded into a DaVinci system or other application systems related to image quality, and has very high practical value.

The invention relates to the field of computer database processing, in particular to a big data real-time enquiry system load balancing method based on copy selection. The method comprises the processes of node load information collection and node load balancing, and the node load balancing process comprises the stages of preprocessing and the copy selection. The method has the advantages that the problems that an existing big data real-time enquiry system load balancing method is too simple and the current state of a machine is not considered are solved, the new big data real-time enquiry system load balancing method based on the copy selection is provided, the load balancing effect is superior to that of the existing big data real-time enquiry system, time complexity is low and is O (n2), and n is the number of blocks; the method is suitable for heterogeneous distributed systems and the conditions of operating other tasks in the systems.

The invention discloses an unstructured road identification method, which comprises the steps of: S1, acquiring a color image in front of a mobile robot in the running process, and preprocessing the acquired color image; S2, performing region segmentation on the image obtained by preprocessing; S3, performing edge detection on the preprocessed image; S4, and performing fusion extraction on the image obtained after edge detection, so as to obtain road regions. The unstructured road identification method is implemented by the steps of: firstly, acquiring the color image in front of the mobile robot, and preprocessing the acquired color image; secondly, subjecting the image to region segmentation, and extracting edge features; and finally, fusing region segmentation and edge feature information to obtain the road regions. According to the unstructured road identification method, an OTSU algorithm is adopted in region segmentation, a multi-directional sobel operator edge detection algorithm is adopted in edge detection, the time complexity is small, the algorithms have good identification effect for a new environment, the robustness thereof is greatly enhanced, and the precision and validity of calculation are improved.

The invention discloses an uncertainty multicast transmission method. The uncertainty multicast transmission method comprises the following steps of: generating a topological graph in a software-defined network, and determining a transmission path among sending ends, receiving ends and forwarding nodes; determining limitation of the number of the sending ends selected by the receiving ends, adjacent edge selection limitation, transmission path selection limitation and multi-wave transmission limitation; and, according to the transmission path among the sending ends, the receiving ends and the forwarding nodes, the transmission cost among any two sending ends, the receiving ends and the forwarding nodes, limitation of the number of the sending ends selected by the receiving ends, adjacent edge selection limitation, transmission path selection limitation and multi-wave transmission limitation, calculating the minimum-cost forest for uncertainty multicast transmission. By means of the uncertainty multicast transmission method disclosed by the invention, uncertainty multicast transmission can work in a software-defined network architecture; and furthermore, the transmission cost and the time complexity are relatively low.

The present invention provides a multi-resource partial order scheduling policy. A large number of tasks with a multi-resource demand and a complicated structure exist in NFV, and in actual task scheduling, task scheduling completion time affects user experience; traditional scheduling mostly focuses on stream scheduling study, and is incapable of considering a task structure, however, when a task has a partial order structure, the stream scheduling cannot ensure task scheduling time to be optimized; according to the multi-resource partial order scheduling policy provided by the present invention, in an NFV network architecture, a multi-resource task scheduling problem based on a partial order structure is formalized into a model of the multi-resource task scheduling problem based on the partial order structure; and a policy of simultaneously optimizing average and tail scheduling time is provided, and according to the policy, a task scheduling order is determined in a dominant resource priority (DRP) based mode, and resources are allocated with a maximum utilization allocation (MUA) method; and the DRP policy involved in the present invention has polynomial time complexity, a resource utilization rate close to 100%, and excellent fairness.

The invention provides a power flow transfer identification algorithm based on multi-branch removal and deviation paths, which includes the following steps: I, analyzing the composition of a power flow transferred to a non-fault branch, wherein, under the condition of multi-branch removal, the transferred power flow on a branch k can be seen as the superposition of power flow transferred to the branch k after multiple branches with an equivalent power flow as an initial power flow are separately broken; II, based on a deviation path algorithm in graph search, searching for the first K shortestpaths between the two end nodes of removed branches, and determining the regional scope with large power flow transfer; and III, giving a transmission section selection principle by considering the real-time running state of the power grid, identifying a branch set with large increase in actual power flow, and finding out lines of which the power flow increases reversely. Important improvement ismade in power flow transfer estimation, K value selection and section criterion for removed branches.

The invention discloses an object prediction area optimization method applicable to target identification, comprising the following steps: expanding an object prediction area; performing image segmentation; performing super pixel bounding box calculation and super pixel significance evaluation; and finally, getting an optimized object prediction area based on sliding window search of super pixels. By controlling the size of super pixels, the scale of image segmentation is increased, the search scope of super pixels is reduced, and therefore, the time complexity of the algorithm is low and is only related to the number of super pixels in an image; as the pixels in the super pixels are consistent and segmentation of the local edge is better, a better locating effect can be produced by taking super pixels as the basic elements of sliding window search compared with single pixels; the search area is reduced effectively, and the traditional pixel-based sliding window search algorithm is accelerated; and in addition, by cascading the method, the target identification accuracy of the existing target identification algorithm can be improved.

The invention discloses a mobile application program behavior identification method based on spectral clustering and a random forest algorithm, and belongs to the technical field of network security.The method comprises the following steps: acquiring encrypted data traffic of mobile application program equipment; performing feature extraction statistics on the encrypted traffic; performing spectral clustering preprocessing on the encrypted data stream feature data set; and finally, modeling the clustered data cluster data set through a random forest algorithm. According to the method, spectral clustering is used as preprocessing to reduce the opportunity of over-fitting of a classifier, and then a random forest ensemble classifier is used to accurately identify various behaviors of an application program.

The invention discloses a method for hiding reversible information of a security encryption domain based on high-level overturn prediction. The high-level overturn prediction method is used for only testing original images and not changing values and positions of pixels, so that a general image exclusive OR/AND scrambling operation can be applied to encryption of images, and meanwhile, protectionof pixel values and pixel positions can be realized, and the security of the encrypted images is improved; According to the testing method based on the high-level overturn prediction disclosed by theinvention, correlation of the images is fully utilized, so that the steganographic capacity is effectively improved; at a receiving end, a receiver only needs an image encryption secret key and does not need a steganographic secrete key, decryption images identical to the original images and plaintext images which contain additional information and are similar to the original images can be acquired; and on the other hand, the receiver only needs an information steganographic secrete key and does not need the image encryption secrete key, the same additional information can be extracted separately from a plaintext domain containing the additional information and a ciphertext domain. According to the method disclosed by the invention, realization of an algorithm is simple, the security is high, and the steganographic capacity is large.