Extensive experiments demonstrate that the proposed framework achieves better performance.
Our modular pathway framework achieves carbon-chain extension by two different mechanisms.
This general framework achieves high-quality crease surface representations at interactive frame rates.
Results: The implemented framework achieved encouraging results in human activity recognition.
The results demonstrate that the proposed framework achieves higher detection accuracy 87% and clustering quality 0.99 compared to existing approaches.
Theoretical analysis and experimental results show that our framework achieves optimal communication and computation efficiency compared to other protocols.
Simulation results show that the proposed framework achieves higher throughput, lower end-to-end delay, and an increased network longevity.
The results indicate that our proposed selection framework achieves significant performance gains over the naive selection methods under different scenarios.
Experimental results demonstrate that on average our framework achieves performance yield improvement of 45% than the deterministic scheme.
The proposed multimodal framework achieves 1.84% and 2.60% gains as compared to uni-modal framework on single and double hand gestures, respectively.
