Why Combine FlexRIO with Python for ML?

NI FlexRIO hardware enables real-time data acquisition at high bandwidths, while Python provides a robust ecosystem for signal analysis and machine learning. When combined, these platforms allow engineers and scientists to build edge-classification systems, adaptive filtering, and AI-assisted diagnostics in test environments.

Architecture Overview

The standard signal pipeline looks like this:

1. ⏺️ **Data Capture:** Using a FlexRIO digitizer module (e.g., NI PXIe-5172)
2. ???? **Preprocessing:** Real-time FPGA filtering, decimation, windowing
3. ???? **Python ML Inference:** Classify, predict, detect using scikit-learn, PyTorch, or TensorFlow
4. ???? **Post-Processing / Storage:** Logging results to PostgreSQL or triggering test actions

This setup typically runs on a PXI controller or adjacent host PC.

Example 1: RF Spectrum Classification

Using FlexRIO’s wideband digitizers and Python-based models, you can classify RF spectrum in real time. Applications include:

• ???? Military: Classify friendly vs unknown transmissions
• ???? Telecom: Identify LTE, 5G, or Wi-Fi bands in complex environments
• ???? Space: Detect spectral interference near ground stations

Python model: Random Forest or 1D CNN trained on FFT slices

Example 2: Anomaly Detection in Analog Sensor Data

In industrial or automotive test benches, FlexRIO captures vibration, acoustic, or electrical signals. Python ML then detects outliers or patterns indicating failure or misalignment. Useful models:

• Isolation Forest for unsupervised anomaly detection
• Autoencoders for pattern deviation in waveforms
• Time-series clustering to separate normal vs faulty cycles

FPGA Offloading & Real-Time Considerations

FlexRIO FPGAs can offload repetitive preprocessing tasks like decimation, FIR filtering, and spectral transformations—leaving Python to focus on ML logic. LabVIEW FPGA or HLS tools can help build these blocks.

Latency tip: Ensure PCIe throughput is optimized and models are quantized if deploying on embedded CPUs.

Best Practices

• ???? Use pyvisa for control and numpy/scipy for feature engineering
• ???? Avoid oversized models; quantize for speed
• ???? Log test results and classification confidence to Data Acquisition & Control infrastructure
• ???? Store code and test conditions in GitLab CI for version control

FAQs

Do I need GPU acceleration?

Not necessarily. Most real-time applications work with CPU-optimized models if data sizes are reasonable.

Can I train models on captured FlexRIO data?

Yes. Use saved waveform files or streaming data logs to build your datasets in Python or MATLAB.

Is LabVIEW required?

Only for FPGA programming. Otherwise, Python can control FlexRIO via NI-VISA or system APIs.

FlexRIO with Python unlocks a new generation of test systems that not only measure, but also interpret signals with machine intelligence. From RF classification to anomaly detection, real-world applications are already transforming R&D and production. Explore PXI Modules and Data Acquisition & Control solutions to get started with signal-aware automation today.