15 results about "Spectral coefficient" patented technology

The invention relates to the field of artificial intelligence deep learning model optimization, and discloses an ultra-low parameter efficient fine tuning method based on sparse frequency domain projection. Comprising the steps of setting an upper projection matrix which is frozen after random initialization in an attention mechanism layer or a feedforward mapping layer of a pre-training model for mapping a low-dimensional reconstruction result to an original weight dimension; constructing a sparse spectrum matrix in a frequency domain, generating a non-zero index set according to a frequency priority and a random sparse mixed strategy, and keeping the whole training process fixed; performing inverse transformation on the sparse spectrum matrix to obtain a time domain reconstruction matrix, and generating a weight increment through linear mapping of an upper projection matrix; only updating the non-zero spectrum coefficient under the limited parameter budget, and keeping the pre-training weight and the upper projection matrix frozen; optionally, spectrum regularization, energy constraint and gradient clipping are applied in the training stage, and quantification and distillation joint optimization is performed in the reasoning stage.

The invention discloses a rapid prediction method for a particle thermal-mechanical coupling process, and the method comprises the steps: scanning energy carrier particles based on a microfocus computed tomography technology, and generating a three-dimensional digital model; importing the three-dimensional digital model into a multi-physics field simulation platform, and establishing a transient full-order model; based on the transient full-order model, generating a training data set and a test data set under different working conditions and corresponding temperature fields and stress fields; performing decomposition calculation on the training data set by using an intrinsic orthogonal decomposition POD reduced-order model to obtain a POD primary function and a spectral coefficient; establishing a mapping model from an input working condition to an output spectral coefficient based on a BP neural network; inputting the target working condition into the mapping model, and outputting a POD spectral coefficient; and on the basis of the calculation result and the POD spectral coefficient, reconstructing full-field physical quantity distribution at any moment under the target working condition through linear combination. The method solves the problems that traditional numerical simulation is low in calculation efficiency and large in resource consumption.

The invention proposes a method and a device for arithmetic encoding of a current spectral coefficient using preceding spectral coefficients. Said preceding spectral coefficients are already encoded and both, said preceding and current spectral coefficients, are comprised in one or more quantized spectra resulting from quantizing time-frequency-transform of video, audio or speech signal sample values.Said method comprises processing the preceding spectral coefficients, using the processed preceding spectral coefficients for determining a context class being one of at least two different context classes, using the determined context class and a mapping from the at least two different context classes to at least two different probability density functions for determining the probability density function, and arithmetic encoding the current spectral coefficient based on the determined probability density function wherein processing the preceding spectral coefficients comprises non-uniformly quantizing absolutes of the preceding spectral coefficients for use in determining of the context class.

Method for applying a P-dimensional discrete Fourier transform (DFT) to a sequence of N samples of a sensor signal, with P, N > 2, wherein the method comprises the following step, performed by a processor circuit (20): receiving one of the samples after the other and whenever a new sample or a group of successive new samples, comprising a predefined number J of samples, is received, with 1 <J<N, empfangen wird: • Provide x for each new sample value n , a sequence index n indicating which of the 1 to N samples has been received, where n is in 1,..., N; and • Select or generate a corresponding DFT vector for each new sample value. d → ( n ) depending on the order index n of the sampled value, where the DFT vector d → ( n ) P comprises pointer values ​​consisting of frequency points f p The P-dimensional DFT can be derived by S * exp(-i 2 π f p t n ), where p in 1,...,P is the index of the pointer value in the DFT vector d → ( n ) is and S is a scaling factor and t n the sampling time of the new sample value x n is and i is the imaginary unit with i 2 = -1 is; • Applying the respective new sample value x n as a multiplication factor on its corresponding DFT vector d → ( n ) to generate a respective spectral contribution vector Δ y → (n) = d → (n) xn; • Adding the respective spectral contribution vectors Δy → (n) to an accumulation vector Δ y → (n) = Δ y → (n) + y → (n − 1); and when, for all N samples, their corresponding resulting contribution vector has been added to the accumulation vector, providing the accumulation vector y → ( N ) as DFT spectral coefficients of the N samples, characterized in that a) the procedure further includes: • Generating at least some or all sampled values ​​at non-equidistant sampling times t n and • Providing a rasterized time pattern by defining a minimum time step size t̂0 and • Generating the respective DFT vector d → ( n ) for the respective sample value of the sequence index n using a P-dimensional basis DFT vector d → ^ ( 0 ) and the DFT vector d → ( n − 1 ) by applying element-wise multiplication, where d → ^ ( 0 ) Pointer elements exp(-i 2 π f pt̂0) with p in 1,...,P includes a time difference t n - t n-1 the sampling time of the last sample value x n and the preceding sample value x n-1 is expressed as the integer multiple, or the next larger integer multiple, or the next smaller integer multiple μ = [tn − tn − 1t^0] of t̂0, where [·] is the rounding operator, such that d → (n) = diag { d → ^ (0)} μ d → (n − 1), or b) the procedure further includes: • Generating at least some or all sampled values ​​at non-equidistant sampling times t n and • Retain storage of M > 1 pre-calculated vectors diag { d → ^ ( m )} for different time values ​​{t̂1,...,t̂ M} in the memory as d → ^ ( m ) , , m = 1 ... M, with phasor elements exp(-i 2 π f p t̂ m ) , p = 1,...,P, and for a given sample value x n , at the sampling time t n , the vector for index m = arg min|{t n - t n-1 - t̂ m} applied to the preceding DFT vector with the nearest time difference by element-wise multiplication to provide the DFT vector d → (n) = diag { d → ^ (m)} d → (n − 1) for n > 1, or c) the procedure further includes: • Generating at least some or all sampled values ​​at non-equidistant sampling times t n and • Provide, for M > 1, a set of possible time step sizes {t̂1,..., t̂ M} and for the sample value x n The time step t̂ m taken, the one closest to the time difference t n - t n-1 of two consecutive samples d → ( n ) = diag { d → ^ ( m )} d → ( n − 1 ) for rn > 1 and m = 1,..., M with d → (m) = (exp (−i 2 π f 1 t ^ m) exp (− i 2 π f 2 t ^ m) ⋮ exp (− i 2 π f P t ^ m)) and m = argmin | { tn − tn − 1 − t ^ m} | , or d) the procedure further includes: • Generating at least some or all sampled values ​​at non-equidistant sampling times t n and • successive approach to the respective time difference t n - t n-1 of two consecutive samples x n-1 and x n , where for a maximum time difference max{t n - t n-1} = Δt̂ for the respective order index n and a time difference resolution of M bits, a minimum time step to Δt 2 M will be and for a given time difference t n - t n-1≤ Δt̂ a combination of predefined time steps is obtained by taking the time difference between the current and the previous sample value Δt n = t n - t n-1 is and Δt n is quantized with M bits, where m = 1 is the most significant bit and m = M is the least significant bit, and • the approach with a time difference of Δτ1 = Δt n starts • the degree of quantization is defined by T m = Δ t ^ 2 m • for m = 1 T 1 = Δ t ^ 2 will and • the m-th bit is derived from wm = ⌊ Δ τ m T m ⌋ results, with Δ τ m = Δ τ m − 1 − wm − 1 T m − 1 for rm > 1 Δτm = Δtnfu ¨rm = 1 as a quantized time difference, ⌊ ⋅ ⌋ denotes the rounding operator and for m = 1, w 1 = ⌊ Δ tn T 1 ⌋ , • when all bits have been calculated, the binary word is represented by the vector w → = ( ​​w 1 w 2 ⋯ w M ) T with w m ∈ {0,1} for m = 1, ...,M • and for a set of possible step sizes resulting from T m results { t ^ 1 ,..., t ^ M} = { T 1 , T 2 ,..., TM} The quantized time difference is calculated as Δ τ = (T 1 T 2 ⋯ TM) w → = ∑ m = 1 M wm T m, which is mapped to the subset of predefined DFT vectors (37) that are used to obtain the DFT vector d → ( n ) for the current time t n required: d → ( n ) = ∏ wm diag { wmd → ^ ( m )} d → ( n − 1 ) for rwm = 1 and m = 1,..., M , where only those predefined vectors d → ^ ( m ) be selected for the w m = 1 applies.

This invention provides a seismic image processing method and apparatus, relating to the field of image processing technology. The method includes: decomposing a seismic image based on its sub-bands and pixel count to obtain multiple image block matrices; performing an orthogonal transformation on each image block matrix to obtain a transformed image block; extracting spectral coefficients from each transformed image block; performing an arc-orthogonal transformation on each set of spectral coefficients to obtain an approximate matrix corresponding to each image block matrix; subtracting each image block matrix from each approximate matrix to obtain a difference image matrix; and decomposing the difference image matrix based on sub-band update values ​​and pixel count update values ​​until all sub-bands and pixels are traversed to obtain all transformed image blocks. The apparatus executes the above method. The method and apparatus provided by this invention can accurately and efficiently perform seismic image processing.

An encoder (100) for encoding a current frame of an audio signal from one or more previous frames of the audio signal is provided according to embodiments. The one or more previous frames precede the current frame, wherein each of the current frame and the one or more previous frames comprises one or more harmonic components of the audio signal, wherein each of the current frame and the one or more previous frames comprises a plurality of spectral coefficients in a frequency domain or in a transform domain. To generate an encoding of the current frame, the encoder (100) will determine an estimate of two harmonic parameters for each of the one or more harmonic components of a most previous frame of the one or more previous frames. Further, the encoder (100) will determine the estimate of the two harmonic parameters for each of the one or more harmonic components of the most previous frame using a first group of spectral coefficients constituted by three or more of the plurality of spectral coefficients of each of the one or more previous frames of the audio signal.

An encoder for encoding an audio signal. The encoder is configured to encode the audio signal in a transform domain or a filter bank domain, wherein the encoder is configured to determine spectral coefficients of the audio signal for a current frame and at least one previous frame, wherein the encoder is configured to selectively apply a predictive encoding to individual spectral coefficients or groups of spectral coefficients separated by at least one spectral coefficient.

This application discloses a Bluetooth audio pitch shifting method, apparatus, medium, and Bluetooth device, belonging to the field of audio encoding and decoding technology. It mainly includes: calculating the amplitude pseudo-spectrum of each spectral coefficient using each spectral coefficient in the current frame audio spectral coefficients obtained through discrete cosine transform, and calculating the fundamental frequency of the current frame audio signal corresponding to each spectral coefficient using a predetermined number of adjacent spectral coefficients before and after each spectral coefficient; calculating the fundamental frequency of the current frame audio signal corresponding to the current frame audio spectral coefficient using the amplitude pseudo-spectrum; processing the phase and frequency of the current frame audio fundamental frequency to obtain the pitch-shifted current frame audio fundamental frequency; generating the pitch-shifted current frame audio spectral coefficients using the pitch-shifted current frame audio fundamental frequency, and completing the remaining Bluetooth audio encoding and / or decoding steps. This application can reduce system complexity, reduce computational load, reduce system latency, improve user experience, and flexibly set pitch shifting parameters to meet the needs of different users.

The present application relates to a kind of network converter broadband oscillation polynomial base function prediction method, belong to the field of power system automation and power electronics control technology, a kind of network converter broadband oscillation polynomial base function prediction method, comprising the following steps: S1: establishing network converter system state space model and extracting time domain analytical solution;S2: construct polynomial frequency domain base function, establish frequency domain analytical characterization framework;S3: derive time-frequency analytical mapping operator matrix;S4: online acquisition system initial value, calculate time domain power series expansion coefficient;S5: execute analytical mapping, solve the frequency domain spectral coefficient of current step;S6: recursive update state, realize the rolling prediction of broadband oscillation frequency spectrum;S7: output frequency spectrum prediction result (frequency, amplitude) and be used for upper layer damping control, the present application, establish a kind of direct mapping mechanism from "time domain analytical solution" to "frequency domain spectral coefficient", realize the unified characterization and closed-form solution of spectral feature in system state evolution process.

The invention relates to the technical field of hydrological simulation, and particularly discloses an extra-small watershed layered three-dimensional hydrological-hydrodynamic full-coupling simulation method, which comprises the following steps: collecting multi-source data to construct a three-dimensional space discrete grid and dividing initial time units; the control equation set is converted into an algebraic system based on a Legendre polynomial and a Gaussian-Loabar node; adaptively adjusting a time unit length and a spectral basis function order according to the local time gradient change rate; a convergence spectral coefficient is obtained through spectral delay correction iteration solution; and finally, forcibly executing mass conservation constraint, controlling step recalculation according to a local time error, and gradually outputting a flow hydrograph and water depth and infiltration rate distribution according to time unit endpoints. According to the method, the dual self-adaption of the time step length and the polynomial order is realized, the numerical oscillation of the infiltration rate caused by the sudden change of the ponding depth can be effectively inhibited, the mass conservation is ensured, and the stability and the efficiency of the rainstorm flood simulation of the extra-small watershed are improved.

The invention relates to a multi-mode satellite signal simulation method, which is used for a user verification test and comprises the following steps of: constructing a multi-mode satellite signal; carrying out discrete sampling on the multimode satellite signal, calculating original discrete energy of each mode, and generating a coupling coefficient of each mode based on an energy ratio; performing normalization processing on the sampled multimode satellite signal to obtain a standardized signal; extracting the frequency domain spectral coefficients of each mode in the standardized signal, and performing weighted synthesis on the frequency domain spectral coefficients of each mode by taking the coupling coefficient as a weight to generate an overall spectrum signal; and converting the total spectrum signal to a time domain to obtain a discrete simulation satellite signal. The method can improve the customization capability, energy balance, feature richness and safety of multimode satellite signal simulation, and is suitable for scenes of high-precision navigation test, anti-interference verification and the like.

The application discloses a kind of projection parallel single-pixel measurement methods based on time multiplexing coding, first using projector to the object to be measured cyclic projection is constituted by full white pattern, full black pattern and four step phase shift base stripe graph sequence graph, camera synchronously shoots the object to be measured, then on the projection sequence similar full white pattern and full black pattern, calculate background light intensity, calculate spectral coefficient, finally all camera shooting image pixel executes Fourier single-pixel imaging in parallel.This application can reduce the number of projection patterns by time multiplexing parallel single-pixel coding method, while ensuring the effectiveness of parallel single-pixel measurement, greatly improve the coding efficiency.

The invention relates to the technical field of intelligent control for high polymer material production, and discloses an intelligent agent system for high polymer material production and an intelligent control method. Obtaining the length of a reactor and the number of sensors, and axially and uniformly segmenting and mapping; performing node coordinate normalization to construct a Chebyshev orthogonal basis, and performing composite trapezoidal integration to generate and correct an inner product matrix and a projection vector; based on correction matrix augmentation and offline Gauss-Jordan inversion, extracting a spectrum expansion pre-calculation coefficient; calculating a spectral coefficient on line according to a sampling interval, reconstructing a temperature field, calculating heating power in combination with thermal parameters, and issuing and iterating in real time; and after the batch is finished, adaptively correcting the temperature curve according to the number-average molecular weight deviation to form closed-loop optimization. The method ensures that monitoring corresponds to a model, matrix numerical value is stable, operation is efficient, whole-field uniform heating and cross-batch performance are consistent, and temperature acquisition accuracy, heating efficiency and product quality are improved.

22753568_1 (GHMatters) P106789.AU.5 Audio Encoder, Audio Decoder, Method for Encoding an Audio Signal and Method for Decoding an Encoded Audio Signal Abstract 5 An encoder for encoding an audio signal. The encoder is configured to encode the audio signal in a transform domain or filter-bank domain, wherein the encoder is configured to determine spectral coefficients of the audio signal for a current frame and at least one previous frame, wherein the encoder is configured to selectively apply predictive encoding to a plurality of individual spectral coefficients or groups of spectral coefficients which are 10 separated by at least one spectral coefficient. Fig. 1 15 Abstract 5 10 15 20 26 20 47 33 18 J un 2 02 6 2 0 2 6 2 0 4 7 3 3 1 8 J u n 2 0 2 6 A b s t r a c t 5 wo 2016 / 142357 PCT / EP2016 / 054831 1 / 5 120 f5 112 f5 112 f4 encoded audio signal 112 f2 104 110 t0 f5t0 f5 to f5 110 t0 f4 to f4t0 f4 110 t0 f2 108 t-2 108 t-1 108 t0 106 t0 t0 f6 f6 106 t0 f5 106 t0 f4 106 t0 f3 t0 f3 106 to f2 t0 f2 t0 f2 106 t0 f1 t0 f1 t0 106 t-1 f6 t-1 f6 106 t-1 f5 t-1 f5 106 t-4 f4 t-4 f4 106 t-1 f3 t-1 f3 106 t-1 f2 t-1 f2 106 t-1 f1 t-1 f1 t-1 FIG 1 106 t-2 f6 t-2 f6 106 t-2 f5 t-2 f5 106 t-2 f4 t-2 f4 106 t-2 f3 t-2 f3 106 t-2 f2 t-2 f2 106 t-2 f1 t-2 f1 t-2 100 Encoder f6 f5 f4 f3 f2 f1 114 f5 114 f4 114 f2 102 audio signal 20 26 20 47 33 18 J un 2 02 6 P C T / E P 2 0 1 6 / 0 5 4 8 3 1 0 1 H O T 4 1 1 1 1 2 - 1 I f 1 1 1 2 1 2 2 1 2 1 0 1 8 E t 1 - 1 1 2 2 1 E t ) t - 4 f 4 t - 2 f 4 I t 1 - 1 t - 2 f 5 f 5 9 0 1 9 1 1 - 1 1 1 2 9 6 9 1 S 2 0 2 6 2 0 4 7 3 3 1 8 J u n 2 0 2 6

This application discloses an audio decoding method, apparatus, and storage medium based on post-filtering. The method includes: performing a pre-decoding step on an L2HC audio bitstream before post-filtering to obtain a set of spectral coefficients; sequentially calculating pseudo-spectral coefficients corresponding to the current spectral coefficient, the previous spectral coefficient, and the next spectral coefficient in the set of spectral coefficients, thereby obtaining a set of pseudo-spectral coefficients; calculating short-term filter coefficients and / or long-term filter coefficients for post-filtering based on the set of pseudo-spectral coefficients; calculating a set of enhanced spectral coefficients based on the set of spectral coefficients, the short-term filter coefficients, and the long-term filter coefficients; and performing an inverse time-frequency transform on the set of enhanced spectral coefficients to obtain the audio signal after decoding the L2HC audio bitstream. This application effectively solves the problem of reduced voice quality in low bit-rate transmission scenarios by adding post-filtering during the decoding process to enhance sound quality.