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RidgeRun Software Developer Blog
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Overcoming the Challenges of Embedded Vision in Robotics
Explore the key challenges of embedded vision in robotics and how RidgeRun helps teams integrate cameras, LiDAR, ROS 2, Edge AI, real-time perception, and low-latency teleoperation into production-ready robotic systems.
Dennise Alvarado


Complete Guide for Embedded Vision in Medical and Healthcare
Embedded vision and Edge AI are transforming medical and healthcare systems by enabling real-time imaging, low-latency streaming, patient privacy protection, and smarter medical device workflows directly at the edge.
Dennise Alvarado


Complete Guide for Embedded Vision in Industrial Automation
RidgeRun helps industrial automation teams integrate embedded vision, Edge AI, real-time video processing, and sensor systems to improve quality inspection, remote monitoring, predictive maintenance, and smarter decision-making on the production floor.**
Dennise Alvarado


NVIDIA Holoscan and the Streaming AI Pipeline Landscape: A Technical Deep Dive
Real-time AI at the edge is reshaping industries from medical devices to broadcast media. NVIDIA’s ecosystem—GStreamer, DeepStream, Holoscan SDK, and Holoscan for Media—offers multiple approaches to building streaming AI pipelines, each with distinct trade-offs in latency, scalability, and architecture. This deep dive clarifies how these frameworks differ and when to use each.
allannavarro1


Evaluating the NXP i.MX95 Platform for Edge AI and Embedded Systems
The NXP i.MX95 platform is designed to meet the growing demands of edge AI and modern embedded systems, combining heterogeneous processing, a dedicated Neural Processing Unit (NPU), and advanced multimedia capabilities. This article provides a decision-focused overview of its architecture, software ecosystem, and key use cases, helping technical leaders evaluate its suitability for industrial, automotive, and intelligent IoT applications.
allannavarro1


Selecting the Proper Device for AI and Deep Learning Inference Acceleration
Select the best hardware device for AI and deep learning inference acceleration
Luis G. Leon-Vega


Action Recognition for Assembly Lines using Deep Learning
A research showing the process of automating action detection of an assembly sequence of a hardware part in a real production line
Michael Gruner


MMA Fighter Detection using Deep Learning and NVIDIA Jetson Nano
RidgeRun ran a research on state-of-the-art algorithms in object detection and trained them for MMA fighters detection in video.
Michael Gruner


NVIDIA DeepStream Reference
RidgeRun is creating a set of Python reference designs to be used as starting points for applications using deep learning in some form.
Michael Gruner


Announcing New Partnership: Coral from Google!
RidgeRun is thrilled to announce its new partnership with Coral from Google. Coral is a complete toolkit that allows you to build...
Michael Gruner


NVIDIA Jetson TX2-Camera image capture latency measurement techniques.
For better understanding of this latest blog from RidgeRun, please read the RidgeRun blog on Diving deep into NVIDIA Jetson TX2 - Video...

ridgerun
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