Hakki Karaman

Sampling rate matters for IMU estimation accuracy. This new paper investigated the influence of IMU sampling frequency on orientation estimation across a spectrum of movement speeds. Widely used IMU sensor fusion algorithms computed orientations at 10, 25, 50, 100, 200, 400, 800, and 1600 Hz and were compared with marker-based optical motion capture orientations to determine accuracy. Results suggest that the sufficient IMU sampling rate for walking is 100 Hz, running is 200 Hz, and high-speed cyclic movements is 400 Hz. The accelerometer sampling rate is less important than the gyroscope sampling rate. These findings could serve as a foundation to inform wearable IMU development or selection across a spectrum of human gait movement speeds. Paper link: https://lnkd.in/gKDjEqj4 Congrats to: 范冰飞, Luobin Zhang, Shibo Cai, Mingyu Du, Tao Liu, and Qingguo Li! #imu #samplingrate #orientation #wearable

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Introducing Project Loch Ness - a FREE initiative by Ansys seeking to provide users with a detailed library (or data lake) of synthetic aperture radar (SAR) training data to enable the training and validation of AI Models for ground-based target detection. AI represents a distinct breakthrough in processing massive amounts of complex SAR data, but ask anyone involved in this work, and getting your hands on SAR imagery with corresponding accurate ground truth and raw I/Q data, is incredibly challenging. Enter Project Loch Ness, providing a free, detailed training library with all the necessary data for AI model training and validation, all simulated in Ansys RF Channel Modeler. For more information, and to access the complimentary dataset yourself, check out: https://lnkd.in/gUcC8d5n #radar #SAR #AI #defence #aerospace #maritime #ISAR #STK #TestAndEvaluation

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From maps to full #simulation with real-world scenarios 🛣️🔍 In a recent post, Mobileye shared their mapping capabilities at the Dachauerstraße-Wintrichring intersection in Munich. DeepScenario brings such maps to life by providing real-world #scenarios, enabling the full simulation loop. This intersection is part of our vast database covering hundreds of thousands of kilometers of already processed traffic data from all around the globe. The video below shows a quick preview from the same location. 👉 Explore more scenarios in our web app (link in the comments). Kfir Viente, Chris Lichtmannecker #AD #TrafficBehavior #Simulation

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𝗗𝗔𝗙𝗼𝗮𝗺: 𝗗𝗶𝘀𝗰𝗿𝗲𝘁𝗲 𝗔𝗱𝗷𝗼𝗶𝗻𝘁 𝘄𝗶𝘁𝗵 𝗢𝗽𝗲𝗻𝗙𝗢𝗔𝗠 "DAFoam develops an efficient discrete adjoint method to perform high-fidelity multidisciplinary design optimization." 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: 📌 OpenFOAM for multiphysics analysis. 📌 Jacobian-free discrete adjoint approach. 📌 competitive speed, scalability, and accuracy. 📌 convenient Python interface to couple with OpenMDAO for multidisciplinary design optimization (MDO). 📝 Read the full paper: https://lnkd.in/eQwNwJDh 💻 Source code available on github: https://lnkd.in/e-ksd4PD 🌐 Documentation: https://dafoam.github.io/ #OpenFOAM #OpenSource #CFD #Simulation #Optimization #Technology #B2B #Engineering

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How fast can CFD be? 🌊 https://lnkd.in/eDieJeNA 🌊Yesterday, in my livestream, I had an interesting side-chat about water surfaces in computer games. 🌊 Ofc I said CFD is to slow for that. 🌊 BUT IT ISN'T. 🌊 And no. you don't need SPH or LBM or GPU or any fancy three-letter-combination to achieve this. 🌊 It's VoF on CPU, (like 8 cores or something, nothing crazy) 🌊 Implicit Multistepping, Adaptive Mesh Refinement, Modified HRIC. 🤓 True gamers do sound a bit nerdy. Is your VoF simulation slower than realtime? Sounds like a skill issue to me! 😂 #meshedpotato #cfd #vof #visualization #multiphase #simcenter

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