Soonshin Seo¹, Junseok Oh², Eunsoo Cho², Hosung Park², Gyujin Kim², Ji-Hwan Kim^2,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3291-3303, 2022, DOI:10.32604/cmc.2022.026259

Abstract Acoustic scene classification (ASC) is a method of recognizing and classifying environments that employ acoustic signals. Various ASC approaches based on deep learning have been developed, with convolutional neural networks (CNNs) proving to be the most reliable and commonly utilized in ASC systems due to their suitability for constructing lightweight models. When using ASC systems in the real world, model complexity and device robustness are essential considerations. In this paper, we propose a two-pass mobile network for low-complexity classification of the acoustic scene, named TP-MobNet. With inverse residuals and linear bottlenecks, TP-MobNet is based on MobileNetV2, and following mobile blocks,… More >

Adeeb Salh¹, Lukman Audah^1,*, Qazwan Abdullah¹, Nor Shahida M. Shah², Shipun A. Hamzah¹, Shahilah Nordin³, Nabil Farah²

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3189-3214, 2021, DOI:10.32604/cmc.2021.014746

Abstract Energy efficiency (EE) is a critical design when taking into account circuit power consumption (CPC) in fifth-generation cellular networks. These problems arise because of the increasing number of antennas in massive multiple-input multiple-output (MIMO) systems, attributable to inter-cell interference for channel state information. Apart from that, a higher number of radio frequency (RF) chains at the base station and active users consume more power due to the processing activities in digital-to-analogue converters and power amplifiers. Therefore, antenna selection, user selection, optimal transmission power, and pilot reuse power are important aspects in improving energy efficiency in massive MIMO systems. This work… More >