452 results about "Signal encoding" patented technology

The invention discloses a morphological gradient region replacement method based on SAM semantic segmentation and user guidance, and relates to the technical field of computer vision and image processing, and the method comprises the steps: carrying out the semantic segmentation of a to-be-processed image through an SAM model, extracting a multi-level semantic feature, carrying out the standardization and dimension reduction, extracting a causal factor based on independent component analysis, and carrying out the user guidance. A directed causal factor association graph is generated through Granger causal relationship test, and a causal attribution probability graph is generated through reverse mapping; constructing a structured causal graph, and generating a causal mask through a graph convolutional network; encoding the original interaction signal into a guide thermodynamic diagram; constructing a diffusion equation, forming a gradual change control equation by dynamically fusing and guiding the intensity distribution of the thermodynamic diagram and an image semantic diffusion item, and iteratively solving the gradual change control equation; generating an anisotropic morphological operation kernel according to the geometric curvature characteristics of each region in the replacement mask; and fusing the optimized replacement mask with the target content based on a gradient domain optimization algorithm to generate a gradient replacement image.

The invention provides a quantum circuit optimization method, device and equipment for a ZUC algorithm subcomponent, and the method comprises the steps: encoding a signal transmitted by communication into quantum bits, and inputting the quantum bits into a ZUC algorithm structure for quantum implementation; representing a first linear subcomponent in the ZUC algorithm structure as a matrix in a binary field; after a matrix in the binary field is expressed as a block matrix, a low-dimensional matrix M in the binary field is derived from the block matrix, and quantum implementation of the M is searched by using a search algorithm designed based on a matrix decomposition principle; and according to a quantum gate parallel rule, carrying out optimized multi-layer division on the quantum of the M again, obtaining the quantum of the M, carrying out optimized layering operation, and optimizing the quantum of the first linear subcomponent. According to the method provided by the invention, the quantum circuit of the subcomponent of the ZUC algorithm is optimized, so that the resource overhead of quantum implementation of the ZUC algorithm is saved.

The invention discloses a mechanical fault diagnosis method and device, equipment and a storage medium, and relates to the technical field of mechanical equipment state monitoring, and the method comprises the steps: carrying out the signal collection of target rotating equipment, so as to obtain a target vibration signal, and carrying out the encoding of the target vibration signal through an original signal encoder, obtaining a feature vector corresponding to the target vibration signal; obtaining fault diagnosis information corresponding to the target vibration signal from a preset diagnosis knowledge base according to the feature vector, and generating a spectrum analysis conclusion corresponding to the target vibration signal by using a target large language model and the target vibration signal; generating a fault diagnosis analysis conclusion corresponding to the target rotating equipment by using the fault diagnosis information corresponding to the target vibration signal, the spectrum analysis conclusion and the target large language model; wherein the target large language model is a model obtained by training historical fault diagnosis information based on different types of rotating equipment. By using the large language model and the diagnosis knowledge base, fault diagnosis of multiple devices can be realized.

Systems and methods for detecting and diagnosing machine faults are discussed. An exemplary system includes at least one sensor node to sense a signal indicative of an operation status of a machine part, and a machine health analyzer circuit to generate a computational machine fault model comprising an autoencoder (AE) network and an associative module. The AE network encodes the sensed signal into signal features in a latent feature space, and decodes the signal features to produce a reconstructed signal. The associative module transforms the encoded signal features into an associative output using a dynamically updatable codebook. The machine health analyzer circuit detects a presence or absence of fault in the machine part based on reconstruction losses determined respectively from the reconstructed signal and the associative output. The detected fault can be presented to a user or to a process such as fault diagnosis or fault correction.

Techniques and solutions are described for encoding and decoding signals, such as audio data. Disclosed innovations can find particular use in speech coding applications, such as for real time communications. Using a neural network, contextual coding can be used to encode latent features for a current frame using a prediction from reconstructed latent features of past frames as a context. An extractor learns a residual-like feature based on such prediction and latent features of the current frame obtained using an encoder. The residual-like feature is then quantized. At a decoder portion of a coding framework, the quantized feature is dequantized and then combined with a prediction from prior reconstructed latent features to provide reconstructed features of a current frame, which can then be processed by a decoder to provide a reconstructed signal.

The invention provides a motor imagery-based rehabilitation evaluation method and system and a storage medium, and the method comprises the steps: obtaining electroencephalogram signal data collected by a subject during the execution of a motor imagery-based rehabilitation task, and extracting the frequency domain features of the electroencephalogram signal data; inputting the frequency domain characteristics of the electroencephalogram signal data into a pre-trained encoder for encoding, and outputting to obtain embedded representation of the electroencephalogram signal data; inputting the embedded representation of the electroencephalogram signal data into a natural language decoder, and outputting to obtain a rehabilitation evaluation result of the subject; wherein in the process of training the signal encoding module to obtain the encoder, the signal encoding module is updated through encoding loss, so that the embedding representation of the label text and the embedding representation of the corresponding electroencephalogram signal data realize cross-modal alignment. According to the method, the end-to-end generation from the original electroneurographic signal to the multi-modal evaluation information can be realized.

The invention relates to the technical field of voice semantics, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a high-fidelity music reconstruction method, device, equipment and medium, and the method comprises the steps: obtaining a to-be-processed music signal, carrying out the signal coding of the to-be-processed music signal, and obtaining a low-dimensional music signal; performing multi-scale convolution on the low-dimensional music signal to obtain a multi-scale music feature; performing multi-head self-attention calculation on the multi-scale music features to obtain attention values of the multi-scale music features; performing feature fusion on the multi-scale music features according to the attention values to obtain fused music features; performing non-autoregression decoding on the fused music features to obtain target acoustic features; and performing waveform reconstruction on the to-be-processed music signal according to the target acoustic feature to obtain a target music signal. According to the invention, the music signal reconstruction quality and reconstruction efficiency can be improved.

The invention belongs to the technical field of wireless communication and artificial intelligence, and discloses a multi-mode semantic communication method and device oriented to multiple tasks, which avoid low-layer signal coding transmission of original data through extraction and transmission of semantic features, realize end-to-end optimization and channel adaptive robust transmission of semantic level communication, and improve the communication efficiency. The bandwidth occupation is obviously reduced; and the anti-noise performance is improved. Semantic transmission and task execution of multi-source data such as images, texts and voices under a unified framework are achieved through a shared neural network encoder and a channel interface, the robustness of tasks is kept under the complex channel condition, and cross-domain semantic expression among modes such as vision, texts and voices is achieved through the framework. Radio frequency data are sent and received in a real hardware platform, physical combination of an algorithm and a radio frequency link is achieved, and implementation of deep semantic communication in an actual wireless link shows that the method has engineering deployability and expansibility.

The present invention provides a video signal encoding / decoding method and an apparatus for the method. The video decoding method of the present invention includes the following steps: determining whether to apply a merged motion difference encoding method to a current block; generating a merged candidate list for the current block; determining a merged candidate for the current block based on the merged candidate list; and deriving a motion vector for the current block based on the merged candidate.

The invention provides an equipment fault diagnosis method and device based on a large language model and electronic equipment, and the method comprises the steps: collecting a vibration signal of the equipment, and extracting the time-frequency characteristics of the vibration signal through a signal encoder; a description text corresponding to the time-frequency feature is retrieved from the description text data set; and editing the cue word template based on the description text and the time-frequency features, and inputting the cue word template into the large language model to obtain an equipment fault diagnosis result, an equipment fault diagnosis basis and an equipment fault maintenance scheme output by the large language model. By using a large language model and a retrieval enhancement generation technology, the problem of cross-working-condition fault diagnosis of equipment in a complex environment is solved, so that the accuracy of fault diagnosis is enhanced, and the generalization of diagnosis is improved.

The invention discloses a quaternary data processing system based on a multi-level signal and a dual-storage architecture, and relates to the technical field of data processing, in particular to a quaternary data processing system combining multi-level signal coding, a dual-storage architecture synchronization strategy and a quantum compatibility design. According to the system, through collaborative design of multi-level signals and a double-storage architecture, the density bottleneck and anti-interference shortages of a traditional binary system are solved, meanwhile, a classic data interface is provided for quantum calculation, and the system can be widely applied to the fields of power line communication, industrial automation, quantum calculation acceleration and the like.

A wind turbine generator variable pitch bearing fault diagnosis method based on signal coding specifically comprises the steps that original vibration signal data of a variable pitch bearing are acquired through a wind turbine generator state monitoring system arranged in a wind power plant; bearing vibration signals are respectively coded into a time-frequency diagram and a position matrix diagram by adopting continuous wavelet transformation and position matrix transformation, and the characteristic information of the signals is enhanced by increasing the visual dimension of data; a global feature extraction module and a local feature extraction module are respectively designed based on a global attention mechanism and a window attention mechanism, so that the model can fully learn feature information of different levels; a dual-channel parallel fusion network model is constructed according to global and local feature extraction modules to fuse global and local feature information, and the purpose of suppressing strong random noise interference is achieved. According to the method, the vibration signal characteristics of the variable pitch bearing in different states can be fully captured, and the problems of poor diagnosis performance and low precision of the variable pitch bearing caused by strong random noise interference can be effectively solved.

A signal processing circuit for a spiking neural network includes an interface for converting an analog input signal to a corresponding pulse-time representation of the analog input signal. The interface includes: an analog-to-information (A / information) converter configured to: generate a modulated signal representative of one or more characteristics of an analog input signal; a feature detector circuit configured to: compare the modulated signal to a reference signal representative of a reference feature, and configured to: generate an error signal indicative of a difference between the modulated signal and the reference signal; a feature extractor circuit comprising a locked loop circuit having an input for receiving the error signal and configured to: generate an output signal representative of an occurrence of one or more features represented by the modulated signal; and an encoder circuit configured to encode the output signal into a pulse sequence for input to the spiking neural network.

A signal encoding and decoding protocol to convey discrete sets of information combined in a single carrier signal is disclosed. The protocol uses modulation of both amplitude and pulse width to carry multiple sets of information. A first signal is pulse width modulated to encode a first data and a second signal is pulse amplitude modulated to encode a second data. The two modulated signals are combined to generate a carrier signal encoded with both the first and second data.

The video decoding method of the present invention includes the following steps: determining whether to divide an encoded block into a first prediction unit and a second prediction unit; when it is determined that the encoded block is divided, determining the division type of the encoded block; deriving first motion information of the first prediction unit of the encoded block and second motion information of the second prediction unit; and obtaining prediction samples in the encoded block based on the first motion information and the second motion information.

A computer system comprising processing circuitry configured to: receive an engine restart request for restarting an engine of a vehicle while the vehicle is moving; receive, directly from the engine, a signal encoding a real-time rotational speed of the engine; and control an actuator to gradually engage a clutch of a transmission coupled to rotating wheels of the vehicle in such a way that a rate of change of the real-time rotational speed of the engine is kept below a predefined threshold rate of change.

A method of processing an amplitude modulated, AM, signal, the method comprising: receiving an AM signal comprising an encoded sequence of symbols, each symbol in the encoded sequence being from a set of predefined symbols, wherein each predefined symbol in the set of predefined symbols, when encoded, comprises an AM signal portion comprising one or more high amplitude signal periods and a low amplitude signal period, wherein the low amplitude signal period is in a different position in the AM signal portion for different predefined symbols in the set of predefined symbols; processing the received AM signal to measure an interval between two successive low amplitude signal periods in the received AM signal; based at least in part on the measured interval, identifying a symbol of the sequence of symbols encoded in the received AM signal; and performing an action based on the identified symbol.

The present disclosure relates to the technical field of communications, and provides a signal transmission method and apparatus, a storage medium and a program product, which are beneficial to reducing interference between signal transmissions. The signal transmission method comprises: sending, to a second node, configuration information of a signal to be transmitted, wherein the configuration information at least comprises an encoding configuration; if the signal to be transmitted is a first signal, the encoding configuration comprises parameters related to a first encoding mode; and if the signal to be transmitted is a second signal, the encoding configuration comprises parameters related to a second encoding mode.

A method for training a non-autoregressive TTS model includes receiving training data that includes a reference audio signal and a corresponding input text sequence. The method also includes encoding the reference audio signal into a variational embedding that disentangles the style / prosody information from the reference audio signal and encoding the input text sequence into an encoded text sequence. The method also includes predicting a phoneme duration for each phoneme in the input text sequence and determining a phoneme duration loss based on the predicted phoneme durations and a reference phoneme duration. The method also includes generating one or more predicted mel-frequency spectrogram sequences for the input text sequence and determining a final spectrogram loss based on the predicted mel-frequency spectrogram sequences and a reference mel-frequency spectrogram sequence. The method also includes training the TTS model based on the final spectrogram loss and the corresponding phoneme duration loss.

An audio communication method, an audio conversion method, a bitstream processing method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product. The audio communication method comprises: in response to a first communication request for an audio signal, acquiring, from a plurality of communication modes, a voice transformation mode for the audio signal (101); performing feature coding on the audio signal, so as to obtain a coded feature of the audio signal (102); acquiring a target timbre corresponding to the voice transformation mode, and determining a timbre feature of the target timbre (103); performing timbre conversion on the coded feature on the basis of the timbre feature, so as to obtain a target coded feature (104); and performing signal coding on the target coded feature, so as to obtain a target audio bitstream conforming to the target timbre, and transmitting the target audio bitstream to a decoding terminal (105).

An encoding method for a light signal, an electronic device, a non-transitory computer-readable storage medium, and a light signal detection circuit are provided. The encoding method for a light signal includes: detecting whether there is a change in a light intensity signal representing a light intensity, the light intensity signal being converted by a photosensitive unit from a received photon stream; encoding, in response to detecting a change in the light intensity signal, the changed light intensity signal to obtain an encoding result; and arranging the encoding result into an encoded sequence according to a time sequence relationship corresponding to changes in the light intensity, the encoded sequence representing time sequence code of the photon stream, and the time sequence relationship representing an order of the changes in the light intensity within a time period.

An algorithm and architecture for sense transfer function estimation injects one or more test signals from a signal generator into a MEMS gyroscope to detect an output signal (e.g., proof mass output sense signal), including an in-phase (e.g., Coriolis) component and a quadrature component. The in-phase and quadrature components are encoded with reference signals to determine phase and / or gain variation and are processed via a variety of components (e.g., matrix rotation, digital gain, tones demodulator, transfer function errors estimation, etc.) to estimate a sense transfer function of the MEMS (e.g., Hs(fd)) and corresponding phase and / or gain offset of Hs(fd). The in-phase and quadrature components are also compensated for phase and / or gain offset by system components.

The invention discloses an unmanned aerial vehicle control system and method, and relates to the technical field of unmanned aerial vehicle control, the control system comprises a ground control end and an unmanned aerial vehicle end, the ground control end comprises a light source module, a signal coding unit and a light beam directional emission module, the unmanned aerial vehicle end comprises a photoacoustic receiving module, a signal decoding and processing unit and a flight control execution module, the system detects emitted sound waves through a photoacoustic effect, reversely deduces characteristic information of the intensity and frequency of optical signals, uses the optical signals as an information carrier, and performs photoacoustic conversion and acoustic signal processing, so that the flight control performance of the unmanned aerial vehicle is improved. According to the system, remote control over the unmanned aerial vehicle is achieved, meanwhile, a photoacoustic conversion and encoding and decoding mechanism is introduced, high-precision and long-distance transmission of control instructions of the unmanned aerial vehicle is achieved, and therefore the capacity of a receiving end for capturing weak signals is enhanced, and the system can flexibly deal with the signal attenuation problem under different illumination conditions and flight environments.

The invention discloses a robot grasping recognition method and system based on visual and tactile information fusion. The method comprises the following steps that S1, tactile signals are preprocessed; the method comprises the following steps: S1, integrating data acquired by each tactile sensor into a four-dimensional tensor, and constructing a basic tactile snapshot sequence; a three-level coding network composed of a CNN (Convolutional Neural Network), an LSTM (Long Short Term Memory) and a Transform Encoder is adopted to code the tactile information; S3, visual signal preprocessing and coding; s4, carrying out cross-modal alignment; and S5, large model reasoning. According to the robot grabbed object recognition method and system combining the visual features and the tactile features, the grabbed object of the robot is understood and analyzed based on the visual information and the tactile information, and the accuracy of robot actions and the completeness of tasks can be improved.

The application discloses a kind of haptic signal encoding processing method, decoding processing method and related equipment, belong to haptic codec field, the haptic signal encoding processing method of the embodiment of the application includes: obtaining at least one of the key frame sequence and first signal corresponding to each haptic signal in multiple haptic signals, the key frame sequence is obtained based on second signal, first signal is obtained based on target residual signal and third signal, target residual signal is obtained based on second signal and key frame sequence, second signal and third signal are obtained based on to haptic signal, and the frequency of second signal is less than the frequency of third signal;At least one of the following is performed: first masking processing is carried out to key frame sequence based on first masking model, and the signal after first masking processing is encoded to obtain first encoding sequence;Second masking processing is carried out to first signal based on second masking model, and the signal after second masking processing is encoded to obtain second encoding sequence.

The present disclosure relates generally to signal encoding for elements within PDF files. One implementation smooths saw-toothed edges of rasterized elements meant for encoding. Other implementations are provided too.

A system for encoding and decoding signals, such as pictures and video, intended for machine use is disclosed. The encoder implements a method for kernel-based transformation of signal samples that reduces and preferably minimizes the entropy of the input signal, but is still equivalent to the input signal for expected machine operation. Accordingly, the transformed signal samples may be compressed using fewer bits than the original samples. The encoded signal is received by a complementary decoder and decoded for use by a machine.

The present disclosure relates generally to signal encoding for value documents. One aspect of the technology relates to authentication of value documents comprising line art patterns. In one example, authentication relies of detection of signals encoded within the line art, and expected frequency domain analysis of the line art. Other combinations are described as well. For example, authentication may rely on detection of signals encoded within encoded and then printed documents, expected frequency domain characteristics encoded signals, presence or absence of halftoning, a color check and / or trained classifier analysis. Other aspects, technology, claims and combinations are described as well.

The invention belongs to the field of communication, and relates to an audio signal transmission method and related equipment, and the method comprises the steps: collecting audio signals which comprise a public network audio signal and a private network audio signal; preprocessing the public network audio signal and the private network audio signal; mixing the preprocessed public network audio signal and the preprocessed private network audio signal to obtain a mixed audio signal; encoding the mixed audio signal into a Bluetooth protocol format through an application program, and generating a Bluetooth audio signal; and transmitting the Bluetooth audio signal to a Bluetooth device. According to the application, protocol analysis and mixing processing are carried out on the private network data packet through a software level, seamless audio fusion of the public network and the private network and cooperative output of the Bluetooth are realized, the problem that audio fusion of the public network and the private network and intercommunication between the Bluetooth and the private network need additional hardware modules is solved, low-delay and high-compatibility transmission is realized, and the transmission efficiency is improved. And the user experience and the operation efficiency in a complex communication scene are improved.

The invention relates to the technical field of communication, and discloses a converged communication method for an analog signal interphone and a public network interphone, which comprises the following steps of: directly sensing the physical on-off state of a speaking right key of an analog interphone without voice signal encoding and decoding processing so as to generate a binary state signal; according to the method and the device, the state signal is mapped into a public network standardized uplink request signaling which does not bear user data, the public network standardized uplink request signaling is sent to serve as a cross-domain state beacon, and then selection scheduling of heterogeneous communication resources is triggered, so that inherent processing delay and power consumption of signal translation in an existing fusion mode are avoided, and the user experience is improved. In a content-independent lightweight beacon mode, nearly instantaneous state synchronization and efficient resource selection between heterogeneous networks are realized, and the operation reliability and adaptability of the system under complex working conditions are improved.