18results about How to "Improve decoding accuracy" patented technology

The invention provides an OFDM signal receiving and processing system and method suitable for an NAVDAT system. According to the method, the characteristics that NAVDAT system signals have synchronous headers, pilot frequencies and the like are fully considered, firstly, the synchronous headers are used for carrying out correlation operation on received data to search a frame starting point, and a signal frame to be demodulated and decoded is obtained in combination with a constant false alarm detection algorithm; and subsequently, carrying out symbol synchronization to determine the FFT window position. Secondly, in combination with a discrete pilot signal, estimating an integral multiple frequency offset error, namely coarse synchronization, and further realizing estimation of a decimal multiple frequency offset error, namely fine synchronization, so as to realize frequency synchronization. And then, channel estimation is carried out by using the pilot signal and adopting a least square method to obtain the channel frequency response of the pilot, the frequency response of the complete signal is obtained through interpolation, and the rough estimation result is further processed through the designed RCAFCN network to obtain accurate channel estimation, so that the original OFDM signal is recovered. And finally, extracting a data part in the OFDM signal according to a frame structure, realizing soft demodulation through a log-likelihood ratio mode, decoding data of a signaling part by using a polarization code, and decoding data of a service part by using a low-density parity check code, thereby realizing complete demodulation and decoding. The method provided by the invention can effectively reduce inter-symbol interference and inter-carrier interference existing in OFDM signals, compensate channel distortion, reduce noise influence, improve decoding precision, improve communication quality, realize optimization of NAVDAT system baseband processing, and guarantee sailing safety of ships on the sea by combining with an existing marine broadcast system.

The invention provides a dialect recognition method, device and equipment and a computer program product, and the method comprises the steps: carrying out the coding and decoding of a to-be-recognized dialect voice, and obtaining a voice decoding feature; decoding the voice decoding features through a pre-trained language model to obtain a dialect recognition result; wherein the dialect text in the decoding network of the language model and the mandarin text with the same semantics as the dialect text correspond to the same language model probability. The dialect recognition method can improve the dialect recognition effect.

The invention relates to the technical field of brain-computer interfaces, and discloses a non-invasive brain-computer interface system based on deep learning and a signal processing method thereof, and the system comprises an EEG signal collection module, a signal preprocessing module, a deep learning model training module, and a decoding and control module. The EEG signal acquisition module is used for acquiring an EEG signal of a user; the signal preprocessing module is used for preprocessing the collected electroencephalogram signals, including noise suppression, filtering and feature extraction; the deep learning model training module is used for training a deep learning model and decoding the preprocessed electroencephalogram signal into a control instruction; the decoding module is used for decoding the electroencephalogram signals collected in real time into control instructions according to the trained model; and the control module is used for controlling external equipment according to the decoded control instruction. The method has the advantage of improving the performance and applicability of the non-invasive brain-computer interface.

The invention discloses a motion intention decoding method and system. The method comprises the following steps: acquiring first signal data; based on the first signal data and a prediction model, a corresponding prediction result is generated, and the prediction result is used for indicating the motion intention corresponding to the next action of the user; based on the first signal data and an intention recognition model, a corresponding decoding result is generated and output, and the decoding result is used for indicating a motion intention corresponding to the current action of the user; calculating a deviation degree between the decoding result and the prediction result; and when the deviation degree exceeds a preset deviation threshold value, performing reverse updating on the intention recognition model based on the deviation degree. While the current action is decoded, prospective prediction is performed on the next action, and the intention recognition model is automatically optimized based on the deviation condition of the prediction result and the decoding result, so that the intention recognition model is suitable for a complex collaborative task or a multi-stage task.

The present invention discloses a brain-controlled grasping robot system and control method for visual semantic target stimulation, including an EEG signal acquisition unit, a visual induced unit, a signal processing unit, and a grasping robot body; the grasping robot body acquires the semantic target image and posture information of each object in the environment; and grasps the object according to the brain-controlled grasping intention; the visual induced unit flashes the visual induced paradigm based on the semantic target stimulation in a preset flashing mode; the EEG signal acquisition unit acquires the EEG signal induced by the visual induced paradigm of the disabled operator; the signal processing unit decodes the EEG signal to obtain the brain-controlled grasping intention, and sends it to the grasping robot body, and also uses the knowledge graph to infer the next step of the brain-controlled grasping intention of the disabled operator, and feeds back to the visual induced unit to generate a visual induced sub-paradigm. The intelligent decision-making and execution capabilities of the brain-controlled grasping robot for assisting the disabled are improved, and the disabled operator is helped to achieve basic self-care.

The invention discloses a face key point detection network training method, a face key point detection method and a medium, and belongs to the technical field of face key point detection, and the method comprises the steps: obtaining a face image data set; in the face image data set, each sample is a face image marked with key point coordinates; for each sample in the face image data set, key points in the sample are divided into boundary endpoints and boundary internal points, and coordinates of the key points are converted to a heat map coordinate system; under the heat map coordinate system, generating a true value heat map for each boundary endpoint by adopting isotropic Gaussian distribution, generating a true value heat map for each boundary internal point by adopting anisotropic Gaussian distribution, and forming a training data set by the face image and the true value heat map of each key point in the face image; and training a face key point detection network used for predicting a heat map of each key point in the face image by using the training data set to obtain a trained face key point detection network. According to the invention, the detection precision of the face key points can be effectively improved.

Embodiments of the present application provide an audio data processing method, a server and a program product. The method is applied to a server, and the method comprises: performing redundant coding processing on to-be-processed audio data to obtain coded audio data, in a case where it is determined based on network state information that the redundant coding process is to be performed; and performing a first redundancy processing operation on the coded audio data to obtain processed audio data. The method significantly improves the quality of audio transmission in a weak network environment, reduces audio delay, freezing and distortion, and improves user experience.

The invention relates to the technical field of remote control, in particular to a remote control method and device based on a low-power-consumption Hall sensor. The method comprises the following steps: acquiring remote control equipment type data; performing Hall sensor deployment based on the remote control equipment type data to generate Hall sensor deployment data; performing sensor working magnetic field direction calibration on the Hall sensor deployment data to generate Hall sensor deployment correction data; performing real-time signal acquisition on the Hall sensor deployment correction data to obtain a standard remote control equipment magnetic field detection signal; performing lightweight signal decoding on the standard remote control equipment magnetic field detection signal to generate remote control equipment lightweight decoding data; and performing remote control mode identification on the remote control equipment lightweight decoding data to generate remote control intention data. By optimizing Hall sensor deployment, signal decoding, response delay and multi-device cooperative control, the precision and efficiency of remote control are improved.

The invention discloses a low-duration calibration electroencephalogram decoding method and system based on invariant feature extraction, and relates to the technical field of electroencephalogram signal decoding, and the method comprises the steps: obtaining electroencephalogram data with labels, and carrying out Fourier transform to obtain amplitude information and phase information; inputting the phase information and the tag into a teacher network for training to obtain feature output of the teacher network; performing random pairing and reconstruction on the labeled electroencephalogram data to obtain reconstructed electroencephalogram data, inputting the reconstructed electroencephalogram data into a student network, and learning phase information extracted from a teacher network through the student network to obtain feature output of the student network; and acquiring real-time data, and inputting the real-time data into a student network to obtain an electroencephalogram decoding recognition result. Similarity features among different individuals are extracted through an invariant feature extraction method, when electroencephalogram signals of a new user are analyzed, the similarity features can be directly used for electroencephalogram analysis of a new subject, and the use efficiency of a non-intrusive brain-computer interface and the electroencephalogram recognition accuracy are remarkably improved.

The invention discloses a single-line transmission method, device and equipment and a computer readable storage medium, which are applied to the technical field of computers, and comprise the following steps: packaging internal bus parallel data into a parallel data message according to a register read-write message protocol, and converting the parallel data message into a serial bit stream; mapping each logic bit of the serial bit stream into a level signal of a corresponding duty ratio according to a duty ratio coding rule; the duty ratio coding rule is that a logic bit 0 represents that a single high level exists in a clock period, and the duty ratio of the single high level does not reach half, and a logic bit 1 represents that a plurality of discontinuous high levels exist in the clock period, and the duty ratios of the plurality of discontinuous high levels reach half; and controlling the level conversion of the single signal line through a switching circuit according to the level signal, and transmitting the coded bit stream to the slave device bit by bit. According to the invention, the problems of high PCB design difficulty, high cost, signal interference and data error risk caused by a large number of signal lines are solved.

The invention relates to a track circuit signal decoding method and device based on nonlinear transformation enhancement and a storage medium. The method comprises the steps that S1, track circuit signals are collected; s2, the collected track circuit signals are preprocessed; s3, performing first processing on the pre-processed signal, the signal of the positive half cycle and / or the signal of the negative half cycle to obtain a target signal; s4, performing FFT calculation on the target signal to obtain a spectrogram; s5, calculating a rough value of the center frequency of the carrier frequency signal and a rough value of the low-frequency signal based on the frequency doubling in the spectrogram; s6, determining the rough value of the center frequency of the target frequency multiplication based on the rough value of the center frequency of the carrier frequency signal; and S7, on the basis of the obtained rough value of the center frequency of the target frequency multiplication, identifying and obtaining the frequencies of the two spectrum peaks of the target frequency multiplication in the frequency diagram, and calculating the accurate value of the center frequency of the target frequency multiplication and the accurate value of the low-frequency signal. Compared with the prior art, the method has the advantages that the decoding precision is improved under the condition that the chip calculation amount is basically unchanged, and the like.

The application discloses a pinyin decoding method and related equipment, and relates to the technical field of natural language processing. The application first acquires a first text sequence containing a historical input text sequence and a current to-be-decoded pinyin string, and extracts semantic features corresponding to the text sequence by using a neural network model; then, corresponding statistical information is acquired by relying on a statistical language model. The statistical language model can store various types of text static statistical information, including candidate word entries and corresponding probability information, and the statistical information is mapped to a feature space consistent with the semantic features to obtain statistical features. The semantic features and the statistical features are fused to obtain fused semantic features, and a decoding result corresponding to the pinyin string is generated according to the fused semantic features. The application combines the semantic understanding ability of the neural network and the prior statistical advantage of the statistical language model, and takes into account the decoding accuracy and the use stability, is efficient in reasoning, and is suitable for various input method use scenarios.

The invention discloses an optical sharing crystal module, a detector and a height decoding method thereof, and emission imaging equipment. The light sharing crystal module comprises a scintillation crystal array, wherein the size of the scintillation crystal array is 2N * 2N; wherein N is a natural number; in the scintillation crystal array, except the scintillation crystals in the first row and the first column, the first row and the 2N column, the 2N row and the first column, and the 2N row and the 2N column, one side surface of each of the other scintillation crystals is provided with a light-transmitting window, and the light-transmitting windows of two adjacent scintillation crystals are arranged in a face-to-face manner; wherein the light-transmitting windows are regularly arranged, the light-transmitting windows in the first column and the 2Nth column are located in the 2nth row, and n is larger than or equal to 1 and smaller than or equal to N-1; the light-transmitting windows in the first row and the 2Nth row are located in the 2nth column, and n is larger than or equal to 1 and smaller than or equal to N-1.

The invention discloses a resolving system suitable for a split type round inductosyn, and belongs to the technical field of angle measurement system equipment.The resolving system comprises a pre-amplification module, an excitation module, a resolving module, a power module and a software algorithm module, comprising a differential input amplification circuit and a second-order active band-pass filter circuit and is used for outputting an amplified angle signal; the excitation module comprises a two-stage second-order active filter circuit and a power amplification circuit; the resolving module comprises a second-order active band-pass filter circuit, an AD conversion circuit, a temperature compensation circuit, a synchronizer auxiliary installation indicating lamp circuit, an FPGA, a DSP data processing circuit and a power supply module. The system has the characteristics of modular design and higher reliability and programmability.

The invention discloses an auditory space attention decoding method based on a low-density electroencephalogram cross-hemisphere attention mechanism, electronic equipment and a medium, and belongs to the field of brain-computer interface and neural signal processing. The method comprises the following steps: preprocessing low-density electroencephalogram signals, and respectively mapping the low-density electroencephalogram signals into three-dimensional tensors with a time dimension and a two-dimensional space dimension according to a left hemisphere and a right hemisphere; a space-channel attention module is utilized to extract key spatio-temporal characteristics of each hemisphere; dynamic interaction between left and right hemisphere features is modeled through a cross-hemisphere attention module, and information fusion is realized; and finally, classifying the fused features, and outputting auditory attention directions. According to the method, an electroencephalogram space-time structure is reserved through three-dimensional tensor mapping, feature discrimination is enhanced in combination with a double attention mechanism, high-precision decoding can still be achieved under the low-density condition that only 4-8 channels are used, meanwhile, the method has the advantages of being low in calculation complexity and high in physiological interpretability, and the method is suitable for wearable brain-computer interfaces and intelligent hearing-aid equipment.

Embodiments of the present application provide encoding and decoding methods, an encoder, a decoder, and a storage medium. The decoding method comprises: a decoder decodes a code stream, and determines a first indication parameter; when the first indication parameter indicates that a sub-block image unit that does not comprise geometric information exists, a second indication parameter corresponding to the sub-block image unit is determined; and when the second indication parameter indicates that the sub-block image unit does not comprise geometric information, reconstruction processing is performed according to texture information corresponding to the sub-block image unit.

This application discloses a random access method, user equipment UE, a base station, and a random access system, to resolve a prior-art problem of relatively long service delay. The method includes: after obtaining downlink synchronization with a cell, determining, by UE, a preamble sequence of the UE in the cell, where the UE has not obtained uplink synchronization with the cell; obtaining, by the UE, service data; determining, by the UE, a scrambling code; scrambling, by the UE, the service data based on the scrambling code, to obtain scrambled service data; configuring, by the UE based on a predetermined resource mapping relationship, time-frequency resources respectively occupied by the preamble sequence and the scrambled service data; sending, by the UE on the configured time-frequency resources, the preamble sequence and the scrambled service data to a base station to which the cell belongs; and receiving, by the UE, a random access response sent by the base station based on the preamble sequence.

The application relates to a local-global time sequence interaction network for unilateral directional motor imagery electroencephalogram decoding, and belongs to the technical field of brain-computer interfaces. The method comprises the following steps: a shallow CNN module is used to extract a localized time-frequency representation from an original EEG signal; a multi-scale time sequence feature extraction and compression module is used to extract complementary features through four parallel time sequence processing paths of a short time sequence convolution kernel, a long time sequence convolution kernel, an average pooling layer and a step convolution layer; a multi-scale fusion feature is reduced in dimension based on a deep separable convolution; a local-global time attention module is used to replace fixed position coding with learnable position coding in an encoder, model global time sequence dependence, perform adaptive average pooling on a segmentation length based on a time pyramid pooling module, and output multi-scale local features and splice. The organic fusion of multi-scale local features and global time sequence dependence features is realized, and the decoding performance of unilateral directional motor imagery electroencephalogram signals is effectively improved.