38 results about "Linear dependency" patented technology

A new method for bias estimation on multiple frequencies with a Kalman filter is proposed. It consists of four steps: First, a least-squares estimation of ranges, ionospheric delays, ambiguities, receiver phase biases and satellite phase biases is performed. The code biases are absorbed in the ranges and ionospheric delays, and a subset of ambiguities is mapped to the phase biases to remove linear dependencies between the unknown parameters. In a second step, the accuracy of the bias estimates is efficiently improved by a Kalman filter. The real-valued a posteriori ambiguity estimates are decorrelated by an integer ambiguity transformation to reduce the time of ambiguity resolution. Once the float ambiguities have sufficiently converged, they are fixed sequentially in a third step. Finally, a second Kalman filter is used to separate the receiver and satellite code biases and the tropospheric delays from the ranges.

The invention pertains to a method for modeling and simulating entities whose interdependencies, as well as the resulting system behavior, can be used to make statements about real behavior. It is comprised of the following steps: the real entities are each represented by an individual software-object which stores the individual behavior of the corresponding entity, wherein this behavior is extracted from real data about the entity and its environment using machine learning methods, in order to then store the individual behavior within the software-object via a set of probabilistic networks (PN), wherein each PN models one sub-behavior of the entity as quantified linear or non-linear dependencies between a set of influence factors and behavior aspects, the influence factors and behavior aspects are represented by the corresponding nodes in the PN. The global interdependencies between the entities are extracted from real data and stored as linear or non-linear dependencies between the entities in the meta-PN and the meta-PN are generated by merging local PNs and by adding the extracted global interdependencies.

A method of evaluating a water sample for the presence or possible future presence of nitrification comprises obtaining data values of a number of parameters, processing the data values to determine correlation coefficients, to identify any linear dependencies, to standardize the scales, evaluating the data values over a plurality of proliferation time periods and neuron numbers, calculating MSEs and R²'s from the evaluations, and estimating a valid likelihood of nitrification of the water sample. A method of evaluating a water sample for the presence or possible future presence of nitrification, comprises obtaining data values of a number of parameters, statistically pre-processing the data values and supplying the pre-processed data values to a neural network. Apparatus, media and processors which are used in performing such methods.

A method and system for power node current waveform modeling provides improved accuracy for logic gate and functional block power node current models in computer-based verification and design tools. An output voltage waveform is generated, with each point a linear function of a set of input values corresponding to times at which the output voltage reaches predetermined fractional values of the supply voltage. A set of coefficients is used for each point, as each output voltage waveform point has a different linear dependency on the set of input values. The output voltage waveform model is then differentiated and multiplied by an effective load capacitance to determine the output current waveform. The method and system retain compatibility with existing software by using input values already present in the digital simulation models (e.g., the 70%-30% delay time or delay time from other voltage pairs, and the 50% switch point time) that yield a subset of output voltage points. The coefficients used in the model are predetermined for the particular circuit from a principle components analysis. The model is highly accurate as the coefficients that determine the linear functions are determined via a principle components analysis that determines the coefficients by factoring input value dependence down to three input variables while maintaining high correlation values between the model and circuit simulations over various input and circuit conditions.

Methods and systems using Pade' Approximant expansion ratios provide mappings between nonlinear sensors and a more linear output domain. The method includes (a) generating a variably amplified version of the input signal in accordance with a produced and variable gain defining signal; (b) generating an output signal that exhibits a substantially linear dependency from a sum of a supplied offset signal and the variably amplified version of the input signal; (c) multiplying the output signal by a supplied gain correction factor to produce a feedback gain correction signal; and (d) using the feedback gain correction signal to produce the variable gain defining signal.

An electronic circuit for adjusting a property of an input signal of a non-linear element is disclosed. A detector detects a strength of the input signal. An analog controller forms a control signal as a piecewise approximation of a non-linear dependency between the property and the strength of the input signal detected by the detector. An adjusting circuit adjusts the property of the input signal according to the control signal from a controller.

A computer-implemented method for simultaneous metric learning and variable selection in non-linear regression is presented. The computer-implemented method includes introducing a dataset and a target variable, creating a univariate neighborhood probability map for each reference point of the dataset, and determining a pairwise distance between each reference point and other points within the dataset. The computer-implemented method further includes computing a Hessian matrix of a quadratic programming (QP) problem, performing optimization of the QP problem, re-weighing data derived from the optimization of the QP problem, and performing non-linear regression on the re-weighed data.

An optical detector(110) is disclosed, comprising: at least one optical sensor(122) adapted to detect a light beam(120) and to generate at least one sensor signal, wherein the optical sensor(122) has at least one sensor region(124), wherein the sensor signal of the optical sensor(122) exhibits a non-linear dependency on an illumination of the sensor region(124) by the light beam (120) with respect to a total power of the illumination; at least one image sensor(128) being a pixelated sensor comprising a pixel matrix(174) of image pixels(176), wherein the image pixels(176) are adapted to detect the light beam(120) and to generate at least one image signal, wherein the image signal exhibits a linear dependency on the illumination of the image pixels(176) by the light beam(1,6) with respect to the total power of the illumination; and at least one evaluation device(132), the evaluation device(132) being adapted to evaluate the sensor signal and the image signal. In a particularly preferred embodiment, the non-linear dependency of the sensor signal on the total power of the illumination of the optical sensor(122) is expressible by a non-linear function comprising a linear part and a non-linear part, wherein the evaluation device(132) is adapted to determine the linear part and/or the non-linear part of the non-linear function by evaluating both the sensor signal and the image signal. Herein, the evaluation device(132), preferably, comprises a processing circuit(136) being adapted to provide a difference between the sensor signal and the image signal for determining the non-linear part of the non-linear function.

A tunable, all-optical, coupling method for a high-Q silica microsphere and an optical waveguide is disclosed. By means of a novel optical nanopositioning method, induced thermal expansion of an asymmetric microsphere stem for laser powers up to 211 mW is observed and used to fine tune the microsphere-waveguide coupling. Microcavity displacements ranging from (0.61±0.13)−(3.49±0.13) μm and nanometer scale sensitivities varying from (2.81±0.08)−(17.08±0.76) nm/mW are obtained. Additionally, an apparent linear dependency of coupling distance on stem laser heating is achieved. Using these methods, coupling can be altered such that the differing and customizable coupling regimes can be achieved.

The invention provides an unmanned aerial vehicle edge computing network linear dependency task unloading method. The method mainly comprises the following steps: 1, generating a task description set Taskk = (Lk, Ik, Ck, Ok), and constructing a mathematical model P1 of task unloading, resource allocation and unmanned aerial vehicle trajectory optimization in an unmanned aerial vehicle edge computing network; 2, under the conditions of given frequency, unloading decision and unloading data volume, constructing a mathematical model P2, solving a problem P2 by adopting convex optimization, solving an optimal unmanned aerial vehicle trajectory, calculating system energy consumption, and recording a target value as E '; 3, constructing a mathematical model based on the obtained unmanned aerial vehicle trajectory; P3, obtaining an unloading decision and a resource allocation scheme by adopting a dynamic programming algorithm and convex optimization, calculating system energy consumption, and recording a target value as E; and 4, comparing the difference between the new weighted total energy consumption value E and the new weighted total energy consumption value E ', if E-E' is less than epsilon, quitting, otherwise, repeating the step 2 and the step 3. By applying the method, the energy consumption of dependent task execution in the mobile edge computing network of the unmanned aerial vehicle is reduced, and the service time of the unmanned aerial vehicle and the terminal equipment is prolonged.

An object of the invention is to allow cipher strength evaluation when available resources such as the complexity and the number of plaintext available for decryption have conditions, and to allow comparison of cipher strength under given conditions. The invention combines the exhaustive search with an algebraic method, sets conditions for resources such as the complexity and the number of plaintext available for decryption beforehand, and utilizes the linear dependency of a decryption equation for use in decryption to optimize a decryption method as the maximum number of available plaintext is secured. Thus, it reduces the complexity and allows efficient search of solutions for the decryption equation.

The invention relates to a COTDR curve smoothing and event detection method and device. The method comprises the following steps: a window division step of dividing to-be-processed data into multiple data windows with a preset data segment length, and computing the linearly dependent coefficient of the data in the window; a reference value computing step of comparing the linearly dependent coefficient with the preset threshold value, taking the original data of the data window as the reference data if the linearly dependent coefficient is greater than the predetermined threshold value, or taking the original data after the smoothing processing as the reference data if the linearly dependent coefficient is less than the predetermined threshold value; a filter processing steps of performing the weighted average on the original data in the window and the reference data corresponding to the window to obtain a filter result; a wavelet decomposition step of decomposing the filter result through the wavelet, and performing the wavelet reconstruction after performing zero-setting on the low-frequency coefficient. The scheme disclosed by the invention has the advantages that the smoothing detection curve can be obtained, the amplitude jittering is lowered, and the event can be automatically detected.

Provided is a computer-implemented method and platform for context aware sorting of items available for configuration of a system during a selection session, the method including the steps of providing a numerical input vector, V, representing items selected in a current selection session as context; calculating a compressed vector, V_comp, from the numerical input vector, V, using an artificial neural network, ANN, adapted to capture non-linear dependencies between items; multiplying the compressed vector, V_comp, with a weight matrix, E_I, derived from a factor matrix, E, obtained as a result of a tensor factorization of a stored relationship tensor, T_r, representing relations, r, between selections of items performed in historical selection sessions, available items and their attributes to compute an output score vector, S; and sorting automatically the available items for selection in the current selection session according to relevance scores of the computed output score vector, S.

An embodiment of the invention discloses a method for generating vehicle straight driving direction indications. The method comprises: determining whether a driving state of a vehicle is a straight driving state; when the driving state of the vehicle is the straight driving state, obtaining a plurality of three-dimensional coordinates output by a three-axis acceleration sensor of the vehicle in preset duration and caching; according to the plurality of three-dimensional coordinates output by the three-axis acceleration sensor, generating a straight line indicating a vehicle straight driving direction. An embodiment of the invention also discloses a device for generating vehicle straight driving direction indications. The method and the device make linear dependency of acquired points high, so as to accurately determine the straight direction of vehicle driving, and reduce errors.