Sensor Data Clipping With Adaptive Thresholding for Noise Separation
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Solution Overview
Problem
Separating signal from noise in sensor data is challenging due to environmental, operational, and inherent noise sources, making it difficult to accurately and consistently identify noise components over time and across various sensors.
Innovation Solution
An apparatus and method involving a processor-based system with clip and threshold modules to analyze sensor data distributions, clip excessive values, and determine adaptive thresholds based on variance between data classes, enabling effective separation of noise from signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sensor data is collected from various sources, then the quantity of information increases, but noise components from environmental, operational, and inherent sources contaminate the data
Solution Approach 1:
The patent extracts and removes noise components from sensor data through automated processing. The system identifies and separates noise from actual signal components, effectively taking out the harmful noise elements while preserving the useful information from multiple sensor sources.
Solution Approach 2:
The patent introduces an automated noise identification and separation system as an intermediary between raw sensor data collection and final data analysis. This intermediary processing layer handles the complex task of distinguishing signal from noise, enabling effective use of multi-source sensor data without manual intervention.
2Measurement precision
If manual methods are used to separate signal from noise, then some level of processing can be achieved, but accuracy, consistency, and speed deteriorate over time and across various sensors
Solution Approach 1:
The patent implements a self-service automated system that performs noise identification and separation without requiring manual intervention. The system autonomously processes sensor data, adapts to different sensor types and environmental conditions, and maintains consistent performance across varying operating conditions, eliminating the limitations of manual processing.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on the specific characteristics of different sensors and environmental conditions. By changing processing parameters adaptively rather than using fixed manual thresholds, the system maintains high accuracy across diverse sensor types and operating scenarios while reducing processing complexity.
3Productivity
If automated processing is implemented, then processing speed and consistency improve, but the complexity of the processing system increases
Solution Approach 1:
The patent creates a universal automated processing system that handles multiple sensor types and various noise conditions through a single integrated platform. This multi-functional approach improves processing speed and consistency across different applications while managing system complexity through standardized, reusable processing modules rather than separate systems for each sensor type.
Data Source
AI summary
Apparatus and method to facilitate automatic detection of a device state are disclosed herein. Selectively constraining a sensor based data set associated with one or more states of a device, wherein selectively constraining the sensor based data set includes analyzing a distribution of the sensor based data set to determine whether to constrain the sensor based data set, the sensor based data set including a first class and a second class of data values. Determining a threshold associated with the sensor based data set by selecting the threshold based on a variance between the first and second classes of the sensor based data set, wherein selecting the threshold includes using a constrained sensor based data set when the sensor based data set is determined to be constrained, and wherein the threshold indicates the data values associated with the first and second classes.