50 results about "Signal variance" patented technology

An edge adaptive system and method for image filtering. The method maps each output pixel onto input image coordinates and then prefilters and resamples the input image pixels around this point to reduce noise and adjust the scale corresponding to a particular operation. Then the edge in the input image is detected based on local and average signal variances in the input pixels. According to the edge detection parameters, including orientation, anisotropy and variance strength, the method determines a footprint and frequency response for the interpolation of the output pixel. In a more particular implementation, the method divides the input pixel space into a finite number of directions called skews, and estimates the edge orientation with the nearest skew direction. This further facilitates pixels inclusion in the interpolation of the output pixel.

According to one aspect of the invention, a method for determining a power control set point is provided. In one embodiment, the power control set point is determined based on one or more factors including a first factor corresponding to a pilot bit error rate, a second factor corresponding to a normalized signal variance, and a third factor corresponding to an average number of fingers in lock.

A method and apparatus for eliminating defective pixels and noise in which each pixel value of an image is extracted using a color filter. A mean value of the extracted image is calculated A noise variance, a first threshold for determining whether a pixel is defective, and a second threshold for calculating a weight value for a pixel are all estimated from the mean value. There is a determination as to whether each pixel is defective, and a weight value, a weighted signal mean and a weighted signal variance of the each pixel are all calculated, and noise eliminated from the image using the noise variance, the weighted signal mean, and the weighted signal variance.

The invention relates to a method for evaluating WLAN (Wireless Local Area network) indoor single-source gauss location fingerprint locating performance based on conditional information entropy and belongs to the field of information system. The invention aims to solve the problems that the traditional medical sensing platform has larger volume, high price, not strong function expansibility and higher energy consumption of a wireless transmission protocol. The method comprises the following steps of: 1, establishing an initial reference point distribution model N'*M'; 2, refactoring the initial reference point distribution model N'*M' from a low dimensional asymmetric model to an N*M high dimensional symmetric model; 3, calculating to obtain a mathematical dependency relationship among the conditional information entropy, location fingerprint locating accuracy and an anticipation error according to the N*M high dimensional symmetric model refactored in the step 2; and 4, evaluating performance influences on the entity location fingerprint positioning system from signal variances of different test points or density changes of reference points according to the mathematical dependency relationship obtained in step 3 by utilizing change situations of the conditional information entropy.

The invention provides a virtual outlier noise reduction method of micro-inertial device signals, which comprises the following steps: (1) for continuous signals with noise, intercepting fixed-length data quantity to be recorded as a sequence A; (2) carrying out symmetric extension on the sequence A to finish wavelet decomposition; (3) averaging wavelet functions after sequence decomposition, and computing signal variance; (4) computing the outlier, and eliminating a threshold; (5) carrying out noise reduction treatment on the wavelet functions by utilizing the computed threshold T; (6) repeating step (2) to step (5) until the number of the decomposed layers reaches the set value; (7) finishing reconstruction of the signals by utilizing the treated wavelet functions, and realizing noise reduction treatment on the signals at the intercepted section; and (8) repeating step (1) to step (7) to realize continuous noise reduction of the signals. When filtering the measured signals of a micro-mechanical gyroscope with the outlier and noise by aopting the method of the invention, oscillation near an outlier point is reduced, and a signal-to-noise ratio which is higher than that acquired by the universal threshold noise reduction method can be acquired.

The invention relates to a direction-of-arrival (DOA) estimation method based on off-grid sparse Bayesian, which includes the following steps: S1, initializing a sparse base matrix: Phi(beta)=A+Bdiag(beta); S2, updating the mean and variance of estimation signals based on the sparse base matrix, and updating and iterating the noise variance, signal variance and off-grid parameters based on the mean and variance of estimation signals; S3, judging whether the maximum value of the absolute values of the iterated and updated off-grid parameters is greater than a set threshold C, executing S4 if the maximum value is greater than the set threshold C, otherwise, executing S5; S4, migrating the grid according to the off-grid parameters and the threshold C, updating matrixes A and B, and updating the sparse base matrix; S5, judging whether convergence is achieved or the number of iterations reaches the upper limit, returning to S2 if convergence is not achieved and the number of iterations doesnot reach the upper limit, otherwise, ending iteration; and S6, using the updated sparse base matrix to update an estimation model of off-grid DOA to get an estimation result. Compared with the priorart, the method of the invention has the advantage of high accuracy.

The invention discloses an indoor positioning method based on a wireless local area network, and belongs to the technical field of intersection of a wireless network technology and a pattern recognition technology. The method includes the steps that according to the rule that signal variance is reduced along with enhancement of signal strength, an existing fingerprint positioning method based on the local area network is improved, when a fingerprint distance is calculated, the variance of received signal strength of all wireless signal sources is estimated according to test positions, and the influence on the fingerprint distance calculation result by the received signal strength of the different signal sources is corrected correspondingly. The influence of error signal strength values caused by a multipath effect and non-line-of-sight factors can be effectively reduced, precision and accuracy of indoor positioning are improved, and compared with an existing method, the indoor positioning method is higher in robustness and stability.

The invention discloses a turbo iterative equalization detection method based on MCMC. The method comprises the steps of establishing an OFDM (Orthogonal Frequency Division Multiplexing) system receiver model; determining prior distribution of channel impulse response, frequency domain interference signal variance and a frequency domain sending signal in an OFDM system; calculating conditional distribution of the channel impulse response, the frequency domain interference signal variance and the frequency domain sending signal; calculating estimated values of the channel impulse response and the frequency domain interference signal variance and a posterior probability of the frequency domain sending signal; carrying out iterative equalization processing to obtain a decoded output signal through utilization of a turbo iterative structure; and the like. According to the method, under the condition that impulse noise prior knowledge is unknown, the estimated values of the channel impulseresponse and the interference noise variance are obtained, moreover, the signal detection is realized, and the system reliability is improved. Influences of impulse noises on the OFDM system can be effectively suppressed, so the system performance is improved. The influences of the impulse noises can be eliminated, so the channel estimation precision is improved.

Dynamic steered spatial compounding is provided in ultrasound imaging. The compounding adjusts for variance. The compounding dynamically reacts to variance due to motion and/or view direction. For each location, the weights are set based on the motion or signal variance for the respective location. The weighting used for compounding or the results of the compounding adapt to maintain or increase signal fidelity.

An edge adaptive system and method for image filtering. The method maps each output pixel onto input image coordinates (110) and then prefilters and resamples the input image pixels around this point to reduce noise and adjust the scale corresponding to a particular operation. Then the edge in the input image is detected based on local (125) and average (127) signal variances in the input pixels. According to the edge detection parameters (129), including orientation, anisotropy and variance strength, the method determines a footprint and frequency response for the interpolation of the output pixel. In a more particular implementation, the method divides the input pixel space into a finite number of directions called skews, and estimates the edge orientation with the nearest skew direction. This further facilitates pixel inclusion in the interpolation of the output pixel.

A biologic chip image has a larger noise signal, which is caused by factors, such as manufacture, hybridization and cleaning of a biological chip, pollution of dust in a testing process, interference of a testing sample and an instrument noise, hybridization non-specific reaction and the like. The invention provides a biologic chip image wavelet de-noising method based on the Bayesian estimation, which comprises the following steps of: firstly expanding the biologic chip image containing the noise into a wavelet coefficient through wavelet conversion; determining a Bayesian shrinkage threshold on the basis of estimating signal variance and noise variance; extracting an important wavelet coefficient by using the Bayesian shrinkage threshold to complete threshold processing and de-noising processing of the image; and finally enabling the de-noised wavelet coefficient to subject the wavelet reverse conversion to reconstruct the image and outputting the de-noised image. The invention has the advantages that the method has favorable effect of de-noising the biologic chip image, thereby the background noise is smoothened, and edge details of sample points are reserved so as to lay the foundation on further analyzing the chip data and ensuring the correctness of a detection result.

The invention relates to a method for typing or identification of a micro-organism using vibrational spectroscopy, characterised in that, differences in one or more vibrational spectroscopic signal contributions from one or more bleachable components between different spectra obtained of a sample of said micro-organism, and/or between spectra of different samples of said micro-organism, and/or between spectra of samples of different micro-organisms, are substantially eliminated as a source of signal variance in a dataset of said spectra, said bleachable components being molecules possessing vibrational spectral bands that upon exposure to light show a reduction in signal intensity.

The invention discloses a contourlet domain Wiener filtering image denoising method based on two-dimensional Otsu, which mainly solves the problem that a denoising effect of a traditional denoising method is not good. The contourlet domain Wiener filtering image denoising method based on the two-dimensional Otsu comprises the following implementation steps of: 1, performing contourlet decomposition on a noise-contained image TI; 2, respectively performing two-dimensional Otsu partition on all decomposed high-frequency sub-bands to obtain an important coefficient and a non-important coefficient; 3, respectively calculating elliptical windows of the high-frequency sub-bands, estimating signal variances of the high-frequency sub-bands according to the elliptical windows, and respectively performing Wiener filtering on the important coefficient and the non-important coefficient; 4, performing contourlet inverse transformation on the denoised high-frequency sub-bands to obtain a denoised image FI; and 5, performing non-local average filtering on the denoised image FI to obtain denoised output. According to the contourlet domain Wiener filtering image denoising method based on the two-dimensional Otsu, noise in a natural image containing Gaussian white noise can be effectively removed. The contourlet domain Wiener filtering image denoising method based on the two-dimensional Otsu can be used for preprocessing the image when change detection and target identification are performed.

A method and a system is provided, useable in electrical control sensors for shaft speed signal frequency change rate tests, detecting intermittent or “in-range”failures. The method estimates a short-term variance of the measured signal or signal rate of change, using the equation: Var[x]=E[x²]−E²[x], where E[x²] is an estimated average of the squared measured signal or rate of change over the short term, and E²[x] is a squared estimated average of the measured signal or rate of change over the short term. The estimated variance is compared with a predefined variance limit for a predefined amount of time, and if the estimated variance exceeds the predefined variance limit for the predefined amount of time, the measured signal is deemed invalid. A latching counter is used for timing, and its time out rate is preferably proportional to the time period the measured input is true. The step for estimating a short-term variance of the measured signal uses several filters performing averaging function.

The invention discloses a coherent noise filtering method and a system. The filtering method comprises the following steps: obtaining measured power signal parameters; starting from the first measuredpower signal of the measured power signal sequence, an iterative algorithm being used to select L power signals to determine the power vector; determining a historical background noise according to the power vector; determining a filter parameter of a coherent noise filter according to the historical background noise; determining a weight of the coherent noise filter according to the historical background noise using an optimization criterion of minimizing signal variance; obtaining the power and the serial number of the measured power signal to be filtered out; according to the historical background noise, the filtering parameter, the weight, the power and the serial number of the measured power signal to be filtered, filtering the background noise of the measured power signal to be filtered, and determining a noise-free power signal. The filtering method and the system provided by the invention can improve the impulse noise detection accuracy and the coherent noise filtering accuracy.

The invention discloses a DC signal variance adaptive detection method and a device. The method comprises the steps of conducting the adaptive smooth adjustment on reference signals and conducting the negative-feedback intervention and adjustment on output signals. Firstly, an original DC signal is tracked in real time to generate a reference signal in combination with the algorithm. After that, the reference signal and the original signal are subjected to differential amplification, and then an output signal is led in for adaptive feedback adjustment. In this way, the problem in the prior art that the correctness and the precision of the DC signal variance detection are influenced by environmental factors and human factors can be solved. The precise output of multiple-type and multiple-channel detection signals is enabled. The above method is suitable for original signals significantly affected by environmental factors and high in static output and small in variance.

The invention discloses a Massive MIMO information and energy simultaneous transmission system optimization method for hardware damage, and the method comprises the steps: establishing a Massive MIMOdownlink signal-energy simultaneous transmission system model, and respectively discussing the hardware damage at a base station transmitter, the hardware damage at an information receiver of a user,and the hardware damage at an energy receiver of the user; according to hardware damage models at a base station, an information receiver and an energy receiver, ensuring that the total power constraint sent by the base station and the energy collected at the user energy receiver are greater than a threshold, and expresing the maximum and minimum reachable rate optimization problem of the user; introducing an auxiliary variable and taking a nonlinear energy collection function as a monotone increasing function, and converting the problem P1 into a problem P2; expressing the energy collection constraint of the user, the total power constraint of the base station and the reachable rate constraint of the information user as a function of a sending signal variance and a pseudo variance; and solving the maximum and minimum reachable rates of the users, and realizing system optimization by adopting an iterative optimization algorithm. According to the method, an additional degree of freedomis provided by using an asymmetric Gaussian signal.

The invention discloses a voice separation method, device, medium and equipment. The method includes: acquiring a time-frequency domain mixed signal to be processed; performing voice separation processing on the to-be-processed time-frequency domain mixed signal, and obtaining a desired signal variance of the to-be-processed time-frequency domain mixed signal according to a result of the voice separation processing; performing de-reverberation processing on the to-be-processed time-frequency domain mixed signal according to the desired signal variance of the to-be-processed time-frequency domain mixed signal, so as to obtain a de-reverberation time-frequency domain mixed signal; and obtaining a time-frequency domain signal of each sound source according to the time-frequency domain mixed signal after de-reverberation. The technical solution provided by the invention is beneficial to realizing online voice separation in a high reverberation environment for a micro double microphone, isbeneficial to improving accuracy of voice separation and is beneficial to facilitating the real-time performance of voice separation.

A bio-molecular contrast agent (BMCA) is introduced into a biological organism such that the agent binds or reacts with target tissue within that organism. The BMCA is also signal-giving, allowing control of particle beam therapy by tracking the signal given by BMCA. The BMCA gives multiple signals which interact with different tissue elements in different ways. The variance in these signals is utilized to determine the constituency of the tissue either before and/or during therapy.

The invention discloses a hand-held electronic apparatus and a detection method for a subscriber identity module (SIM) card. The hand-held electronic apparatus comprises an antenna, the SIM card and a processor. The antenna is configured on the removable back cover of the hand-held electronic apparatus for receiving wireless signals. The processor is coupled to the SIM card and is used for detecting the signal intensity and signal variance of the wireless signals received by the antenna. If the signal intensity is smaller than a first critical value or the signal variance is greater than a second critical value, the processor polls the SIM card so as to detect whether the SIM card is already removed.

An object of the invention is to provide a mass spectrometry apparatus capable of obtaining a highly accurate quantitative result and being low-cost. A small section measurement instruction unit 101 instructs a detector 9 to perform measurement on a plurality of small sections 5 in a channel 4, the signals detected by the detector 9 are stored in a data storage unit 102, the signals are integrated by a small section signal amount integration unit 103, and variance of the integrated signals is calculated by a signal variance calculation unit 104. A signal variance evaluation unit 105 evaluates the signal variance of signals of each small section 5 in the same channel 4. When the signal variance is evaluated to be stable, an operation control unit 106 controls operations of the ion source 6 to continue measurement without warning. When the signal variance is evaluated to be unstable, the warning is performed during the measurement or after the measurement.

The invention provides a modulation format recognition method and system based on variance analysis. The modulation format recognition method comprises: a test signal generation step of performing asynchronous delay sampling on a test signal to obtain a test signal X_test; a test signal variance calculation step of calculating the variance of the test signal X_test; and a processing step of calculating a Euclid distance between the variance of the test signal X_test and a library vector C_lab in a comparison library, judging whether the Euclid distance is a minimum Euclid distance or not, if so, performing modulation format output, otherwise, not performing output. The method and the system have the beneficial effects that a modulation format of an optical communication signal is recognized effectively through a variance analyzing algorithm, and recognition is performed by features of the signal under the condition of low chromatic dispersion, so that complex calculation in the prior art is avoided. The method is simple, and online recognition can be performed. The requirement on the system is lower than that of a conventional method; and the recognition accuracy for the modulation format is high.

The invention relates to the technical field of micro-seismic monitoring, and concretely relates to a mining micro-seismic signal filtering method. The method comprises the following steps: acquiring a micro-seismic monitoring signal with a preset duration as a to-be-analyzed signal; in the time domain, extracting a background noise signal as a first signal according to the signal variance ratio in the front and rear time periods of the sliding window; and performing synchronous extrusion wavelet transform on the to-be-analyzed signal to remove noise, and performing inverse transform on the denoised signal wavelet coefficient to obtain a denoised time-domain micro-seismic signal. When the to-be-analyzed signal is subjected to synchronous extrusion wavelet transform, the wavelet coefficient of the to-be-analyzed signal is used as a second wavelet coefficient, the second wavelet coefficient is filtered based on a wavelet coefficient threshold, and the wavelet coefficient threshold is determined based on a probability distribution function of the wavelet coefficient of the first signal. According to the method, the signal-to-noise ratio can be improved, the characteristics of the original vibration signal monitored by microseism are reserved to a great extent, and the influence on subsequent microseism signal processing and analysis is reduced.