Overview
Co-host: Optica
A boxcar averager enhances the signal-to-noise ratio (SNR) of periodic signals. This powerful tool is commonly employed in characterization techniques such as microscopy, sensor signal acquisition, and telecommunications. While alternative approaches like lock-in detection can also filter out high-frequency noise components, the boxcar averager offers distinct advantages for processing low-duty-cycle signals. In this webinar, we explore the fundamental principles of the boxcar averager and compare its applications to other averaging techniques. We also demonstrate its capabilities using Moku Cloud Compile, a versatile tool included on FPGA-based Moku devices that enables users to quickly deploy custom functions with no FPGA programming experience required. For expert insights on extracting signals from periodic, low-duty-cycle data using a boxcar averager, deploying premade bitstreams with Moku Cloud Compile, and optimizing your setup for real-time analysis, check out our webinar Q&A blog.
Watch on demand to:
- Learn how a boxcar averager with a static gate extracts key information from a noisy, low-duty-cycle signal, with a trigger signal and configurable system parameters.
- See the waveform recovery mode of the boxcar averager with the Moku Python API.
- Learn how to subtract DC baselines using a dual boxcar averager.
- Find out new ways to utilize the boxcar averager in conjunction with other FPGA-based Moku instruments to create custom signal processing pipelines in Multi-Instrument Mode.
The webinar includes a presentation, demonstration, and live Q&A session.
Who should attend:
- AMO physicists
- Optics and photonics researchers, engineers, and academics
- Scientists working in microscopy and spectroscopy
- Scientists working in laser frequency stabilization and interferometry
- R&D scientists
- Lab managers