Nasopharyngeal carcinoma (NPC) is a category of tumours with a high incidence in head-and-neck. To treat nasopharyngeal cancer, doctors invariably need to perform focal segmentation. However, manual segmentation is time consuming and laborious for doctors and the existing automatic segmentation methods require large computing resources, which makes some small and medium-sized hospitals unaffordable. To enable small and medium-sized hospitals with limited computational resources to run the model smoothly and improve the accuracy of structure, we propose a new LW-UNet network. The network utilises lightweight modules to form the Compound Scaling Encoder and combines the benefits of UNet to make the model both lightweight and accurate. Our model achieves a high accuracy with a Dice coefficient value of 0.813 with 3.55 M parameters and 7.51 G of FLOPs within 0.1 s (testing time in GPU), which is the best result compared with four other state-of-the-art models.

Behavioral context recognition aims to identify an individual’s situational state and has broad application prospects across multiple fields. With the proliferation of smart devices such as smartphones and wearables, sensor data have become powerful tools for recognizing human behavior. Although deep neural networks have shown strong performance in behavioral context recognition, they typically rely on static inference structures. However, given the inherently multimodal and multi-label nature of this task, such static designs exacerbate computational complexity, leading to excessive computational demands that hinder deployment on resource-constrained devices. To address these issues, we propose EBCR, a computation-efficient framework for multimodal multi-label behavioral context recognition. The framework employs a hierarchical modality aggregation mechanism to reduce redundant multimodal computation and introduces a relation-aware loss function to explicitly model label dependencies, thereby improving both recognition accuracy and the reliability of early-exit decisions. On the Extrasensory and ETRI Lifelong 2020 datasets, EBCR achieves a balanced accuracy of 74.6% and 63.0%, respectively, comparable to state-of-the- art methods. When deployed on a Raspberry Pi 5, EBCR operates with only 19.21–25.95 MFLOPs, achieving 4–5 ms latency and 0.020–0.032 J energy consumption, resulting in 1.1×–2.5× faster inference and 17.6%–66.7% lower energy cost compared to existing models. These results demonstrate the effectiveness of EBCR in achieving accurate and efficient multi-label behavioral context recognition under real-world resource constraints.

Timely detection of incipient slip is critical for delicate robotic grasping and dexterous manipulation. However, existing learning-based methods suffer from detection latency and high computational demands. In this paper, we present an ultra-fast lightweight incipient slip detection framework based on hyperdimensional (HD) computing, using the PapillArray optical tactile sensor. Our approach introduces a novel graphical-spatial-temporal HD encoding scheme coupled with a context-driven training and inference strategy, achieving a slip detection accuracy of 91.78% in offline evaluation. The resulting model is exceptionally compact and highly edge-compatible, with a size of only 0.375 kB. Furthermore, hardware acceleration on an FPGA enables inference within 0.42 microseconds, representing an over $10^{4}\,\times$ speedup compared to optimized CPU implementations. Online robotic experiments involving grip-force control based on the proposed slip detection method further validate its practical effectiveness. This work offers a practical and scalable solution for real-time slip detection in robotic manipulation tasks.