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With the fifth generation (5G) communication technology, the mobile multiuser networks have developed rapidly. In this paper, the performance analysis of mobile multiuser networks which utilize decode-and-forward (DF) relaying is considered. We derive novel outage probability (OP) expressions. To improve the OP performance, we study the power allocation optimization problem. To solve the optimization problem, we propose an intelligent power allocation optimization algorithm based on grey wolf optimization (GWO). We compare the proposed GWO approach with three existing algorithms. The experimental results reveal that the proposed GWO algorithm can achieve a smaller OP, thus improving system efficiency. Also, compared with other channel models, the OP values of the 2-Rayleigh model are increased by 81.2% and 66.6%, respectively.

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He, C., Liu, J., Zhu, Y., & Du, W. (2021). Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review. Frontiers in Human Neuroscience, 15, 765525. https://doi.org/10.3389/fnhum.2021.765525

Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.

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Chau, M., & Lei, W. C. (2021). Nowcasting Gross Domestic Product and Gross Gaming Revenue of Macau: a Theoretical and Conceptual Framework. 113–136. https://www.researchgate.net/publication/362831464_Nowcasting_Gross_Domestic_Product_and_Gross_Gaming_Revenue_of_Macau_a_Theoretical_and_Conceptual_Framework

This paper aimed to build up the theorical and conceptual understanding of future forecasting study of Macau’s GDP and Gross Gaming Revenue (GGR) by co-movement of economic indicators. Macau GDP and GGR showed co-movements with a number of time series economic indicators, including China’s exports and imports, China’s manufacturing PMI, non-manufacturing PMI, China's electricity production growth, share price of some Macau’s gaming operators, etc. These time series data can be found in statistics departments of China, Macau and Hong Kong, stock exchanges, and international organizations such as the International Monetary Fund (IMF), the World Bank, the World Trade Organization (WTO). Burns and Mitchell’s study in 1946 identified co-movements between economic indicators and being further carried out and developed leading, coincident and lagging indicators, which is essential for future econometric models and nowcasting techniques developments to study these co-movements. In particular, with the proper application of nowcasting techniques, future studies can exploit the data of leading and coincident economic indicators to forecast Macau’s GDP and GGR within an acceptable level of error. Since Macau is a “monotown,” where the gaming revenue makes a significant contribution to the economy. The forecasting of gaming revenue is crucial as it aids the gambling and tourism industries in preparing supply and provides information to policymakers to plan for the near future. This research also contributes to understand Macau’s economy by investigating its internal and external economic variables.

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Ge, Y. (2021, December). 澳門教師文化回應教學認知與實踐研究 [Vitural Conference]. 3rd Summit of Youth Education and Development in Hong Kong and Macau, Shenzhen.

Marques, J. A. L., Han, T., Wu, W., Madeiro, J. P. do V., Neto, A. V. L., Gravina, R., Fortino, G., & de Albuquerque, V. H. C. (2021). IoT-Based Smart Health System for Ambulatory Maternal and Fetal Monitoring. IEEE Internet of Things Journal, 8(23), 16814–16824. https://doi.org/10.1109/JIOT.2020.3037759

The adoption of IoT for smart health applications is a relevant tool for distributed and intelligent automatic diagnostic systems. This work proposes the development of an integrated solution to monitor maternal and fetal signals for high-risk pregnancies based on IoT sensors, feature extraction based on data analytics, and an intelligent diagnostic aid system based on a 1-D convolutional neural network (CNN) classifier. The fetal heart rate and a group of maternal clinical indicators, such as the uterine tonus activity, blood pressure, heart rate, temperature, and oxygen saturation are monitored. Multiple data sources generate a significant amount of data in different formats and rates. An emergency diagnostic subsystem is proposed based on a fog computing layer and the best accuracy was 92.59% for both maternal and fetal emergency. A smart health analytics system is proposed for multiple feature extraction and the calculation of linear and nonlinear measures. Finally, a classification technique is proposed as a prediction system for maternal, fetal, and simultaneous health status classification, considering six possible outputs. Different classifiers are evaluated and a proposed CNN presented the best results, with the F1-score ranging from 0.74 to 0.91. The results are validated based on the diagnosis provided by two specialists. The results show that the proposed system is a viable solution for maternal and fetal ambulatory monitoring based on IoT.

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