Acoustic Probe Compressive Sampling for Lower Data Rates
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Solution Overview
Problem
Non-destructive inspection probes face challenges with high hardware complexity and data rate due to the large amount of data generated by full matrix sampling of sensing elements, which increases with the count of elements.
Innovation Solution
Implement compressive sensing techniques to under-sample acoustic data from a matrix of sensing elements, reconstructing images using a subset of samples, thereby reducing hardware size and data rate without sacrificing image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full matrix sampling is used to capture acoustic data from all sensing elements, then image quality and defect detection accuracy are improved, but data quantity and hardware complexity increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for image reconstruction by applying compressive sensing techniques. Instead of capturing all N samples from the full matrix of sensing elements, the system selectively acquires a reduced subset of M samples (where M < N) that contain sufficient information to reconstruct the acoustic image, thereby reducing hardware complexity while maintaining measurement precision
Solution Approach 2:
The patent applies partial action by acquiring less than the full set of samples (M out of N samples) needed for complete matrix capture. The compressive sensing algorithm processes this partial data set to reconstruct the full image, demonstrating that complete sampling is not necessary when using appropriate reconstruction techniques
2Measurement precision
If full matrix sampling is used to capture acoustic data from all sensing elements, then image quality and defect detection accuracy are improved, but data quantity and transmission requirements increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for image reconstruction by applying compressive sensing techniques. Instead of capturing all N samples from the full matrix of sensing elements, the system selectively acquires a reduced subset of M samples (where M < N) that contain sufficient information to reconstruct the acoustic image, thereby reducing data quantity while maintaining measurement precision
Solution Approach 2:
The patent changes the sampling parameter from full matrix (N samples) to compressed sampling (M samples). By modifying the sampling rate and selecting specific subsets of sensing elements to activate, the system reduces the total number of samples acquired while maintaining image quality through compressive reconstruction algorithms
3Adaptability or versatility
If the count of sensing elements is increased to improve inspection coverage, then measurement capability is improved, but hardware size and data rate increase
Solution Approach 1:
The patent makes each sensing element in the matrix multi-functional by enabling them to serve both as transmit and receive elements. The compressive sensing approach allows the system to achieve full matrix capture functionality with reduced hardware activation, as not all elements need to be simultaneously active to achieve complete inspection coverage
Solution Approach 2:
The patent applies partial action by activating only a subset of sensing elements at any given time rather than using all elements simultaneously. This reduces the instantaneous hardware requirements and data processing burden while still achieving comprehensive inspection coverage through the compressed sensing reconstruction process
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Compressive sensing significantly reduces data quantity and hardware requirements while maintaining image quality, achieving comparable defect detection to full matrix sampling with a fraction of the data and resources.
Implementation Method 1
obtaining signals representative of one or more acoustic waves received using a matrix of sensing elements
Data Source
AI summary
Examples of the present subject matter provide techniques for compressive sampling of acoustic data. A probe may sample in a compression mode, such that the entire matrix is not sampled at full-time resolution or spatial resolution. Therefore, the initial amount of data captured by the probe is reduced, allowing for lower density hardware (e.g., fewer analog-to-digital conversion channels or related analog front-end hardware) to be used at a lower data rate.