Sensing technology has been widely investigated and utilized for gas detection. Due to the different applicability and inherent limitations of different gas sensing technologies, researchers have been working on different scenarios with enhanced gas sensor calibration. This paper reviews the descriptions, evaluation, comparison and recent developments in existing gas sensing technologies. A classification of sensing technologies is given, based on the variation of electrical and other properties. Detailed introduction to sensing methods based on electrical variation is discussed through further classification according to sensing materials, including metal oxide semiconductors, polymers, carbon nanotubes, and moisture absorbing materials. Methods based on other kinds of variations such as optical, calorimetric, acoustic and gas-chromatographic, are presented in a general way. Several suggestions related to future development are also discussed. Furthermore, this paper focuses on sensitivity and selectivity for performance indicators to compare different sensing technologies, analyzes the factors that influence these two indicators, and lists several corresponding improved approaches.

In recent years, the concept of the Metaverse has attracted considerable attention. This article provides a comprehensive overview of the Metaverse. First, the development status of the Metaverse is presented. We summarize the policies of various countries, companies, and organizations relevant to the Metaverse, as well as statistics on the number of Metaverse-related publications. Characteristics of the Metaverse are identified: 1) multitechnology convergence; 2) sociality; and 3) hyper-spatio-temporality. For the multitechnology convergence of the Metaverse, we divide the technological framework of the Metaverse into five dimensions. For the sociality of the Metaverse, we focus on the Metaverse as a virtual social world. Regarding the characteristic of hyper-spatio-temporality, we introduce the Metaverse as an open, immersive, and interactive 3-D virtual world which can break through the constraints of time and space in the real world. The challenges of the Metaverse are also discussed.

The intent of this paper is to provide an overview of basic concepts, types, techniques, and experimental studies of the current state-of-the-art Frequency Selective Surfaces (FSSs). FSS is a periodic surface with identical two-dimensional arrays of elements arranged on a dielectric substrate. An incoming plane wave will either be transmitted (passband) or reflected back (stopband), completely or partially, depending on the nature of array element. This occurs when the frequency of electromagnetic (EM) wave matches with the resonant frequency of the FSS elements. Therefore, an FSS is capable of passing or blocking the EM waves of certain range of frequencies in the free space; consequently, identified as spatial filters. Nowadays, FSSs have been studied comprehensively and huge growth is perceived in the field of its designing and implementation for different practical applications at frequency ranges of microwave to optical. In this review article, we illustrate the recent researches on different categories of FSSs based on structure design, array element used, and applications. We also focus on theoretical breakthroughs with fabrication techniques, experimental verifications of design examples as well as prospects and challenges, especially in the microwave regime. We emphasize their significant performance parameters, particularly focusing on how advancement in this field could facilitate innovation in advanced electromagnetics.

With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.

Internet of things (IoT) is fascinating; its future architecture is still under construction. Based on the analysis on the basic and essential characters of IoT, this paper deals with Future IoT architecture in two aspects: Unit IoT and Ubiquitous IoT. Focusing on a special application, the architecture of the Unit IoT is built from man like neural network (MLN) model and its modified model. Ubiquitous IoT refers to the global IoT or the integration of multiple Unit IoTs with "ubiquitous" characters, and its architecture employs social organization framework (SOF) model. The models for Future IoT are not only helpful to interpret the relationship between IoT and reality world, but also beneficial to the implementation of IoT in its current development milieu.