AI Spectrum Categorization via Image Conversion
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Solution Overview
Problem
Conventional spectrum analysis methods rely on human operators to manually inspect numerical data, which is time-consuming, error-prone, and may miss signals due to limitations in human perception and processing.
Innovation Solution
The method involves converting processed spectrum data into digital image files, allowing for the use of convolutional neural networks (CNNs) for AI-based spectrum categorization, thereby improving efficiency, accuracy, and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual inspection methods are used for spectrum analysis, then human operators can detect signals, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces the mechanical human inspection process with an automated computer-based system that converts spectrum data into image format and applies convolutional neural networks for automatic classification, eliminating manual intervention while improving both speed and accuracy
Solution Approach 2:
The patent introduces an intermediary image conversion step that transforms numerical spectrum data into visual image format, enabling the use of powerful image recognition algorithms (CNNs) that can rapidly and accurately classify signals without direct human intervention
2Productivity
If numerical data processing methods are used for spectrum categorization, then the process can be automated, but the accuracy and efficiency are limited compared to image-based AI analysis
Solution Approach 1:
The patent changes the data representation parameter from numerical values to image format, transforming the spectrum data into a visual representation that can be processed by CNNs, thereby simultaneously improving both processing efficiency and categorization accuracy
Solution Approach 2:
The patent adds a visual dimension to the spectrum data by converting it into image format with spatial and frequency dimensions, enabling the application of two-dimensional convolutional operations that are highly efficient for pattern recognition and significantly improve categorization performance
Data Source
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AI summary
A device for artificial intelligence-based spectrum categorization is provided. The device allows for identifying different signal types present within the spectrum and automatically categorizing them using image based artificial intelligence algorithms. The device is configured to receive a radio signal comprising an IQ sample, calculate a spectrogram of the IQ sample, convert the spectrogram into an image, and provide the image to a pre-trained convolutional neural network, CNN, stored in the device. The CNN is configured to determine, whether the radio signal corresponding to the image belongs to one of the following categories: noise floor, target signal, interference signal, target signal with interference signal.