Tunable Signal Sampling for Neural Network Training Data
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Solution Overview
Problem
Constant rate signal sampling is suboptimal for applications like video frame extraction for training artificial neural networks, as it fails to capture potentially useful training samples with different video characteristics, leading to missed key-data.
Innovation Solution
A tunable signal sampling system that identifies data partitions, compares them using predetermined metrics, selects candidate sample partitions, and extracts samples using a weighting factor to optimize key-data extraction, enabling automated and adaptive sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If constant rate signal sampling is used, then signal quality control is maintained, but key-data extraction is insufficient
Solution Approach 1:
The patent implements dynamic sampling by adjusting the sampling rate based on signal characteristics. The system transitions from constant rate sampling to variable rate sampling, where the sampling interval adapts according to detected signal features such as motion magnitude in video frames. This allows the system to maintain signal quality while capturing more key-data during dynamic periods and reducing sampling during static periods.
Solution Approach 2:
The patent changes the sampling parameter (sampling rate) based on signal conditions. By monitoring signal characteristics and modifying the sampling rate accordingly, the system optimizes both signal quality control and key-data extraction. The sampling rate becomes a dynamic parameter rather than a fixed value, enabling adaptation to different signal states.
2Ease of operation
If constant rate sampling extracts frames periodically, then sampling simplicity is maintained, but potentially useful training samples are missed
Solution Approach 1:
The system replaces simple periodic sampling with dynamic sampling that responds to signal characteristics. The sampling process becomes adaptive, automatically adjusting to capture important frames while maintaining operational feasibility through automated detection and selection algorithms.
Solution Approach 2:
The sampling system performs self-adjustment by automatically detecting signal features and determining optimal sampling points without external intervention. The system serves itself by making intelligent decisions about which frames to capture based on intrinsic signal properties, eliminating the need for manual sampling rate adjustment.
3Stability of the object's composition
If constant rate sampling is used for video frame extraction, then uniform sampling is achieved, but high intensity actions are missed
Solution Approach 1:
The patent transforms uniform sampling into dynamic sampling by introducing adaptability to signal variations. The system detects changes in video content such as motion intensity and adjusts sampling accordingly, capturing high-intensity actions while maintaining compositional stability through systematic sampling strategies.
Solution Approach 2:
The system applies different sampling strategies to different portions of the signal based on local characteristics. High-intensity regions receive higher sampling density while low-intensity regions use lower sampling rates, optimizing the capture of important events while maintaining overall sampling efficiency.
Data Source
AI summary
A tunable signal sampling system includes a computing platform having a hardware processor and a memory storing a software code that when executed receives a communications signal, identifies data partitions included in the communications signal, performs, using a first predetermined metric, a first set of comparisons each comparing a different sequential pair of the data partitions with each other, and selects, based on the first set of comparisons, a subset of the data partitions as candidate sample partitions of the communications signal. The software code also determines multiple default sample partitions of the communications signal, performs, using a second predetermined metric, a second set of comparisons each comparing a different one of the default sample partitions with a respective one of the candidate sample partitions, and extracts, using a predetermined weighting factor applied to the results of the second set of comparisons, a sample of the communications signal.