This recorded webinar had industry leaders NVIDIA, Connect Tech and e-con Systems converge to discuss the latest advancements in next-generation autonomous mobility systems. The collaborative event provided attendees with valuable insights into cutting-edge technologies and innovative solutions that drive the evolution of autonomous mobile robots (AMRs). Webinar Resources
Patrick Dietrich, Chief Technology Officer of NVIDIA partner Connect Tech, demonstrates the company’s latest edge AI and vision technologies and products at the 2021 Embedded Vision Summit. Specifically, Dietrich demonstrates how to integrating vision sensors with NVIDIA’s Jetson edge applications. Since the inception of NVIDIA’s Jetson platform, the capabilities of edge AI and vision-enabled systems have grown. [...]
Presented at Embedded World 2021 Digital Since the inception of NVIDIA’s Jetson platform, the capabilities of Edge AI Vision-Enabled systems have grown. Finding solutions to easily integrate vision sensors within embedded hardware has often has led to lengthy product development cycles and additional software requirements. This session will explore how various sensor technologies can [...]
Presented at Embedded World 2021 Digital The capability to deploy advanced AI programs within mobile, robust devices is now a reality. By combining the latest generation of CPU and GPU technology, processing huge data payloads in remote and mobile locations is now possible. Join Connect Tech, NVIDIA’s largest Jetson hardware provider, to learn more [...]
Presented at Embedded World 2021 Digital Edge AI and autonomous machine designs are no longer constrained by lengthy product development cycles. The NVIDIA Jetson platform offers small form factor embedded GPUs that present ideal size vs. power capabilities for AI projects. Join Connect Tech, NVIDIA’s largest Jetson hardware provider, to learn how to reduce [...]
The ever-increasing number of connected devices requiring real-time responses led to the invention of edge computing, computations are completed on the device rather than centralized servers. Applications requiring edge devices to be deployed at scale or with deep learning programs presents a logistical and maintenance challenge — on top of the resource constraints typically present [...]