Faucet Detector Using Frequency Domain Signal Analysis
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Solution Overview
Problem
Conventional faucets using radio wave detectors often experience miss detection and false detection due to environmental factors, object color, and material, especially when water is discharged at high pressure or in the presence of sunlight, making it difficult to accurately determine the presence of objects near the water outlet.
Innovation Solution
A faucet equipped with a detector that sends radio wave signals, converts the received signals into frequency domain signals using individual filter banks, and performs recognition based on frequency distribution and signal intensity ratios, stored in a database for comparison, to accurately detect objects regardless of environmental effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional detector using radio waves is used, then the faucet can detect objects in the vicinity, but it causes miss detection and false detection due to environmental factors, object color, material, and water flow conditions
Solution Approach 1:
The patent divides the detection signal into multiple frequency bands using filter banks, analyzing different frequency components separately to identify object-specific patterns and reduce false detections caused by environmental factors
Solution Approach 2:
The patent transitions from time-domain signal analysis to frequency-domain analysis by converting sensor signals into frequency domain signals, enabling detection based on frequency distribution patterns rather than simple intensity thresholds
2Measurement precision
If a photosensor using infrared is used, then the detector can sense thermal radiation, but it has difficulty in detecting black or transparent objects and is affected by sunlight
Solution Approach 1:
The patent replaces photosensor-based optical detection with radio wave-based detection, eliminating susceptibility to sunlight and object color/material properties while maintaining detection capability through Doppler effect-based motion sensing
3Ease of operation
If simple threshold-based detection is used, then the detection process is simple, but it cannot accurately distinguish between water flow and objects under high pressure water shower conditions
Solution Approach 1:
The patent segments the detection signal into multiple frequency bands and analyzes the frequency distribution across these bands, creating a more sophisticated detection method that maintains accuracy while managing complexity through systematic signal processing
Solution Approach 2:
The patent performs preliminary frequency domain conversion and filter bank analysis on the sensor signal before making detection decisions, preparing the signal in advance to enable more accurate distinction between water flow and objects
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The faucet effectively reduces miss detection and false detection, accurately determining object presence near the water outlet, even in challenging conditions such as high water pressure or sunlight, and can detect black or transparent objects.
Implementation Method 1
a sensor configured to send a radio wave signal and receive the radio wave signal reflected by an object to output a sensor signal corresponding to motion of the object
Data Source
AI summary
The faucet includes a detector including: a frequency analyzer to convert a sensor signal into a frequency domain signal, and extract, by use of a group of individual filter banks with different frequency bands, signals of the individual filter banks from the frequency domain signal; a recognizer; and a database device, for storing sample data. The recognizer performs a recognition process of detecting a detection object based on detection data containing a frequency distribution of signals based on the signals of the individual filter banks. The recognizer performs the recognition process based on comparison between the detection data and the sample data.