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Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However, it remains challenging to extract more distinguishable features from different decomposed components for driver fatigue recognition. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. Four decomposition methods are employed to disassemble the EEG signals into components of different complexity. Instead of handcraft features, the CNNs in this framework directly learn from the decomposed components. In addition, a component-specific batch normalization layer is employed to reduce subject variability. Moreover, we employ two ensemble modes to integrate the outputs of all CNNs, comprehensively exploiting the diverse information of the decomposed components. Against the challenging cross-subject driver fatigue recognition task, the models under the framework all showed superior performance to the strong baselines. Specifically, the performance of different decomposition methods and ensemble modes was further compared. The results indicated that discrete wavelet transform-based ensemble CNN achieved the highest average classification accuracy of 83.48% among the compared methods. The proposed framework can be extended to any CNN architecture and be applied to any EEG-related tasks, opening the possibility of extracting more beneficial features from complex EEG data.

Among various time series (TS) forecasting methods, ensemble forecast is extensively acknowledged as a promising ensemble approach achieving great success in research and industry. Due to the high diversification of individual model assumptions, heterogeneous information fusion contributes to generating effective and robust forecasts for Economics, Meteorology, and Transportation. This paper proposes a Bayesian optimization-based dynamic ensemble (BODE) that overcomes the single model-based methods limitation and provides a dynamic ensemble forecast combination for TS with time-varying underlying patterns. The proposed BODE method combines ten disparate model candidates, including statistical methods, machine learning (ML)-based models, and the latest deep neural networks (DNN). We take into consideration their prediction performance for the recent past to adjust their weights for combination and apply the model-based Bayesian optimization algorithm (BOA) for the combination hyperparameter (HP) tuning to endow our method with higher adaptability and better generalization performance. Besides, the frequency impact of TS data on the ensemble forecast methods is under-researched in the current literature. Therefore, four groups of distinct seasonal TS datasets are investigated in this paper. The empirical result demonstrates that our method performs robustly better performance with the main reasons analyzed in a detailed ablation study.

Over the past few decades, code-division multiple-access (CDMA) systems have been widely used for military and commercial applications. However, the multiple-access interference (MAI) greatly degrades the system performance. To overcome this drawback, many researchers have put forward multiuser detectors (MUDs) to handle the MAI properly. The MUDs make use of the MAI to help make accurate decisions, which is unlike the conventional detectors. In this project, existing multiuser detectors are studied thoroughly. This dissertation first presents several typical multiuser detectors and gives the analysis of system performance. In some linear detectors, inversion of correlation matrix is not welcomed in practice. Interference cancellation schemes in parallel or serial structures are also studied. Then both blind and non-blind adaptive detection algorithms are discussed. The blind multiuser detectors are very welcomed because they do not need training sequence and the a-priori knowledge of interferential users. The simulation results show that all these multiuser detections can achieve a better system capacity and a lower bit error rate, however, different receivers can gain its best performance under different environments. At last, several practical problems and future research topics are presented. With the multiuser detection technique becoming increasingly important, their applications will also be expanded continuously. Master of Science (Communications Engineering)