53 results about "Noise weighting" patented technology

An image is reconstructed based upon a filtered backprojection (FBP) technique using a reconstruction filter which is customized or shaped by parameters including a weight value that is determined for weighing projection data according to a predetermined noise model. The weight value is determined based upon rays or views in the projection data. A regularization term is also combined with the noise-weighted FBP.

The embodiment of the invention discloses an image iteration reconstruction method and a device. The method comprises steps of acquiring projection data subjected to water hardening correction after a target object is subjected to ray scanning, and according to the projection data, generating an initial reconstruction image and a noise weight matrix; according to the initial reconstruction image, acquiring an initial regularization penalty factor and an initial bone image segmentation matrix; according to the projection data, the initial reconstruction image, the initial bone image segmentation matrix, the noise weight matrix and the initial regularization penalty factor, using a pre-established bone hardening iteration reconstruction model to carry out image iteration reconstruction and acquiring a reconstruction image subjected to the bone hardening correction. In this way, it is achieved that bone hardening artifacts are gradually eliminated in an image iteration reconstruction process; bone hardening artifacts in the iteration reconstruction image can be effectively reduced; and quality of the iteration reconstruction image is ensured.

Disclosed herein is a noise reduction method for automatic speech recognitionl. A noise reduction method for automatic speech recognition, including: computing a magnitude spectrum of a noisy speech containing a clean speech to be recognized and noise affecting the clean speech; computing a power spectrum of the noisy speech; computing an estimate of a power spectrum of the clean speech; computing an estimate of a power spectrum of the noise; computing an estimate of an a priori signal-to-noise ratio as a function of the estimate of the power spectrum of the clean speech and the estimate of the power spectrum of the noise; computing an estimate of an a posteriori signal-to-noise ratio as a function of the power spectrum of the noisy speech and the estimate of the power spectrum of the noise; computing an attenuation gain as a function of the estimate of the a priori signal-to-noise ratio and the estimate of the a posteriori signal-to-noise ratio; and computing an estimate of a magnitude spectrum of the clean speech as a function of the magnitude spectrum of the noisy speech and the attenuation gain. Computing the estimates of the a priori and the a posteriori signal-to-noise ratios includes computing a noise weighting factor for weighting the estimate of the power spectrum of the noise in the computation of the estimates of the a priori and the a posteriori signal-to-noise ratios; computing a spectral flooring factor for flooring the estimates of the a priori and the a posteriori signal-to-noise ratios; and computing the estimates of the a priori and the a posteriori signal-to-noise ratios also as a function of the noise weighting factor and the spectral flooring factor

An apparatus and method for evaluation of hearing loss is disclosed. The apparatus and method use evoked Auditory Brainstem Responses (ABR) to determine if the subject is able to hear repeatedly administered click stimuli. In order to optimize evaluation, the present invention uses normative data to accurately weight the auditory responses, so that evaluation will be possible in different or changing noise conditions.

The invention relates to a signal-noise-ratio-post-filtering-and-characteristic-space-fusion minimum-variance ultrasonic imaging method. The signal-noise-ratio-post-filtering-and-characteristic-space-fusion minimum-variance ultrasonic imaging method includes the steps that sampling signals received by array elements are delayed, subjected to forward-backward smoothing and subjected to diagonal loading treatment, and an estimated sample covariance matrix is obtained; the estimated covariance matrix is subjected to characteristic decomposition, and signal subspace is constructed; in the desired signal subspace, according to the minimum-variance criterion, an adaptive beam-forming weight value is calculated; then a post-filtering coefficient is designed according to the signal coherence, a noise weighting coefficient is introduced according to the input signal noise ratio, and a signal-noise-ratio-filtering coefficient is calculated; the adaptive beam-forming weight value and the signal-noise-ratio-filtering coefficient are fused to obtain the novel weight vector; finally, multiple pieces of data subjected to forward-backward smoothing treatment are weighted and summed through the obtained minimum-variance weight value fusing the signal-noise-ratio post filtering and the characteristic space, and an adaptive beam signal is obtained. By means of the signal-noise-ratio-post-filtering-and-characteristic-space-fusion minimum-variance ultrasonic imaging method, the properties of ultrasonic images in the resolution ratio aspect, the contrast ratio aspect, the noise robustness aspect and the like can be improved, and therefore the quality of ultrasonic imaging is improved as a whole.

Pressure control in a fuel cell is achieved by using an H-infinity controller coupled in a feedback loop between a reactant feed gas valve and a pressure sensor on gas flows to the membrane electrode assembly of the fuel cell. To maintain pressure balance across the membrane, the pressure of the oxidant reactant is used to regulate fuel reactant flow. An integrator windup compensator manages integral windup in the H-infinity control scheme. Control weight, sensor noise weight, and performance weight matrices are incorporated into the H-infinity control model. Respective to PID control, the H-infinity model provides superior performance in the presence of high frequency feedback noise enabling use of low cost control components in the fuel cell and a minimum of EMI shielding.

The invention discloses a square root volume fuzzy adaptive Kalman filtering SLAM (Simultaneous Localization And Mapping) method. The method comprises the steps of: modeling a mobile robot, to establishing a dynamic model and an observation model; by means of a fuzzy adaptive noise dynamic adjustment algorithm, setting control weights for motion noise and observation noise in the dynamic model andthe observation model and fuzzily adjusting the noise weights by dynamically adjusting innovation mean and variance; predicting pose information of the robot at the moment k through the pose information of the robot at the moment k-1; and updating the post information in a calculator after carrying out iteration preset times. The method has the beneficial effects that an iterative method is combined with strong tracking and aiming at the problems of the motion noise and the observation noise of the robot, different noises are adaptively dynamically adjusted by adopting an improved fuzzy adaptive method; and the complexity of the algorithm is reduced, the distortion problem of a sampling point in the nonlinear case is solved, the trajectory deviation phenomenon caused by the increase of feature points is corrected and the pose accuracy is increased.

The invention relates to a time domain digit weighting method for non-stable noise signals. The method includes the following steps that (1) data acquisition is conducted on the non-stable noise signals to acquire noise digital signals p(Tau); (2) a weighting amplitude curve A(f) is established according to the octave sound level modification value of a noise weighting network; (3) mirror transformation processing is conducted on the weighting amplitude curve A(f), and a non-periodic real even function A'(f) of a frequency domain weighting network is acquired; (4) inverse Fourier transform is carried out to acquire an impulse response function A'(t) of the weighting network; (5) a window function Wbeta(t) is selected to conduct windowing truncation processing on the A'(t); (6) a windowing weighting wavelet function A'wbeta(t) is acquired; (7) interpolation resampling is conducted on the windowing weighting wavelet function, and the time interval of the windowing weighting wavelet function is made to be consistent with the time interval of collected original noise digital signals; (8) the acquired non-stable noise digital signals p(Tau) and the windowing weighting wavelet function are subjected to related comparison transform, so that time domain weighting fluctuation signals pw(t) are acquired; (9) a weighting overall sound pressure level curve Lw(t) of the non-stable signals is obtained through sound level transform calculation.

The invention belongs to the technical field of radar array signal processing, and discloses a COLD array arrival direction and polarization parameter joint estimation method based on resampling. Theproblems of large computation load and greatly reduced parameter estimation precision under low signal to noise ratio in the prior art are solved. According to the main idea, the range of the incomingwave direction and polarization parameters is pre-estimated and combined with resampling, so that resampled parameter estimation values are normal values rather than abnormal values; a pseudo noise adding method is changed; and the optimal pseudo noise weight is selected for each group of resampling. According to the invention, spectrum peak search can be avoided; the root polynomial order is reduced; the computation load and the hardware complexity are reduced; the arrival direction and polarization parameter estimation performance under low signal to noise ratio is greatly improved; and thedisadvantages of large computation load and severely reduced parameter estimation performance under low signal to noise ratio, which are caused by spectrum peak search in the prior art, are overcome.

The invention provides a magnetotelluric impedance estimation method. The method comprises the following steps: performing Fourier transform on a time domain magnetotelluric signal measured from an observation station to a frequency domain; estimating initial impedance by using a least square method, obtaining an initial residual error, distinguishing various noises, obtaining a weight value corresponding to magnetic field normalization, obtaining estimated magnetotelluric impedance, judging a numerical value of a parameter for measuring whether the noise is close to Gaussian noise, outputtingthe impedance and the like. According to the method, impedance estimation is carried out on the basis of a noise weighting mode, the relation between noise and signals can be well utilized, weightingnormalization is carried out by utilizing the correlation, and Gaussian noise increased along with increase of signal strength is restored into Gaussian noise after weighting normalization; comparedwith a traditional least square method and a traditional steady method, the method based on noise weighting truly solves the problem that Gaussian noise is increased along with increase of signal strength.

To address the need for choosing values of harmonic noise weighting (HNW) coefficient (ε_p) so that the amount of harmonic noise weighting cam be optimized, a method and apparatus for performing harmonic noise weighting in digital speech coders is provided herein. During operation, received speech is analyzed to determine a pitch period. HNW coefficients are then chosen based on the pitch period, and a perceptual noise weighting filter (C(z)) is determined based on the harmonic-noise weighting (HNW) coefficients (ε_p).

Method and device for reconstructing a target signal from a noisy input signal The invention relates to a method (300) for reconstructing at least one target signal (361) from an input signal (301) corrupted by noise, the method (300) comprising: determining (310) a first set of feature vectors (311) from the input signal (301), the first set of feature vectors (311) forming anon-negative input matrix (V, 331) representing signal characteristics of the input signal (301); determining (320) a second set of feature vectors from the first set of feature vectors (311), the second set of feature vectors forming a non-negative noise matrix (B, 341) representing noise characteristics of the input signal (301); decomposing (350) the input matrix (V, 331) into a sum of a first matrix and a second matrix, the first matrix representing a product of a non-negative bases matrix (W, 351) and a non-negative weight matrix (H, 352), and the second matrix representing a combination of the noise matrix (B, 341) and a noise weight vector (hb, 353); and reconstructing (360) the at least one target signal (361) based on the non-negative bases matrix (W, 351) and the non-negative weight matrix (H, 352).

The embodiment of the invention discloses a video image enhancement method and device. The method can comprise the following steps: acquiring the frame level noise intensity indicator value of a current frame according to corresponding pixel points of the current frame and a previous frame of a video image; acquiring noise weights and direct current components corresponding to the pixel points according to the pixel points of the current frame and a preset first window; acquiring a high-frequency coefficient group corresponding to the current frame from the pixel points of the current frame according to a preset sequencing strategy; acquiring an image enhancement gain value according to the frame level noise intensity indicator value of the current frame and the high-frequency coefficient group; acquiring high-frequency values corresponding to the pixel points according to the image enhancement gain value, the pixel points of the current frame and the direct current components corresponding to the pixel points; and performing image enhancement on the pixel points of the current frame according to the high-frequency values corresponding to the pixel points of the current frame and the noise weights corresponding to the pixel points based on a preset image enhancement strategy to obtain a corresponding frame of the current frame after image enhancement.