Soonshin Seo^1,2, Ji-Hwan Kim^2,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2833-2856, 2023, DOI:10.32604/cmc.2023.042816

Abstract Automatic speech recognition (ASR) systems have emerged as indispensable tools across a wide spectrum of applications, ranging from transcription services to voice-activated assistants. To enhance the performance of these systems, it is important to deploy efficient models capable of adapting to diverse deployment conditions. In recent years, on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios. However, these methods often confront substantial trade-offs, particularly in terms of unstable accuracy when reducing the model size. To address challenges, this study introduces two crucial empirical findings. Firstly, it proposes the incorporation of… More >

Qian Sun^1,2, Zhiping Shen^1,2, Jinglin Liang^1,2, Xiaoyi Wang^1,2,*, Jiping Xu^1,2, Li Wang^1,2, Huiyan Zhang^1,2, Jiabin Yu^1,2, Ning Cao³, Ruichao Wang⁴

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3967-3977, 2021, DOI:10.32604/cmc.2021.017252

Abstract Water quality sensor networks are promising tools for the exploration of oceans. Some key areas need to be monitored effectively. Water quality sensors are deployed randomly or uniformly, however, and understanding how to deploy sensor nodes reasonably and realize effective monitoring of key areas on the basis of monitoring the whole area is an urgent problem to be solved. Additionally, energy is limited in water quality sensor networks. When moving sensor nodes, we should extend the life cycle of the sensor networks as much as possible. In this study, sensor nodes in non-key monitored areas are moved to key areas.… More >