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This paper addresses road anomaly detection by formulating it as a classification problem and applying deep learning approaches to solve it. Besides conventional road anomalies, additional ones are introduced from the perspective of a vehicle. In order to facilitate the learning process, the paper pays a close attention to pattern representation, and proposes three sets of numeric features for representing road conditions. Also, three deep learning approaches, i.e. Deep Feedforward Network (DFN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), are considered to tackle the classification problem. The detectors, with respect to the three deep learning approaches, are trained and evaluated through data collected from a test vehicle driven on various road anomaly conditions. The comparison study on the detection performances is conducted by setting key hyper-parameters to certain sets of fixed values. Also, the comparison study on performances of each detector with respect to different pattern representations is conducted. The results have shown the effectiveness of the proposed approaches and the efficiency of the proposed feature representations in road anomaly detection. INDEX TERMS Convolutional neural network, deep feedforward network, deep learning, pattern representation, recurrent neural network, road anomaly detection.

Transmission of a quantum state is essential for performing quantum information processing tasks. The communication channel will be inevitably immersed in its surrounding environment under realistic conditions. In this paper, we investigate the influence of environment noise on the transmission fidelity when transferring a quantum state through a spin chain. The non-Markovian open system dynamics is systematically analyzed by using the quantum state diffusion equation method. With each spin immersed in its own finite temperature and non-Markovian heat bath, we consider three types of system–bath interaction: dephasing, dissipation and spin-boson. The transmission fidelity is found to decrease with the increasing bath temperature and system–bath coupling strength. Interestingly, we find that the bath non-Markovianity can help enhancing the transmission fidelity.

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