144 results about "Spatial prediction" patented technology

Procedures, methods, architectures, apparatuses, systems, devices, and computer program products directed to reduction of signaling overhead in connection with processing of reference signals. In an embodiment, an apparatus may be configured to receive, from a network node, configuration information comprising channel state information (CSI) spatial prediction parameters; receive a first plurality of reference signals transmitted from a plurality of antenna ports of the network node; estimate, based on the plurality of the received reference signals, first CSI measurements; and determine, based on the first CSI measurements and the CSI spatial prediction parameters, a first subset of antenna ports among the plurality of antenna ports of the network node and one or more parameters associated with the first subset of antenna ports, wherein the first subset is less than the plurality of antenna ports.

The invention relates to the technical field of mineral resource estimation, and discloses a mineral reserve estimation method based on artificial intelligence, comprising the following steps: step 1, collecting and preprocessing data, acquiring multi-source data, and performing data standardization and feature extraction; 2, carrying out ore body modeling through a 3D convolutional neural network, constructing the 3D convolutional neural network, and carrying out ore body prediction through three-dimensional convolution calculation; step 3, optimizing ore body space prediction in combination with Kriging interpolation, preliminarily predicting ore body space distribution through Kriging interpolation, and further optimizing by using a deep learning model; and step 4, estimating reserves based on Bayesian optimization. The technical scheme of combining multi-source data fusion, a 3D convolutional neural network and Kriging interpolation is adopted, the effect of accurately capturing ore body space distribution under the complex geological condition is achieved, and compared with an existing scheme depending on a traditional geological statistical method, the problem that the fault zone and heterogeneous region modeling capacity is insufficient is solved.

The invention belongs to the technical field of data processing, and particularly relates to a hidden ore body evaluation and positioning method based on multi-source data processing. The method mainly aims at the problems of incompleteness and isomerism of multi-source geological data in acquisition, fusion and modeling. Comprising the following steps: acquiring hyperspectral, geochemical and magnetic anomaly multi-source data of an evaluation area; intelligently complementing missing modal data by using a generative adversarial network based on geological constraints and modal outburst to form a complete multi-source data set; an unsupervised clustering algorithm combining geological correlation and entropy weight analysis is adopted to construct high-confidence-coefficient pseudo-label data, and knowledge mining of unlabeled samples is achieved; feature purification and dimension reduction are carried out through multi-modal feature fusion and hierarchical principal component analysis, and key feature vectors representing the existence of the ore body are extracted; and finally realizing space prediction of the concealed ore body by utilizing the classification model. According to the method, a high-quality data basis and a unified processing framework are provided for intelligent recognition of the hidden ore body, and efficient and accurate positioning of the hidden ore body is achieved.

Example systems and techniques are described for controlling operation of a vehicle. An example system includes one or more memories configured to store a machine learning model and one or more processors. The one or more processors are configured to obtain two-dimensional (2D) image data and three-dimensional (3D) point cloud data. The one or more processors are configured to generate one or more multimodal fused 3D stixels based on the 2D image data and the 3D point cloud data. As part of generating the one or more multimodal fused 3D stixels, the one or more processors are configured to execute a machine learning model, the machine learning model having been trained with a 3D stixel correction. The one or more processors are configured to control operation of a vehicle based on the one or more multimodal fused 3D stixels.

The invention relates to the technical field of civil engineering, and discloses a road subgrade settlement prediction method and system based on multi-source data. The method comprises the following steps: acquiring and correcting a precipitation sequence and roadbed soil moisture distribution data; analyzing rainfall accumulation characteristics and carrying out penetration risk classification, thereby extracting a penetration depth estimation value, and utilizing finite element analysis to simulate a soil body saturation state change trend; quantifying a soil body supporting capacity reduction range, constructing and calibrating a settlement initiation probability calculation model, and obtaining settlement probability distribution; identifying a high-risk evolution area, integrating path weights and accumulated influence factors, and generating a settlement prediction interval subjected to multi-dimensional verification; and fusing the prediction interval and actual roadbed structure data through a geographic information system to generate a comprehensive evaluation report. According to the method, the whole-process accurate evaluation of the roadbed settlement risk from multi-source perception, mechanism simulation to space prediction is realized, and the accuracy and timeliness of the roadbed settlement risk prediction and the pertinence of engineering maintenance are improved.

The invention discloses a soil organic matter digital mapping method and device based on crop rotation historical change, and the method comprises the steps: bringing crop rotation interannual change and crop rotation change frequency into an environment variable set, and carrying out the screening and combination with other environment covariables; the historical crop rotation change and the long-term frequency thereof are brought into the environment variable set, and the environment variable set is enriched, so that the influence of human activities on soil spatial-temporal heterogeneity is considered in soil organic matter prediction, and the precision of model prediction is improved. By utilizing the method, the influence of historical agricultural practice can be reflected, and the spatial heterogeneity of the farmland soil organic matters can be better described, so that the performance of a soil organic matter spatial prediction model is improved.

The invention relates to the technical field of risk monitoring, in particular to a brucellosis risk monitoring and early warning method based on remote sensing and GIS (Geographic Information System), which comprises the following steps of: acquiring multi-stage remote sensing images to construct an ecological grid set, hanging cases, positioning and generating a disease source boundary map layer, identifying a high vegetation and temperature intersection area to form a risk structure, and carrying out early warning on the risk structure. And extracting the display area and superposing the GIS map to output early warning information. According to the method, the multi-stage remote sensing image covering the key epidemic area is obtained, region segmentation and geocoding registration are executed, by means of coupling detection of the normalized vegetation index and the land surface temperature, the spatial intersection region with the pathogen propagation suitable ecological condition can be identified, the risk index is calculated according to the graph block structure distribution, and the risk level label is given; according to the method, accurate positioning and hierarchical evaluation of a high-risk ecological environment and historical case space overlapping region are realized, the space prediction precision and the intuition of risk prompt are improved, and the capability of responding to the brucellosis potential outbreak trend in advance is effectively enhanced.

The invention discloses a shield tunneling machine attitude prediction method and system, and relates to the field of shield tunneling machine construction.The shield tunneling machine attitude prediction method comprises the steps that firstly, shield tunneling machine construction parameters, geological environment parameters and tunnel planning parameters are obtained and preprocessed, and related characteristics are extracted; secondly, mapping the extracted features into nodes by using a heterogeneous graph model, and updating node features in real time through weighted processing of a graph attention mechanism; then, the node features and the connection relation are input into the trained hybrid drive prediction model, and time sequence, physical and space prediction values of the attitude angle of the shield tunneling machine are obtained; and finally, constructing a Bayesian causal graph to quantify the influence of each factor on the attitude angle, carrying out dynamic weighted fusion based on a quantization result, and outputting a final predicted value and a confidence interval. The problem that the prediction effect is poor under the complex geological condition due to the fact that prediction only depends on single shield tunneling machine parameters in the prior art is solved, and accurate prediction of the attitude angle of the shield tunneling machine can be achieved.

The invention provides a cultivated land non-cultivation data processing and analysis system and method. The system comprises a non-cultivation rate influence factor analysis module and a non-cultivation rate prediction module. The non-farming rate influence factor analysis module integrates a geographic detector model, a panel model, a space model, a space-time geographic weighted regression model and other modules to obtain a significant influence factor result of non-farming in a target area; and the non-farming rate prediction module comprises a panel model prediction module and a time t prediction module to predict the non-farming rate corresponding to each administrative region in the next year. Based on the technical scheme of the invention, multi-source heterogeneous data such as land utilization, climate environment and the like are integrated; index classification is more detailed and comprehensive, so that an analysis result is more effective; time and space factors are fused, and variables of different regions are analyzed more accurately; the factor identification accuracy is remarkably improved, the space prediction precision is remarkably improved, and the time sequence prediction error is remarkably reduced.

The invention relates to the technical field of information retrieval, in particular to an academic supply and demand dynamic sensing method and system based on space-time big data, and the method comprises the following steps: obtaining the path length of a source and a target college, screening migration direction stable labels, recognizing a trend consistent region, evaluating the local transfer capacity, and fusing a high-frequency path region. And extracting behavior track data, performing sequence comparison on the associated nodes, and generating a prediction chain space offset mark set. According to the method, space label units with stable migration characteristics are effectively distinguished by processing enrollment information and population migration trends and constructing a path length filtering and direction section repeating mechanism, and supply and demand migration origin areas are accurately locked in combination with change trends reflected by continuous direction consistency; spatial aggregation judgment is enhanced through a statistical means of a neighborhood capacity proportion and path intersection frequency, the sensitivity and dynamic response capability of a spatial prediction structure are improved, and active sensing and intervention prompting of a supply and demand change trend under multiple space-time dimensions are realized.

The invention relates to the technical field of data processing, in particular to a space intelligent reasoning method and system, and the method comprises the steps: generating a plurality of initial space reasoning tasks corresponding to a to-be-executed task instruction of a robot according to a pre-constructed space intelligent machine, and determining a first type of reasoning tasks and a second type of reasoning tasks from the initial space reasoning tasks, obtaining target space environment information corresponding to the first type of reasoning tasks and the second type of reasoning tasks, and obtaining structured data corresponding to the first type of reasoning tasks and the second type of reasoning tasks through a deep learning model, performing calculation according to the structured data corresponding to each first type of reasoning task by using a target calculation program corresponding to each first type of reasoning task in the sandbox simulator to obtain an accurate space prediction result; through the combination of the deep learning model and the sandbox simulator, the calculation accuracy can be improved, and the accuracy and reliability of overall space intelligent reasoning are enhanced.

The invention discloses a soil erosion resistance prediction method and system based on earth surface parameters, and relates to the technical field of soil erosion and water and soil conservation monitoring and prediction, and the method comprises the steps of earth surface parameter collection, parameter preprocessing, erosion response modeling, erosion resistance threshold tensor inversion and erosion resistance prediction. Collecting multi-source earth surface parameter data; secondly, constructing a unified earth surface parameter feature set; obtaining erosion response simulation data by adopting an erosion response modeling method combining physical process simulation and lightweight machine learning residual correction; an explicit Bayesian neural network and a variational reasoning framework are constructed, observation data are combined, and a four-dimensional anti-corrosion threshold tensor is obtained through inversion; introducing a double-branch space-time multi-task fusion prediction model of an anti-erosion threshold tensor, and outputting a prediction value and a prediction grade of the soil anti-erosion capability; according to the scheme, dynamic and spatial prediction of the erosion resistance of the soil can be realized, and a scientific basis is provided for water and soil conservation and ecological environment management.

The invention relates to the field of computer storage, and provides a cache space capacity adjusting method and computer device.The method comprises the steps that cache access information of a processor executing cache access operation is obtained; performing feature extraction on the cache access information to obtain data feature information corresponding to the cache access information; predicting the space size of a data cache space required by a processor according to the data feature information to obtain the predicted capacity of the cache space; determining an adjustment strategy for adjusting the space size of the data cache space based on the cache space prediction capacity; and adjusting the space size of the data cache space according to the adjustment strategy. Due to the fact that the size of the cache space needed by the equipment is predicted, and the space size of the cache space is adjusted according to the prediction result, the size of the cache space can adapt to the requirements of the equipment, the flexibility of the size of the cache space is improved, and the utilization rate of hardware is improved.

A water-rich tunnel permeability dynamic partitioning method based on multi-source data comprises the following steps: acquiring a permeability coefficient, rock compressive strength, an integrity coefficient, RQD and longitudinal wave velocity data of a tunneling area, and constructing a multi-source feature matrix through space-time alignment and standardization processing; dynamically calculating the weight of each index based on an entropy weight method, and adaptively adjusting the contribution degree of the parameter in combination with the geological type; a standard cloud parameter is generated by using a cloud model, the membership degree of real-time data to each penetration grade is calculated through a forward cloud generator, and a final grading result is comprehensively judged; and mapping a grading result to a three-dimensional BIM model to generate a dynamic penetration cloud picture, and triggering a real-time early warning mechanism. According to the method, through multi-source data fusion and dynamic weight distribution, the static and experience dependence of a traditional grading method is broken through, and the complex stratum permeability evaluation precision is remarkably improved; the level boundary fuzziness is quantified by the cloud model, and the risk of misjudgment of a critical interval is avoided; through deep integration of advanced geological forecast data, permeability space prediction of an unexcavated area is achieved, minute-level early warning and grouting parameter intelligent recommendation are combined, the probability of water inrush accidents is effectively reduced, and an intelligent solution is provided for water-rich tunnel safety construction and resource optimization.

The invention discloses a phytoplankton space distribution prediction method based on environment DNA and machine learning, and belongs to the technical field of ecological environment evaluation and treatment. Comprising the following steps: determining a sampling site, and collecting a phytoplankton eDNA sample; carrying out DNA extraction and treatment on the collected eDNA sample, and calculating a species abundance matrix; screening a training data set for training a prediction model according to the sampling time and the sampling site of the eDNA sample; inputting the training data set as an independent variable of a training set, inputting the species abundance matrix as a dependent variable of the training set, and training a prediction model; and inputting remote sensing image data into the prediction model to obtain a species spatial distribution diagram. Compared with the prior art, the method has the advantages that efficient and high-species-resolution spatial distribution prediction of algae species such as blue-green algae can be achieved, the phytoplankton prediction model is trained through the machine learning model, automatic operation of spatial prediction of various phytoplanktons is achieved, the arrangement density of sampling points is reduced, and the monitoring efficiency is improved.

The invention relates to the technical field of bioinformatics, in particular to an esophageal cancer RNA-protein interaction space prediction method and device based on a graph neural network, and can solve the problem that it is difficult to accurately distinguish the difference of cancer tissue and normal tissue on RPI in a traditional method to a certain extent. The method comprises the steps of obtaining multi-stage space transcriptome data related to esophageal cancer, and performing preprocessing to obtain a multi-modal feature tensor and a canceration stage tag; using the multi-modal feature tensor and the canceration stage tag, using RNA and protein as nodes, defining a static edge based on sequence complementarity and structure matching degree, defining a dynamic edge based on spatial co-expression correlation, and constructing a space-time dynamic graph updated along with the canceration stage; the space-time dynamic graph serves as input, interaction existence, site coordinates and binding energy are predicted through space-time double-branch coding and attention mechanism fusion features, and an interaction prediction result is output.

The invention provides an energy monitoring method based on user working condition identification and energy-saving space prediction, and the method comprises the steps: carrying out the dynamic adjustment and collection of multi-source data based on a self-adaptive data collection strategy according to a user load rate, carrying out the data fusion of the collected multi-source data according to the spatial-temporal characteristics, and constructing high-dimensional working condition feature data; based on the high-dimensional working condition feature data, generating a working condition identification result by using a pre-trained light-weight deep learning working condition classification model in combination with an adaptive sliding window mechanism; and constructing a dynamic energy efficiency reference model, calculating optimal energy consumption under the current working condition based on matching of historical data and a real-time working condition identification result, and quantifying a single-point energy-saving space, predicting multi-point collaborative energy-saving potential and performing real-time energy efficiency monitoring intervention based on a preset layered energy-saving potential evaluation framework. According to the invention, dynamically changing user working conditions can be effectively identified, and energy space prediction can be accurately detected and quantified.

A multi-medium three-dimensional modeling and rock-soil mechanical response coupling method comprises the steps that a collapse risk assessment model is established according to time sequence evolution data of deformation accumulation, if the local deformation rate exceeds a preset threshold value, a high-risk area is judged, and if the deformation accumulation amount reaches a critical value, a potential collapse position is determined; constructing a spatial prediction model through the high-risk area and the potential collapse position information, and performing quantitative calculation on collapse probabilities of different areas by adopting a risk assessment algorithm to obtain a spatial distribution prediction result of karst collapse; and updating numerical model parameters by adopting spatio-temporal information output by the early warning mechanism, and optimizing and adjusting the prediction precision through a feedback correction mechanism to obtain a karst collapse prediction technical system adapted to complex geological conditions. According to the method, accurate prediction and early warning of the karst collapse are achieved in a data driving and model coupling mode, technical support is provided for prevention and treatment of the karst collapse under complex geological conditions, and the method has important practical value.

The invention provides an industrial site soil pollutant concentration prediction method using three-dimensional distribution interpolation, and belongs to the field of soil pollution prediction, and the method comprises the following steps: S1, extracting soil pollution distribution characteristics based on sample point positions and attribute information; s2, measuring the anisotropy of the pollution concentration value in the two directions by estimating the ratio R of the vertical direction gradient to the horizontal direction gradient of the soil pollutant concentration through the soil sample point pollution concentration and a difference method, and multiplying the z value in the original coordinate space by the R by taking the R as an expansion factor to obtain a new vertical coordinate value; s3, expanding the spatial position representation module of the DKNN from a two-dimensional space to a three-dimensional space; s4, selecting a space encoder; and S5, on the basis of the spatial encoder, performing spatial prediction by using a general Kriging equation. According to the method, a GeoAI framework based on deep learning and geoscience knowledge fusion is introduced and expanded, the problem of three-dimensional distribution simulation of industrial site soil pollutants can be solved, and soil pollution can be accurately predicted.

An adaptive motion vector prediction unit configured to adaptively perform spatial prediction that performs prediction using a motion vector around a target block and temporal prediction that performs prediction using a motion vector of a collocated picture is included, and in the temporal prediction performed by the adaptive motion vector prediction unit, the collocated picture to be referred to is designated on a per picture basis, and a reference list is designated on a per slice basis.

The invention discloses a landslide susceptibility evaluation method fusing Bayesian optimization and a sparse residual network, and relates to the technical field of mountain disaster space prediction. According to the method, a landslide multi-source factor set is constructed, space standardization is completed, feature compression is performed by using Bayesian optimization XGBoost, and primary probability features are output; inputting the primary features into a sparse gating residual network, and extracting high-order nonlinear coupling features through a sparse attention and residual contraction dual mechanism; training a network by adopting cross entropy loss and an adaptive learning rate, and outputting a landslide occurrence probability; and dividing susceptibility grades based on a GIS natural breakpoint method and drawing. The method is different from an existing landslide model in the aspects of primary feature extraction and deep network architecture, landslide susceptibility prediction precision and interpretability are remarkably improved, and a scientific basis is provided for traffic line selection and disaster prevention planning in a cold region.

The invention provides an intelligent control method for heat treatment furnace temperature, and relates to the technical field of metallurgical heat treatment control. The method comprises the following steps: establishing a blank temperature state space prediction model based on adaptive parameter estimation for predicting the time sequence change of the blank temperature; an optimization objective function is constructed, and an optimal furnace temperature setting sequence is calculated in a prediction time domain; establishing a system parameter adaptive adjustment mechanism, and updating system parameters in real time by adopting a recursive least square method; and valve control is executed based on self-adaptive PID control, and a gain compensation strategy is introduced to deal with equipment aging. According to the method, the furnace temperature can be accurately controlled under different working conditions and different equipment aging degrees without depending on the experience of operators, and the product quality stability is effectively improved.

Systems and methods for providing a neural network system for time series forecasting are described. A time series dataset that includes datapoints at a plurality of timestamps in an observed space is received. The neural network system is trained using the time series dataset. The training the neural network includes: generating, using an encoder of the neural network system, one or more estimated latent variables of a latent space for the time series dataset; generating, using an auxiliary predictor of the neural network system, a first latent-space prediction result based on the one or more estimated latent variables; transforming, using a decoder of the neural network system, the first latent-space prediction result to a first observed-space prediction result; and updating parameters of the neural network system based on a loss based on the first observed-space prediction result.

The invention provides a geomagnetic daily variation prediction method and system based on sub-band Kriging interpolation, and relates to the technical field of geomagnetic daily variation data processing, and the method comprises the steps: obtaining a measurement geomagnetic daily variation signal in a target region; based on the measured geomagnetic daily variation signal, determining a plurality of frequency data corresponding to the measured geomagnetic daily variation signal by adopting wavelet decomposition; on the basis of the multiple pieces of frequency data, adopting a Kriging interpolation prediction model to predict interpolation data corresponding to each piece of frequency data; based on the plurality of interpolation data, the geomagnetic daily variation prediction value of the geomagnetic daily variation observation station to be solved in the target area is obtained through linear superposition reconstruction, and the precision and reliability of geomagnetic daily variation space prediction are effectively improved.

The invention belongs to the technical field of mineral resource prediction, and provides a mineral resource space intelligent prediction method and device based on multi-model ensemble learning, and the method comprises the steps: S1, obtaining a feature data set of a to-be-predicted geological space; s2, performing feature extraction on the feature data set by using a trained convolutional neural network to obtain a new feature data set; s3, performing mineral resource prediction according to the new feature data set and the trained Stacking multi-model integrated learning space intelligent modeling; through accurate mathematical operation and activation function processing, the key information in the input feature map can be efficiently extracted, the feature map with higher representation capability is generated and output, the information redundancy is reduced, the problem that deep and weak feature information is difficult to extract in the mineral resource prediction process is solved, and the mineral resource prediction efficiency is improved. Therefore, the accuracy and effectiveness of feature extraction are improved, and a better feature data basis is provided for subsequent multi-model ensemble learning.

The invention belongs to the technical field of disaster management, discloses an urban flood emergency material demand space prediction method and related equipment, and aims to construct a multi-dimensional risk assessment system by fusing flood disaster-inducing factors and disaster-bearing body vulnerability data so as to significantly improve the accuracy and space directivity of emergency material demand prediction. The subjective limitation of a traditional expert scoring method is broken through by adopting a machine learning weighting mechanism, and objective quantitative evaluation of the flood risk level is realized by dynamically optimizing the influence factor weight through an AdaBoost model. A spatial principal component analysis method is introduced to carry out dimension reduction processing on multi-source social economic indexes, key vulnerability factors are effectively extracted, and data redundancy is eliminated. Spatial coupling of disaster exposure and social vulnerability is realized through an emergency spatial coefficient model, and a spatial association mechanism of disaster risk and population distribution is established, so that the recognition precision of people needing rescue reaches a grid level. The finally formed material demand prediction map can accurately reflect differentiated demands of emergency materials in different geographic units.

The invention relates to the technical field of smart tourism management, and discloses a dynamic management system based on smart scenic spot resources. The system establishes a resource real-time state portrait through a state portrait module, and a load analysis module extracts a periodic load waveform of each resource point according to a historical operation log and calculates a current theoretical load phase. The offset calculation module compares a real-time state with a standard state under a theoretical phase to generate a real-time offset vector, and a global offset field is constructed through the global fusion module. And the phase calibration module reversely corrects a theoretical load phase according to the space gradient and intensity distribution of the biased field to obtain a predicted load phase after feedback calibration. And the load prediction module extracts a load prediction value sequence of a plurality of time slices in the future. According to the method, through periodic phase modeling and full-field dynamic calibration, finer time sequence description and more collaborative space prediction of scenic spot resource states are realized, and the perspectiveness and adaptability of management are improved.

The application discloses a magnetic resonance signal denoising method combining a shaping Prony algorithm and a spatial prediction filter. Three-dimensional coordinate axis parameters are determined, and a spatial adaptive prediction filter structure is constructed. After data preparation is performed on received noisy signal data of multiple measuring points, a three-dimensional data body is established, Prony decomposition is performed on signal data in the three-dimensional data body by using a Prony algorithm, a shaping regularization method is introduced to solve a least square value, Prony transformation values of the noisy signal of the measuring points and spatial prediction filter coefficient values corresponding to the measuring points are obtained. A pure signal of a target measuring point is predicted and approximated by using Prony components of adjacent measuring points and the spatial prediction filter coefficient values, and suppression of random noise of the noisy signal of the target measuring point is realized. By using the method, coil laying work is reduced, detection efficiency is improved, random noise can be further effectively suppressed, complex effective signals are protected, and the signal-to-noise ratio of the magnetic resonance signal is improved.

The invention provides a lithium battery residual life prediction method and system based on data space position analysis, and the method comprises the steps: building a battery residual life space prediction model based on a convex polytope mechanism according to the basic measurement physical quantity of a lithium battery and the health condition parameter of the battery; determining the shortest length of the online trend as a spatial trend constraint according to the battery health condition parameter of the lithium battery; extracting spatial trend characteristics based on the characteristic parameters of the online operation data of the lithium battery to be detected, and determining a spatial trend straight line by combining spatial trend constraints; and determining a residual service life prediction value corresponding to the space trend characteristics of the lithium battery to be detected by integrating the battery residual service life space prediction model. By adopting the scheme, the defects of complicated operation and insufficient prediction result precision in the prior art can be overcome, and a battery residual life prediction result with high physical significance and high interpretability can be efficiently obtained.

An apparatus includes a decoder including a spatial prediction engine, a temporal prediction engine, a reconstruction engine, and a decoded picture buffer. The apparatus also includes a controller configured to cause the decoder to reconstruct a base resolution version of the block of the frame in a base resolution mode using at least one of the spatial prediction engine or the temporal prediction engine and the reconstruction engine. The controller is further configured to cause the decoder to generate an enhanced resolution version of the block in an enhanced resolution mode using at least one of the spatial prediction engine or the temporal prediction engine and the reconstruction engine.