詳細
カテゴリ: 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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カテゴリ: 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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カテゴリ: Power and Energy

Onshore drilling rigs operate in remote, hazardous environments where safety, reliability, and operational continuity are pivotal. The complexity of these environments requires robust OT (Operational Technology) security to protect sensitive industrial control systems (ICS) from cyber threats, unauthorized access, and operational disruptions.

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カテゴリ: 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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カテゴリ: SD-WAN

To enhance secure, resilient, and efficient communication between remote ATMs, bank branches, and headquarters in the era of digital banking. As financial organizations increasingly adopt advanced self-service and digital solutions, deploying a robust SD-WAN architecture using uCPE (Universal Customer Premises Equipment) is essential for maintaining reliable end-to-end connectivity across distributed locations.

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カテゴリ: Network Computing

In today’s AI-driven landscape, modern AI infrastructure faces several core challenges: handling massive data volumes, managing high-speed GPU-accelerated computations, and ensuring low-latency networking across AI resources. These  infrastructures must support parallel processing for demanding tasks like AI training and inferencing, necessitating an advanced, high-throughput network to deliver seamless, secure, and efficient AI workloads.

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カテゴリ: 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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