Signal Synchronization via Cross-Correlation Phase Shift
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Solution Overview
Problem
Computer systems face difficulties in synchronizing signals from different data sources due to lead-lag phase discrepancies caused by varying sampling rates and unsynchronized clocks, making it challenging to generate coherent data streams for analysis.
Innovation Solution
A method that generates correlation coefficients between signals with different phase shifts, determining a synchronizing phase shift associated with the highest correlation coefficient to synchronize the signals, and re-sampling signals to a common sampling rate if necessary, allowing for accurate synchronization of power, temperature, and performance parameter signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data acquisition devices with different sampling rates and unsynchronized clocks are used to gather operational parameters, then the quantity of data collected is improved, but phase coherence between data streams deteriorates
Solution Approach 1:
The patent applies preliminary action by calculating cross-correlation coefficients between signal pairs before final synchronization. This pre-processing step identifies the optimal time shifts needed to align signals from different data acquisition devices, enabling phase coherence to be established before generating synchronized relationships between the data streams.
Solution Approach 2:
The patent uses cross-correlation coefficients as an intermediary to bridge signals with different sampling rates and time stamps. By computing correlation coefficients across multiple time shifts, the method finds the optimal alignment without requiring direct synchronization of the original unsynchronized signals, thus maintaining data quantity while achieving phase coherence.
2Adaptability or versatility
If data acquisition devices with different sampling rates are used, then the adaptability of the system is improved, but the complexity of synchronizing signals deteriorates
Solution Approach 1:
The patent changes the parameter of time shift by calculating correlation coefficients across multiple discrete time shifts. This transforms the synchronization problem from a complex continuous alignment task into a discrete parameter search, where the optimal time shift is identified by finding which discrete shift produces the highest correlation coefficient between signal pairs.
Solution Approach 2:
The patent applies partial action by computing correlation coefficients only for a finite set of discrete time shifts rather than continuously. This partial sampling of the time shift space is sufficient to identify the optimal alignment while significantly reducing computational complexity compared to exhaustive continuous synchronization methods.
3Reliability
If time stamps from unsynchronized clocks are used, then the independence of data sources is improved, but the accuracy of synchronized relationships deteriorates
Solution Approach 1:
The patent uses feedback by calculating cross-correlation coefficients to measure the alignment quality between signals with different time stamps. The correlation coefficient serves as feedback that indicates how well-synchronized the signals are at each time shift, allowing the system to iteratively identify the optimal time shift that maximizes synchronization accuracy while preserving data source independence.
Data Source
AI summary
Some embodiments of the present invention provide a system that synchronizes signals related to the operation of a computer system. During operation, a set of correlation coefficients between a first signal and a second signal is generated, wherein each correlation coefficient is associated with a different phase shift between the first signal and the second signal. Then, a synchronizing phase shift associated with the highest correlation coefficient in the set of correlation coefficients is determined in order to synchronize the first signal and the second signal.