Cooperative inference in distributed sensor networks is challenged by limited communication bandwidth and the risk of node failures. This paper introduces Compressed Feature Diffusion for Decentralized Classification (CFD-DC), a novel framework that addresses these challenges. Each node performs local inference using its own features and compressed feature representations received from other nodes. Our approach relies on two key components: first, a trainable feature compressor at each node that learns compact representations, reducing communication while preserving critical discriminative information; second, an adaptive node weighting mechanism that dynamically adjusts the influence of local and remote features, providing robustness to unreliable or failed nodes. Experiments on multi-view image classification and a simulated multi-node underwater acoustic target classification task demonstrate the effectiveness of the framework. The results show competitive performance compared to centralized and state-of-the-art multi-view methods, reduced communication costs, and superior robustness in scenarios with node failures.

This paper presents the design and implementation of a multimodal person-following system for a mobile telepresence robot. A color histogram matching and position matching algorithm was developed for a person-recognition function using Kinect sensors. Robot motion was controlled by adjusting its velocity according to the humans position in relation to the robot. The robot was able to follow the targeted person in various person-following modes, such as the back-following mode, the side-by-side accompaniment mode as well as the front-guiding mode. An obstacle avoidance function was also implemented using the virtual potential field algorithm.