84 results about "Circular convolution" patented technology

The present invention relates to a novel matrix-valued transform framework to process digital signals. According to one aspect of the invention, matrix-valued methods and apparatus are described to transform a vector-valued discrete-time data sequence from time-domain into frequency-domain. In another aspect, matrix-valued methods and apparatus are described to transform a vector-valued data sequence from frequency-domain into time-domain. Furthermore, the new framework disclosed in this invention also provides a plurality of methods and apparatus for basic signal processing functions and operations of a matrix-valued communication system. These functions and operations include but are not limited to matrix-valued fast Fourier transformation, matrix-valued linear and circular convolution, matrix-valued correlation, matrix-valued multiplexing and de-multiplexing, and matrix-valued data coding and decoding.

Channel estimation of block signals containing pilot frequency and balanced method characterizes in containing side channels of force and after of a main channel component in a movable window to determine to get a correct channel estimation interval again to get channel impulse response estimation with the length of N, M or M+2XN, then to take position of home and end of the said window as the positioning information needed by circular convolution of the signal and channel inputs response structure and process the data block via channel transmission as the one after channel distortion via frequency domain balance offset or make time-domain over-sampling channel estimation in the sliding window interval to inerase time-domain resolution of channel estimation. The data block can be IDFT of OFDM or modulated by single carrier.

The primary synchronization signal detection method in an LTE system. The method comprises the following steps: converting cross-correlation process to linear convolution, converting the linear convolution into circular convolution in a zero filling mode, and performing Fourier transform FFT operation to obtain a primary synchronization signal PSS sequence dmf(n) in a local frequency domain; performing the Fourier transform FFT operation on a received signal sequence, and performing point-to-point multiplication on the received signal sequence and the local primary synchronization signal PSS sequence to obtain a cross-correlation power sequence, judging whether the primary synchronization signal PSS is successfully retrieved by judging the peak to average ratio of the cross-correlation power sequence, and if so, calculating a PSS signal position according to the peak position, and calculating a call intra-group identifier; and otherwise, indicating that the retrieval is failed. The primary synchronization signal detection method has the beneficial effects of reducing the complexity of the cross-correlation operation while keeping the wide adaptability of the cross-correlation operation in PSS synchronization, improving the operation accuracy while reducing the single-step operation data size and improving the operation efficiency and accuracy.

The invention discloses a method and a device for decoding an iterated internal code and evaluating a channel for an orthogonal frequency division multiplexing system. The iterating method comprises the following steps: at a transmitting end, carrying out internal code encoding and intraframe interleaving by a code with a long short code; at a receiving end, calculating circular convolution of an OFDM time domain data block and an evaluation value of channel time domain impulse response based on an initial evaluation value of the channel time domain impulse response; completing constellation de-mapping according to the channel evaluation and the result of circular convolution to acquire bit soft information; carrying out intraframe de-interleaving and internal code decoding on the bit soft information to acquire decoded bit soft information, carrying out intraframe interleaving on the decoded bit soft information, and remapping the constellation; and updating the evaluation value of the channel time domain impulse response according to the remapped result, and carrying out channel evaluation post-treatment. The method and the device can improve channel evaluation precision, and reduce system error rate, thereby improving system performance, in particular mobile performance, and ensuring complexity and controllable time delay of the system.

Transmitted signals are modified to facilitate the emulation of circular convolution in non-contiguous transmission environments. These modified signals may be derived from well-known signature sequences. In an exemplary method, a tail portion of a final segment of a base signal is prefixed to an initial segment of the base signal, to form a first transmit segment. One or more additional transmit segments are formed by prefixing, to each of the one or more segments of the base signal other than the initial segment, a tail portion of the immediately preceding segment of the base signal. The transmit segments so formed are transmitted in respective ones of the plurality of non-contiguous transmit-time intervals. Corresponding methods for receiving the transmitted segments and reconstructing the base signal are also described, as are corresponding transmitting and receiving apparatuses.

A region segmentation method for naturally placing grape bunch pedicel by machine vision is provided. A binocular vision system is constructed by adopting a multi-degree-of-freedom bracket, and that irradiation mode of diffuse reflective light source is designed to obtain grape bunch image, Then, the image is decomposed in HSI model, the circular convolution kernel with radius of 3 pixels is designed, the median filter is carried out pixel by pixel along the grape string image, and the LOG kernel function is used to sharpen the edge of the grape string image. Then, based on the distribution characteristics of grape edge region, the minimum distance between disconnected domains in the minimum neighborhood is used to represent the edge distance of grape, According to the distribution histogram of edge distance, the segmentation points of stem edge and kernel edge of grape bunch are obtained, and the morphological kernels of open and close are designed by using the segmentation points ofedge distance, and the morphological operation is carried out by using the adaptive convolution check image. Finally, the high precision and high efficiency segmentation of naturally placing grape bunch pedicel region is realized by machine vision.

The invention relates to a method that low complexity suppression of PAPR in FRFT-OFDM system, which belongs to the field of broadband wireless digital communications technology and can be used to reduce the PAPR in FRFT-OFDM system. The method is based on fractional random phase sequence and fractional circular convolution theorem, which can effectively reduce the PAPR of system. The method of the invention has the advantages of simple system implementation and low computational complexity. In this method, the PAPR of the system can be effectively reduced while keeping the system reliability. When the number of candidate signals is the same, the PAPR performance of the proposed method was found to be almost the same as that of SLM and better than that of PTS. More importantly, the proposed method has lower computational complexity than that of SLM and PTS.

The invention discloses a high-speed parallel equalizer and an equalizing method thereof, relating to the field of a digital receiver. The method comprises the following steps of: changing a carrier wave-recovered data which is sent by a front end receiver into parallel 2n lines; converting the data by means of frequency domain; filtering the wave of the data by means of equalizing in the frequency domain; outputting the wave-filtered data after being converted into frequency domain; selecting the latter n lines of output data according to the principle of circular convolution; extracting error information; converting the error information by means of frequency domain; and completing the update of a weight coefficient in the frequency domain, thereby completing a data-equalizing and a weight coefficient-updating process. The method is quite suitable for processing high-speed digital communication and conquers the limitation of hardware resources.

The invention discloses an offshore oil-gas platform detection method based on a structural recurrent neural network. The method comprises: according to serialization structure characteristics of a space-time image, a structural recurrent neural network model of a seaborne target is established; parameter optimization adjustment is carried out on the established structural recurrent neural networkmodel and then a high-order space-time characteristic model of the seaborne target is established; on the basis of the high-order space-time characteristic model of the seaborne target, a high-orderspace-time characteristic of the seaborne target is obtained; a bi-directional circular convolution layer is constructed based on the high-order space-time characteristic of the seaborne target and abi-directional circular convolutional neural network with fusion of front and rear time phase features is established; and on the basis of parallel optimization adjustment of the bi-directional circular convolution layer, accurate detection of the seaborne moving target under the single time phase is realized. With the deep learning, the capability of obtaining the target feature is enhanced; andwith full utilization of the time-space features of the target, the target in the complex background interference can be detected. Moreover, the universality of the algorithm is enhanced.

The invention provides a circular convolution hexagonal multicarrier transmission method. The method comprises the following steps: determining the number of sub symbols contained in a sent data block; determining the number of to-be-sent data symbols according to the number of the sub symbols and the number of subcarriers; carrying out shift and zero fill on each to-be-sent data symbol according to the position of a belonging sub symbol on the time to obtain a corresponding sending vector; separately carrying out circular convolution operation on each sending vector and a Gauss pulse shaping function, and carrying out frequency modulation on a circular convolution result according to the sub symbols and the subcarriers belonging to the sending vector to obtain a pulse signal carrying the data symbol information; accumulating the pulse signals to obtain a multicarrier signal; and adding a cyclic prefix for the multicarrier signal according to the maximum delay extension of a channel to form a to-be-send baseband signal. The circular convolution hexagonal multicarrier transmission method provided by the embodiment of the invention has very strong double-selection channel resistance, realizes higher spectrum effectiveness of the to-be-send signal, and meanwhile reduces the peak-to-average power ratio.

The invention is a time-domain known sequence inserted OFDM-based channel estimating and balancing device, applied to handheld mobile digital multimedia terminals, comprising: data separator, separating the received signal into time-domain known sequence and DFT data frame; time-domain known sequence interference eliminating unit, making reconstruction and circular convolution on the received data according to the obtained channel impulse response; time-domain channel estimating unit, obtaining the channel impulse response and sending it to frequency- domain balancing unit; local time-domain known generator unit, generating local time-domain known sequence; FFT unit; frequency-domain balancing unit, implementing frequency-domain balance. And the device can implement channel estimation and reception of a system with high spectrum utilization ratio and has not high complexity, and can meet the requirements of handheld mobile equipment.

The invention provides a deep learning super-resolution method based on an enhanced upsampling and discrimination fusion mechanism, which comprises the following steps of: directly extracting original features of a low-resolution input image by using a single layer based on a deep learning convolutional layer in a residual error branch; using 6 cascaded multi-layer feature fusion-based circular convolution units to extract deep features, and enabling the deep features output by each circular convolution unit to be subjected to up-sampling through 6 deconvolution layers; fusing the up-sampled high-resolution features by applying a feature fusion mode with a discrimination mechanism, and performing dimensionality reduction on the up-sampled features by using a single-layer convolutional layer to obtain a residual error of the high-resolution image; carrying out up-sampling on the low-resolution image by using a bicubic interpolation method in a mapping branch; and adding the mapping and the residual of the high-resolution image pixel by pixel to obtain a final high-resolution image. According to the invention, an up-sampling opportunity is provided for low-resolution characteristics of each stage, and higher reconstruction accuracy is obtained.

An FBMC equalization and demodulation unit and corresponding method to process an FBMC signal includes FBMC symbols, each FBMC symbol comprising data mapped over M subcarriers, oversampled by a factor K, filtered by a prototype filter and transposed in the time-domain, comprising: a frequency domain transposition unit, configured to transpose a block of P*KM samples comprising at least one FBMC symbol into frequency domain samples, where P is an integer greater than one, an equalizer unit configured to multiply the frequency domain samples by one or more coefficients computed from a propagation channel estimate, at least one circular convolution unit, configured to perform P circular convolutions between subsets of the equalized samples and a frequency domain response of a frequency shifted version of the prototype filter, and adders, to sum corresponding outputs of each of the P circular convolutions.