Signal Processing Device Dual-Resolution Abnormality Detection
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Solution Overview
Problem
Existing signal processing methods for biological samples struggle to detect measurement abnormalities that have a slight influence on time-series signals, leading to potential errors in concentration measurements.
Innovation Solution
A signal processing apparatus and method that utilize both low and high time-resolution measurement units to acquire and analyze time-series signals, extracting waveform shape features and using Mahalanobis generalized distance for abnormality determination, enabling detection of subtle measurement abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-resolution measurement methods are used, then device complexity is low, but measurement precision deteriorates because subtle abnormalities cannot be detected
Solution Approach 1:
The measurement system is segmented into two distinct measurement units: a first measurement unit that captures low-time-resolution signals for normal measurements, and a second measurement unit that captures high-time-resolution signals for abnormality detection. This segmentation allows each unit to be optimized for its specific function, improving overall detection precision without requiring a single overly complex system
Solution Approach 2:
The second measurement unit performs excessive measurement by capturing signals at higher time resolution than necessary for normal measurements. This partial or excessive action provides additional detailed information that enables detection of subtle abnormalities, with the understanding that not all high-resolution data will be used in every measurement scenario
2Measurement precision
If high-time-resolution measurement is performed continuously, then abnormality detection precision improves, but productivity deteriorates due to increased processing time
Solution Approach 1:
The system implements periodic action by using the high-time-resolution second measurement unit selectively rather than continuously. The control unit determines when abnormality detection is needed and activates the second measurement unit at those periodic intervals, while relying on the first measurement unit for routine measurements, thus maintaining productivity while enabling precise abnormality detection when necessary
Solution Approach 2:
High-time-resolution measurement is performed as a partial or excessive action only when abnormality detection is required, rather than for every measurement. This approach provides the necessary detection precision while minimizing the impact on overall measurement throughput by limiting high-resolution measurements to specific cases
3Reliability
If multiple measurement units are used, then reliability improves through better abnormality detection, but device complexity increases
Solution Approach 1:
The measurement system is divided into two specialized measurement units, each optimized for specific detection needs. The first unit handles normal measurements with lower complexity requirements, while the second unit specializes in abnormality detection. This segmentation improves reliability by ensuring each unit is optimized for its function while keeping individual unit complexities manageable
Solution Approach 2:
The control unit serves multiple functions by managing both measurement units, determining when to switch between them based on measurement requirements. This multi-functionality allows the system to achieve high reliability through appropriate unit selection without requiring completely separate independent systems, thereby controlling overall device complexity
4Measurement precision
If conventional single-feature abnormality determination is used, then ease of operation is high, but measurement precision deteriorates because subtle abnormalities are missed
Solution Approach 1:
The system transitions from single-feature abnormality determination to multi-dimensional analysis by extracting multiple waveform shape feature amounts (amplitude, width, area, peak position) from the high-time-resolution signals. This dimensional expansion in the feature space enables detection of subtle abnormalities that would be invisible in single-feature analysis, while the automated feature extraction and comparison processes maintain operational simplicity
Data Source
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AI summary
[Task] To provide a signal processing apparatus and a signal processing method which implement measurement with higher reliability by detecting an abnormality even though such abnormality has only a slight influence on a time-series signal. [Solution] Provided is a signal processing apparatus including a first measurement unit which acquires a first time-series signal with a first time resolution; a second measurement unit which acquires a second time-series signal with a second time resolution higher than the first time resolution; and a determination unit which determines a measurement abnormality based on the second time-series signal. The normal measurement is performed based on the first time-series signal while the measurement abnormality determination is performed based on the acquired second time-series signal.