505 results about "Signal decoding" patented technology

A video signal decoding device comprises a processor which: determines a particular quantizer for reconstructing a first quantized transform coefficient, the particular quantizer being one of a first quantizer and a second quantizer which are different from each other, the particular quantizer being determined on the basis of the state of the first quantized transform coefficient; reconstructs the first quantized transform coefficient on the basis of the particular quantizer to obtain a reconstructed transform coefficient; and updates the state of a second quantized transform coefficient that is reconstructed after the first quantized transform coefficient, wherein the first quantized transform coefficient and the second quantized transform coefficient are transform coefficients in the current block.

The invention discloses a sparse dynamic graph convolution-based electroencephalogram signal decoding method and system. The method comprises the following steps of: acquiring a multi-channel electroencephalogram signal and preprocessing the multi-channel electroencephalogram signal; performing multi-band filtering on each channel signal, extracting statistical characteristics on each band signal, calculating a covariance matrix of a task electroencephalogram signal, constructing image electroencephalogram data by taking an electroencephalogram channel as an image node, the multi-band spliced statistical characteristics on the channel as a node feature vector, and the covariance matrix between the channels as an adjacent matrix; finally, a dynamic graph convolutional neural network model is constructed, the model constructs a graph convolutional neural network based on an autoregression moving average filter, graph electroencephalogram data is used as input, the category of electroencephalogram signals is used as output, an adjacency matrix is dynamically generated in combination with bilinear mapping, fuzzy label learning and sparse constraint are added to improve the decoding capacity of the model, and the dynamic graph convolutional neural network model is obtained. And the frequency domain response capability and robustness of the model to the graph structure are enhanced.

The invention discloses a power distribution network fault positioning method and device based on a satellite time service inverter, and belongs to the technical field of power system fault diagnosis, and the method comprises the steps: obtaining a time service signal of a satellite navigation system, decoding the time service signal to generate a unified space-time reference, distributing the unified space-time reference to a plurality of inverter nodes in a power distribution network, and forming a cooperative measurement network; a coding sequence current signal is injected into a power distribution network line through a master control inverter node; synchronously acquiring response voltage traveling wave data by each inverter node, performing cross-correlation analysis on the response voltage traveling wave data and the coded sequence current signal, and extracting propagation time delay of each inverter node; and correcting the reference line wave velocity by using the current line parameters and the current meteorological data, and calculating fault point position data in combination with propagation time delay. According to the method, an inverter cooperative measurement network is constructed by using satellite time service, a coding sequence current signal is actively injected, and a real-time wave velocity is corrected by combining an intelligent model, so that rapid, accurate and high-reliability positioning of a power distribution network fault can be realized.

The invention provides a signal decoding method based on a PSI5 interface, and relates to the technical field of signal decoding, and the decoding method comprises the following steps: processing and adjusting a received PSI5 signal to obtain an accurate identification signal; and extracting a clock signal from the PSI5 signal, designing a clock compensation algorithm according to the quantum neural network, and dynamically adjusting the clock signal to obtain clock synchronization data. And determining a data frame boundary, and decoding the accurate identification signal to obtain original data. And performing comparative analysis on the original data and the PSI5 signal to obtain comparative difference data, judging whether the comparative difference data accords with a preset error type or not, and performing corresponding processing. According to the invention, fractional calculus processing is carried out on the received signal, then signal distortion is corrected by using a channel equalization method, and the quantum neural network is constructed to dynamically adjust the clock signal, so that clock synchronization is realized, a reliable time reference is provided for accurate processing of the signal, and the accuracy of signal decoding is improved.

The invention belongs to a decoding method, and provides a multi-modal brain-computer interface decoding method and a related device for solving the technical problems that an existing non-intrusive brain language decoding method is insufficient in adaptability in global context and weak in generalization performance in a cross-subject scene, and multi-modal neural feature collaborative enhancement is difficult to achieve. And determining the called execution module. The execution module comprises a feature extraction module, a cross-subject standardization module, a multi-mode semantic fusion module, a language recognition module and a semantic consistency module. By obtaining the unified semantic representation and combining the beam search algorithm, the fairness and universality in different language groups are remarkably improved, multiple modes can be supported, and the brain signal decoding precision and robustness are improved. In addition, cross-subject semantic representation generalization can be realized, and individual specificity is effectively reduced.

A system comprises an encoder configured to compress attribute information and / or spatial for a point cloud and / or a decoder configured to decompress compressed attribute and / or spatial information for the point cloud. To compress the attribute and / or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. Additionally, an encoder is configured to signal and / or a decoder is configured to receive a supplementary message comprising volumetric tiling information that maps portions of 2D image representations to objects in the point. In some embodiments, characteristics of the object may additionally be signaled using the supplementary message or additional supplementary messages.

The invention relates to the technical field of brain-computer interfaces and neural signals, in particular to a light-weight electroencephalogram signal decoding method based on spatial grouping enhancement, which comprises the following steps: acquiring motor imagery electroencephalogram signal data, and preprocessing the motor imagery electroencephalogram signal data; constructing a space grouping enhancement network model, inputting the preprocessed motor imagery electroencephalogram signal data for training, calculating an importance coefficient, determining a loss function, and marking a corresponding motor imagery category; and obtaining to-be-decoded motor imagery electroencephalogram signal data, inputting the to-be-decoded motor imagery electroencephalogram signal data into the trained space grouping enhancement network model, and performing decoding in combination with the importance coefficient to obtain a corresponding classification result. According to the space grouping enhancement network model, the space-time characteristics of the EEG signals can be synchronously optimized, coupling optimization of the space-time characteristics of the EEG signals is achieved, the technical problem that an existing EEG signal decoding method is difficult to balance between model complexity and classification precision is solved, and the decoding accuracy and real-time performance are improved.

The invention discloses an attention monitoring method and related equipment. Monitoring is realized through coordination of five steps of target detection, risk assessment, visual stimulation, electroencephalogram decoding and attention guidance. The method specifically solves the defects in the prior art: electroencephalogram signal decoding is adopted to replace a subjective report, so that emotion and cognition deviation is avoided, instantaneous attention monitoring is realized, and the hysteresis and non-objectivity of a subjective method are overcome; visual stimulation and electroencephalogram response association is used for replacing operation behavior inference, a mouse or touch equipment is not needed, remote monitoring is adapted, and insufficient real-time performance caused by operation delay is eliminated; the focus target is decoded through the SSVEP response frequency, the limitation of the eye tracker on the monitoring angle and the fixed posture is eliminated, and the method is suitable for the complex scene with multiple screens, multiple angles and multiple postures. Meanwhile, key target list dynamic generation and guide strategy automatic activation form closed-loop management, the key target omission risk is significantly reduced, and the man-machine cooperative monitoring efficiency and reliability are improved.

The invention belongs to the technical field of medical apparatus and instruments, and particularly provides an eye-brain organ complex visual restoration system based on cooperative training, comprising: an eye-brain organ complex construction module for constructing a functional eye-brain organ complex; the signal processing module is used for collecting and processing an initial nerve spike signal generated by the brain organ; the stimulation signal generation module is used for generating an electrical stimulation signal for simulating the response of the photoreceptor through an adaptive modulation algorithm; the signal decoding and mapping module is used for stimulating a brain organ to generate a new nerve spike signal based on the electrical stimulation signal and decoding the new nerve spike signal into an external equipment control instruction; and the closed-loop feedback control module is used for performing closed-loop feedback control and optimization on the eye and brain organ complex visual restoration system based on cooperative training. According to the invention, the visual information in the biological signal can be correctly identified and utilized.

The invention discloses a real-time position obtaining method and system based on Beidou satellite positioning, and relates to the technical field of high-precision positioning, and the method comprises the steps: receiving a Beidou satellite signal, obtaining an original observation value, obtaining precise ephemeris, clock correction information and error correction parameters, carrying out the decoding and preprocessing of the obtained data, and carrying out the decoding and preprocessing of the obtained data; and carrying out position calculation according to the preprocessed data and the error correction parameters to obtain real-time position coordinates, transmitting the real-time position coordinates to a remote monitoring center, continuously updating the satellite signals and the error correction parameters, and carrying out dynamic monitoring. Through the steps of receiving Beidou satellite signals, acquiring precise ephemeris and error information, decoding and preprocessing the signals, resolving the position, dynamically monitoring and the like, high-precision and stable real-time positioning is realized, the influence of ionosphere and troposphere errors and a multi-path effect is effectively reduced, and the positioning precision and reliability are improved.

The invention provides a wide-range unmanned aerial vehicle sensing system and method based on a wireless relay technology, and relates to the technical field of unmanned aerial vehicle sensing. The system comprises a radio detection device and a wireless relay device deployed on the periphery of the radio detection device, the wireless relay device comprises a receiving antenna, a signal optimization unit and a broadband directional antenna, the receiving antenna is used for capturing radio signals transmitted by the unmanned aerial vehicle, the signal optimization unit is used for amplifying and processing the received signals, and the broadband directional antenna is used for receiving the radio signals transmitted by the unmanned aerial vehicle. The broadband directional antenna is used for re-transmitting a signal to the direction where the radio detection equipment is located; the radio detection device is used for passively receiving and decoding radio signals and capturing information of the unmanned aerial vehicle in real time, and the method comprises the steps of signal receiving and optimizing, signal redirection and transmission, signal decoding and analysis, information feedback and processing and the like. According to the invention, the unmanned aerial vehicle detection requirements of flakey areas such as airports with larger protection requirement ranges can be met, and low cost, low power consumption and high efficiency are ensured.

A method and system for decoding speech based on recorded brain signals is provided. The method can include receiving recorded brain signals via a microelectrode array. The method can include extracting one or more features from the recorded brain signals. The method can include converting the one or more extracted features into one or more feature embeddings. The method can include transforming, by one or more encoders, the one or more feature embeddings. The method can include predicting, by one or more decoders, phonemes based on the one or more transformed feature embeddings. The method can include predicting speech based on the predicted phonemes.

The invention discloses a signal decoding method and device based on an OFDM (Orthogonal Frequency Division Multiplexing) system and electronic equipment, and belongs to the technical field of communication. The signal decoding method comprises: obtaining signal to noise ratios and bit information of subcarriers corresponding to symbols in an effective signal, the effective signal comprising a plurality of symbols, and the symbols comprising a plurality of subcarriers; based on the bit information of the subcarrier corresponding to the symbol, determining a bit information set, the bit information in the bit information set corresponding to the same original bit; on the basis of a comparison result between the signal-to-noise ratios corresponding to every two pieces of bit information in the bit information set, diversity combining is carried out on the bit information set, and combined bit information is obtained; and performing channel decoding based on the combined bit information. According to the method, the diversity combining strategy is determined according to the difference between the signal-to-noise ratios of every two pieces of bit information in the bit information set, and high combining performance is guaranteed while hardware resource consumption is reduced.

The invention discloses a motor imagery electroencephalogram signal decoding method, system and device based on a double-path hierarchical hybrid architecture. The method comprises the following steps: acquiring a multi-channel motor imagery electroencephalogram signal; extracting the preliminary spatio-temporal features through a convolution embedding module to obtain embedded features; the embedded features are input into a double-path hierarchical mixing module formed by stacking a plurality of feature processing sub-modules, feature extraction is performed on the embedded features layer by layer through a main path and an auxiliary path which are arranged in parallel, and different feature extraction strategies are adopted according to stacking hierarchies; in each layer, the dual-path output features are fused through an adaptive fusion mechanism to obtain depth features; and finally, outputting the prediction probability of the motor imagery category through a classifier module. The method effectively solves the problems that an existing method is high in calculation complexity, unbalanced in feature extraction and insufficient in robustness, and the decoding precision and efficiency of the motor imagery electroencephalogram signals are remarkably improved.

The embodiment of the utility model relates to the technical field of automobile control valves, and discloses a valve driving device and an electronic valve.The valve driving device comprises a rotor, a circuit board, a first magnetic inductor and a second magnetic inductor, the circuit board is arranged on the outer side of the circumference of the rotor, and the rotor can rotate around the axis direction of the rotor relative to the circuit board; the first magnetic sensor is arranged on one side of the rotor and used for sensing the radial magnetic flux of the rotor, and the second magnetic sensor is arranged on one side of the rotor in the radial direction of the rotor and used for sensing the tangential magnetic flux of the rotor. In this way, according to the embodiment of the invention, the rotation angle of the rotor can be obtained through decoding calculation according to the radial magnetic flux signal and the tangential magnetic flux signal. Changes of different components of the same magnetic field are sensed through the first magnetic sensor and the second magnetic sensor, comprehensive assembly errors are effectively avoided, the orthogonality of two paths of sensing signals is improved, and the calculation precision of angle decoding is improved.

An endoscope with voice control. A data processor for the endoscope obtains voice training data for a specific surgeon, and / or information indicating spoken utterances associated with a surgical procedure for which the endoscope is to be used. Voice utterances signals are received from a surgeon during the surgery. Speech recognition technology decodes the voice utterance signals into commands to control the endoscope, based at least in part on the data obtained from the database, and control commands are issued to implement the decoded voice utterances. Concurrently with the accepting and decoding of voice utterances, the processor accepts and decodes control signals from at least one other input device. The processor issues control commands to components of the endoscope to implement the decoded other input control signals. The voice utterances are accepted, decoded, and executed concurrently with the other input control signals.

A video signal decoding device comprises a processor, wherein the processor predicts a sample of a chroma component corresponding to a sample of a luma component of a current block on the basis of the sample of the luma component, and predicts the current block on the basis of a predicted value of the sample of the chroma component. The predicted value of the sample of the chroma component is obtained using a linear equation, and the linear equation may include a term for a gradient value of the sample of the luma component.

The invention relates to a system and method for frequency domain copy mode transmission using orthogonal codes. A system for providing copy mode orthogonal frequency division multiple access transmission while maintaining a relatively low peak-to-average power ratio is disclosed. Data to be transmitted are converted into symbols, and the symbols are copied to a certain number of resource units. A phase shift is applied to a number of symbols prior to transition to the time domain for transmission. The phase shift may be removed at the receiver prior to decoding the signal into transmitted data.

The invention relates to a Manchester coded signal decoding method and a Manchester coded signal decoding system, and belongs to the field of electronic information, communication and power system automation. A plurality of clocks with the same frequency and different phases are generated according to Manchester encoding signals, the clocks are used for sampling the signals, a phase-shifting clock sampling method is used, dependence on a high-frequency clock is reduced, most interference in communication signals is determined to be eliminated according to a majority voting strategy for sampling results, and the accuracy of the signals is improved. And the seriously distorted signals are corrected according to special value enumeration, so that the interference of signal duty ratio distortion and burrs is reduced, and the decoding reliability is further improved. And finally, determining an original data stream of the signal according to the two level states of each code element. The method is used for decoding high-speed and ultra-high-speed Manchester encoding signals under the conditions of no high-frequency clock, poor data signal quality or unstable clock, and simultaneously realizing self-adaptive communication of different encoding formats.

The invention relates to the field of electroencephalogram signal processing and neural engineering, and discloses a multi-mode electroencephalogram feature fusion decoding method. The method comprises the following steps: carrying out noise reduction, calibration and standardization processing on an original electroencephalogram signal to generate a standard signal; extracting mu / beta rhythm time-frequency features and Hjorth time-domain features of the signals, and generating a fusion feature vector through PCA dimension reduction fusion; performing pre-classification and weight optimization by using an SVM classifier, performing space and time sequence feature extraction and classification through a CNN-LSTM network, and outputting a classification probability; model parameters are updated according to the probability and verified, and finally real-time control signals suitable for various communication interfaces are generated. According to the method, the accuracy and robustness of electroencephalogram signal decoding are improved, and efficient and self-adaptive motor imagery brain-computer interface control is realized.

The invention provides a clinical motion control device, method and system, electronic equipment and a medium, and the device comprises an ultra-micro array implantation probe which is configured to be implanted into a brain motor cortex of a patient and collect a neuroelectric signal of the brain motor cortex; the integrated data processing module is electrically connected with the ultra-micro array implanted probe and is configured to convert the neuroelectric signals into digital signals and perform neural decoding on the digital signals to obtain motion control signals containing the motion intention of the patient; and the data transmission module is in communication connection with the integrated data processing module and is configured to transmit the motion control signal to external rehabilitation equipment through a wired interface or a wireless communication mode, so that the external rehabilitation equipment drives the limbs of the patient to execute corresponding rehabilitation motions. According to the invention, large-scale, high-precision and synchronous monitoring can be carried out on brain motor cortex neural activities, the signal acquisition quality is improved through an integrated structure design, and the signal decoding precision is improved at the same time.

A video signal decoding apparatus is disclosed. The decoding apparatus comprises a processor. The processor: acquires a model that models the relationship between a value of a luma sample of the current block and a value of a chroma sample of the current block, on the basis of a value of at least one sample from among a neighboring luma sample of the current block, a chroma sample corresponding to the neighboring luma sample of the current block, a neighboring luma sample of a reference block, and a chroma sample corresponding to the neighboring luma sample of the reference block; and predicts a chroma block of the current block by using the acquired model and a luma block of the current block.

The invention provides a model training method and device based on electroencephalogram signals and an electroencephalogram signal decoding method and device, and relates to the technical field of artificial intelligence. According to the method, a unified shared potential representation space is constructed, multi-granularity alignment targets are utilized in the space to conduct joint training on electroencephalogram signals and multi-modal data such as vision, audio and text, and therefore the multi-modal data of the electroencephalogram signals and the multi-modal data of the vision, the audio and the text are obtained. The electroencephalogram signals with low signal-to-noise ratio and high individual difference are semantized and standardized successfully, efficient and semantic-level fusion of an electroencephalogram mode and a general multi-mode large model system is achieved, and the model obtained through training can directly map the electroencephalogram signals of any user into semantic-rich vector representation.

The invention discloses a visual evoked brain signal decoding method and system based on a multi-modal diffusion model, and the method can reconstruct a high-resolution image from an fMRI signal and generate a descriptive text. According to the method, an fMRI signal is mapped to an image-text detail potential feature space and an image-text advanced semantic feature space of a CLIP model through a lightweight regression model, and an image and a text are generated under the guidance of a joint condition by using a multi-modal diffusion model. According to the method, multi-condition semantic information of image and text features is fused, high-fidelity image and text description are generated from brain signals at the same time by using a multi-mode potential diffusion model for the first time, and the superior ability of functional brain region analysis in the aspect of specific semantic content decoding is revealed. The invention provides a solution for brain-computer interface, neuroscience research and medical auxiliary diagnosis.

A unit equipment (UE) configured to establish a protocol data unit (PDU) session including one or more quality of service (QoS) flows and multiple data radio bearers (DRB) for communications with a network, decode, from signals received from the network, or determine an initial mapping table for a QoS flow to DRB mapping based on a network configuration of mapping parameters, decode, from signals received from the network, or determine information regarding current or upcoming traffic on the one or more QoS flows or one or more DRBs, and configure transceiver circuitry to transmit an indication to the network based on the information regarding current or upcoming traffic on the one or more QoS flows or one or more DRBs.

The invention relates to the technical field of signal decoding, in particular to a single-molecule signal decoding method based on an artificial intelligence algorithm, which specifically comprises the following steps: performing baseline correction on an original current signal sequence to obtain a corrected current signal dynamic baseline sequence; conducting conductance fluctuation component separation through power spectrum density and frequency band division to obtain a main current signal of the molecular event; dividing into independent molecular event segments, and extracting stable features to obtain a complete local segment stable feature set; splicing and correcting to obtain a corrected complete single molecule track; and performing similarity contrastive analysis with a standard control single-molecule trajectory to obtain an optimized single-molecule trajectory. According to the method, the problem that in the prior art, the application range and precision of the technology in complex biomolecule analysis are limited due to the fact that a single-molecule signal is difficult to be accurately matched with a standard control track in a high-salt environment is solved.

Technologies directed to preamble detection in co-existence environments of WLAN and LR-WPAN signals are described. One wireless device has a first radio that sends a first RF signal to a second wireless device and a second radio that operates according to the IEEE 802.15.4 standard. The wireless device includes a circuit coupled between the first radio and the second radio. The circuit detects a presence of the first RF signal. The circuit receives a second RF signal from a third wireless device and attenuates the second RF signal to obtain a third RF signal in response to the presence of the first RF signal being detected. The second radio receives the third RF signal and decodes a preamble using the third RF signal.

The invention discloses a motor imagery electroencephalogram signal decoding method and system, and the method comprises the steps: obtaining the time sequence data of each channel of a motor imagery electroencephalogram signal, carrying out the averaging of the time sequence data along the time dimension, obtaining the features after the time average pooling, carrying out the standardization of the features in the channels, and obtaining the time sequence data of each channel of the motor imagery electroencephalogram signal. Gaussian weighting coefficients are generated through Gaussian weighting harmonic, the coefficients are broadcasted along the time dimension, weights matched with the time dimension are obtained, the weights and the motor imagery electroencephalogram signals are multiplied element by element, and the dynamically harmonic motor imagery electroencephalogram signals are obtained. Extracting multi-channel multi-scale spatial-temporal features of the signal, windowing a feature sequence, constructing a feature vector with a fixed length, and performing classification through a classifier to obtain a probability corresponding to each predefined motor imagery category; according to the method, the decoding performance can be effectively improved through cooperative work of adaptive coordination, feature heterogeneous fusion and channel attention optimization of the motor imagery electroencephalogram signals.

The invention discloses an electroencephalogram signal decoding method and system based on characteristic decomposition and adversarial training, and relates to the technical field of brain-computer interfaces and neural engineering. Then, the extracted features are decomposed into target feature vectors and irrelevant feature vectors through a feature decomposition module; information related to a motor imagery task is reserved in a target feature vector through supervised learning of a classification module, meanwhile, task related information is not included in an irrelevant feature vector through confrontation training of a judgment module and a feature decomposition module, and therefore separation of target features and individual irrelevant features is achieved. In the test stage, only the target feature vectors are used for motor imagery task classification, individual differences are effectively overcome, and the generalization ability of the model across subjects is improved.

This disclosure provides a voice signal decoding method and apparatus and an electronic device. An encoding apparatus encodes an original voice signal, to obtain an encoded bitstream. The encoded bitstream includes an acoustic feature encoding result. A decoding apparatus obtains the acoustic feature encoding result in the encoded bitstream, obtains a style feature from the acoustic feature encoding result, obtains an excitation feature, performs style fusion processing on the excitation feature and the style feature, to obtain a fused voice feature, and reconstructs a decoded voice signal based on the voice feature. Because the style feature indicates a voice style of an original voice signal, a voice feature in the original voice signal can be restored from the voice feature obtained by performing style fusion on the excitation feature and the style feature.