Enhancing Machine Monitoring with ML at the Edge
Labs is working with Tulip’s hardware team to incorporate AI and Machine Learning for anomaly detection at the edge and other potential use cases. Automatic outlier detection can alert personnel to help them manage risks.
Improving Production with ML Insights and Advanced Analytics
The Labs team is working on several projects including using Machine Learning (ML) to streamline operator workflows. Another core project is using process mining and advanced analytics to help engineers automate steps and gain insights for optimizing operations.
Augmenting Operators with RealWear and Hololens
Labs is enabling operators with voice-controlled Tulip applications using wearables to implement hands-free operations like reviewing dashboards and image capturing.
Democratizing Computer Vision Technology
A core Labs project has been developing no-code computer vision detectors that work with off-the-shelf cameras and within Tulip apps. Use cases include anomaly detection, automate document reading, and automated material flow tracking.
Enhancing Tulip Apps with Hyper-Accurate Spatial Intelligence
Tulip and ZeroKey integrated RTLS technology with Tulip apps for automating material arrival alerts and analyzing real-time material location data.
Rapid development, real-world deployment, and evaluation of projected augmented reality applications
Forward Propagation, Backward Regression, and Pose Association for Hand Tracking in the Wild
Huang, Mingzhen; Narasimhaswamy, Supreeth; Vazir, Saif; Ling, Haibin; Hoai, Minh
Tulip Vision: A New Look into Frontline OperationsDec 22, 2021
Bundle Adjustment Optimization for 3D Jigs in Tulip VisionMay 13, 2021
Optimizing the Neural Compute Stick for Vision Models Inference on the EdgeMay 13, 2021
Tulip on Wearable Devices for Hands-Free Frontline AppsFeb 24, 2022
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