This work investigates the feasibility of using the diffusion network, a stochastic recurrent neural network, to recognise continuous-time biomedical signals.
Diffusion Network to model biomedical signals, and identi- fies a hardware-amenable method of recognising signals in real time with trained Diffusion Networks.
This work investigates the feasibility of using the diffusion network, a stochastic recurrent neural network, to recognise continuous-time biomedical signals.
Real-time recognition of continuous-time biomedical signals using the Diffusion Network. In Proceedings of the International Joint Conference on Neural Networks ...
Yu-Su Hsu, Tang-Jung Chiu, Hsin Chen: Real-time recognition of continuous-time biomedical signals using the Diffusion Network. IJCNN 2008: 2628-2633.
Yu-Su Hsu, Tang-Jung Chiu, Hsin Chen: Real-time recognition of continuous-time biomedical signals using the Diffusion Network. IJCNN 2008: 2628-2633.
Aug 20, 2024 · Strategies for real-time implementation include using lightweight models (shallow neural networks, decision trees), hardware acceleration ...
Missing: Diffusion | Show results with:Diffusion
Jan 27, 2024 · Through rigorous benchmarking against contemporary time-series synthesis models, our findings illuminate the BioDiffusion model's superior ...
The Diffusion Network(DN) is a stochastic recurrent network which has been shown capable of modeling the distributions of continuous-valued, continuous-.
This work has been able to demonstrate the feasibility of real-time continuous recognition of knee motion using multi-channel MMG signals detected on clothes.