278 results about "Spatial encoding" patented technology

The invention relates to the technical field of people flow prediction, and discloses an urban people flow prediction method based on an urban point-of-interest spatio-temporal data set, which comprises the following steps: acquiring the urban point-of-interest spatio-temporal data set; performing multi-modal feature dynamic fusion by adopting a hierarchical perception attention fusion mechanism to obtain a point-of-interest feature vector; performing spherical spatial dependency modeling by adopting a regional adaptive spherical convolutional coding method to obtain a spatial coding vector; using a periodic mask generator to carry out business time constraint coding to obtain a time feature vector; and according to the interest point feature vector, the space coding vector and the time feature vector, space-time coupling features are extracted, regional perception time sequence modeling is performed on the space-time coupling features, and a people flow prediction value and crowding degree grade probability distribution are generated through multi-task sharing prediction. According to the urban people flow prediction method based on the urban interest point spatio-temporal data set, the urban people flow prediction performance is improved.

The invention belongs to the technical field of power system load prediction, and relates to a long-range multivariable load prediction method and system based on time-frequency domain collaboration, and the system carries out the normalization and stabilization of a multivariate load time sequence through a data preprocessing module; the feature embedding module performs linear embedding on the block sequence to construct high-dimensional feature representation; the state space coding module extracts long-range dependency features and generates depth time sequence representation; the decoding prediction module maps the coding features into a preliminary prediction sequence; the time sequence alignment module identifies a leading-lagging relation among multiple variables and aligns a time sequence; the frequency domain optimization module realizes frequency domain component fusion based on adaptive filtering; and the model training optimization module is used for performing training and optimization through a signal attenuation loss function. The method can effectively improve the precision and robustness of long-range multivariable load prediction, and especially has obvious advantages in the aspects of processing complex dependency relationships and dynamic time delay.

The invention relates to the field of ocean data buoy data quality control, and discloses a buoy meteorological data restoration method based on a space-time double-attention neural network, and the method comprises the following steps: data collection and preprocessing; establishing a space-time double-attention neural network model, wherein the model comprises a time coding link, a space coding link and a feature fusion layer; model training: inputting the time features containing the target elements to be predicted into a time coding link, inputting the meteorological elements into a space coding link, and outputting the target elements at the moment to be predicted by the model; after model training, evaluating the model by using the test set; and predicting a vacancy value or an abnormal value in the multi-dimensional meteorological element data acquired in real time by using the model which is qualified in evaluation, so as to realize data restoration. According to the method disclosed by the invention, time and space constraints are effectively combined, and abnormal values and vacant values in the meteorological data are predicted and replaced, so that the meteorological data are repaired.

A pharmaceutical hyperspectral reconstruction method based on a coded aperture snapshot spectral imaging (CASSI) system includes: collecting and processing original pharmaceutical hyperspectral images to obtain augmented pharmaceutical hyperspectral images; performing simulated spatial encoding on the augmented pharmaceutical hyperspectral images to obtain encoded measurement images; performing spectral inverse shift on the encoded measurement images, then performing inverse encoding to obtain inversely encoded three-dimensional hyperspectral images, using the augmented pharmaceutical hyperspectral images as target images, and constructing a training set and a testing set according to the inversely encoded three-dimensional hyperspectral images and the target images; constructing a deep symmetric neural reconstruction network, and training and testing the deep symmetric neural reconstruction network; and deploying a tested deep symmetric neural reconstruction network onto the CASSI system, real-time collecting pharmaceutical measurement images using the snapshot coded imaging system, and performing computational reconstruction on the pharmaceutical measurement images to obtain reconstructed three-dimensional hyperspectral images.

The invention discloses a spatio-temporal data compression method and system based on lightweight processing, and relates to the technical field of image data processing, and the method comprises the steps: based on spatio-temporal data to be compressed, establishing standardized data entries, forming a window set through time axis segmentation, carrying out the periodic discrimination of windows according to the frequency domain energy distribution, and constructing spatio-temporal blocks, establishing an error control parameter table based on the global error budget; generating a time coding stream, performing integerization on the three-dimensional coordinates and the attribute values, generating a space coding stream through double difference and run length coding, performing edge folding lightweight and texture compression on the grid data, and generating a joint coding result; and based on a joint coding result, dividing the compressed data into a base layer and a plurality of enhancement layers, and establishing a relevance hierarchical storage structure and a block-level index table to generate the compressed data capable of being transmitted in a streaming manner. According to the method, collaborative compression can be carried out by utilizing spatial-temporal data internal relevance.

The invention discloses an underwater image enhancement method based on dual-path feature decoupling and gating fusion. The method comprises the following steps: firstly, constructing a training data set; then, an encoder-bottleneck layer-decoder is used as a trunk, and a dual-path encoding and decoding mechanism is adopted to construct an underwater image enhancement model; the encoder is used for extracting direction encoding features and space encoding features; the bottleneck layer is used for extracting global and local features and fusing and outputting bottleneck features; the decoder is used for decoding and multi-scale refinement so as to reconstruct and obtain an underwater enhanced image; then training is carried out to obtain a trained underwater image enhancement model; and finally, the device is deployed to acquire the underwater image in real time for underwater image enhancement. According to the method, independent modeling is carried out for anisotropic scattering / edge attenuation and spatial non-uniform atomization / local brightness imbalance, and mutual interference of different degradation causes in the same feature space can be reduced.

The invention discloses a tooth CBCT image super-resolution reconstruction method based on a potential diffusion model, and relates to an image processing and generating technology in the field of computer vision. The in-vivo medical image is trained based on hidden space coding and probability diffusion, so that the network has a better super-resolution effect on the real in-vivo tooth CBCT image.

The invention discloses an end-to-end polarization hyperspectral image classification method and system, belongs to the technical field of deep learning and optical imaging, and solves the technical problems of low reconstruction process speed, limited precision, low information utilization rate in a classification process and weak feature extraction capability in the prior art. The method comprises the following steps: carrying out target shooting based on a snapshot type space coding hyperspectral polarization imaging system, and carrying out system coding on a shot image to obtain two-dimensional aliasing data; reconstructing the two-dimensional aliasing data into a polarization hyperspectral data cube by using the trained reconstruction network; training the classification network based on the polarization hyperspectral data cube to obtain a trained classification network; and performing joint fine tuning on the trained reconstruction network and the trained classification network to obtain a polarization hyperspectral image classification model, and performing polarization hyperspectral image classification by using the polarization hyperspectral image classification model. The method is used for realizing high-quality reconstruction and accurate classification of the polarization hyperspectral image.

The invention discloses a multi-frame reconstruction multispectral imaging system based on dynamic space-time joint coding. The system comprises a filtering window module used for limiting the spectral range of imaging; the spatial code modulation module is used for providing spectral codes in a spatial dimension; the time coding modulation module is used for carrying out inter-frame modulation on the spectrum codes provided by the space coding modulation module; the optical imaging module is used for imaging the light field modulated by the time coding modulation module on the color acquisition module; the color acquisition module is used for carrying out further spectral coding on the light field to obtain a measurement image; a calculation reconstruction module; the method is used for recovering a high-dimensional multispectral image data cube by using a measurement image. The method has the advantages of being light in weight, low in cost, high in underqualitative relieving capacity, efficient and stable in reconstruction method, high in adaptability and practicability and the like.

The invention provides a medical magnetic resonance image reconstruction method and system, and belongs to the technical field of image reconstruction, and the method comprises the steps: obtaining sampling coding and under-sampling k space data; processing the under-sampled k space data through inverse Fourier transform, and converting the k space data into an image domain to obtain under-sampled image data with blurring or artifacts; carrying out preliminary restoration on the undersampled image data based on sampling coding, further applying data consistency operation on the image to obtain a rough image, and taking the rough image as an image condition vector after underspace coding; acquiring a text serving as a cue word, inputting the text into a text encoder, and encoding the text into a high-dimensional semantic embedding vector by the text encoder; and inputting the high-dimensional semantic embedding vector and the image condition vector into a reconstruction model based on the correction flow to predict a full-sampling MRI image, and generating a final reconstructed MRI image.

The invention discloses a three-dimensional radar echo reflectivity variational auto-encoder pre-training method, which belongs to the technical field of meteorological radar data analysis, and comprises the following steps: extracting advanced features from three-dimensional high-resolution radar data through a space encoder based on an attention mechanism; mapping the advanced features into determined probability distribution parameters using a probabilistic encoder; sampling according to the distribution parameters by using a re-parameterization technique to obtain continuous potential variables; mapping the potential variables back to a pixel space by using a probability decoder based on an attention mechanism to complete data reconstruction; and finally, constructing a mixed loss function consisting of a mean square error and KL divergence by utilizing a reconstruction result and a distribution parameter to train the model. According to the invention, by optimizing the network architecture and introducing the attention mechanism, high-quality reconstruction of high-resolution three-dimensional radar data is realized while extremely low video memory requirements and low parameter quantity are ensured, and the practical value of data synthesis and enhancement is remarkably improved.

The invention discloses an overhead power line icing thickness detection method and system based on a structure perception framework, and the method comprises the steps: firstly obtaining point cloud data of a power transmission line, preprocessing the point cloud data, and inputting the preprocessed point cloud data into a structure perception semantic segmentation network; the network adopts a four-level encoder-decoder architecture, an encoder extracts multi-scale features through local space encoding, cross-scale space attention fusion and local aggregation operation, and a decoder realizes feature fusion in combination with a jumper connection mechanism and outputs a lead semantic segmentation result; extracting a conductor point set based on a segmentation result and carrying out Euclidean clustering strand splitting; performing biplane projection and polynomial fitting on each strand of wire to construct a three-dimensional center line; and finally calculating the distance from the point to the center line, selecting a peripheral point set to calculate the wire envelope radius, and combining the radius of the bare wire to obtain the icing thickness. Measurement deviation caused by point cloud shielding, wire bending and asymmetric icing is effectively solved, millimeter-level precision non-contact ice thickness detection in a complex environment is achieved, and reliable technical support is provided for power grid disaster prevention.

The invention discloses a lightweight SDN (Software Defined Network) attack detection method based on multi-scale iterative attention, relates to the technical field of network security, and solves the problem that an SDN attack detection method based on deep learning in the prior art is insufficient in feature selection static state and spatial modeling and gives consideration to both lightweight and high precision. The method is based on a feature contribution degree evaluation mechanism, the most critical features for attack discrimination are screened out in real time, redundant information is eliminated, and the calculation burden is reduced. Moreover, the attack feature map is generated through normalization, time window overlapping slicing and multi-channel space coding, so that the perception capability of a complex attack mode is improved. Besides, a multi-scale iteration attention mechanism is embedded in a lightweight network architecture, key features are highlighted and redundant information is suppressed through multi-granularity convolution extraction and iteration weight fusion, and both lightweight and high-precision detection are realized, so that the method is suitable for real-time network environment and edge device deployment.

The invention discloses an accelerator architecture design method and electronic equipment, relates to the technical field of computers, and performs dynamic fractal space coding on a design parameter matrix of an accelerator to obtain an analog coding vector. And performing meta-learning dynamic weight entropy search on the analog coding vector by using a historical task data set to determine a new design scheme. And when the difference between the sensitivity parameters in the new design scheme and the sensitivity parameters in the neighbor set meets a difference condition, performing multi-objective optimization simulation on the new design scheme. And when the difference does not meet the difference condition, multiplexing the simulation result of the neighbor set. And performing iterative search according to Bayesian optimization, and determining a final accelerator architecture design scheme in the selected design scheme and the simulation result thereof. Dimension reduction is performed on a high-dimensional discrete space, and a search direction is guided by means of an efficient meta-learning dynamic weight entropy acquisition function. And rapid exploration of an accelerator architecture design space under multi-objective optimization is realized.

The invention provides a single-pixel calculation hyperspectral imaging system and method, and the technical scheme of the invention comprises a spatial light modulator which is used for carrying out the spatial coding of a light beam from an imaging scene; and the reconfigurable spectrum detector is used for detecting the light beams after space coding. The reconfigurable spectrum detector is provided with a group of spectral response functions which are tunable along with external bias voltage, broadband and correlative, and the reconfigurable spectrum detector forms a core sensing unit of the micro-computing spectrometer. A barrel signal sequence is obtained by synchronously collecting light intensity integral values under different space coding patterns and different bias voltages. And finally, in a data processing module, a spectral data cube of the target scene is jointly solved from the bucket signal sequence according to the spatial coding matrix and the spectral response function set by utilizing a calculation reconstruction network. According to the invention, two computational imaging technologies are creatively fused, the structure is very compact, and real-time and wide-spectrum spectral imaging can be realized at a low sampling rate.

The invention discloses a direction of arrival estimation method based on a residual network. A space encoder and an improved residual network cascade module are included. According to the system, complex signals received by an antenna array element are used as input, a covariance matrix is extracted, and a multi-channel characteristic pattern is constructed. Direction-of-arrival coarse estimation is carried out through space encoder partition, and further direction-of-arrival fine estimation is carried out through an improved residual network. Through reasonable combination of the deep neural network and the residual network, high estimation precision and operation speed can still be maintained in complex scenes of low signal-to-noise ratio, small signal source angle interval, multipath interference and the like. Compared with the traditional direction of arrival estimation method, the method provided by the invention has a remarkable advantage in the direction of arrival estimation precision under the complex test conditions of low signal-to-noise ratio and the like. Meanwhile, on the premise that high precision is kept, the method has the higher operation speed, and good real-time performance and engineering application prospects are embodied.

The invention provides a space-time fusion coding method and a data relation generation method and system, and is applied to the technical field of physical world-oriented multi-source heterogeneous data organization management. Space information of a target data object is mapped to a geographic space subdivision grid coding system to generate a space code; the method comprises the following steps: standardizing time information according to preset time granularity (such as year, month, day and hour), mapping the time information to a layered time coding system to generate a time code, and fusing a space code, the time code and object identification information according to a preset rule to generate a unique space-time fusion code; and finally, the space-time fusion codes and attribute information are bound to form standardized data records, data objects of different sources and different formats are mapped to a unified space-time coding system, data islands are broken, and a data foundation is laid for constructing a unified space-time relation network.

The invention relates to the technical field of visible light communication, and discloses an intelligent lamp data transmission system based on visible light communication. The system comprises an optical signal processing module, a transmission management module and a terminal control module. The optical signal processing module collects and processes the visible light signal and outputs a light intensity space coding signal; after the transmission management module receives the signal, the signal is decoded by the signal analysis unit to obtain an original data stream signal and a channel interference characteristic signal, the channel optimization unit processes the original data stream signal and the channel interference characteristic signal to obtain an optimal modulation parameter signal, and the transmission strategy unit outputs a dynamic bandwidth allocation signal; in the terminal control module, an adaptive modulation execution unit adjusts the driving parameters of the light emitter according to the optimal modulation parameter signal, a lamp array control unit adjusts the working state of a lamp light-emitting unit and collects a real-time link state signal, and a link monitoring module outputs a communication quality index. The system is suitable for an intelligent illumination and data transmission fusion scene.

The invention relates to the technical field of deep learning, and discloses a deep learning prediction method and system for multi-source space-time lattice point data, and the method comprises the steps: obtaining live lattice point data, forecast lattice point data and static lattice point data, and carrying out the preprocessing, thereby obtaining uniform lattice point feature data; and generating missing mask lattice point data, generating confidence coefficient lattice point data according to observation coverage information, interpolation distance information, a time-space consistency test result and a forecast aging attenuation rule, and forming model input lattice point data. And constructing a training sample and generating forecast aging coding data. And constructing and training a deep learning prediction model, wherein the model comprises a space coding network, a time coding network and a forecast aging segmentation prediction network. In the training stage, confidence coefficient is used for weighting regression loss. In the reasoning stage, a release prediction result is obtained. According to the method, the learning stability of the multi-source grid point data under the missing measurement and filling conditions is improved, and the reliability of continuous value prediction and grading alarm probability under different prediction time periods is improved.

The invention provides a text guidance-based generative reverse face recognition method. The method comprises the following steps of: obtaining an initial latent vector and a fine tuning generator by using a pre-trained editing encoder and an original face image; taking the initial latent vector as an initial vector during gradient updating, and starting to circularly update until a final latent space code is obtained; and inputting the subsurface space code into the fine-tuned generator, and taking the generated image as a protected image corresponding to the original face image. The total loss function comprises an adversarial loss for explicitly promoting diversity and a loss function of a visual effect; the fine-tuned image generated by the generator is closer to an auxiliary image randomly selected from the auxiliary data set so as to realize protection of dynamic tracking of face recognition; meanwhile, it is ensured that the image generated by the fine-adjusted generator follows the specification of target text prompt and keeps the visual similarity with the original face image. The method has excellent performance in the aspect of preventing the face image of the user from being identified by the dynamic FR strategy.

The invention relates to a remote sensing interpretation method and system integrating multi-source spatio-temporal spectrum characteristics and a visual model. The method comprises the following steps: firstly, acquiring and preprocessing an optical image, harmonic and synthetic aperture radar data of a target area; time spectrum features of the optical image and the harmonic data are extracted respectively, and fusion features are generated through cross attention mechanism fusion; carrying out image serialization, time coding and space coding processing on the fused features and synthetic aperture radar data, and extracting spatio-temporal features by using a self-attention mechanism; and finally, decoding the spatio-temporal features and fusing a multi-source feature convolution result to generate a pixel-level land coverage classification result. According to the method, harmonic data are introduced to capture the time change trend of the ground features, a visual Transform self-attention mechanism is utilized to capture the space-time interdependence relationship, multi-source data are combined to capture the multi-dimensional features of the ground features, the limitation of a single data source is avoided, the multi-source time sequence features are fused to enable the model to adapt to different geographical environments, and the generalization performance is improved.

The invention relates to the field of intelligent scheduling allocation, in particular to an edge cloud computing resource allocation optimization method based on deep learning, which adopts a space-time prediction algorithm based on multi-head attention and gating mechanism optimization to design time coding and space coding. The spatial relationship and interaction between time sequence characteristics of the computing power load and edge server nodes are captured, and meanwhile, a multi-head attention mechanism and expansion causal convolution are combined, so that instantaneous computing power load fluctuation can be captured, and the long-term trend of the computing power load can be mined; therefore, a reliable basis is provided for subsequent computing power scheduling by predicting an accurate computing power load. The invention designs an alternating direction multiplier method based on genetic algorithm optimization, which is not only suitable for a nonlinear and multi-constraint optimization problem, but also can be expanded to a larger-scale distributed edge node cloud computing system, and meanwhile, a global optimal solution is quickly approached through the genetic algorithm, so that the quality of an initial solution is improved, and model convergence is accelerated; and the distributed collaborative allocation scheduling efficiency is improved.

The invention discloses an ore analysis data returning method. The method comprises the following steps: sending ore scene image data acquired by each camera to an encoder to form a video stream, and dynamically allocating a cache space; the encoder sends the video stream to the multicast group address, and the back-end analysis equipment joins in the multicast group address; the multicast router receives a video stream to form (S, G) table entries; the encoder sends multicast data to back-end analysis equipment; the multicast router analyzes the IGMP (S, G) member report message sent by the back-end analysis equipment to update the (S, G) table item, and sends a PIM (S, G) update message to be transmitted along the multicast tree; and respectively comparing the priorities of the corresponding back-end analysis devices by using the multicast routers, recording the information of the back-end analysis device with the highest priority and forwarding the information to the encoder, and emptying the backup video stream and updating the metadata by the encoder according to a received sequence number confirmation message. The method is high in storage utilization rate, and feedback flow can be reduced while data integrity is ensured.

The invention discloses a multi-temporal remote sensing crop extraction method in combination with MoCo self-supervised learning. Relates to the technical field of agricultural resource monitoring, in particular to a multi-temporal remote sensing crop extraction method combined with MoCo self-supervised learning. According to the method, label-free satellite images are utilized, MoCo self-supervised learning is adopted to carry out label-free pre-training on a space encoder, so that a crop classification model can still learn spatio-temporal characteristic representation with discriminative power under limited label data, and the classification precision problem caused by insufficient labeled samples is effectively relieved. The method comprises the following steps: acquiring a multi-temporal crop label-free remote sensing satellite image data set and a label data set; constructing a time-phase crop classification model: pre-training a spatial feature encoder by MoCo self-supervised learning; performing supervised learning on the crop classification model by adopting the labeled data set to obtain a final time phase crop classification model; and classifying the multi-temporal crops through the final temporal crop classification model.

Various embodiments of the present disclosure provide for a method and apparatuses that perform spatial encoding in a multipath environment such that transmissions on different beams are separately encrypted with complex codes such that when the transmissions on the different beams are received at the receiver, the separate encryptions are cancelled out. The transmissions can also have time delay, gain, and phase modifications made to the transmissions such that the automatic self-decryption is performed within a predefined distance of where the receiver is determined to be. In this way, encryption / decryption keys do not have to be sent to the receiver, and unauthorized devices that intercept the beams at a location other than the receiver location will not be able to decrypt the communication.

A binocular visual field (VF) testing system and a method employing spherical (non-Euclidean) geometric modeling and eye-tracking technology to generate cyclopean visual field maps are disclosed. The system projects dichoptic stimuli to each eye independently at controlled fixation distances and dynamically records vergence responses using eye tracking. A non-Euclidean spatial encoding model based on logarithmic spirals and Jacobian analysis is applied to the resulting binocular data, enabling three-dimensional topographic field mapping. This approach extends traditional monocular field testing by accounting for retinal curvature, binocular integration, and fixation depth effects, resulting in volumetric visual field models more representative of real-world spatial perception. The system includes hardware (BINOSCOPE) and software modules that implement real-time depth encoding, topological mapping, and boundary detection of peripheral visual fields.

The application discloses a semi-implicit neural map construction method based on grid-like cell space coding. The method uses grid-like cell space coding to abstractly encode a three-dimensional space, inputs the coding result into a neural network for decoding, and generates a new visual view through rendering. The grid-like cell space coding on the three-dimensional space can improve the correlation between data, eliminate redundant information in the neural map, realize compression sensing of the environment, maximize the use of information, and thus improve the map reconstruction quality. The semi-implicit neural map construction method can be applied to technical fields such as medical imaging, automatic driving, game development or indoor design, and can automatically generate a three-dimensional scene model according to an input two-dimensional image.

The application discloses a real scene video deblurring system and method based on a single-step video diffusion model, comprising an encoding module, a denoising module and a decoding module, wherein the encoding module is used for respectively performing latent space encoding on each frame in a blurred video sequence to be recovered, generating a frame-by-frame latent space representation corresponding to the input video frame by frame; the denoising module is used for performing single-step denoising on the frame-by-frame latent space representation to obtain a latent space representation corresponding to a clear video; the decoding module is used for decoding the latent space representation corresponding to the clear video into image frames frame by frame and outputting according to the original time sequence of the input video to obtain a deblurred video result. Through frame-by-frame latent space encoding, frame-by-frame blur differences can be preserved, and through single-step diffusion distillation, reasoning delay can be reduced, and deblurring quality and reasoning efficiency are considered.

An end-to-end polarization hyperspectral image classification method and system, belonging to the field of deep learning and optical imaging technology, solves the technical problems of slow reconstruction speed, limited accuracy, low information utilization, and weak feature extraction capabilities in existing technologies. The method involves capturing images of a target using a snapshot-style spatially coded hyperspectral polarization imaging system. The captured images are then encoded by the system to obtain two-dimensional aliased data. A trained reconstruction network is used to reconstruct the two-dimensional aliased data into a polarization hyperspectral data cube. The classification network is then trained based on the polarization hyperspectral data cube to obtain a trained classification network. The trained reconstruction network and the trained classification network are jointly fine-tuned to obtain a polarization hyperspectral image classification model, which is then used to classify polarization hyperspectral images. This invention achieves high-quality reconstruction and accurate classification of polarization hyperspectral images.

The application provides an X-ray communication device and method based on a laser-driven matrix photocathode. The device is applied to the technical field of X-ray tubes and comprises a laser control circuit, a matrix transmission photocathode array, a collimating microchannel plate, a matrix transmission anode target array, a vacuum tube shell and a beryllium window. The method comprises: generating a dynamic pattern, loading data information to be input into an optical signal; and exciting photoelectron emission with spatial resolution characteristics at a corresponding photocathode pixel area; each channel cluster is aligned with a single photocathode pixel at the front end; after the electrons enter the microchannel, continuous secondary electron emission occurs under the action of the electric field on the inner wall of the channel, and an avalanche effect is generated; and through mechanisms such as bremsstrahlung radiation, a bundle of microfocus X-rays is generated at each target point. In this way, the technical problems of the X-ray source in the prior art, such as the limitation in spatial coding capability, response speed, integration degree and communication dimension, can be solved.