315 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 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.

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 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.

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 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.

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 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.

The invention discloses a method and a related device for decoding natural continuous voice by a neural electrophysiological signal, and relates to the technical field of neural signal processing and voice synthesis, and the method comprises the steps: obtaining a neural electrophysiological signal generated when a user reads a target text or listens to a corresponding audio, breaking the single signal modal limitation, and obtaining a voice signal; various non-intrusive application scenes are considered; slicing according to a preset time window to obtain continuous neural electrophysiological signal fragments; inputting the segments into a pre-trained deep learning physiological signal decoding model, outputting corresponding speech feature segments, directly mapping neural signals and speech features, omitting an intermediate conversion step, and reducing error accumulation; and converting the feature fragments into time-domain voice waveforms through a pre-trained neural vocoder, and splicing the time-domain voice waveforms to obtain natural continuous voices. Practicality and naturalness of brain-computer interface communication are remarkably improved, and an efficient and convenient communication way is provided for people with language dysfunction.

This invention discloses a differential VGA signal decoding and display device, relating to the display field. It includes a signal input module, an ESD protection module, a differential receiving module, a synchronization separation module, an image decoding driver module, a display module, an audio processing module, and a control module. These modules are interconnected and work together to decode, process, and display differential VGA signals. This differential VGA signal decoding and display device solves the problems of short transmission distance, weak anti-interference capability, limited resolution, and high power consumption associated with traditional VGA interfaces. It also achieves full compatibility with traditional VGA devices, integrating audio processing, local playback, intelligent signal enhancement, and remote monitoring and management functions. It achieves reliable conversion from analog to differential signals, supports high-definition display, and has a transmission distance of up to 50 meters, while maintaining full compatibility with traditional VGA devices. This technology is particularly suitable for professional fields such as industrial automation and medical imaging, meeting the diverse display and intelligent operation and maintenance needs of professional scenarios.

The invention discloses a method and a system for unvarnished transmission of logic signals on a two-wire power supply circuit, and the system comprises a power supply, a power isolator, the two-wire power supply circuit, a load isolator, an impedance matcher, at least one transmitting node and at least one receiving node. One of the sending nodes sends the logic signal, encodes the sending logic signal into a first narrow pulse code signal, and then converts the first narrow pulse code signal into a second narrow pulse code signal on the two-wire power supply line for transmission; and the receiving node receives the logic signal, converts a second narrow pulse code signal on the two-wire power supply line into a third narrow pulse code signal, and decodes the third narrow pulse code signal into a receiving logic signal. The method has the advantages of high transmission rate, strong real-time performance, high reliability, low cost, wide application range and the like.

A multi-channel signal encoding method includes determining a downmixed signal of a first channel signal and a second channel signal in a multi-channel signal, and reverberation gain parameters corresponding to different subbands of the first channel signal and the second channel signal, where the obtained reverberation gain parameters belong to at least two reverberation gain parameter groups. The method further includes selecting, from the at least two reverberation gain parameter groups, a target reverberation gain parameter group. The method further includes generating parameter indication information, where the parameter indication information indicates the target reverberation gain parameter group. The method further includes encoding reverberation gain parameters corresponding to the target reverberation gain parameter group, the parameter indication information, and the downmixed signal to obtain a bitstream.

The utility model discloses an inductance encoder for a rotating motor, which relates to the technical field of encoders and comprises a signal receiving and demodulating module, the signal receiving and demodulating module comprises a rotor plate, a stator coil plate and a dual-channel induction inductance sensor chip, the rotor plate is connected with a rotating shaft portion of the rotating motor, and the stator coil plate is connected with the inductance sensor chip. The stator coil plate comprises an exciting coil and a receiving coil, non-contact electromagnetic induction is formed among the rotor plate, the exciting coil and the receiving coil, and the inductance sensor chip is used for demodulating amplitude modulation signals on the receiving coil and converting the amplitude modulation signals into sine and cosine signals; the signal processing module is used for receiving the sine and cosine signals output by the inductive sensor chip and processing the sine and cosine signals to obtain signal information; the signal decoding and outputting module is used for receiving the signal information and converting the signal information into angle position information of a motor rotor plate; the inductance encoder has the characteristics of high anti-interference capability, strong environmental adaptability and low cost.

The application discloses a brain-computer interface method and system based on real-time closed-loop vibration enhancement, relates to the technical field of brain-computer interface, and comprises the following steps: processing EEG signals through a filtering and feature extraction algorithm, generating a regression vector and identifying an event-related potential as an expected signal; constructing a distributed EEG signal decoding network, optimizing node cooperation through task clustering; adopting an adaptive algorithm update node estimation of a diffusion strategy, optimizing signal decoding through dynamic adjustment of combination coefficients and inter-node cooperation; generating a virtual feedback signal according to a decoding result, fusing the virtual feedback signal with the EEG signal and feeding back to the system, adopting machine learning to optimize classification output, and adjusting model parameters to enhance brain rhythm. The method disclosed by the application can accurately capture the change characteristics of the electroencephalogram signal, dynamically adjust the cooperation mode between nodes, and improve the signal decoding accuracy. The decoder parameters can be adjusted in real time, the electroencephalogram rhythm synchronization is enhanced, and the recognition effect of the motor imagination task is improved.

The invention discloses a neural signal decoding method, a brain-computer system, electronic equipment and a storage medium. The method comprises the following steps: acquiring a tested neural signal; determining the language state of the subject according to the neural signal and a preset language state detection model; determining a pre-trained language decoding model according to the language state; and decoding the neural signal by using the language decoding model to obtain a language decoding result. The technical problem that the neural signals corresponding to different language states cannot be decoded in real time when facing the neural signals corresponding to different language states is solved.

The invention discloses a mobile emergency broadcast and alarm system, which belongs to the technical field of alarm devices, and comprises a central signal wireless transmitting end used for receiving and encoding an audio alarm signal and a flashing alarm signal, and sending the encoded signals to a mobile alarm terminal in a wireless mode; the mobile alarm terminal is used for receiving the coded signal sent by the wireless signal transmitting terminal, decoding the coded signal and then giving an alarm; wherein the mobile alarm terminal comprises a portable terminal and an area coverage type terminal. The central signal wireless transmitting end is used for transmitting the alarm signal, the mobile alarm terminal is arranged for the target personnel to give an alarm in various modes such as sound, light and vibration, the system can flexibly adapt to the field environment, and the effectiveness and accuracy of alarm coverage are improved.

The invention discloses an RSVP normal form electroencephalogram electrode offset self-adaptive compensation method for an autonomous motion scene, is suitable for an electroencephalogram signal decoding task in a motion state, and belongs to the crossing field of computers and biomedical engineering. For the problem of artifact interference in a motion state, positive and negative sample pairs obtained by sampling in an anchoring feature space without artifacts and a feature space to be compensated with artifacts are introduced through supervised contrast learning based on a decoupling training strategy, and the compensation capability of a feature extractor is trained by applying InfoNCE loss. Meanwhile, on the basis of small-batch sample data characteristics, a lightweight feature extractor is designed, and the EEG signals are downsampled into spatiotemporal features based on spatiotemporal convolution for subsequent comparative learning. Compared with a complex feature extractor using a self-attention mechanism, a multi-scale feature extraction module and the like, the lightweight design has the advantages that classification indexes such as BA and RECALL are obviously improved, and the calculation cost is greatly reduced.

The invention discloses a heterogeneous audio and video independent routing transmission method and device and a storage medium, and relates to the technical field of audio and video signal processing. According to the method, an HDMI signal is decoded at a matrix input side to form a first signal carrying video data and control / auxiliary information and a second signal carrying audio data, and the first signal and the second signal enter a matrix switching module through two sets of independent physical links to realize independent routing of audio and video; and the output end recovers the HDMI video signal frame based on the first signal, recovers the audio interface signal based on the second signal, and re-embeds the recovered audio interface signal into the HDMI video signal frame, thereby realizing independent source selection output of different-source audio and video.

The application discloses a wireless communication security transmission method, and belongs to the field of wireless communication security. The method comprises the following steps: an information sender encodes information to be sent to each legal receiver into a code word on a fine lattice by using a nested lattice code, wherein a secret message and an auxiliary message are generated for each information, the auxiliary message is a lattice point randomly selected from a coarse lattice, and the secret message is formed by mapping a mapping function to a coset of the coarse lattice; a precoding technology is used to isolate the information to be sent to each legal receiver, and a final sending signal is generated; and a legal receiver decodes the received signal to obtain the message to be sent by the information sender. The method uses a nested lattice code encoding scheme to minimize the information leakage rate of an eavesdropper, so that strong security of a communication system is achieved. Meanwhile, the secret message transmission between legal channels is realized by using a multi-user precoding technology, and the data security transmission between each point in a communication network is realized.

A method and device for processing a video signal using a reduced transform are provided. A method for decoding a video signal includes splitting a current block into a plurality of subblocks based on a first flag for a split direction of the current block, determining a subblock, to which a transform is applied, among the plurality of subblocks based on a second flag for a position of a transform target block, determining a transform target area in the subblock, and applying an inverse transform including a horizontal transform and a vertical transform to transform coefficients of the transform target area. The transform target area is determined based on a transform type applied to the subblock and a size of the subblock.

The application discloses a BTM intelligent anti-interference decoding method, system, device and medium for a railway train control system, and belongs to the technical field of signal processing in the railway train control system. The method is used for BTM intelligent anti-interference decoding of the railway train control system, acquires an uplink signal and constructs a frame segment, extracts multi-scale features through a parallel one-dimensional convolution branch after preprocessing, generates gating coefficients for feature fusion according to decoding side metrics, obtains deep layer representation by introducing a deep separable convolution of a structure sparsification mechanism, predicts noise based on the representation and obtains a denoising signal through residual learning, then performs decision and verification to output a decoding result, and updates the gating coefficients and convolution structure parameters online and adaptively according to the decoding result and metric feedback, so that closed-loop control and optimization of anti-interference decoding are realized. The application can significantly improve the accuracy of signal decoding, reduce the calculation complexity and time delay, and meet the real-time deployment requirements of the vehicle-side device.