152 results about "Frequency map" patented technology

This invention pertains to the extraction of the slowness dispersion characteristics of acoustic waves received by an array of two or more sensors by the application of a continuous wavelet transform on the received array waveforms (data). This produces a time-frequency map of the data for each sensor that facilitates the separation of the propagating components thereon. Two different methods are described to achieve the dispersion extraction by exploiting the time frequency localization of the propagating mode and the continuity of the dispersion curve as a function of frequency. The first method uses some features on the modulus map such as the peak to determine the time locus of the energy of each mode as a function of frequency. The second method uses a new modified Radon transform applied to the coefficients of the time frequency representation of the waveform traces received by the aforementioned sensors. Both methods are appropriate for automated extraction of the dispersion estimates from the data without the need for expert user input or supervision

An apparatus and method for SFBC coding and decoding in a communication system using a plurality of (N₁) Tx antennas are provided. In a transmitter using N_t transmit antennas, a pre-coder pre-codes an input symbol sequence using a pre-coding matrix. The pre-coding matrix is produced by puncturing a unitary matrix in a predetermined method. An encoder generates a plurality of vectors by grouping the symbols of the pre-coded symbol sequence by twos, space-frequency maps each of the vectors by encoding each of the vectors in an Alamouti coding scheme, and provides each of the space-frequency mapped symbols to a predetermined OFDM modulator.

A method for increasing the performance and utilization of cache memory by combining the data block frequency map generated by data de-duplication mechanism and page prefetching and eviction algorithms like Least Recently Used (LRU) policy. The data block frequency map provides weight directly proportional to the frequency count of the block in the dataset. This weight is used to influence the caching algorithms like LRU. Data blocks that have lesser frequency count in the dataset are evicted before those with higher frequencies, even though they may not have been the topmost blocks for page eviction by caching algorithms. The method effectively combines the weight of the block in the frequency map and its eviction status by caching algorithms like LRU to get an improved performance and utilization of the cache memory.

A method of removing returns from rain in Doppler radar systems (and corresponding apparatus and computer software) comprising receiving a Doppler radar return signal, transforming the signal into a range versus frequency map, for a plurality of range cells, performing a fit over frequency, for the plurality of range cells, calculating a total energy, for the plurality of range cells, comparing the calculated total energy to a threshold value, and replacing the return signal for a range cell with another signal if for the plurality of range cells the comparing step is positive.

The invention relates to a wavelet transformation and multi-channel PCNN-based hyperspectral image fusion method, which comprises the following steps: firstly, performing preprocessing of registering and grey level adjustment on hyperspectral images of N wave bands to be fused, and performing the wavelet transformation to obtain low-frequency sub-band images and high-frequency sub-band images; secondly, performing primary nonlinear fusion processing on the low-frequency sub-band images and the high-frequency sub-band images respectively by using a multi-channel PCNN model, obtaining corresponding ignition frequency map, performing linear mapping of corresponding coefficient range on the ignition frequency map for the low-frequency sub-band images, and taking a mapping result as a fusion result; thirdly, performing the region segmentation on the high-frequency sub-band images in each direction by using the ignition frequency map, and performing the fusion processing on different regions by using different fusion rules; and finally, processing wavelet reconstruction and obtaining a final result image. The method can realize the hyperspectral image fusion of a plurality of hyperspectral wave bands and can achieve a better fusion effect.

Spiral imaging has recently gained acceptance for rapid MR data acquisition. One of the main disadvantages of spiral imaging, however, is blurring artifacts due to off-resonance effects. Dixon techniques have been developed as methods of water-fat signal decomposition in rectilinear sampling schemes, and they can produce unequivocal water-fat signal decomposition even in the presence of B₀ inhomogeneity. Three-point and two-point Dixon techniques can be extended to conventional spiral and variable-density spiral data acquisitions for unambiguous water-fat decomposition with off-resonance blurring correction. In the spiral three-point Dixon technique, water-fat signal decomposition and image deblurring are performed based on the frequency maps that are directly derived from the acquired images. In the spiral two-point Dixon technique, several predetermined frequencies are tested to create a frequency map. The techniques can achieve more effective and more uniform fat signal suppression when compared to the conventional spiral acquisition method with SPSP pulses.

The invention discloses an estimation method for train running state parameters based on time-frequency map processing of optical fiber sensing which comprises the following steps of: acquiring signals in real time through an optical fiber buried in the train track to acquire a time-space vibration signal of the running process of the train along the track; acquiring a time-frequency map of an observation time window signal when the train passes through each spatial position based on the acquired time-space vibration signals; performing NLM nonlinear de-noising on the time-frequency map to acquire a de-noised time-frequency map; performing image processing on the de-noised time-frequency map to obtain a train outline image; and extracting the basic outline and boundary parameters of the train based on the train outline image, and calculating train running state parameters according to the basic outline and boundary parameters estimated at different positions. According to the estimation method for train running state parameters based on time-frequency map processing of optical fiber sensing, the online detection and estimation of continuous time-space of train running states in long distance, large range and strong noise background are solved.

A controller that controls a switching element of an inverter using pulse width modulation is applied to a power converter including an inverter, to realize a stable change of a carrier frequency, current control responsiveness, and inverter loss suppression. The controller includes a carrier-frequency setting unit that sets a carrier frequency command used for pulse width modulation of an inverter corresponding to a current command and a current-command change rate. The carrier-frequency setting unit includes a carrier frequency map having mapped thereon information of a carrier frequency corresponding to a current command expressed in a vertical axis and a current-command change rate expressed in a lateral axis, and outputs information of a carrier frequency on the carrier frequency map corresponding to the input current command and the input current-command change rate to a switching pattern calculator.

A method of analyzing a fingerprint, the method comprising the step of acquiring a fingerprint image (20) together with the following steps:

• • performing filtering processing on the fingerprint image to estimate, for each pixel of the fingerprint image, a first frequency of the ridges (21) in the fingerprint, and using the first frequencies associated with the pixels of the fingerprint image to produce a first frequency map (22) of the fingerprint image;
  • subdividing the fingerprint image into a plurality of windows each comprising a plurality of pixels, calculating a Fourier transform for each window in order to estimate a second frequency of the ridges for all of the pixels in said window, and using the second frequencies associated with the pixels of the windows to produce a second frequency map of the fingerprint image; and
  • merging the first frequency map and the second frequency map in order to obtain a map of consolidated frequencies of the fingerprint image.

The embodiments of the invention disclose a music quality evaluation method, a music quality evaluation apparatus, a computer device and a storage medium. The method includes the following steps that:to-be-evaluated audio information is acquired; the to-be-evaluated audio information is converted into a frequency spectrum with a frequency adopted as a limiting condition; and the frequency spectrum of the to-be-evaluated audio information is inputted into a preset sound quality evaluation model, so that the evaluation information of the to-be-evaluated audio information is obtained, wherein the sound quality evaluation model is a convolutional neural network model that is pre-trained to achieve convergence. The to-be-evaluated audio information is converted into the frequency map, and thefrequency spectrum is evaluated with the sound quality evaluation model obtained by means of convolutional neural network model raining, so that the evaluation information of each piece of music is obtained, and therefore, a user can be facilitated to screen music according to the evaluation information, and therefore, the interference of low-quality music on the user can be avoided, and a networkenvironment can be purified.

The invention relates to a radar and multi-network fusion based pedestrian pose recognition method and system. The method comprises: pre-processing an echo signal of a radar signal to obtain an outputsignal; suppressing a stationary target in the output signal; searching a pedestrian located distance unit in the suppressed output signal; performing time-frequency analysis on the echo signal corresponding to the pedestrian located distance unit, and obtaining a an echo signal time-frequency map; using a plurality of convolutional neural networks to recognize the echo signal time-frequency map,and obtaining a recognition result of each convolutional neural network; and fusing the recognition results of all the convolutional neural networks, and obtaining the fused pose recognition result.The pedestrian pose recognition method provides a pedestrian pose recognition result in real time by analyzing the radar echo, can ensure high pose recognition accuracy through multi-neural network fusion, is free from interference of factors such as illumination conditions, weather, smoke and the like, and is able to work all day and all weathers.

The invention provides an aircraft electromechanical system fault identification method based on a random convolutional neural network. The method includes the following steps that: firstly, the short-time Fourier transform is adopted to construct a vibration acceleration signal into a two-dimensional time-frequency map with good spatial correlation, and the problem of two-dimensional transformation of a one-dimensional signal is solved; secondly, the transformed two-dimensional time-frequency map is input into the random convolutional neural network, wherein the network adopts a random dropout mechanism to suppress the model overfitting and enhance the generalization ability, and adopts a momentum stochastic gradient descent algorithm to update network parameters and further complete theconstruction of an identification model; and finally, the random convolutional neural network is adopted to identify the fault of an aircraft electromechanical system. The scheme of the invention is good in identification effect and strong in practicability, is simple and practicable, and is suitable for fault identification of an aircraft system.

The invention relates to a residual network rolling bearing fault diagnosis method based on time-frequency analysis. The method comprises the following steps of S1. collecting vibration signal data, converting a vibration time domain signal of a rolling bearing into a time-frequency map by using short-time Fourier transform, and converting the time-frequency map into a two-dimensional gray level time-frequency map; and S2. performing feature extraction on the signal by using a residual network, and diagnosing a fault type of the bearing, wherein the input of the residual network is the gray level time-frequency map generated in the step S1, and the output of the residual network is a fault diagnosis result. According to the residual network rolling bearing fault diagnosis method provided by the invention, the bearing vibration data are converted into the time-frequency map by using the short-time Fourier transform, the time domain and frequency domain features during the vibration of afaulty bearing can be clearly reflected, and the network can accurately diagnose different fault types conveniently. Since the time-frequency signal contains both the time domain and frequency domaininformation of the bearing, and the deepening of a network layer of the residual network does not cause the problem of gradient disappearance or gradient explosion, higher accuracy can be obtained bythe method while performing fault diagnosis on the bearing.

The invention discloses an unmanned aerial vehicle classification method and device based on radar multipath signal time-frequency characteristic fusion. The method includes collecting radar data of multiple UAVs; radar data are processed by Fourier transform to obtain radar echoes of different range units and different time, and static clutter of echo signals is suppressed by high-pass filter toextract different multipath echo signals of different range units. The multi-path echo signal is analyzed by short-time Fourier transform to obtain the time-frequency map of each UAV's multi-path echosignal. Extracting Time-Frequency Features of Multipath Echo Signal from Time-Frequency Diagram; and fusing the time-frequency characteristics of the multipath echo signals to obtain the classification results of each unmanned aerial vehicle. This method can acquire the data of different targets for learning and training, and does not need to modify the hardware, so as to accurately distinguish between bird and UAV, different types of UAV function, and high accuracy is achieved.

A quantitative image (resonance frequency map) of a resonance frequency difference is obtained using a high-speed phase compensated pulse sequence of a gradient echo (GE) system. A signal function of the pulse sequence used when obtaining the resonance frequency map is generated by a numerical simulation. The high-speed phase compensated pulse sequence uses a BASG sequence, for example.

The invention provides a sort of communication equipment and an indication method of the running status of the communication equipment, wherein the communication equipment comprises at least one functional board and each functional board is provided with at least one indicator lamp. The indication method comprises the following steps that: each functional board is respectively divided into at least one functional module; various states of each functional module are respectively mapped to an indicator lamp and a frequency of the functional board on which the functional module is arranged; the occurrence state of at least one functional module is determined; synchronized signal of the communication equipment is adopted to generate a frequency mapped by the status of a status functional module; moreover, the mapped frequency is used to drive the indicator lamp (mapped on a functional board on which the status functional module is arranged) to work. The communication equipment and the indication method can reflect more types of status by means of fewer indicator lamps.

The invention provides a UAV classification method and device. The method comprises a step or unit of obtaining time domain data of a target UAV; a step or unit of converting the time domain data into a time-frequency map; a step or unit of extracting a feature vector representing the motion state of the target UAV motion from the time-frequency map; and a step or unit of sending the feature vector into a support vector machine to classify the target UAV. According to the invention, the classification is performed by acquisition of the time-domain data of the UAV in flight and extraction of the feature vector from the time-domain data as the basis for the classification of the UAV, the effective recognition of the UAV class is realized, the classification accuracy is improved and the influence of environmental factors and the error of judgment are reduced; and by the use of the support vector machine for classification, a good identification capability is gained for the type of samples with a small number.

A method and apparatus for concealing data to be transmitted within an environment. A sound frequency map is identified based on sounds detected within the environment. A number of audio symbols for use in representing a number of data blocks in the data to be transmitted within the environment are selected using the sound frequency map. An encrypted audio signal is formed using the number of audio symbols.