Neural-Network Digital Pulse Processing for Photon Spectrometry
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Solution Overview
Problem
Existing spectrometers and digital pulse processors face challenges in providing accurate and fast signal analysis while requiring complex algorithms and expensive hardware, necessitating a simpler and more efficient processing method.
Innovation Solution
Utilizing multiple neural networks, including Feedforward Neural Networks, Deep Neural Networks, and Convolutional Neural Networks, to process digital pulse signals, with preprocessing steps like amplification and filtration to enhance signal-to-noise ratio and handle pile-up conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional digital pulse processing algorithms are used, then measurement precision and reliability are improved, but device complexity and development cost increase
Solution Approach 1:
The patent replaces traditional mechanical/digital signal processing algorithms with a neural network-based system. The neural network is trained offline to learn optimal signal processing patterns, then deployed for rapid inference. This substitution transforms complex algorithmic processing into a more manageable neural network architecture that achieves comparable or superior precision with reduced development complexity.
Solution Approach 2:
The neural network is trained in advance using labeled signal data to learn optimal processing patterns. This preliminary training phase allows the network to internalize complex signal characteristics and processing rules, so that during actual operation, the network can perform rapid analysis without requiring complex real-time algorithms. The preprocessing and feature extraction are performed during training rather than during live signal processing.
2Measurement precision
If traditional digital pulse processing algorithms are used, then measurement precision is improved, but hardware cost increases
Solution Approach 1:
The patent replaces expensive, specialized hardware components with a neural network implementation that can run on standard computing platforms. By training the neural network offline and deploying it as software, the system achieves high-precision signal analysis without requiring specialized hardware accelerators or complex processing units, thereby reducing hardware costs while maintaining measurement precision.
3Measurement precision
If complex processing algorithms are used, then signal analysis accuracy is improved, but processing time increases
Solution Approach 1:
The neural network is trained offline using comprehensive signal data and labeled examples. During this preliminary training phase, the network learns optimal processing patterns and decision boundaries. Once trained, the network can perform rapid inference on new signals without requiring complex real-time computations, thus achieving high accuracy with reduced processing time during actual operation.
Solution Approach 2:
The patent replaces traditional sequential signal processing algorithms with a neural network that can process signals in parallel. The neural network's distributed architecture allows multiple signal features to be analyzed simultaneously during inference, reducing processing time while maintaining the accuracy benefits of complex analysis.
Data Source
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AI summary
The present disclosure provides systems, devices, and methods for a digital pulse processor for a spectrometer, the methods include the steps of: obtaining a signal from a radiation detector configured to detect one or more photons, processing the signal using at least one neural network, and providing information indicative of properties of detected one or more photons based on the processing.