52 results about "Energy (signal processing)" patented technology

An energy signal processing system (10) includes a first shaft assembly (14) rotatable about an azimuth axis (16), and a second shaft assembly (18) coaxially mounted for rotation about the azimuth axis (16). The first shaft assembly (14) defines a zenith plane (20) inclined with respect to the azimuth axis (16). The system (10) includes an energy signal processing element (22) rotatable about a processing element axis (24) that intersects and is generally perpendicular to the azimuth axis (16), as well as a means for rotating the element (22) about the element axis (24) such that: energy signals travelling substantially along a preselected path axis (12) and impinging the energy signal processing element (22) are processed or deflected substantially along the azimuth axis (16); or vice versa; or energy signals generated by the energy signal processing element (22) are directed substantially along the preselected path axis (12).

The present invention provides a apparatus and a method used for measuring the detonation time and the first arrival time of seismic waved caused in the seismic exploration field, wherein said apparatus includes a signal measuring means used for measuring current and voltage of the detonation circuit, a test signal generating means used for adding to the detonation circuit a omega-frequency sine wave test signal in order to smooth filter out noise and interference to enhance accuracy of measurement and a signal-processing means used for detecting and measuring current and voltage with same frequency as the omega-frequency test signal from current and voltage measured by the signal measuring means, and thus computing the curve of impedance changes and, in some extent to eliminate noise and interference and to determine accurately the detonation time TB. Said apparatus also include a or several detector used for measuring seismic waves and converting them to electrical signals, and a signal processing means connected to it. Wherein, the signal processing means carry out an average energy calculation on said electronic signal so as to determine whether the first arrival wave has arrived, and by using a set of smooth filtering method to compute accurately the first arrival time backward from the time of appearance of the first maximum value in the average energy curve after time ts. The above mentioned two signal processing means could be combined into a single one.

The invention relates to an LDoS attack detection method based on integrated wavelet transform in an SDN environment. The invention relates to the technical field of signal processing. The method comprises the steps that entropy sets of different wavelet energy spectrums are obtained through calculation by means of multiple different wavelet transform basis functions; randomly selecting a wavelet basis function from a wavelet basis function library; judging whether the number of the selected wavelet basis functions reaches the number of specified integrated wavelet basis functions or not, and decomposing by utilizing three different wavelet basis functions; extracting the detail coefficients of each coefficient matrix are extracted, calculating an integrated wavelet energy value to acquire an entropy set of an integrated wavelet energy spectrum, allocating corresponding labels, and selecting a part of data set to train a support vector machine model and a full-connection neural network model; and detecting LDoS attacks in the SDN network by using the trained support vector machine model and the full-connection neural network model, sending a warning message if the LDoS is detected, and discarding data packets corresponding to the flow table entries, thereby reducing the load of the SDN network.

The invention discloses a seismic signal detection method based on waveform characteristics, and relates to the field of seismic signal processing. The method comprises the following steps: firstly, selecting seismic signals and noise signals in historical events collected by an array as a data set, extracting an amplitude characteristic alpha, a ratio characteristic rho and a specific frequency band energy mean value characteristic gamma in each signal, normalizing, and normalizing an energy and characteristic lambda; dividing all seismic signals and noise signals into training samples and test samples; forming a corresponding matrix by the characteristic parameters of all seismic signals in the training samples, substituting the corresponding matrix into a Gaussian function, optimizing by using a gradient descent method to obtain an optimal hyper-parameter corresponding to each characteristic, and calculating a posterior mean value and a covariance of a Gaussian process of each characteristic to obtain four characteristic models; predicting the occurrence probability of a new event by using the verified feature model and a Bayesian thought, and judging whether the event is a seismic event or not according to the occurrence probability of the event. According to the invention, the correct detection rate is improved, and the applicability is stronger.

The invention discloses a signal instantaneous frequency estimation method, and relates to the technical field of signal processing methods. The method comprises the following steps: inputting a signal and initializing each variable; performing two-dimensional search on the i-th signal to obtain an optimal parameter, and extracting instantaneous frequency estimation of the i-th signal under the optimal parameter; solving the time domain representation of the i-th signal, and subtracting the i-th signal from the original signal to obtain the remaining (i+1)-th signal; repeating the above stepsuntil the energy of the remaining components is less than a set threshold value; and finally, adding the obtained instantaneous frequency estimation of the components to realize instantaneous frequency estimation of the multi-component non-stationary signal. The method provided by the invention not only has adaptability to the number of signal components and relatively strong noise robustness, butalso improves the signal time-frequency distribution energy aggregation and instantaneous frequency estimation precision.

The invention provides a method for solving signal processing crossing loss based on interpolation and single-point DFT filtering, and the method comprises the steps: carrying out the target detection on an obtained distance-Doppler-energy spectrum, and obtaining the dimension indexes and energy of a target point and a Doppler adjacent point; interpolation processing is carried out, accurate Doppler coordinates are obtained according to an interpolation processing result, and a single-point DFT filtering twiddle factor is generated; and filtering the slow time data vector by using a twiddle factor to obtain distance-Doppler processing data of the target, and carrying out angle measurement to obtain target angle information. According to the method, the accurate Doppler frequency of the target is estimated through interpolation operation, the signal-to-noise ratio of the distance-Doppler two-dimensional FFT processing result is increased through single-point DFT filtering, the single-point DFT filtering twiddle factor is solved through the compensation factor in engineering, the calculation time is short, the accuracy is high, and the problem of radar signal crossing loss is effectively solved.

The invention relates to a crack detection method for a double-layer metal compound pipe based on an interface wave. The method comprises the following steps: analyzing a signal which is generated by a surface wave acting on the double-layer metal compound pipe, thereby confirming if a crack exists, and if the crack exists, and further confirming an axial position and a circumferential position of the crack by utilizing the surface wave which is converted into the interface wave on a joint surface of an inner layer metal pipe and an outer layer metal pipe. The interface wave is free from frequency dispersion in the diffusing process on the joint surface of the metal compound pipe, the energy is concentrated on the interface, the interface wave is sensitive to the damages, such as, cracks, on the interface, the model number of the interface wave in the metal compound pipe is less and the recognition for a crack reflected signal in the signal processing process is benefited, so that the validity and accuracy of the crack recognition and positioning can be increased. According to the invention, the detection operation is convenient, the recognition for the damage on the interface of the whole compound pipe can be realized by stimulating and receiving the signal at one end of the pipe and the crack detection efficiency is increased.

The invention discloses a simulation method for nuclear signal generation and processing based on Matlab. Using Matlab based on the Monte Carlo method particle transport model, the deposition energy in the detector can be obtained only by inputting the energy and quantity of γ-ray photons. The simple simulation method is based on Gaussian broadening to simulate statistical fluctuations and noise effects, use the double exponential function to simulate the physical characteristics of the detector to fit the nuclear signal waveform, and use Simulink to build a signal processing system based on amplification and filtering to perform electrical processing on the nuclear signal. , to realize the simulation of the whole process of nuclear signal generation and processing.

The invention discloses a bistatic ISAR imaging method based on weighted L1 norm constraints, which belongs to the technical field of radar signal processing, and adopts the following steps: Step 1. Establishing a bistatic ISAR imaging echo model to obtain full aperture echo data; Step 2. Construct a sparse basis matrix to obtain the sparse representation of the full aperture echo data, construct an observation matrix to obtain the sparse aperture echo data, and establish a bistatic ISAR sparse aperture imaging model based on compressed sensing; Step 3. Set each pixel of the target to be sparse The same distribution is subject to Laplace prior, and the maximum posterior probability estimation function of the target image is established by Bayesian criterion, which is approximated as a weighted L1 norm constraint solving problem; step 4, using the Cauchy-Newton algorithm for weighted L1 norm constraint The solution of the optimization problem realizes the reconstruction of the target image; the invention can make better use of the energy accumulation and structural characteristics of the target, and is beneficial to improve the imaging quality.

The invention discloses a seismic signal detection method based on waveform characteristics, and relates to the field of seismic signal processing. The method comprises the following steps: firstly, selecting seismic signals and noise signals in historical events collected by an array as a data set, extracting an amplitude characteristic alpha, a ratio characteristic rho and a specific frequency band energy mean value characteristic gamma in each signal, normalizing, and normalizing an energy and characteristic lambda; dividing all seismic signals and noise signals into training samples and test samples; forming a corresponding matrix by the characteristic parameters of all seismic signals in the training samples, substituting the corresponding matrix into a Gaussian function, optimizing by using a gradient descent method to obtain an optimal hyper-parameter corresponding to each characteristic, and calculating a posterior mean value and a covariance of a Gaussian process of each characteristic to obtain four characteristic models; predicting the occurrence probability of a new event by using the verified feature model and a Bayesian thought, and judging whether the event is a seismic event or not according to the occurrence probability of the event. According to the invention, the correct detection rate is improved, and the applicability is stronger.

The invention relates to the technical field of EEG signal processing, in particular to an emotional EEG signal recognition method based on multi-dimensional information in the EMD domain. First, the EMD is used to adaptively decompose the EEG signal into eigenmode functions IMFs with different oscillation frequencies. , and then extract the waveform difference, phase difference and normalized energy of the eigenmode function, and combine the extracted multi-dimensional information into eigenvectors as the representation of different emotional EEG signals. The classification and recognition of electrical signals greatly improves the classification accuracy.

The invention relates to a dynamic extrapolation method of a bandwidth-limited signal, which belongs to the technical field of signal processing. The invention solves the problems of low efficiency of the traditional extrapolation method when the observation time is relatively small and low reliability of the traditional extrapolation method when the iterative filter bandwidth is larger than the signal bandwidth. The present invention improves the extrapolation accuracy when the initial energy of the observation signal is relatively small by treating the extrapolation signal segmented and sequentially extrapolating, and solves the problem of the traditional Gerchberg-Papoulis extrapolation algorithm (referred to as GP algorithm) when the observation time is relatively small. Low-efficiency extrapolation problem, and the low-reliability extrapolation problem when the iterative filter bandwidth is greater than the signal bandwidth, when the computational complexity is fixed, under the requirements of a certain extrapolation performance index, the dynamic extrapolation method of the present invention can improve the GP algorithm The effectiveness of the GP algorithm improves the reliability of the GP algorithm in scenarios where the signal bandwidth is uncertain within a certain range. The invention can be applied to the technical field of signal processing.

The invention provides a method for solving event-related potential waveform diagrams based on sparse modeling. Including: data preprocessing, de-drying, framing and energy normalization; sparse dictionary learning, dictionary initialization, sparse modeling, dictionary learning and convergence judgment; waveform dictionary clustering; waveform dictionary property determination. The advantage of the present invention is that: the ERP waveform is acquired for the inherent event-related potential activity pattern in the data instead of the label given by the cognitive experiment, not only can process the EEG data in the experimental environment, but also can process the acquisition in the real environment without labels EEG data with better universality and practicability. In addition, the use of a signal processing method for single-shot and single-frame data processing can effectively improve the flexibility of EEG analysis and processing, and it also has a significant application prospect in the field of brain-computer interface.

The least squares channel estimation method based on the minimum energy wavelet framework, the steps are as follows: use the LS algorithm for preliminary channel estimation; perform energy normalization processing on the preliminary channel estimation results to obtain a normalized sequence; use the minimum energy wavelet framework to normalize The decomposed sequence is decomposed into the first layer and the second layer to obtain the decomposed sequence; based on the hard threshold function, the decomposed sequence is denoised; the wavelet tower reconstruction algorithm is used to reconstruct the signal, and the reconstructed new signal is reversed. The energy is normalized and calculated to obtain the final estimated value. The present invention adds a signal processing link based on the minimum energy wavelet frame on the basis of the preliminary channel estimation result obtained by the traditional LS algorithm, and can eliminate the error components in the estimation result obtained by the LS algorithm, thereby improving the accuracy of channel estimation. Moreover, the wavelet tower decomposition and reconstruction algorithm has low computational complexity and is easy to implement in engineering, laying the foundation for subsequent channel equalization and decoding performance improvement.

The invention discloses a signal processing method and system for blast furnace lining impact echo detection. The material of a detected object is subjected to wave velocity calibration through a timedifference ultrasonic method, calibration wave velocity information is obtained, empirical modal decomposition is conducted on impact echo signals, and an intrinsic model component is obtained; on the basis of the intrinsic model component, the impact echo signals are filtered to obtain filtered signals and energy operators based on the filtered signals; and the filtered signals are classified, and on the basis of a classification result and the wave velocity information, thickness information of the innermost layer of a blast furnace lining is obtained. The technical problem that in the prior art, elastic waves generate the aliasing effect during propagation in a non-uniform layered medium and accordingly the deviation of an erosion state detection result of the blast furnace lining is large is solved. By denoising the collected impact echo signals and further utilizing the energy operators to classify the filtered signals, echoes of various types can be effectively distinguished, sothat corresponding features of a boundary surface are accurately extracted, and finally the thickness information of the innermost layer of the blast furnace lining is accurately extracted.

The invention discloses an adaptive microseismic data compression sensing method based on dictionary learning, which belongs to the technical field of signal processing. The invention constructs an adaptive redundant dictionary, and determines the number of samples according to the energy of the signal and the sparse decomposition coefficient on the adaptive dictionary , and then the signal is compressed and sampled according to the compressed sensing technology, and the signal is reconstructed after being stored and transmitted to the terminal. The present invention adopts the K-SVD algorithm to construct an adaptive redundant dictionary according to the characteristics of the microseismic signal, which ensures that the peak value of the signal will not be deviated after sparse decomposition and reconstruction, and then adaptively determines the number of samples according to the energy and sparsity of the signal to reduce the number of samples , which increases the effective sampling rate and reduces the pressure of storage and transmission. This algorithm is simple and easy to implement, and the effect is ideal. It can effectively compress and sample mine microseismic signals, and has good technical value and application prospects.

The invention provides leading signal generation, sending and receiving methods and systems thereof, wherein the methods and the systems are suitable for an asynchronous communication system. The leading signal generation method comprises the following steps of generating first symbols A and second symbols B, wherein the first symbols A and the second symbols B are time-domain symbols with a same length; and arranging at least 2 K first symbols A and the 2 second symbols B in a cascade mode so as to generate a leading signal, wherein the K expresses a search frequency point number. The leading signal receiving method comprises the following steps of receiving a signal from a channel; carrying out signal processing on the received signal so as to acquire a baseband signal; intercepting the baseband signal into a plurality of sequences according to a sliding window; calculating an autocorrelation value corresponding to a plurality of intercepted sequences; and detecting an autocorrelation energy value corresponding to the autocorrelation value according to a preset detection threshold to determine whether to receive the leading signal. In the invention, a receiving end is ensured to be capable of detecting the leading signal, frequency offset estimation precision is guaranteed and timing estimation accuracy is increased.

The invention discloses an ultrasonic image quantitative diagnosis system and a signal processing method thereof. The ultrasonic image quantitative diagnosis system comprises a data receiving module, a first denoising module and a second denoising module, an empirical mode decomposition module in the first denoising module is used for carrying out empirical mode decomposition on each row of signals / column of signals of an ultrasonic image to obtain an eigenmode function and a margin, and then secondary denoising of the signals is achieved through an energy denoising method and a Hilbert transform denoising method. For speckle distribution of the liver ultrasonic image, the ultrasonic image is denoised through a row signal method and a column signal method, and noise residues in a certain direction occurring in denoising through a single method can be effectively improved by taking the mean value of the row signal method and the column signal method; and during signal processing, through an energy denoising method and a Hilbert denoising method, secondary denoising can further eliminate noise of a low-frequency signal after primary high-frequency denoising, so that the quality of a liver ultrasonic image is improved, and an accurate basis is provided for diagnosis of a doctor.