Automatic Filter Selection by Frequency Spectrum for Noise Removal
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Solution Overview
Problem
Existing weight measuring units face challenges in automatically selecting suitable filters to effectively filter out noise from input signals, relying on user experience and limited computer software capabilities, which are inconvenient and inefficient.
Innovation Solution
An automatic filtering method and device that analyzes input signals to obtain their frequency spectrum, selects appropriate filters from a preset list based on the spectrum, and fine-tunes filtering parameters to effectively remove various noise types, ensuring reliable filtering performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If filter selection is based on user experience and experiments, then filtering effectiveness can be improved, but operation complexity and time consumption increase
Solution Approach 1:
The system automatically analyzes the frequency spectrum of the input signal and selects appropriate filters without requiring user experience or manual experimentation. The microprocessor performs spectral analysis and autonomously determines the most suitable filter configuration, making the system self-sufficient in filter selection while maintaining high filtering effectiveness.
Solution Approach 2:
The system changes the parameter selection approach from manual user-based selection to automatic spectral analysis-based selection. By analyzing the frequency characteristics of the input signal and comparing them with the frequency response characteristics of available filters, the system dynamically selects the most appropriate filter parameters, eliminating the need for user experience while maintaining filtering effectiveness.
2Ease of operation
If filter selection is based on simple signal change rate, then ease of operation is improved, but filtering effectiveness and adaptability deteriorate
Solution Approach 1:
The system transitions from one-dimensional signal change rate analysis to two-dimensional frequency spectrum analysis. By examining the frequency domain characteristics of the input signal rather than just temporal changes, the system gains deeper insight into signal characteristics, enabling more accurate and versatile filter selection across different application scenarios.
Solution Approach 2:
The system replaces simple mechanical comparison of signal change rates with sophisticated spectral analysis using a microprocessor. The microprocessor performs Fourier transformation or other spectral analysis algorithms to extract frequency domain features, then compares these with filter frequency responses to automatically select the most suitable filter, greatly enhancing adaptability while maintaining ease of operation.
3Reliability
If computer software is used for signal analysis, then filtering capability is improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The system merges the signal analysis and filter selection functions into a single integrated microprocessor unit. Rather than using separate computer software systems, the microprocessor performs both spectral analysis and filter selection within the weight measuring unit itself, combining multiple functions into one compact component and reducing overall system complexity.
Solution Approach 2:
The microprocessor serves multiple functions: it performs spectral analysis of the input signal, compares the spectrum with filter characteristics, selects the appropriate filter, and can adjust filter parameters. This multi-functional approach replaces what would otherwise require separate specialized software components, reducing implementation difficulty while maintaining strong signal analysis capability.
Data Source
AI summary
An automatic filtering method includes: a step of analyzing an input signal so as to obtain a frequency spectrum of the input signal; and a step of selecting, on the basis of the frequency spectrum, at least one filter from among a plurality of preset filters and filtering the input signal. The step of selecting includes determining, on the basis of the frequency spectrum of, a signal type in the input signal; selecting, on a basis of the signal type, a corresponding filter corresponding to the signal type; filtering the input signal with use of a first parameter of the corresponding filter and setting a second parameter for the corresponding filter on a basis of a result of the filtering; and filtering the input signal with use of the second parameter set for the corresponding filter. The method may ensure a filtering effect against noise.