The Smart Hat is an advanced wearable assistive system designed to improve mobility and independence for visually impaired users. It integrates voice-guided navigation, real-time object detection, ultrasonic obstacle sensing, and contextual feedback to enable safe and independent movement. Built on a Raspberry Pi 5 platform with edge computing capabilities, the system ensures low-latency processing while utilizing secure cloud services (Firebase, Firestore, Google Cloud Platform) for data storage and analysis. Real-time remote monitoring is facilitated through Ngrok. Experimental validation demonstrates robust performance in obstacle avoidance, accurate navigation, and responsive feedback across diverse environments. By enhancing user autonomy and confidence, this innovative solution highlights the transformative potential of wearable assistive technologies in improving accessibility for the visually impaired.
Cyber attacks on AI systems are recognized as a significant threat. In particular, adversarial attacks, which digitally perturb input data to cause AI to make incorrect predictions, are a serious concern. Recently, such attacks have been pointed out to be applied in the physical world. For instance, attackers place transparent stickers with adversarial perturbations on a camera lens to disrupt AI image perception. When AI-based factory automation (FA) systems are exposed to such threats, the AI systems can, for example, fail to detect defective products or internal system faults. These failures can lead to economic losses and system disruptions. In this paper, we implement the physical adversarial attacks and confirm their feasibility against AI-based FA systems through two experiments: (1) simulation experiments using the open-source FA-specific dataset (MVTec AD), and (2) a real-world experiment where physical stickers perturb camera lenses. As a result, in both evaluations, it is quantitatively shown that our attacks successfully caused malfunction in AI-based FA systems and that images perturbed by our method maintained high similarity to the original images. These experimental results reveal that adversarial attacks are feasible and pose a serious threat to FA systems.
Neural processing units (NPUs) have become essential in modern client and edge platforms, offering unparalleled efficiency by delivering high throughput at low power. This is critical to improve the TOPS/W of the NPU, leading to longer battery life. While NPUs were initially designed to efficiently execute computer vision (CV) workloads such as CNNs, the rising demand to run transformer-based large language models (LLMs) locally now calls for significant architectural and software adaptation. This paper presents LLM-NPU, a comprehensive software-hardware co-optimization framework that enables scalable, power-efficient LLM deployment on NPUs under tight compute and memory budgets. We present software solutions such as vertical and horizontal operator fusion, quantization-aware weight compression, hybrid key-value (KV) quantization, eviction strategies, and static-shape inference that target memory bottlenecks and compute inefficiencies in LLM execution. On the hardware side, we explore domain-specialized NPU enhancements, including processing-in-memory architectures, extended input channel accumulation, structured sparsity acceleration, GEMM engine optimizations, mixed precision, microscaling format support, and fusion-aware execution pipelines. These co-designed innovations can collectively improve the energy, throughput, and latency of NPUs for LLM workloads.
Small Modular Reactors (SMRs) are an emerging nuclear energy technology offering the potential to reduce industrial loads on the electric grid to improve frequency stability and power quality. These compact nuclear facilities provide reliable energy, especially in remote areas. However, integrating SMRs into the bulk electric system introduces new cyber-physical vulnerabilities, including increased exposure to cyberattacks and potential cascading disruptions. This paper introduces a Cyber-Informed Design framework tailored to Cyber-Physical Energy Systems incorporating SMRs. The framework employs attack trees to model threat scenarios and identify vulnerabilities across four functional layers of SMR stations. We apply the threat modeling approach to a reference architecture and map the attack paths to the MITRE ATT&CK for Industrial Control Systems to provide insights into improving the resilience of cyber-physical energy systems.
Caching systems require not only a high hit rate but also adaptability and scalability to effectively manage diverse workloads. This study explores the possible directions of using machine learning for adaptive caching. We explore the implementation of several caching strategies from recent literature, including SIEVE, S3-FIFO, and TinyLFU, highlighting the limitations of traditional methods. We leverage the development of these new caching strategies in combination with machine learning to improve adaptability at small cache sizes.We test LeCaR, a model-driven approach that switches between LRU and LFU, on a real-world trace from Microsoft Azure. We build on it by incorporating multiple combinations of traditional and modern methods. We note that modified_LeCaR, with LRU, LFU, and S3-FIFO, tested on real-world traces, demonstrates improved performance over the original LeCaR, particularly in scenarios with smaller cache sizes. modified_LeCaR’s strong performance on smaller cache sizes makes it well-suited for resource-constrained systems (e.g., embedded systems). Especially because we do not use resource-heavy algorithms (e.g., neural networks).
