Stochastic Sparse Tree Grid Spatial Filtering
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Solution Overview
Problem
Existing data filtration solutions struggle with managing extremely large datasets where signals of interest are embedded within a vast amount of unimportant data, leading to computational overload due to O(N^2*M^2) complexity in spatial matching across an N×M grid, necessitating impractical increases in computational resources.
Innovation Solution
The implementation of a stochastic sparse tree grid for data processing, which assigns spatial information to data elements on a grid, determines sparseness through a multiple resolution level sparse tree filter, and filters data using a sparse histogram method, allowing efficient handling of large datasets without excessive computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing data filtration solutions are used to manage large datasets, then spatial matching can be performed, but computational complexity increases to O(N^2*M^2) requiring impractical increases in computational resources
Solution Approach 1:
The patent divides the large N×M grid into multiple smaller sub-grids or blocks. By processing spatial matching within these smaller segments rather than across the entire grid, the computational complexity is reduced from O(N^2*M^2) to a more manageable level, while still achieving comprehensive spatial matching through hierarchical or iterative processing of the segmented regions.
Solution Approach 2:
The patent introduces additional dimensions to the spatial filtering process by incorporating temporal dimensions (processing data across multiple time frames) and hierarchical dimensions (multiple resolution levels). This transforms the problem from a purely two-dimensional spatial matching task into a multi-dimensional processing task, enabling more efficient computation through temporal smoothing and hierarchical refinement strategies.
2Measurement precision
If computational resources are increased to overcome spatial matching complexity, then matching accuracy can be maintained, but sensor systems become impractical
Solution Approach 1:
The patent applies partial action by performing spatial matching on a subset of grid elements at each processing stage rather than exhaustively processing all N×M elements simultaneously. Through iterative refinement and temporal smoothing, the system achieves adequate spatial matching accuracy without requiring computational resources proportional to the full grid size, making the sensor system practically implementable.
3Device complexity
If simplification in processing is applied to manage computational load, then computational resources are reduced, but artificial reduction of grid dimensions or matching with other information is compromised
Solution Approach 1:
The patent employs periodic action through temporal smoothing, where spatial matching is performed periodically across multiple time frames. By accumulating and averaging matching results over time, the system maintains spatial matching accuracy despite using simplified processing at each individual time step, thus resolving the contradiction between computational simplicity and matching precision.
Solution Approach 2:
The patent applies preliminary action by performing coarse spatial matching and filtering operations before refined processing. Through pre-processing steps that identify and filter obvious patterns or noise, the system reduces the computational burden on subsequent processing stages while preserving the accuracy needed for final spatial matching decisions.
Data Source
AI summary
A computer-based method for processing data is described. The method includes receiving, at a processing device, data to be processed, processing the data to assign spatial information to the data, the spatial information defining an element on a grid, determining, with the processing device, a sparseness of the processed data through application of the processed data to a multiple resolution level sparse tree filter, placing the processed data within a histogram based on the determined sparseness, and filtering the data based on spatial statistics using a sparse histogram method.