Adaptive Sampling for Static Data Sets
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Solution Overview
Problem
Existing methods for sampling static data sets, such as in terrain mapping and ray tracing, are inefficient as they often require arbitrary adjustments in sample density, leading to over or under sampling, resulting in time-consuming processes and suboptimal results.
Innovation Solution
Adaptive sampling technique where two or more rays are cast onto a surface, with the distance between intersection points calculated and compared to a desired sample density, allowing for real-time adjustments in sample density by altering the angle between adjacent rays to achieve the desired density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If arbitrary adjustments in sampling density are made and more samples are taken, then sampling accuracy is improved, but time consumption increases
Solution Approach 1:
The patent applies dynamics by making the sampling density adjustable and adaptive rather than fixed. The system dynamically modifies the number of samples taken based on real-time analysis of the data characteristics, allowing it to increase sampling where needed and decrease it where sufficient data already exists, thereby resolving the contradiction between achieving high sampling accuracy and reducing time consumption
Solution Approach 2:
The patent implements feedback by continuously analyzing the collected data to determine whether the current sampling density is sufficient. This feedback mechanism allows the system to adjust future sampling decisions based on past performance, ensuring that sampling accuracy requirements are met while avoiding unnecessary additional sampling that would increase time consumption
2Measurement precision
If sampling density is increased to avoid under sampling, then data accuracy is improved, but processing efficiency deteriorates
Solution Approach 1:
The patent applies local quality by allowing different sampling densities in different regions or contexts within the data set. Rather than uniformly increasing sampling density across all data, the system identifies specific areas where higher accuracy is needed and applies increased sampling only there, thereby maintaining data accuracy while preserving processing efficiency in regions where lower sampling is sufficient
3Measurement precision
If repeated sampling adjustments are performed to achieve desired density, then sampling quality is improved, but complexity of the process increases
Solution Approach 1:
The patent applies self-service by enabling the sampling system to automatically adjust its own parameters without external intervention. The system autonomously analyzes data quality, determines appropriate sampling densities, and modifies its sampling strategy in real-time, thereby improving sampling quality while reducing process complexity by eliminating the need for manual repeated adjustments
Data Source
AI summary
A sampling module is provided. Two or more rays are cast onto a surface from a single point of origin. The ray or rays intersect the surface at various locations. The distance between the intersection points of each pair of adjacent rays is calculated. This distance is the current sample density. The current sample density is compared to the desired sample density. If the current sample density is not equal to the desired sample density then the sample density of the next casting of rays is adjusted accordingly.