3439 results about "Rate of convergence" patented technology

An equalizer for equalizing channel multi-path distortion includes digital filters. To improve the convergence speed and tracking ability of the equalizer while lowering noise and power consumption, the digital filters are divided into sections. Various parameters of the sections, such as step-size, shutdown and update rates can be controlled. Control of the various parameters can be realized either in software on an embedded or external processor or by dedicated hardware.

Systems, methods, and a computer readable medium for performing auto exposure (AE) techniques that are beneficial in variable lighting conditions—and particularly applicable to handheld and/or mobile videoconferencing applications—are disclosed herein. Handheld and/or mobile videoconferencing applications—unlike their fixed camera counterparts—are often exposed to a wide variety of rapidly changing lighting and scene conditions, and thus face a difficult trade-off between adjusting exposure parameter values too frequently or not frequently enough. In personal electronic devices executing such handheld and/or mobile videoconferencing applications, it may be desirable to: use a small, centered, and center-weighted exposure metering region; set a relatively low brightness target value; and adjust the camera's exposure parameter values according to a distance-dependent convergence speed function. The use of such techniques, in conjunction with a relatively large stability region, may also improve the quality of a video encoder's temporal predictions—and thus video quality—in videoconferencing applications.

The present application relates to tracking a position of a device, e.g. for detecting slow and rapid earth deformation, by making use of a recursive filter having the filter characteristic adapted to a detected type of motion. If the motion of the position tracking device is rapid, the filter characteristic is set such that the rapid motion can be tracked with the necessary speed. On the other hand, if the motion is slow, e.g. during times of a normal tectonic drift, the filter characteristic is set such that the motion is slowly tracked with the advantage of efficient noise reduction, i.e. noise in the input signal is effectively barred and does not pass through the filter to the output signal. Thus, in times of rapid motion the convergence speed of the filter output signal to the input signal is set high for fast convergence and in times of slow motion the convergence speed of the filter output signal to the input signal is set low for a slow convergence. The filter may be a Kalman filter.

This invention relates to one rapid movement estimation method, which comprises the following steps: a, dividing each frame of images into several blocks and determining vector space upper and down message on each current image composed of upper and down messages to determine several prepare motion vector; computing each prepare motion vector matching error values with low motion matching error values with current pixel module designed as motion vector step.

In a burst-mode, high-speed spread-spectrum communications system, faster convergence of a receiver's automatic gain control (AGC) circuit reduces the time required to bring a received signal within the operating range of an operation amplifier and other radio-frequency and digital sections of the receiving system. A gain control circuit includes a coarse-gain feedback loop and a fine-gain feedback loop to improve convergence speed and at the same time maintain the stability of the AGC circuit. The coarse-gain feedback loop quickly brings the received signal, using a large gain signal, to the desired operating range. The fine-gain feedback loop uses a smaller gain signal to gradually smooth the received signal to avoid saturation on the A/D converters.

The invention discloses a method for autonomously localizing robots on the basis of laser radar. The method includes randomly generating N particles to form particle swarms around initial locations ofthe robots, and updating the particle swarms according to robot real-time movement distances and real-time rotation angles measured by sensors of the robots at current operation moments of the robots; computing the superposition quantity of point cloud of the laser radar and obstacles of maps for each particle to use the superposition quantity as a score of the particle, computing weighted position and posture average values of the particle swarms by the aid of the score, which is used as a weight, of each particle and utilizing the weighted position and posture average values as AMCL (adaptive Monte Carlo localization) estimation positions and posture; utilizing the AMCL estimation positions and posture as initial values, acquiring scanned and matched positions and posture by the aid ofscanning and matching algorithms on the basis of Gauss-Newton iterative processes and utilizing the scanned and matched positions and posture as the optimal positions and posture of the robots at thecurrent operation moments; re-sampling the particle swarms by the aid of AMCL algorithms to ultimately obtain the global optimal positions and posture of the robots during operation. The global optimal positions and posture of the robots are used as localization results. The method has the advantage that the localization convergence rate can be greatly increased, and the localization precision andthe localization stability can be greatly enhanced.

The invention discloses a short-term load prediction method based on similar day segmentation and an LM-BP network. According to the method, through quantitative calculation of comprehensive correlative coefficients between meteorological factors and a historical load curve corresponding to a to-be-predicted day, the load curve of the to-be-predicted day is segmented, and according to prediction load curves within different time intervals, corresponding similar days are separately figured out; selection of the similar days is conducted through comprehensive consideration of a multi-feature similarity judgement standard of tendency similarity and shape similarity which are based on historical day meteorological similarity and historical load data. A similar day sample with the highest similarity is selected form historical data of the same kind; different neural network models are constructed through different training samples within different load prediction time intervals, and therefore the prediction precision of the neural network models is further improved. By means of the method, the calculation speed and convergence speed of a prediction algorithm are increased.

The embodiment of the invention provides a model training method and device based on federated learning and a server. The method comprises the steps of sending a to-be-trained model to a plurality ofworking nodes; receiving a local model fed back by the plurality of working nodes, wherein the local model is obtained by training a to-be-trained model by each working node according to own data; determining the precision of each local model according to the test data set; determining a weight coefficient of each local model according to the precision of each local model, wherein the weight coefficient is positively correlated with the precision; and updating the to-be-trained model according to the plurality of local models and the corresponding weight coefficients. According to the method provided by the embodiment of the invention, by increasing the weight coefficient of the high-precision local model and reducing the weight coefficient of the low-precision local model, the convergencerate of the to-be-trained model is increased, and the precision of the to-be-trained model is improved.

The invention provides an unmanned aerial vehicle trajectory planning method based on EB-RRT. The method comprises the steps that grid partitioning is carried out on a map environment; a node xnearst nearest to a random point in existing nodes is found; an insertion point xnew is calculated according to the step length; if the sum of the distance between a root node to xnew and the Euclidean distance between xnew and the end is not greater than the length of the current optimal path, whether the xnew point is in an obstacle is detected; if not, the surrounding environment information of xnearst is collected, and a new insertion point xnew is randomly sampled in the surrounding free area; xnew is inserted into a tree; the nearby node set of xnew is traversed and found in the corresponding grid, and the path of the nearby nodes is optimized; connection detection is carried out on two trees into which the xnew point is inserted; if not, two trees are exchanged, and random points continue to be sampled; if so, a feasible path is found, and downsampling is carried out; and a Bessel cubic interpolation algorithm is used to optimize the new path. The unmanned aerial vehicle trajectory planning method provided by the invention has the advantages of high convergence speed, good flexibility, high running efficiency and good practicability.

The invention discloses an image target recognition method based on optimized convolution architecture. The image target recognition method includes collecting and enhancing an input image to form a sample; training the sample on the basis of the optimized convolution architecture; performing classified recognition on an image target by using the convolution architecture after training, wherein optimization of convolution architecture includes ReLU activation function; locally responding to normalization; overlapping and merging a convolution area; adopting neuron connection Drop-out technology; performing heuristic learning. Compared with the prior art, the image target recognition method has the advantages that tape label samples can be expanded, and the image target recognition method is supportive of classification of many objects and acquiring of high training convergence speed and high image target recognition rate and has higher robustness.

The invention discloses a multisource information fusion method in an evidence high-conflict environment, which comprises the following steps of: determining the mutual support degree of evidences according to evidence distances, further calculating the relative discount factor of each evidence by taking a characteristic vector corresponding to the maximum characteristic value of a evidence support degree matrix pattern as a weighting vector of the evidence, wherein the evidence with the maximum weighing coefficient is a key evidence; and discounting each evidence information, and fusing the information of multisource sensor data by using the D-S (Dempster/Shafer) rule so as to increase the accuracy and precision of a monitoring proposition judgment under an evidence high-conflict status.The method can effectively eliminate the uncertainties of multisource information, increase the accuracy of information evaluation and overcome the limitation that correct evaluation results can not be acquired by using the traditional D-S evidence theory under the evidence high-conflict status, and has the advantages of simple algorithm, high convergence rate and the like.

Methods and systems are provided for reliably providing a prognosis of the life-expectancy of a vehicle battery. A state of degradation of the battery is predicted based on a rate of convergence of a metric, that is derived from a sensed vehicle operating parameter, towards a defined threshold, determined based on past history of the metric. The predicted state of degradation is then converted into an estimate of time or distance remaining before the component needs to serviced, and displayed to the vehicle operator. Vehicle control and communication strategies may be defined with respect to the predicted state of degradation.

A method for iteratively reconstructing image data acquired by a computed tomography system is provided. The method comprises generating a calculated sinogram from an image estimate and generating an error sinogram based on the calculated sinogram and a measured sinogram. Then, one or more backprojections are performed, each based upon a reconstruction parameter. The reconstruction parameter impacts at least one of convergence speed and computational cost of each iterative step and corresponding reconstruction. A filtering step is performed prior to performing the one or more backprojections. Finally, the initial image is updated by adding corresponding results of the one or more backprojections to the image estimate to obtain the reconstructed image.

The invention discloses a non-cooperative spacecraft attitude estimation method based on virtual sliding mode control, and belongs to the technical field of non-cooperative spacecraft navigation. The non-cooperative spacecraft attitude estimation method comprises the following steps: utilizing a virtual control sliding mode controller based on the Lyapunov principle; using target satellite absolute attitude obtained by a stereoscopic vision system as a control objective; according to motion characteristics of the target satellite, establishing a virtual satellite motion model of the target satellite; using a kinetic model of the virtual satellite as a controlled member to obtain attitude parameters of the virtual satellite; using attitude parameters estimated by the virtual satellite and the target satellite absolute attitude obtained by the stereoscopic vision system as controlled input, and calculating the virtual revolving moment on the motion model of the virtual satellite through the virtual sliding mode controller, so as to realize the estimation of the target satellite attitude parameters by the virtual control sliding mode controller. The non-cooperative spacecraft attitude estimation method disclosed by the invention is low in calculated amount, and can still achieve higher convergence rate and higher precision when the initial error of the state variables is high or the system error emerges, so as to meet the requirements of the high performance navigation system.

The invention relates to a mixed measurement analysis method for a satellite antenna. The method effectively solves the problems that various measurement devices are used for co-measurement to reduce detection difficulty and improve detection efficiency during a measurement process of the satellite antenna. The method comprises the steps: cubic mirror collimating measurement is carried out by electronic theodolites during antenna installation and detection processes, scanning measurement of an antenna shaped surface is carried out by a laser radar, a space point position is measured by a laser tracker, and thus the measurement of the satellite antenna is jointly completed by the various measurement devices; union calibration algorithm of 'six freedom degree measurement station three-dimensional network' is employed, a conversion relationship between measurement station coordinates and measurement coordinates is utilized, various observed value error equations are directly listed, so as to overcome shortcomings of a traditional algorithm and improve adaptability of the algorithm. The method provided by the invention is simple, is easy to operate, enables an initial value to be fast acquired, has low requirements for precise degree of the initial value, has a few iteration times, is quick in convergence speed, theoretically is an optimal solution, and has strong algorithm adaptability, high measuring efficiency, fast speed and high precision.

The invention discloses a feedback artificial neural network training method and a feedback artificial neural network calculating system and belongs to the field of calculation of neural networks. According to the artificial neural network training method, the synapse weight is adjusted according to a feedforward signal and a feedback signal at the two ends of each neural synapse; when the signals at the two ends of each neural synapse are an excitation feedforward signal and an excitation feedback signal respectively, the synapse weight is adjusted to the maximum value; when the signals at the two ends of each neural synapse are a tranquillization feedforward signal and an excitation feedback signal respectively, the synapse weight is adjusted to the minimum value. According to the feedback artificial neural network calculating system, each node circuit comprises a calculating module, a feedforward module and a feedback module and the node circuits are connected through the neural synapses simulated by memristors, and a series of pulse signals are adopted to achieve the feedback artificial neural network training method. An artificial neural network provided by the system and the method is high in rate of convergence, and the artificial neural network calculating system is few in control element, low in energy consumption and capable of being applied to data mining, pattern recognition, image recognition and other respects.

The invention discloses a multi-band structure self-adaptive filter switching method for AEC (acoustic echo cancellation). Firstly, a far-end voice signal is acquired; a voice endpoint is detected, and a VAD (voice activity detection) flag bit and an improved envelope decision threshold are output; the voice signal is fed into a loudspeaker to serve as a desired signal and also input into a self-adaptive filter; the self-adaptive filter adopts a switchable multi-band structure and a corresponding self-adaptive algorithm, parameters of the filter are adjusted by use of the least mean square criterion according to feedback information, and the optimal solution is obtained. According to the provided switching method, voice characteristics are considered sufficiently under the condition that steady maladjustment is guaranteed, and optimized configuration of the convergence rate and the algorithm complexity is realized while advantages of the algorithm in the convergence rate are utilized. During actual application of echo cancellation, a single algorithm does not easily meet various variable demands. The variable switching algorithm provides more probability for a user and has great significance in application of self-adaptive echo cancellation.

The invention discloses a non-rigid heart image grading and registering method based on an optical flow field model, which belongs to the technical field of image processing. The method comprises the following steps of: obtaining an affine transformation coefficient through the scale invariant characteristic vectors of two images, and obtained a rough registration image through affine transformation; and obtaining bias transformation of the rough registration image by using an optical flow field method, and interpolating to obtain a fine registration image. In the non-rigid heart image grading and registering method, an SIFT (Scale Invariant Feature Transform) characteristic method and an optical flow field method are complementary to each other, the SIFT characteristic is used for making preparations for increasing the converging speed of the optical flow field method, and the registration result is more accurate through the optical flow field method; and the characteristic details of a heart image are better kept, higher anti-noising capability and robustness are achieved, and an accurate registration result is obtained. Due to the adopted difference value method, a linear difference value and a central difference are combined, and final registration is realized by adopting a multi-resolution strategy in the method simultaneously.