Liquid Instruments

Introducing Moku Version 3.3 and the new Neural Network instrument! Unlock the power of #AI to deploy #machinelearning models to physical systems for fast, flexible signal analysis, denoising, sensor conditioning, closed-loop feedback, and more. Check out all the latest updates: https://hubs.ly/Q02VLDs10

Visvesvaraya Technological University, one of the largest technical universities in #India, recently integrated Moku:Go into its #engineering syllabus, bringing reconfigurable #testandmeasurement to thousands of undergraduate students at more than 150 colleges. With VTU's new foundation in place, Amitesh Kumar of Liquid Instruments shares what's next for expanding this impact, from supporting students and faculty with learning labs to expanding local partnerships. Read the interview: https://lnkd.in/gDpsgyvi

Have you used the new Moku #NeuralNetwork to run real-time #machinelearning algorithms in line with your experimental setups? ➡️ Build and train models with Python, then deploy to your test systems with Moku:Pro. ✅ Make data processing fast, easy, and intelligent. 🔄 Implement real-time, closed-loop feedback systems. 💡 Process large datasets quickly. 💻 Make machine learning accessible and effective. Learn more and review how to update your software: https://hubs.ly/Q02_0ZTG0

Meet the team that started it all, and share in our vision of how reconfigurable instrumentation is changing the future of #testandmeasurement. #FlashbackFriday https://hubs.ly/Q02-L34X0

It's easy to classify complex signals or detect anomalies by building a #neuralnetwork classifier with the Moku Neural Network. First, identify when a trigger signal has occurred or if an anomaly is present. Then, output the likelihood of occurrence to the analog outputs or another embedded Moku instrument for fast, simple signal analysis. Get the example: https://hubs.ly/Q02-D7PH0

Did you know that the flexibility and real-time processing of #FPGAs makes them strong candidates for implementing small-scale neural networks like autoencoders? In this video, Liquid Instruments engineer Jessica Patterson creates a pulsed radar signal and obscures it with noise. Then, she uses the Moku #NeuralNetwork to decode and reconstruct the original signal with an #autoencoder network in real time. Check it out: https://hubs.ly/Q02-qxzy0

🇦🇺 Who else is excited to be at The Australian Institute of Physics 2024 Congress in Melbourne? Come visit us at booth T3 to learn how reconfigurable instrumentation enhances flexibility and speed anywhere you need to test. Plus, see how our latest instrument enables new ways to connect #AI to the physical world. 💡

📣 New #webinar available on demand. Learn helpful strategies to modernize experimental setups with reconfigurable #testandmeasurement instrumentation. Watch now to explore: 🕦 The advantages of real-time digital signal processing. 📏 How to leverage a digital-first approach to quickly adapt instrumentation to your measurement requirements. 🔃 How to build sophisticated signal-processing pipelines with multi-instrument capabilities to construct and characterize PLLs. Check it out: https://hubs.ly/Q02-9Lgw0

"Frankly, Moku:Lab is in great demand in our lab. People are always fighting to use it at any given time — it’s very popular in our group.” When generating frequency combs, maintaining consistent timing between #laser pulses is essential. Slight variations in laser pulse timing can introduce problematic jitter and timing errors into the system. Learn how University of Colorado Boulder researchers are advancing #LiDAR and remote sensing applications with a single-cavity, dual-comb laser source and flexible, #FPGA-based instruments.

📣 November issue of Measuring Up out now. Check out Moku milestones from our year in review and see upcoming events.