39 results about "Spectral mask" patented technology

The invention provides an on-chip integrated multispectral coding TDI sensor and an imaging method thereof. The on-chip integrated multispectral coding TDI sensor comprises a TDI sensor, a multispectral coding mask plate and an imaging model, the TDI sensor comprises a pixel array, a reading circuit, a cache circuit, a reconstruction calculation circuit and a time sequence control circuit, and all parts of the TDI sensor are electrically connected; the multispectral coding mask plate covers the photosensitive surface of the pixel array, the coding pixels of the multispectral coding mask plate are plated with multispectral optical filters, and a part of the optical filters are covered with a light-proof medium. On-chip coding is carried out on the TDI sensor to integrate a multispectral mask plate, and a two-dimensional coding measurement value image obtained by scanning is reconstructed to obtain a multispectral two-dimensional image, so that the TDI sensor realizes a multispectral data acquisition function of a plurality of multispectral TDI spectral sensors on a single sensor; and meanwhile, the defects of high energy consumption cost and high space cost caused by the fact that a multispectral TDI sensor needs a plurality of spectral band sensors are overcome.

Embodiments are disclosed for converting audio data to studio quality audio data. The method includes obtaining an audio data having a first quality for conversion to studio quality audio. A first machine learning model predicts a set of acoustic features. A spectral mask is applied to the audio data during the prediction of the set of acoustic features. A second machine learning model generates studio quality audio from the set of acoustic features and the audio data.

Techniques pertaining to distributed-tone resource unit (DRU) optimization to improve spectrum mask in wireless communications are described. An apparatus (e.g., a station (STA)) generates a DRU according to a tone plan. The apparatus also applies a first shift and a second shift to tones of the DRU in a first half of the tone plan and tones of the DRU in a second half of the tone plan, respectively. The apparatus then performs a wireless communication with the DRU.

The present disclosure provides methods, components, devices, and systems for 480 and 640 megahertz (MHz) transmission in Wi-Fi. Some aspects are more particularly directed to including a bandwidth field and a bandwidth extension field in a preamble of a physical layer protocol data unit (PPDU), the bandwidth field and the bandwidth extension field jointly indicating that a channel bandwidth of the PPDU is a continuous 480 MHz channel bandwidth or a 640 MHz channel bandwidth. In some aspects, parameters of the PPDU may be defined based on the PPDU occupying a 480 MHz or 640 MHz bandwidth. The parameters may include, for example, a tone plan, an allowed puncturing pattern, signaling of a puncturing plan, signaling of a resource element allocation, a short training field, a long training field, a pilot signal sequence, a phase shift, a segment parser, and / or spectrum masking.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may perform a crest factor reduction on a radio frequency (RF) waveform in multiple passes, wherein the multiple passes each include: a clipping operation to reduce a peak-to-average power ratio (PAPR) associated with the RF waveform according to a threshold; and a filtering operation to remove at least a portion of out-of-band emission from the clipped RF waveform. The UE may transmit the RF waveform over a wireless channel, wherein the RF waveform transmitted over the wireless channel satisfies a spectral mask. Numerous other aspects are described.

The invention relates to the technical field of machine learning, in particular to an image generation method and device based on frequency domain autoregression, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the discrete cosine transform of an input image, and obtaining a spectral density matrix; decomposing the spectral density matrix into multiple levels of sub-images, and sorting the sub-images according to a preset value; reconstructing the multi-level sub-images into spatial domain sub-images by using inverse discrete cosine transform, and encoding the spatial domain sub-images into a one-dimensional causal token sequence by using a nested spectrum token module; based on spectrum energy long-tail distribution, performing distribution processing on the one-dimensional causal token sequence, and introducing a causal spectrum mask; and performing autoregressive token prediction on the distributed one-dimensional causal token sequence, and reconstructing a corresponding sub-image. Through nested spectrum tokenization, non-uniform token-frequency mapping and causal spectrum mask, efficient, strict causal and rich-detail visual generation is realized.

Embodiments of the present disclosure provide a method, a transmitter node, a receiver node, and a computer program product for communication of information in a communication channel comprising at least one main band and an adjacent guard band. The method is performed by a transmitter node in a wireless communication network. The method comprises determining whether to transmit information in the guard band based one or more configured transmission parameters associated with the transmitter node. Upon determination to transmit information in the guard band, the method comprises determining transmission power for frequency resources allocated in the guard band based on a spectral mask defining a power density limit across an allocated frequency range. The method further comprises transmitting first information on the frequency resources allocated in the guard band and second information on frequency resources allocated in the main band to a receiver node according to the determined transmission power. Corresponding transmitter node, receiver node and computer program products are also disclosed.

This disclosure describes systems, methods, and devices related to reducing noise in and improving speech signals and radio signals. A device may identify a radio frequency signal received by radio frequency circuity at a time; apply a Short Time Fourier Transform (STFT) to the radio frequency signal to generate a STFT signal; identify a signal-to-noise ratio associated with the radio frequency signal at the time; identify a packet-error-rate associated with the radio frequency signal at the time; identify activity when the multiplexed radio frequency signal is received by the radio frequency circuity at the time; select, based on the signal-to-noise ratio, the packet-error-rate, and the activity, a spectral mask to be applied to the STFT signal; apply the selected spectral mask to the STFT signal to generate a clean STFT signal; and apply an inverse STFT to the clean STFT signal to generate a clean radio frequency signal.

The invention discloses a network flow time sequence decomposition method and system based on hierarchical decoupling, belongs to the technical field of time sequence analysis and network flow prediction, and solves the problems of insufficient global trend and local disturbance signal separation capability and the like in the prior art. The method comprises the following steps: processing an input network traffic time sequence by adopting a sliding window moving average algorithm to obtain a trend component, and extracting a seasonal component; dividing into a plurality of windows, and carrying out Fourier transform to obtain an amplitude sequence; after obtaining an amplitude sequence, carrying out averaging processing and sorting, selecting a frequency index with the maximum amplitude, and constructing a frequency spectrum mask; dividing the frequencies of the trend component and the seasonal component into a global frequency and a local frequency based on a spectrum mask, and respectively executing inverse Fourier transform to obtain four decomposition components; and adopting a multi-component time sequence feature fusion model to reconstruct and optimize the four decomposed components to obtain enhanced time sequence feature representation. The method is used for improving network traffic prediction.

The invention discloses a hyperspectral anomaly detection method based on double-domain multi-scale feature reconstruction, which comprises the following steps: firstly, in order to relieve the influence of abnormal pixels and spectral variation on background recovery, reducing the abnormal pixels through a space-spectral mask strategy, thereby avoiding interference on subsequent feature extraction; then, due to the complexity of the background, the invention designs a multi-scale double-domain feature interaction module which is used for extracting multi-scale features of the background in different domains. Specifically, multi-scale spatial features are extracted in a spatial domain through a multi-scale convolution block, and frequency domain features are extracted in a frequency domain through multi-scale wavelet convolution. In addition, the features of different domains are fully fused through feature interaction, and the background modeling capability of the network is improved. And finally, performing anomaly detection on the residual error of the reconstructed background and the input image through the mahalanobis distance. According to the method, features can be effectively extracted for targets with different shapes and multi-scale targets, so that a relatively good detection effect is achieved.

This disclosure provides methods, components, devices and systems for ambient power downlink bandwidth control. Some aspects more specifically relate to generating, by a transmitting device, a spreading waveform by concatenating a sequence of symbols having a first duration, and modulating an on-off keying (OOK) waveform having a different symbol duration with the spreading waveform. In some examples, the different waveforms may have different symbol durations, and symbol boundaries for the OOK waveform and the spreading waveform may or may not align in time. The spreading waveform may include a set of concatenated orthogonal frequency division multiplexing (OFDM) symbols, Barker sequences, or the like. The OOK waveform may include zero-mean OOK signals. The access point (AP) also may apply filtering to ensure that the generated waveform satisfies a spectral mask.

The application discloses a hyperspectral image target detection method based on spectral mask driving background learning, and belongs to the field of remote sensing image processing. The scheme is as follows: background sample screening is performed on an original hyperspectral image, and high-confidence background samples obtained are used as training samples of a network; a band mask is constructed for the obtained background spectrum as a mask training sample; two parallel automatic encoder networks are constructed, and the original background spectrum sample and the background spectrum sample subjected to the mask are used for training the two automatic encoder networks respectively; a constraint loss is constructed between the encoder outputs of the two automatic encoders during training; and the reconstruction error of a reconstructed hyperspectral image of the automatic encoder trained by using the original background spectrum is calculated and used as a detection result. The application can fully utilize background spectrum information, improve the target detection precision of the hyperspectral image, and can be used for target reconnaissance, mineral exploration and field search and rescue.

1. Name of the product in this design: Spectral Mask (2563). 2. Purpose of this design: For facial beauty and care. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The invention designs an innovative method suitable for link prediction tasks in a dynamic graph convolutional network. Most of existing dynamic graph link prediction methods are focused on independent coding of interaction sequences in a time domain, the potential causal relationship between node interactions cannot be effectively mined, and time migration existing in the interaction process is difficult to capture. The invention designs a multi-view adaptive frequency domain dynamic graph convolutional network. Firstly, multi-view information including nodes, edges, time and node interaction frequency is coded by using a time sequence interaction perception graph embedding module; wherein the node interaction frequency is coded by a neighbor interaction frequency method based on a matrix and is used for describing an interaction evolution rule between nodes. Fusion features are subjected to feature enhancement through a dual-channel spectrum mask module and then transferred back to a time domain, nonlinear features are captured and aggregated by using MLPmultiplier, efficient and accurate prediction of future interaction relationships is realized, and the method has both performance and speed advantages and has wide application value.

Isolating and amplifying a conversation between selected participants is provided. A plurality of spectral masks are received. Each spectral mask of the plurality of spectral masks corresponds to a respective participant of a selected group of participants included in the conversation. A composite spectral mask is generated by additive superposition of the plurality of spectral masks. The composite spectral mask is applied to sound captured by a microphone to filter out sounds that do not match the composite spectral mask and to amplify remaining sounds that match the composite spectral mask.

The present invention belongs to the field of artificial intelligence, and relates to a teaching interactive answering positioning method, device, computer equipment and storage medium. The method includes: obtaining a set of video frame sequences and a corresponding mixed audio file; identifying key frames in the set of video frame sequences, performing feature extraction, and performing short-time Fourier transform on the mixed audio, and then performing feature extraction on the audio; performing multimodal fusion to generate a spectrum mask; calculating the momentum information of continuous video frames and generating momentum embedding, and performing spectrum embedding processing according to the spectrum mask; inputting a momentum-aware Transformer module to generate sequence information; performing residual fusion to generate a spectrum mask; and generating a visual sound source localization map. Multimodal fusion improves positioning accuracy, momentum embedding and spectrum embedding enhance timing sensitivity, residual fusion optimizes feature integration, and a visual sound source localization map enhances intuitiveness, enhances the interactivity and fun of learning, and helps to improve teaching efficiency and quality.

The invention belongs to the technical field of computer vision. The invention provides a hyperspectral spatial-spectral mask variational self-encoding anomaly detection method for local security and protection. According to the space-spectrum joint mask technology designed by the embodiment of the invention, the multi-dimensional information of the space and the spectrum is fully utilized, the difficulty of effectively reconstructing the abnormal region in the reconstruction process of the network is improved, and the network is enabled to pay more attention to normal space and spectrum characteristics when learning image data distribution; and the detection capability of a complex abnormal scene is obviously improved. The designed VAE reconstruction network and mask combination mechanism makes full use of the probability modeling advantage of VAE and the attention mechanism advantage of mask, the accuracy and generalization ability of anomaly detection are improved, and the interpretability of the model is enhanced. And the designed residual image processing method effectively alleviates the noise problem caused by a complex background through a domain transformation recursive filter, and realizes higher detection precision and robustness.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: encoding one or more instance of a received image with spectral mask data, wherein the spectral mask data specifies spectral information of the received image to be masked; training one or more predictive model in dependence on the encoding; querying the one or more predictive model with a query image; and performing processing in dependence on an output from the querying.

The invention relates to the technical field of memory control and artificial intelligence, and discloses a 3D stacked memristor array optimization method based on AI data analysis, and the method comprises the following steps: S1, extracting frequency domain impedance response data containing a resistance component and a reactance component, and constructing a local state vector reflecting a physical state; s2, in combination with the time evolution characteristics of the historical operation sequence, outputting a sensitivity graph representing the interference susceptibility degrees of the target unit and the reference unit to different frequencies; s3, determining the optimal write-in carrier frequency and write-in voltage amplitude of each layer through game iteration; and S4, generating a write-in voltage waveform subjected to frequency spectrum mask filtering, and applying the write-in voltage waveform to the target unit to complete write-in operation. According to the method, the frequency domain impedance is analyzed through the AI to predict interference, the orthogonal frequency is distributed by using the game theory, and the reverse offset signal is applied, so that efficient concurrent writing of the 3D stacked array is realized, interlayer crosstalk is effectively inhibited, and the data stability is improved.

The application provides a generative self-supervised hyperspectral image target detection method based on a spectral mask, features of hyperspectral remote sensing data are encoded and decoded through a generative model and a spectral mask strategy, a general feature representation is learned in the absence of labeled samples, a transfer learning model based on a lightweight transformer encoder, a full connection layer and a Sigmoid layer is constructed, a detector composed of a full connection layer and a Sigmoid layer is fine-tuned in combination with a small amount of training samples and a weighted binary cross-entropy loss, rapid training of the transfer learning model is realized, and the pre-training model is effectively transferred to different target detection tasks. The application has the beneficial effects that: the hyperspectral remote sensing image itself features are fully utilized, the general feature representation is learned from the unlabeled data according to the self-supervised learning theory by using the generation task, and the pre-training model is transferred to various detection tasks, so that the detection precision and time efficiency are improved.

This terminal is provided with an authentication information holding unit and a wireless signal processing unit. The authentication information holding unit holds authentication information relating to authentication of a use region with respect to a spectrum mask when preamble puncturing is used. The wireless signal processing unit transmits authentication information relating to the use region of the terminal to an access point.

The present invention discloses an audio noise reduction system, a noise reduction method and a vehicle-mounted audio system. The noise reduction system comprises an input audio preprocessing module, which preprocesses an input audio signal to generate an audio spectrum; a deep neural network separation module, which is configured with: a feature extraction layer, which extracts audio features, namely, time features and frequency features, from the audio spectrum; an encoder, which is used to compress the audio features output by the feature extraction layer and generate a latent space representation according to the audio features; a decoder, which reconstructs and separates human voice and noise according to the latent space representation; an adaptive mask generator, which generates a time-series spectrum mask according to the frequency difference between noise and human voice and the time feature to separate the human voice audio; and an output reconstruction module, which is configured to inversely transform the frequency features of the separated human voice audio signal to reconstruct time-domain audio data, which can adapt to different noise types and avoid speech distortion or erroneous suppression.

The invention relates to the technical field of vehicle-mounted communication, in particular to a vehicle-mounted wireless communication module real-time interference avoidance method based on edge computing, which comprises the following steps of: capturing burst pulse interference of the Internet of Vehicles and a cellular frequency band through marginalized spectrum sensing, extracting a time domain duty ratio and a spectrum mask feature, and forming a dynamic interference feature set; performing multi-level classification on interference sources by using a lightweight convolutional neural network, calculating confidence coefficients in combination with signal entropy values, and generating interference situation data; fusing the vehicle position, the road topology and the surrounding node state to construct a risk thermodynamic diagram, driving an edge engine to dynamically redistribute spectrum resources, and outputting a channel switching instruction; controlling a radio frequency front end to adjust power and modulate a coding scheme through an instruction, and synchronously acquiring link quality feedback; by comparing a feedback value with a safety threshold value in real time, a redundancy mechanism is triggered to realize seamless switching of a main link and a standby link, an uploaded log is encrypted, and high reliability and safety of communication are ensured.

The invention discloses a self-adaptive spectrum mask electroencephalogram signal cross-session decoding method, a medium and equipment, which are used for solving the problem of insufficient cross-session generalization ability caused by non-stationarity of motor imagery electroencephalogram signals. The method comprises the following steps: performing frequency domain decomposition on an input electroencephalogram signal to obtain a frequency spectrum; calculating an amplitude spectrum and dynamically updating a global average spectrum through moving average; weighting the global average spectrum by using the learnable weight matrix to generate a weighted amplitude spectrum; performing threshold screening based on the learnable threshold parameter to obtain a sparse mask; the mask is normalized and then acts on an original frequency spectrum, so that self-adaptive enhancement and noise suppression of frequency domain characteristics are realized; and finally, outputting a prediction result through time domain reconstruction and a classifier, and optimizing spectrum mask parameters and classifier parameters in an end-to-end manner. Through a dynamic frequency domain screening mechanism, cross-session stable feature components are automatically captured, and the model generalization performance can be improved without depending on target session data.

The embodiments of the present application provide a method, apparatus, intelligent device, and computer storage medium for processing voice data, wherein the voice data processing method includes: obtaining facial image data and voice spectrum data containing multiple faces; processing the facial image data and the voice spectrum data to determine a target face; obtaining facial features and voiceprint features corresponding to the target face, and based on the facial features, the voiceprint features, and the voice spectrum data, determining a spectrum mask for indicating noise data in the voice spectrum data; and performing voice enhancement processing on the voice spectrum data based on the spectrum mask. Through the solution provided by the embodiments of the present application, the voice of the target user can be enhanced to improve the user experience.

According to the broadband oscillation identification and early warning method and system based on the composite spectrum difference measurement, real-time operation data of all nodes of a power grid are obtained, and a time sequence spectrum is obtained; when the oscillation alarm is relieved and steady-state operation in a set time period is passed, taking a frequency spectrum of a statistical average value of historical operation data in a sliding window in the set time period as a reference frequency spectrum; a frequency spectrum mask is introduced to represent a concerned frequency band; calculating the distance between the time sequence frequency spectrum and the reference frequency spectrum in the concerned frequency band, determining the KL divergence of the time sequence frequency spectrum and the reference frequency spectrum in the concerned frequency band according to the energy of the reference frequency spectrum in the concerned frequency band, and constructing a composite frequency spectrum difference index by using the distance and the KL divergence; taking energy and energy variation of the time sequence spectrum in a concerned frequency band as an energy cumulative evolution index; based on the composite spectrum difference index and the energy cumulative evolution index, early warning or warning is carried out after broadband oscillation is identified, and the monitoring sensitivity, the early warning accuracy, the early symptom capturing capability and the adaptability to the dynamic characteristics of the spectrum are improved.

The invention discloses a spectrum mask-based online signature generation method, which is characterized by comprising the following steps of: calculating a spectrum mask between handwritings according to a spectrum difference between an online signature and a sample signature, and constructing a training data set containing the spectrum mask and a handwriting spectrum of the sample signature; training the deep learning model, and predicting to obtain a handwriting mask of the online signature; and generating an online signature frequency spectrum according to the sample handwriting frequency spectrum and the handwriting mask, and obtaining an online signature handwriting sequence through inverse transformation. The feature information of the original signature can be better reserved by adopting a frequency domain analysis method, so that a more vivid scribble signature is generated by restoring echoing. And the privacy and rights and interests of the signer are better protected. And better reversibility of the signature spectrum is realized.

The invention belongs to the technical field of gas pipeline unmanned aerial vehicle leak detection, and discloses an unmanned aerial vehicle inspection control system and method based on acoustic imaging gas pipeline leak detection, an optical imaging auxiliary unit collects environment images in real time, and a scene adaptive switching unit identifies scene types through image features and loads corresponding interference feature templates; the interference characteristic acquisition subunit synchronously acquires three-dimensional data of pipeline vibration, machine body vibration and environmental noise, modeling is performed through the CNN-LSTM interference characteristic learning unit to generate an interference type, an intensity grade and a frequency spectrum mask, the interference type, the intensity grade and the frequency spectrum mask are transmitted to the composite noise reduction preprocessing unit to support a three-stage noise reduction process, a pure leakage point signal is finally generated, residual interference is effectively eliminated, and the noise reduction efficiency is improved. The omission ratio is reduced; the collaboration module establishes a control instruction channel, a detection data channel and an optimization parameter channel, a collaboration instruction priority channel unit ensures that a control instruction is transmitted preferentially, and a block chain data evidence storage unit generates hash values for all transmission data to ensure that full-link data is credible.

Methods and systems are disclosed for customizing the transmit power of a wireless device to parameters of the device, such as process split, voltage, and temperature. The device can identify one or more parameters of the device, such as identifying a process split, or sensing an operating voltage or temperature. The device can determine an updated transmit power based on the identified known parameter values and any unknown parameter values. When the device transmits based on the known parameter values and across a range of variations of the unknown parameter values, the updated transmit power is determined as the maximum transmit power that satisfies one or more target transmit metrics, such as EVM and spectral mask. The device can periodically adjust its transmit power based on the updated transmit power.

The present application relates to the field of vehicle-mounted communication technology, and more particularly to a real-time interference avoidance method for a vehicle-mounted wireless communication module based on edge computing, which captures burst pulse interference in the vehicle networking and cellular frequency bands through edge spectrum sensing, extracts time domain duty cycle and spectrum mask features to form a dynamic interference feature set, classifies interference sources at multiple levels using a lightweight convolutional neural network, calculates confidence levels in combination with signal entropy values to generate interference situation data, constructs a risk heat map by fusing vehicle position, road topology and surrounding node state, drives the edge engine to dynamically redistribute spectrum resources, and outputs channel switching instructions; the instructions control the power and modulation coding scheme of the radio frequency front end, and synchronously collect link quality feedback; by comparing the feedback value with the safety threshold in real time, the standby link is seamlessly switched through the trigger of the redundancy mechanism, and logs are uploaded after encryption, ensuring high reliability and security of communication.