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分類: Edge AI

Lanner, in collaboration with Techman Robot, introduces an advanced AI-powered Robotic Automated Optical Inspection (AOI) solution designed for precision industrial product inspection. This innovative system combines Techman’s collaborative robotic arm, equipped with a built-in AI vision system, and Lanner’s industrial-grade EAI-I730 robotic controller featuring an NVIDIA RTX 6000 ADA GPU. The integration of robotic automation and edge AI enables highly accurate, real-time defect detection in manufacturing environments.

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分類: Edge AI

Hospitals and healthcare facilities face significant challenges in managing pharmaceutical inventories. Manual tracking of drug stocks is prone to errors, leading to shortages, overstocking, and compliance risks. Inefficient inventory management impacts patient care and increases operational costs.

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分類: Edge AI

Smoke detection is a cornerstone of fire safety, as smoke is often the earliest indication of a potential fire hazard. However, traditional smoke detectors can be delayed in identifying smoke, particularly in expansive spaces or areas with high ceilings. Addressing these limitations, the Lanner Edge AI Security Solution—AI Smoke Police (AISP)—offers a sophisticated, AI-based approach to real-time fire and smoke detection, ensuring quicker response times and enhanced safety measures.

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分類: Edge AI

As the global energy grid faces increasing challenges from aging infrastructure and insufficient automation, advanced monitoring solutions are essential to protect critical infrastructure such as substations, drilling rigs, and oil and gas facilities. Modern technologies, such as AI-powered computer vision and thermal detection systems, provide comprehensive solutions to mitigate risks, enhance safety, and ensure operational continuity.

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分類: Edge AI

As generative AI models such as Large Language Models (LLMs) become integral to various industries, ensuring the privacy and security of sensitive data is critical. LLMs often interact with sensitive corporate or even personal data during training and inference, posing significant risks to privacy; such challenges must be addressed by de-identifying and securing sensitive information throughout the AI lifecycle.

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分類: Edge AI

Gun-related violence presents an escalating threat to public safety, emphasizing the urgent need for faster detection and response to minimize casualties. Traditional security systems reliant on human monitoring or reactive alerts often face delays in response times, increasing the risk of harm.

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分類: Edge AI

As 5G networks evolve, they enable ultra-low latency, faster speeds, and massive device connectivity. However, traditional CPU-based systems have struggled to handle computationally intensive virtualized Radio Access Network (vRAN) functions, creating bottlenecks in performance. To address these challenges, integrating AI at the edge—where data processing is closer to the source—has become essential. AI-accelerated infrastructure optimizes latency and bandwidth usage while supporting critical network requirements such as software-programmable Network Operating Systems (NOS) for RAN operations.

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