High-g environments—where acceleration forces greatly exceed normal gravity—pose serious challenges for embedded electronics used in aerospace, defense, automotive crash systems, space missions, and industrial machinery. These extreme forces, often measured in multiples of “g” (with 1g equal to Earth’s gravity), can reach hundreds or even thousands of g during missile launches, projectile deployment, rocket stage separation, or high-speed impacts. Under such conditions, embedded systems must continue to operate reliably without signal degradation, structural failure, or data corruption.
Global Network & Technology Excellence Awards
Program Manager at Scifax
Friday, February 13, 2026
Wednesday, February 11, 2026
How Smart Home Chatbots Boost Your Cybersecurity Game!
The growth of smart home devices does not only increase at-home convenience but also amplifies cybersecurity risks.
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Tuesday, February 10, 2026
Similarity-Aware Surrogate-Assisted NAS with Graph Neural Networks
Similarity surrogate-assisted evolutionary neural architecture search based on graph neural networks sits right at the intersection of efficiency and intelligence in modern AutoML. As neural architectures grow more complex, evaluating every candidate through full training becomes painfully expensive. This approach tackles that bottleneck by using a surrogate model that can predict performance, allowing the evolutionary search to focus its energy on the most promising architectures instead of wasting computation on weak candidates.
At the core of this idea is the representation of neural architectures as graphs. Layers become nodes, connections become edges, and the entire network structure is naturally encoded in a graph format. Graph neural networks (GNNs) are especially well-suited here because they can learn structural patterns and relationships directly from this representation. By processing these architecture graphs, the GNN surrogate learns how different design choices influence performance metrics such as accuracy, latency, or energy consumption.
The “similarity” aspect adds another smart layer to the process. Instead of treating each architecture in isolation, the surrogate model leverages similarity measures between architectures—often based on graph embeddings. Architectures that are structurally similar tend to exhibit similar performance, and the surrogate exploits this intuition to make more accurate predictions, even with limited training data. This significantly improves sample efficiency, especially in the early stages of the evolutionary search.
Within the evolutionary framework, the surrogate model guides selection, mutation, and crossover operations. Poorly predicted architectures can be filtered out early, while promising ones are refined and evolved further. Periodically, a small subset of architectures is fully trained to correct and update the surrogate, ensuring that prediction errors do not accumulate and mislead the search over time.
Overall, this combination of similarity-aware surrogates, evolutionary optimization, and graph neural networks creates a powerful and scalable NAS strategy. It balances exploration and exploitation more effectively than brute-force methods, reduces computational cost, and opens the door to searching much larger and richer architecture spaces—making high-performance neural design more accessible and sustainable.
Wednesday, February 4, 2026
How Physics-Informed AI Makes Robotic Mirror Milling Smarter!
Mirror milling technology is widely used in the aerospace industry for manufacturing thin-walled parts, yet existing machine tool-based mirror milling systems are costly and inflexible.
Monday, February 2, 2026
How to Outsmart Cyberattacks in Supply Chains! 🚀
Consumers increasingly seek online companionship in their daily lives. Alongside traditional real human online companionship, a novel form of online companionship through virtual humans powered by artificial intelligence (AI), has rapidly gained prominence.
Friday, January 30, 2026
Feeling Lonely? Virtual Buddies to the Rescue! 🤖💬
Consumers increasingly seek online companionship in their daily lives. Alongside traditional real human online companionship, a novel form of online companionship through virtual humans powered by artificial intelligence (AI), has rapidly gained prominence.
Level Up Safety: WIST Design Secrets in 60 Seconds!
Immersive virtual reality is increasingly used for safety training, yet many initiatives remain technology-led pilots that enhance scenario realism and engagement without explaining how training becomes embedded in everyday work (e.g., alignment with SOPs, assessment routines, scheduling, and accountable debrief practices) or how skills reliably transfer back to duty.
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