1752 results about "Time complexity" patented technology

A database encryption system and method, the Structure Preserving Database Encryption (SPDE), is presented. In the SPDE method, each database cell is encrypted with its unique position. The SPDE method permits to convert a conventional database index into a secure one, so that the time complexity of all queries is maintained. No one with access to the encrypted database can learn anything about its content without the encryption key. Also a secure index for an encrypted database is provided. Furthermore, secure database indexing system and method are described, providing protection against information leakage and unauthorized modifications by using encryption, dummy values and pooling, and supporting discretionary access control in a multi-user environment.

Apparatus and method use pre-order queuing and scheduling. Reordering of the transmission sequence of packets that could be sent out in one round is allowed according to the quantum consumption status of the flow within the round. Per-packet time complexity is maintained independent from an increase in flow number and packets of variable-length are processed.

The invention discloses a polarization decoding method based on subsection CRC and belongs to the technical field of wireless communication channel coding. The subsection CRC (cyclic redundancy check) is utilized to improve an existing list successive elimination polarization decoding algorithm, and subsection polarization code CRC is carried out on an original information sequence at a coding end; and at a decoding end, the whole list successive elimination and decoding process is enabled to be divided into a plurality of subsections, CRC is carried out in each subsection, and unsuccessful decoding is judged according to the CRC check result or screening of ineffective paths is carried out. The subsection CRC and the list successive elimination polarization decoding algorithm are combined, so that time complexity of polarization decoding can be effectively reduced, and especially in the low SNR (signal to noise ratio) region, the time complexity of decoding is reduced obviously.

The parallel kinematic machine (PKM) trajectory planning method is operable via a data-driven neuro-fuzzy multistage-based system. Offline planning based on robot kinematic and dynamic models, including actuators, is performed to generate a large dataset of trajectories, covering most of the robot workspace and minimizing time and energy, while avoiding singularities and limits on joint angles, rates, accelerations and torques. The method implements an augmented Lagrangian solver on a decoupled form of the PKM dynamics in order to solve the resulting non-linear constrained optimal control problem. Using outcomes of the offline-planning, the data-driven neuro-fuzzy inference system is built to learn, capture to and optimize the desired dynamic behavior of the PKM. The optimized system is used to achieve near-optimal online planning with a reasonable time complexity. The effectiveness of the method is illustrated through a set of simulation experiments proving the technique on a 2-degrees of freedom planar PKM.

The invention discloses a distributed knowledge data mining device and method used for a complex network. The distributed knowledge data mining device adopts a distributed computing platform which is composed of a control unit, a computing unit and a man-machine interaction unit, wherein the innovation key is to finish the calculated amount needed by a multifarious clustering algorithm in the data mining by different servers so as to improve the efficiency of the data mining. Aiming at different knowledge data, the degrees of relation and the weights of knowledge data also can be computed by applying different standards, so that a more credible result is obtained. A second-level clustering mode is adopted in the knowledge data mining process; the result of the first-level clustering is relatively rough, but the computing complexity is very low; and the computing complexity of the second-level clustering is relatively high, but the result is more precise. By combining the first-level clustering with the second-level clustering efficiently, the distributed knowledge data mining device improves the time complexity and clustering precision greatly in comparison with the traditional first-level clustering mode. According to the invention, as a visual and direct exhibition network structure and a dynamic evolutionary process are adopted, references are provided for the prediction in the fields of disciplinary development and hotspot research.

The present invention relates to a method for program transformation and an apparatus for COBOL to Java program transformation. The method consists of: (1) a new approach for statement-to-statement program transformation, facilitated by a predefined target language library, which keeps original comments, program control flow, functionality, and time complexity; (2) a new approach for goto statement elimination, which uses existing exception handling mechanism in target language and its implementation is hidden in a super class in a library; (3) a new extended BNF to distinguish different occurrences of the same term in a BNF production; (4) a new approach for embedded statement as a special marker statement and a comment, (5) in the description of the above, a program transformation specification language is defined to describe relationship between comments in two languages. (6) an apparatus, as the preferred embodiment of the method, is a COBOL to Java program transformation system Cobol2Java; a sample COBOL application and its Cobol2Java translation are given.

The present invention discloses a road boundary detection method based on a three-dimensional laser radar. In the process of intelligent vehicle driving, point cloud data collected by a vehicle-mounted three-dimensional laser radar is subjected to rasterizing processing to generate a binary raster graphic. The binary raster graphic is subjected to a distance conversion operation to obtain a distance grey-scale map, a filing distance is smaller than the narrow space between obstacle points of certain thresholds, the overall contour of the obstacle points is not changed, and an obstacle area contour map is obtained. A region growing method is used, with the position of an intelligent vehicle as a start point, the region growing is carried out forward, the passable area contour map of a road is obtained, and combined with the original binary raster graphic, a road area contour map is obtained. The contours of two sides of the road area contour map are extracted, the second function fitting is carried out, and a road boundary is obtained. The method is applicable to an urban road, a rural road and other roads, the influence of obstacles on a detection effect is small, the time complexity is low, the real-time processing is achieved, day and night work is achieved, and the algorithm robustness is good.

The invention discloses an intra-frame lossless compression coding method based on an HEVC (high efficiency video coding) frame, wherein the method is low in time complexity and high in prediction accuracy and increases compression ratio. By the aid of the HEVC frame, a coding unit can be adaptively divided according to image flatness, the best division scheme is determined, and the optimal prediction mode is selected. DPCM (differential pulse code modulation) differential codes are used for preliminary prediction, prediction errors are possibly large when only the differential codes are used for an area with complex texture, and accordingly, prediction values are corrected for the area by means of error compensation. By combining the two methods, reduction of time complexity and improvement of prediction accuracy can be effectively compromised. Distribution characteristics of residual errors depend on the prediction mode, so that the scanning sequence of the residual errors is determined according to the prediction mode, and entropy coding can be more effective.

The invention discloses a system and method for visual tracking, and belongs to the technical field of visual monitoring. The method comprises the following steps: (1) collecting and inputting real-time monitoring images and target images, (2) matching the local features of the collected images and the target images, (3) finding a dense region of feature points through a clustering classification algorithm, and calculating the coordinate of a centroid where a target is located, (4) cutting out images close to the centroid, multiplying the images by a two-dimensional Gauss template, and pre-processing images to be matched, (5) carrying out overall color matching on the images to be matched and the target images in a RGB three-color channel, (6) analyzing the motion trail of the centroid, predicating a position where the centroid is likely to appear at the next moment, outputting the offset coordinate of the target relative to the center of a monitoring and shooting image in real time, and controlling a camera to track the motion trail of a suspicious target. The method can accurately find the centroid of the suspicious target and reduces the time complexity and the space complexity of target detection, meanwhile, the potential position of the target at the next moment is predicated, and real-time tracking of the suspicious target is achieved.

The invention belongs to the technical field of path or flight path planning of robots as well as low-altitude flight aircrafts, specifically relates to a path planning method of a passable area divided at unequal distance, and is used for solving the problem that existing planning algorithm has large time complexity in time and space complexity. The path planning method comprises the following steps of: calculating convex extreme points of each barrier curve; dividing the passable area by using each convex extreme point as a horizontal line; abstracting each small area obtained by dividing into a peak of a graph; forming an undirected graph by all peaks; finding out a peak serial number corresponding to the small area at which a starting point and a final point are located; finding out all paths for the undirected graph by breadth-first or depth-first scanning; finding out an actual to-be-travelled path of a moving object according to the situation on an actual map. The path planning method disclosed by the invention has the beneficial effect of overcoming the problems of algorithms of A* and the like on memory space and operation time, and overcoming a convergence problem of an ant colony algorithm at the same time. Besides, time complexity and space complexity are improved greatly in comparison with other algorithms.

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.

The invention discloses a memristor based image identification system and method. The system comprises an image signal extracting module, a plurality of memristor based neural network modules, and an identification module, wherein an input end of the identification module is connected with an output end of each neural network module, and an input end of each neural network module is connected with an output end of the signal extracting module. The method includes the following steps that (1) a feature vector of a gray level image to be identified is acquired and input into the neural network modules; (2) each neural network module grades and identifies the feature vector according to an image model of each neural network module; (3) a difference value between a value of each neural network module and obtained standard values during training is calculated, and the classification of an image to be identified is judged according to the difference value. The memristor based image identification system has good expansibility, high integration density, and low power consumption; the method has low time complexity and high identification accuracy.

The invention discloses a low-complexity polarization code decryption SCL algorithm based on segmented verification assistance, and the algorithm selects a parity check code, repeatedly uses the parity check code in a decryption process, and achieves the performances of SCL-CRC24. Moreover, compared with a conventional scheme, the algorithm is better in low signal to noise ratio anti-noise performance and error rate. In addition, the spatial complexity of the algorithm is lower than that of the SCL-CRC24, the time complexity is greatly reduced, and the decoding speed is greatly improved. Compared with a CRC-24 verification algorithm sacrificing multiple information bits, the algorithm employs a parity check method, enables verification elements to be distributed in the information bits, is repeatedly used in the decoding process, and is lower in time complexity than the prior art.

The invention relates to an estimating method of the main curvature and the main direction of point cloud data, which comprises the following steps of: preprocessing, estimating a normal transversal curvature, fitting a Weingarten matrix, computing the characteristic value and the characteristic vector of the Weingarten matrix and computing the main curvature and the main direction. In the method, the main curvature and the main direction faithful to an original object are obtained only by utilizing the scanning data and the estimated normal vector of a laser scanner. The main curvature and the main direction are computed by the method through least square linear fitting and the characteristic value and the characteristic vector of the matrix, the algorithm is simple, the computing result is accurate, and the time complexity is highly effective. The method is called as a normal transversal fitting method, and the computing result thereof has important application value in the fields of virtual reality, computer games, natural scene simulation, urban landscape design, data compression, characteristic extraction, object 3D reconstruction and the like.

The invention discloses a stream table entry generating method and device. The method comprises the following steps: receiving a service path establishing request, wherein the service path establishing request comprises a constraint condition, source equipment and target equipment; finding a service path which satisfies the constraint condition and ranges from the source equipment to the target equipment in a data transmission network according to a cross-layer information model, wherein the cross-layer information model is a model describing an integral topological relation between an IP (Internet Protocol) layer and a light layer on the same level; generating a respective stream table entry corresponding to each piece of forwarding equipment respectively on the service path, and transmitting the stream table entries to corresponding forwarding equipment respectively. According to the scheme, resources of the IP layer and resources of the light layer are considered during calculation of the service path according to the cross-layer information model in which the resources of the IP layer and the resources of the light layer are on the same level, thereby greatly lowering the time complexity of business path calculation, increasing the service path calculation efficiency, and increasing the generation efficiency of the stream table entries.

The invention relates to a visual navigation based multi-crop row detection method, which belongs to the related field of machine vision navigation and image processing, and aims to quickly and accurately extract a plurality of ridge lines in farmland and meet the requirements of the real-time navigation and positioning of agricultural machinery. The invention provides an agricultural machine vision navigation based multi-crop row detection method, which comprises the steps: calibrating camera parameters, acquiring video and image frames, and carrying out distortion correction for the image; dividing a crop ridge line area, extracting navigation positioning points by using the vertical projection method, and calculating the world coordinates of the positioning points; using the random straight line detection method to calculate the positioning points, and detecting the straight lines on which crop ridge rows are positioned; and obtaining the position of each crop ridge row in a world coordinate system relative to the agricultural operation machinery by calculation according to the slope parameters and intercept parameters of the straight lines. Compared with the traditional technology, the technical scheme of the invention greatly reduces time complexity and space complexity, and also improves the accuracy and the real-time of navigation.

The invention discloses a multi-target distribution and flight path planning method for multiple rescue helicopters, which relates to the technical field of aviation emergency rescue. The method comprises the steps of evaluating aviation rescue task execution feasibility, conducting disaster area map image processing, obtaining a feasibility matrix through the evaluation of the rescue task execution feasibility, taking the feasibility matrix as a coefficient matrix for a solution matrix Xij, solving distribution results according to tasks of different quantities of the rescue helicopters and target points through a Hungary algorithm, obtaining a flight path solution space by extracting a safety zone topological structure by adopting an image processing method and morphological characteristics, obtaining the shortest path in the flight path solution space by a heuristic A* search algorithm, combining the results of multi-target distribution for multiple rescue helicopters, forming an emergency plan for rescue task execution of the helicopters, and calling a Google Earth flight simulation API (Application Program Interface) to demonstrate the emergency rescue plan and making preflight preparation, so as to improve the efficiency and safety of the aviation emergency rescue. The method is simple, the time complexity is low, the flight path planning process is visual, and the result is distinct.

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 relates to a multi-frame range image automatic registration method. (1) Any two frames of range images are registered through using SIFT characteristics, and the correctness of a result is judged. First, the SIFT characteristics of the two frames of the range images are calculated, corresponding points are bidirectionally crossly matched, then polar line constraint is calculated by a RANSAC algorithm, error matching is filtered, then the two frames of the range images are accurately registered through using an ICP algorithm, and the correctness of a result is judged. (2) a cycle space of a model diagram is researched, and a global consistent registration result is calculated. First, a derived cycle basis of the model diagram is calculated, an adjacency relational graph of the derived cycle basis is built, then a group basis of the consistent cycle space is calculated, and further a consistent registration result is obtained. The method can effectively increase the research speed of the cycle space, and an exponential time complexity can be increased to a line time complexity in an ideal condition. (3) the consistency of a cycle is judged, and the consistency of the registration result in the cycle is judged by a judgment method of relative differences. The invention can reliably automatically register multi-frame range images, through researching consistent cycles, error registration is taken out, thereby obtaining consistent registration results.

The invention discloses a fatigue driving state detecting system and method based on decision-making level data integration. The fatigue driving state detecting system firstly uses an acceleration sensor to acquire motion acceleration of a steering wheel, judges motion states of the steering wheel based on dynamic acceleration threshold values, and primarily judges fatigue driving states of a driver based on 4s immobility theory. Detecting error tolerance is improved by setting errors of the steering wheel turning from left to right. Pulse time domain values of the driver can be acquired via a pulse sensor, and physiological states of the driver can be detected based on dynamic pulse threshold values. Integrated detecting results can be obtained by integrating two kinds of detecting results in decision making level. By the fatigue driving state detecting method based on decision-making level data integration, a fatigue driving state detecting prototype system is constructed. Compared with existing methods, the fatigue driving state detecting method based on decision-making level data integration has certain advantages in detection accuracy and perfect performance in response time, time complexity, memory occupation and the like of algorithms.

The invention relates to a balanced binary tree-based method for detecting collisions in a large-scale virtual environment. According to the characteristics of large object quantity and complex object types in a large-scale visual environment, the method adopts an expanded balanced binary tree to improve a bounding volume hierarchy tree, accelerates the detection of object collisions and reduces time complexity, caused by environmental changes, of tree reconstruction for overcoming the drawbacks of the conventional bounding volume hierarchy method in the real-time detection of collisions in a dynamic environment. The method adopts different bounding volumes according to hierarchy, combines the simplicity and compactibility of the bounding volumes, and improves a collision method as well as the accuracy of collision detection. Concrete processing flow is described in drawings in the abstract.