The Metaverse, a hypothetical virtual space where people interact with digital environments, has gained attention as a transformative concept in various industries like education, healthcare, and business. However, a comprehensive understanding of the Metaverse’s research landscape and future prospects is essential to guide further exploration and decision-making in this dynamic field. On the basis of the preliminary analysis and the existing literature, the comprehensive bibliometric analysis of the Metaverse is anticipated to reveal a number of themes that span the vast spectrum of its applications. The objective of the analysis is to provide a comprehensive overview of the current state and future prospects of the Metaverse by analyzing past and present research trends and predicting future research directions. The research collected 928 journal articles from the Web of Science database using bibliometric methods. Through co-citation and co-word analysis, the study identifies the most influential publications, outlines the knowledge structure, and forecasts future developments. The study identified four distinct clusters for co-citations analysis and five distinct clusters for co-word analysis. Despite the growing importance of the Metaverse in various aspects of human life, more scholarly efforts are required to provide a comprehensive overview of its research landscape. This article provides illuminating insight into the expanding field of Metaverse research and its applications to human life. The study concludes with a comprehensive explanation of Metaverse research and perspectives on the field’s future development. This is the first study to present a knowledge structure of the Metaverse via a science mapping methodology.

In research of rich media data, how to extract and utilize semantic information based on the data is essential. With the development of deep learning technology, semantic information extraction and analysis is becoming more mature. Regarding the recent research on audio data in a home scene, infants’ crying is a specific topic besides human speech sounds. However, in current research, infants’ crying data are usually generated from quiet laboratory environments with limited noise, and there are few studies on the detection of infants’ crying in a high-noise environment, while noise is inevitable in the home scene. Based on this observation, this paper will study the feasibility of using deep learning techniques to achieve crying detection in high-noise environments. The results have identified that the Convolutional Neural Networks (CNN) combined with spectrogram as crying detectors can achieve the relatively robust performance of 12% over that with MFCC in high-noise environments, which is recommended as a baseline for further work.

New architectures are proposed for the realization of micro-power analog-to-digital data converters. They are based on a time-interleaving and pipelining successive-approximation-register (SAR) structure. The resulting ADCs require very low power dissipation for medium-speed and medium-accuracy data conversion.

A new architecture is proposed to reduce the power consumption and capacitor area in successive-approximation register analog-to-digital converters (SAR ADCs). Two junction-split binary-weighted capacitor arrays are used in a coarse/fine quantization scheme. This reduces both the power consumption and the capacitor area to a small fraction of that of the original split-junction SAR ADC.