Integrating deep learning with environmental perception enhances robotic adaptability to complex tasks. However, its “black-box” nature, such as the lack of uncertainty quantification, poses challenges for safety-critical applications, particularly in unstructured and noisy environments. Bayesian neural networks (BNNs) offer uncertainty quantification but are limited by high hardware overhead, restricting real-time implementation on resource-constrained robots. This paper presents a mixedsignal hardware accelerator for BNNs, utilizing probabilistic quantum tunneling in fully depleted silicon-on-insulator (FDSOI) transistors to enable efficient, real-time uncertainty quantification. Device measurements indicate high-quality Gaussian random variable generation, validated through quantile-quantile plot analysis, with a high correlation coefficient ($r=0.997$) at $200 \mathrm{fJ} /$ sample. Leveraging such compact randomness, the parallel architecture achieved $10^{3}-10^{4} \times$ latency reduction at less than $2 \times$ area cost. Finally, in uncertainty-aware visual localization application of autonomous underwater vehicles, the BNN model effectively distinguishes data noise from model uncertainty, yielding significant information gain and enhancing the resampling efficiency by $4.5 \times$ at same accuracy.

Next-generation Internet of Medical Things (IoMT) must balance efficient local decision-making with strong privacy protection for remote monitoring and diagnosis. However, the resource constraints of IoMT devices make this difficult. This paper introduces a novel bio-signal encoder within the hyperdimensional computing (HDC) framework. It leverages inherent transistor variations for physically unclonable encoding. This technique is called variation-based analog entropy (VAE). VAE reduces memory footprint and power consumption while enhancing security. It offers a scalable, energy-efficient solution that addresses IoT’s resource limitations while ensuring secure, intelligent healthcare applications.The VAE cell achieves high entropy robustness (30.23-57.76 dB signal-to-noise ratio) with only a 10-transistor footprint. It reduces HDC vector dimensions by 14.3× and improves accuracy by 2%. Compared to an SRAM baseline, it shrinks encoder area by 1.3-4.4× and cuts leakage power by 327×. Custom analog circuits for entropy management eliminate data conversion, boosting energy efficiency to 48.5 nJ per query. Evaluations on experimentally collected bio-fluid data demonstrate classification accuracy of 94.9 % and 97.6 % in 5-class and 3-class viscosity sensing, respectively. This highlights the potential of the system for IoMT applications. Furthermore, the VAE significantly enhances security, lowering attacker-restored data peak SNR by 16 dB, and making unauthorized recovery indistinguishable.

Recognizing continuous human action is a fundamental task in many real-world computer vision applications including video surveillance, video retrieval, and human-computer interaction, etc. It requires to recognize each action performed as well as their segmentation boundaries in a continuous sequence. In previous works, great progress has been reported for single action recognition, by using deep convolutional networks. In order to further improve the performance for continuous action recognition, in this paper, we introduce a discriminative approach consisting of three modules. The first feature extraction module uses a two stream Convolutional Neural Network to capture the appearance and the short-term motion information from the raw video input. Based on the obtained features, the second classification module performs spatial and temporal recognition and then fuses the two scores from respective feature stream. In the final segmentation module, a semi-Markov Conditional Field model, capable of handling long-term action interactions, is built to partition the action sequence. As can be seen in the experimental results, our approach obtains state-of-the-art performance on public datasets including 50Salads, Breakfast, and MERL Shopping. We have also visualized the continuous actions segmentation results for more insightful discussion in the paper.