Tensor Builder Interface for ML Input Data Construction
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Solution Overview
Problem
Current machine learning applications in test and measurement systems face challenges in effectively utilizing input data from devices under test, particularly in constructing efficient tensor images for deep learning networks, which limits their predictive capabilities.
Innovation Solution
A user interface and method for building tensor images, allowing users to select and configure types of tensors, such as waveform vectors, images, and S-parameter images, with options for defining short pattern inputs, bar graphs, noise measurements, and user-defined parameters, enabling efficient data organization and transformation into 1D, 2D, or 3D tensor formats for input to deep learning networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data input methods are used for machine learning systems, then the system can process data, but the predictive accuracy and efficiency are limited due to suboptimal data organization
Solution Approach 1:
The patent segments complex measurement data into structured tensor formats with defined dimensions (time, frequency, amplitude). By dividing raw waveform data into organized tensor structures with specific dimensionalities (1D, 2D, 3D), the system improves predictive accuracy while making data organization more manageable through systematic segmentation rather than handling raw unstructured data.
Solution Approach 2:
The patent transforms data representation by introducing dimensional transformations through tensor structures. Raw 1D waveform data is converted into multi-dimensional tensors (2D, 3D) that capture temporal, spectral, and amplitude relationships simultaneously. This dimensional change enables machine learning systems to exploit structural patterns in the data, significantly improving predictive accuracy.
2Productivity
If multi-dimensional tensor data is constructed for machine learning input, then predictive capability improves, but data processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-organizing raw measurement data into structured tensor formats before feeding it to machine learning systems. The system performs preliminary transformations including waveform segmentation, spectral analysis, and tensor construction in advance, so that when data reaches the ML system, it is already optimized for processing, thereby improving prediction efficiency without burdening the ML system with complex data preparation.
Solution Approach 2:
The patent introduces an intermediary data processing layer that acts as a mediator between raw measurement instruments and machine learning systems. This intermediary layer constructs tensor structures from raw data, performing necessary transformations and organization. This mediator handles the complexity of tensor construction, shielding the ML system from complex data processing while enabling efficient predictions.
3Ease of operation
If structured tensor formats are implemented for data input, then data handling efficiency improves, but the initial setup and configuration complexity increases
Solution Approach 1:
The patent implements universality by designing a configurable tensor construction system that can handle multiple data types (waveforms, spectra, time-frequency representations) and multiple tensor dimensionalities (1D, 2D, 3D) through a unified framework. This universal approach allows the same system to process different measurement data types using consistent tensor structures, improving data handling efficiency across various applications while reducing the need for separate processing pipelines.
Data Source
AI summary
A test and measurement instrument includes one or more ports to allow the test and measurement instrument to receive data from a device under test (DUT), a connection to a machine learning network, a display configured to display a user interface, one or more controls to allow the test and measurement instrument to receive inputs from a user, and one or more processors configured to execute code that causes the one or more processors to: render a menu on the display that displays different types of tensors, receive, from the one or more controls, a user selection that identifies a selected type of tensor, and build the selected type of tensor from the data from the DUT and send the selected type of tensor to the machine learning network. A method of providing a user interface is also disclosed.