214 results about "Line search" patented technology

An epileptiform activity patient-specific template creation system permits a user to efficiently develop an optimized set of patient-specific parameters for epileptiform activity detection algorithms. The epileptiform activity patient template creation system is primarily directed for use with an implantable neurostimulator system having EEG storage capability, in conjunction with a computer software program operating within a computer workstation having a processor, disk storage and input / output facilities for storing, processing and displaying patient EEG signals. The implantable neurostimulator is operative to store records of EEG data when neurological events are detected, when it receives external commands to record, or at preset or arbitrary times. The computer workstation operates on stored and uploaded records of EEG data to derive the patient-specific templates via a single local minimum variant of a multidimensional greedy line search process and a feature overlay process.

The invention discloses a PTAM improvement method based on ground characteristics of an intelligent robot. The PTAM improvement method based on ground characteristics of the intelligent robot comprises the steps that firstly, parameter correction is completed, wherein parameter correction includes parameter definition and camera correction; secondly, current environment texture information is obtained by means of a camera, a four-layer Gausses image pyramid is constructed, the characteristic information in a current image is extracted by means of the FAST corner detection algorithm, data relevance between corner characteristics is established, and then a pose estimation model is obtained; two key frames are obtained so as to erect the camera on the mobile robot at the initial map drawing stage; the mobile robot begins to move in the initializing process, corner information in the current scene is captured through the camera and association is established at the same time; after a three-dimensional sparse map is initialized, the key frames are updated, the sub-pixel precision mapping relation between characteristic points is established by means of an extreme line searching and block matching method, and accurate re-positioning of the camera is achieved based on the pose estimation model; finally, matched points are projected in the space, so that a three-dimensional map for the current overall environment is established.

A method of allocating resources in the presence of uncertainty is presented. The method builds upon deterministic methods and initially creates and optimizes scenarios. The invention employs clustering, line-searching, statistical sampling, and unbiased approximation for optimization. Clustering is used to divide the allocation problem into simpler sub-problems, for which determining optimal allocations is simpler and faster. Optimal allocations for sub-problems are used to define spaces for line-searches; line-searches are used for optimizing allocations over ever larger sub-problems. Sampling is used to develop Guiding Beacon Scenarios that are used for generating and evaluating allocations. Optimization is made considering both constraints, and positive and negative ramifications of constraint violations. Applications for capacity planning, organizational resource allocation, and financial optimization are presented.

The present invention provides a system and method for building fast and efficient support vector classifiers for large data classification problems which is useful for classifying pages from the World Wide Web and other problems with sparse matrices and large numbers of documents. The method takes advantage of the least squares nature of such problems, employs exact line search in its iterative process and makes use of a conjugate gradient method appropriate to the problem. In one embodiment a support vector classifier useful for classifying a plurality of documents, including textual documents, is built by selecting a plurality of training documents, each training document having suitable numeric attributes which are associated with a training document vector, then initializing a classifier weight vector and a classifier intercept for a classifier boundary, the classifier boundary separating at least two document classes, then determining which training document vectors are suitable support vectors, and then re-computing the classifier weight vector and the classifier intercept for the classifier boundary using the suitable support vectors together with an iteratively reindexed least squares method and a conjugate gradient method with a stopping criterion.

The invention relates to the technical field of database keyword search, in particular to a graph-index-based graph database keyword vicinity searching method. The method comprises an off-line index constructing step and an on-line searching step, wherein the off-line index constructing step comprises the following steps of: defining a d-distance sub-graph for a weighted graph G=(V, E, w), generating d-distance sub-graphs which take certain nodes in the graphs as centers by an incomplete Dijkstra algorithm, storing the acquired largest d-distance sub-graph in a sub-graph base, constructing indexes from the nodes to the sub-graphs, designing a d-distance graph index structure, and storing the d-distance graph indexes in an interpreted mode; and the on-line searching step comprises the following steps of: searching in a combined sub-graph by a searching algorithm, and outputting a top-k answer. By zooming out a search target from an integral graph to a group of sub-graphs of limited sizes, the searching efficiency can be greatly improved.

A method of allocating resources in the presence of uncertainty is presented. The method builds upon deterministic methods and initially creates and optimizes scenarios. The invention employs clustering, line-searching, statistical sampling, and unbiased approximation for optimization. Clustering is used to divide the allocation problem into simpler sub-problems, for which determining optimal allocations is simpler and faster. Optimal allocations for sub-problems are used to define spaces for line-searches; line-searches are used for optimizing allocations over ever larger sub-problems. Sampling is used to develop Guiding Beacon Scenarios that are used for generating and evaluating allocations. Optimization is made considering both constraints, and positive and negative ramifications of constraint violations. Applications for capacity planning, organizational resource allocation, and financial optimization are presented.

A motion estimation algorithm finds the best match for a given block or macroblock so that the resulting error signal has very low energy level which is computed, for e.g., by the SAD method. The motion estimation algorithm also provides for an optional sub-pixel level estimation and an inter4v search, and allows for restricting the number of searches for a Frame-frame ME (motion estimation) using Top-Top and Bottom-Bottom field MEs. The algorithm provides for a selective early exit and enables selecting a suitable search area with N candidate points (4 to 8) for starting the search. The search is conducted progressively till a minimum error signal (low energy level signal) is reached. The candidate points for search may be in a diamond shaped configuration, and there may be a plurality of successive diamond configurations, the number of which is configurable. The invention has application in MPEG-4 and H.264 standards.

The invention relates to a motion estimation method for extending line search into a panoramic video. The method comprises the following steps: (1) extending images on all the surfaces of a referenceframe by using upper, lower, left and right adjacency images on each surface of the reference frame; (2) selecting a first macroblock without search in macroblocks of a predictive frame, searching theerror point of the smallest block in the extended reference frame by a line search method through the macroblock, and returning a motion vector corresponding to the error point of the smallest block;(3) taking the matched block subjected to line search in the step (2) as the starting point, and performing extended search on the adjacent macroblocks to obtain a corresponding motion vector; and (4) detecting whether the search matching is performed on the macroblocks in the predictive frame or not, and entering in the step (2) if a macroblock is not searched, otherwise, ending. The method caneffectively perform motion estimation on the panoramic video and improve the accuracy of motion estimation and the quality of a compensation image.

The present ivnention relates to a circular / elliptic / sequence meter monitoring method based on conout profile of body in transformer substation, belonging to the field of transformer substation meter monitoring technology. It is characterized by that its total control terminal computer has a remoter meter detection / fetch module capable of implementing circular / elliptic / square search and detection algorithm based on contour profile of body, and said monitoring method includes the following steps: utilizing wireless local area network to control patrol inspection robot in the transformer substation, according to predefined patrol inspection line searching the space position of the meter locked by subprogram in preset search space range, then using visible light camera head to obtain the geometrical image of the motor, then transferring said image into total control terminal, makin data processing so as to attain the goal of implementing remote monitoring and control.