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JP-2026075056-A - Test measurement system and signal generation method

JP2026075056AJP 2026075056 AJP2026075056 AJP 2026075056AJP-2026075056-A

Abstract

[Problem] To replicate signals from a device under test with specific environmental and channel conditions. [Solution] The test measurement system 10 includes a plurality of nodes 12, each having one or more sensors configured to receive signals; one or more generative adversarial network (GAN) models; one or more signal generators configured to generate and transmit signals; and one or more processors 16. The system is configured to receive a request for a signal having a signal profile from any of the plurality of nodes 12, send the signal profile and the request to one or more GAN models, receive a signal profile that matches the signal profile, send the matching signal profile to one or more signal generators, and execute a program that causes one or more processors to perform the process of generating and transmitting a matching signal. [Selection Diagram] Figure 1

Inventors

  • キース・アール・ティンズリー
  • デイビッド・エム・バウス
  • サラ・アール・ボーエン
  • ジョサイア・エイ・バートレット

Assignees

  • テクトロニクス・インコーポレイテッド

Dates

Publication Date
20260507
Application Date
20250926
Priority Date
20250819

Claims (10)

  1. A test and measurement system, A plurality of nodes, each including one or more nodes having sensors configured to receive signals, One or more Generative Adversarial Network (GAN) models, One or more signal generators configured to generate and transmit signals, Equipped with one or more processors, The one or more processors The process of receiving a request for a signal with a signal profile from any of the above multiple nodes, The process of sending the above signal profile and the above request to one of the GAN models among the one or more GAN models, The process involves receiving a matching signal profile that matches the above signal profile, A test and measurement system configured to execute a program that causes one or more processors to perform the following processes: transmit the matching signal profile described above to one or more signal generators, and generate and transmit a matching signal.
  2. The test measurement system according to claim 1, wherein the above signal profile includes the environment in which any of the above nodes among the above-mentioned nodes operates, the metadata of the above request, and the potential target of the above matching signal.
  3. One or more of the above processors further, The process of saving the above matching signal to a database, The process involves accessing the above database and determining whether the above signal profile exists in the above database, The test measurement system according to claim 1, configured to execute a program that causes one or more processors to perform the process of transmitting the above signal profile from the above database to one or more of the above GAN models.
  4. The test and measurement system according to claim 1, further configured such that one or more processors execute a program that causes the one or more processors to perform a process to operate the one or more GAN models in order to generate the matching signal profile.
  5. One or more of the above sensors further include a test and measurement device, and the test and measurement device is A port on the channel of the above-mentioned test measurement device receives an input signal from the device under test (DUT), One or more analog-to-digital converters (ADCs) for converting the input signal from the above DUT into a digital representation of the input signal, Having one or more processors, The one or more processors A process for evaluating the characteristics of the above input signal, Using the local GAN model present in the above test measurement device, the process involves matching the characteristics and duplicating the above input signal as a duplicated signal. The test measurement system according to claim 1, configured to execute a program that causes one or more processors to perform the process of sending updates to the above-mentioned local GAN model to a central server.
  6. The process of receiving a request for a signal with a signal profile from a node in the test measurement system, The process involves sending the above signal profile and the above request to one or more Generative Adversarial Network (GAN) models, A process of receiving matching signal profiles that match the above signal profile from one or more of the above GAN models, A signal generation method comprising the process of transmitting the above-mentioned matching signal profile to one or more signal generators, and generating and transmitting a matching signal.
  7. The process of saving the above matching signal to a database, The process involves accessing the above database and determining whether the above signal profile exists in the above database, The signal generation method according to claim 6, further comprising the process of transmitting the above signal profile from the above database to one or more above GAN models.
  8. The signal generation method according to claim 6, further comprising the process of distributing the updated GAN model obtained from the above request to other nodes in the system where the updated GAN model exists.
  9. The process that receives the above signal request is, The sensor node processes the input signal from the device under test (DUT), A process for evaluating the characteristics of the above input signal, The process involves using the local GAN model present on the above node to match its characteristics and duplicate the above input signal as a duplicated signal, The signal generation method according to claim 6, further comprising the process of transmitting update information for the above-mentioned local GAN model to a central server.
  10. The signal generation method according to claim 9, further comprising the process of transmitting the above-mentioned duplicated signal to one or more databases.

Description

This disclosure relates to a test and measurement system, and more particularly to artificial intelligence (AI) for generating waveforms for the test and measurement system and the environment. A waveform is a graph of a signal over time and has a wide variety of functions in electronic circuit testing. The device under test (DUT) generates signals that form waveforms, enabling the analysis of the DUT's performance. A DUT may generate signals in response to signals applied to it. Some test devices, such as arbitrary waveform generators and arbitrary function generators, generate and apply signals. Japanese Patent Publication No. 2024-074289Japanese Patent Publication No. 2023-183409Japanese Patent Publication No. 2011-107138Special Publication No. 9-510301 Tektronix's website introducing "signal generators," [online], [searched September 25, 2025], Internet <https://www.tek.com/ja/products/signal-generators>Tektronix's website introducing "Mixed-Signal Oscilloscopes," [online], [searched September 25, 2025], Internet <https://www.tek.com/ja/oscilloscope-mixed-signal-oscilloscope> Figure 1 shows a diagram of an artificial intelligence-based system for test measurement.Figure 2 shows a flowchart of an embodiment of the closed-loop optimization test and measurement system.Figure 3 shows an embodiment of a sensor node including a test measurement device.Figure 4 shows an embodiment of a node during reception processing.Figure 5 shows an embodiment of a node during the transmission process.Figure 6 shows a flowchart of an embodiment of a method for generating a matching signal.Figure 7 shows an embodiment of an artificial intelligence-based system for testing and measurement under specific environmental conditions. Figure 1 shows an embodiment of a test and measurement system. The test and measurement system 10 includes a network of multiple nodes, such as node 12; one or more servers, such as server 14; and one or more databases, such as database 22. Server 14 is referred to as a "centralized server," meaning that it communicates with many, if not all, nodes 12, not in terms of its physical location. Server 14 and the nodes 12 constituting the test and measurement device may have one or more processors 16, memory 24, ports 18 for server 14 or node 12 to communicate with the device under test (DUT), and a machine learning system 20. The machine learning system 20 may have a separate processor from the main processor, which is programmed to run the machine learning process, and both are represented by processor 16. In this explanation, the term "artificial intelligence (AI)" refers to machine learning, which includes neural networks and other machine learning architectures that have undergone supervised or unsupervised learning to generate output, as well as "generative AI." Generative AI may have a large-scale language model (LLM) and may include a transformer. Generative AI may also refer to GANs, which are likely to operate without the use of transformers. A GAN typically consists of two neural networks. One network, i.e., a generative network or generator, produces an output that mimics the input. The other network, a discriminative network or discriminator, evaluates the output and determines whether it truly mimics the input. The two networks continue to "compete" with each other until the output mimics the input very faithfully, and the discriminator cannot distinguish the differences that would normally be detected by error handling, thus "losing" the discriminator. In this application, the terms "GAN model" or "GAN" refer to a pair of neural networks. Multiple GAN models, i.e., multiple pairs of neural networks, may exist in AI20. System 10 in Figure 1 employs federative learning, and the basic GAN model or "bootstrap" model is present in a large part, if not the entire system. Some of the nodes 12 in system 10 may consist of forwarding nodes or other types of nodes that do not manipulate inputs or outputs. These nodes handle data distribution (routing) and other tasks that do not involve receiving or transmitting signals from outside the system. In this description, nodes that receive signals are referred to as receiving nodes, and nodes that transmit signals outside the system are referred to as transmitting nodes. Many nodes may be both receiving and transmitting nodes. For ease of understanding, as an example, node 12 of system 10 may be considered a sensor node that receives signals. Since waveforms are generated based on signals over time, the terms "signal" and "waveform" are used synonymously. The signal may be a signal from a DUT, or it may consist of "threat" signals such as incoming radar signals. Node 12 receives the signal and generates a response. This response may include replicating the signal using a GAAN and deriving environmental and channel characteristics based on this replication process. Figure 2 shows the elements of the AI component within the AI component 20 and embodiments of t