Sorry, you need to enable JavaScript to visit this website.

Gait analysis is often used as part of the rehabilitation program for post-stoke recovery assessment. Since current optical diagnostic and patient assessment tools tend to be expensive and not portable, this paper proposes a novel marker-based tracking system using a single depth camera which provides a cost-effective solution suitable for home and clinic use. The proposed system can simultaneously generate motion patterns even within a complex background using the proposed geometric model-based algorithm and autonomously provide gait analysis results.

This paper addresses channel-robust compressed sensing (CS) acquisition of sparse sources under complexity-constrained encoding over noisy channels in wireless sensor networks. We propose a single-sensor joint source-channel coding method based on channel-optimized vector quantization by designing a CS-aware encoder-decoder pair to minimize the end-to-end mean square error (MSE) distortion of the signal reconstruction. As our key target is to obtain tolerable encoding complexity at the resource-limited sensor, the method relies on vector pre-quantization of the measurement space.

In this paper, we propose a video haze removal system that can be used to assist vehicle drivers and autonomous vehicles in hazy weather conditions. Our design objectives are to provide a high speed system with high perceived resolution that can be integrated in vehicle dashboard. We achieved these objectives by targeting TMS320DM6446 platform, where we propose a distribution of the tasks of the algorithm among the heterogeneous processor cores: ARM, DSP, and VICP.