Sensor Configuration Using Optimal Sub-Sampling for Sparse Measurements
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Solution Overview
Problem
Current sensor systems face challenges in efficiently sampling and reconstructing data due to high requirements for sensor density and computational power, leading to increased costs, bandwidth demands, and prolonged acquisition times, while reducing sensor numbers or sampling frequency compromises accuracy.
Innovation Solution
A computer-implemented method for configuring a sensor system using a training dataset and a dictionary to determine an optimal sub-sampling operator, which selects the most important sample positions, allowing for reduced sensor and sample numbers while maintaining measurement criteria, thereby reducing hardware costs and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of sensors and sampling frequency are increased to improve data quality and quantity, then measurement precision and data quantity are improved, but device complexity, cost, and computational requirements increase
Solution Approach 1:
The patent extracts only the most essential sample positions from the complete sensor dataset using optimal sub-sampling operators. By identifying and retaining only the critical samples that preserve measurement accuracy, the system eliminates redundant data collection from excessive sensors and high-frequency sampling, thereby reducing device complexity while maintaining data quality.
Solution Approach 2:
The patent transforms the sensor system configuration by changing the sampling parameters dynamically. Instead of uniformly high sampling across all sensors, the system applies learned sub-sampling operators that adaptively adjust sampling rates and sensor activation based on the actual information content required, optimizing the balance between measurement precision and system complexity.
2Quantity of substance
If the number of sensors and sampling frequency are increased to improve data quantity, then data quantity is improved, but bandwidth requirements and processing requirements increase
Solution Approach 1:
The patent extracts a compressed subset of sensor measurements that contains the essential information content. By using optimal sub-sampling to identify and retain only the most informative samples, the system reduces the total data volume transmitted and processed while preserving the quantity of useful information needed for accurate reconstruction and analysis.
Solution Approach 2:
The patent performs preliminary analysis during the training phase to pre-determine optimal sub-sampling operators and identify critical sample positions. This advance preparation allows the system to efficiently select and transmit only the necessary data points during operation, reducing real-time bandwidth and processing energy requirements while maintaining adequate data quantity for analysis.
3Device complexity
If the number of sensors is reduced to decrease device complexity and cost, then device complexity and cost are reduced, but measurement precision deteriorates
Solution Approach 1:
The patent applies local quality optimization by identifying specific sample positions and sensor locations that contribute most to measurement precision. Instead of uniformly reducing all sensor measurements, the system selectively retains critical local measurements while omitting redundant ones, thereby maintaining accuracy with fewer sensors through intelligent local selection rather than global reduction.
Solution Approach 2:
The patent performs preliminary training with a dense sensor array to learn the optimal sub-sampling pattern and identify which sensor positions and sample moments are most informative. This advance learning enables the system to subsequently use a reduced sensor configuration that strategically samples only at the most critical positions, preserving measurement precision despite having fewer sensors.
4Productivity
If sampling frequency and resolution are reduced to decrease processing requirements, then processing requirements are reduced, but measurement precision deteriorates
Solution Approach 1:
The patent extracts the most informative temporal and spatial samples from the sensor data stream using learned sub-sampling operators. By identifying and retaining only the critical moments and positions that contain essential information, the system reduces the sampling frequency and resolution required while maintaining measurement precision, as the extracted subset preserves the key features needed for accurate reconstruction.
Data Source
AI summary
The present disclosure relates to a computer-implemented method for r configuration of a sensor system, said method (500) comprising providing (510,520), for a training dataset X consisting of at least one data element Xi, for a measurement of the sensor system (200) relating to an initial set-up of the sensor system (200), a dictionary D forming a signal model comprising at least one basis function and at least one set of sparse coefficients S. Each set of sparse coefficients Si and the dictionary D represents one of the data elements Xi of said training dataset X. Determining (530) an optimal sub-sampling operator ψ based on the dictionary D and said at least one set of sparse coefficients S, wherein the optimal sub-sampling operator ψ is a selection of a true subset of the sample positions of the training dataset X, wherein the true subset corresponds to the sample positions containing the most important samples of the training dataset X. Configuring (540) the sensor system (200) based on the initial set-up of the sensor system (200) and the optimal sub-sampling operator ψ.