Sensor Measurement Compression for Standby Power Reduction
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Solution Overview
Problem
Existing methods for processing sensor measurements in portable devices face challenges in efficiently estimating data values during periods of unavailability, leading to increased electrical consumption and delayed processing due to frequent active phase returns, which affects device operation and accuracy.
Innovation Solution
A method that involves recording fewer characteristics of sensor signals during unavailability phases and using a second algorithm to estimate data values upon returning to the active phase, reducing memory usage and accelerating the restart of device operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the second computer goes into standby mode during unavailability phase, then power consumption is reduced, but processing delay increases when returning to active phase
Solution Approach 1:
The first computer performs preliminary compression of sensor measurements during the unavailability phase, storing compressed data in its memory. This preliminary action ensures that when the second computer returns from standby, it receives pre-processed data ready for immediate algorithm execution, minimizing processing delay while maintaining power savings during standby
Solution Approach 2:
The first computer acts as an intermediary between the sensor and the second computer during standby mode. It continuously acquires and compresses measurements, holding them in its memory buffer, and transfers them to the second computer upon wake-up. This intermediary role eliminates the need for the second computer to process raw sensor data immediately, reducing both power consumption during standby and processing delay upon return
2Measurement precision
If the second computer executes the first algorithm frequently to estimate data values, then estimation accuracy is improved, but device operation slows down
Solution Approach 1:
Instead of executing the full first algorithm frequently, the system uses a simplified approach during unavailability phase: the first computer performs partial processing by compressing measurements and storing key characteristics. When the second computer wakes, it executes the algorithm once on the compressed data, achieving sufficient estimation accuracy without the performance penalty of frequent full algorithm executions
Solution Approach 2:
The processing task is segmented between two computers with different capabilities. The first computer (with limited processing power) handles continuous measurement acquisition and compression during standby. The second computer (with higher processing power) executes the intensive first algorithm only when needed, after waking from standby. This segmentation allows accurate estimation without continuously slowing down device operation
3Loss of information
If the first computer records all intermediate measurements during unavailability phase, then data completeness is improved, but memory capacity is exceeded
Solution Approach 1:
The first computer extracts only the essential characteristics from the full sensor measurements during the unavailability phase. Instead of storing complete intermediate measurements, it stores compressed representations that capture the key information needed for later estimation. This extraction maintains sufficient data completeness for accurate reconstruction while staying within memory capacity constraints
Solution Approach 2:
The system changes the parameter representation of measurement data during compression. The first computer transforms detailed sensor readings into compressed formats with fewer parameters, reducing memory requirements. When the second computer processes the data, it uses these changed parameters to reconstruct accurate estimates, maintaining data utility while respecting memory capacity limits
4Measurement precision
If the second computer processes recorded intermediate measurements upon returning to active phase, then estimation accuracy is improved, but processing time increases
Solution Approach 1:
The first computer performs preliminary compression and organization of measurement data during the unavailability phase. By the time the second computer returns to the active phase, the data is already pre-processed and ready for immediate algorithm execution. This preliminary action significantly reduces the processing time required while maintaining the accuracy benefits of using recorded measurements
Data Source
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AI summary
The invention relates to a measurement processing method, wherein: during an operational phase, a second computer executes (122) a first algorithm for estimating a new value Pi k of a datum; during a phase when the second computer is unavailable, a first computer determines and records (114) characteristics of a signal measured by a sensor, the number of characteristics determined during said phase being strictly less than the number of intermediate measurements that the first computer establishes during the operational phase; the unavailability phase is stopped at a time t2 and the method proceeds to an active recovery phase, during which the second computer executes (148) a second algorithm for estimating a value Pi w of the datum at a time tw within the interval [t1; t2] on the basis of the characteristics determined and recorded during the unavailability phase; then the active recovery phase is stopped and the method returns to the operational phase.