In the current open society and with the growth of human rights, people are more and more concerned about the privacy of their information and other important data. This study makes use of electrocardiography (ECG) data in order to protect individual information. An ECG signal can not only be used to analyze disease, but also to provide crucial biometric information for identification and authentication. In this study, we propose a new idea of integrating electrocardiogram watermarking and compression approach, which has never been researched before. ECG watermarking can ensure the confidentiality and reliability of a user’s data while reducing the amount of data. In the evaluation, we apply the embedding capacity, bit error rate (BER), signal-to-noise ratio (SNR), compression ratio (CR), and compressed-signal to noise ratio (CNR) methods to assess the proposed algorithm. After comprehensive evaluation the final results show that our algorithm is robust and feasible.

High buildings in cities need elevators to transport people in daily life. Therefore, early to detect the occurrence of elevator failures by developing the diagnosis method is significant to ensure people’s safety. This paper presents the machine learning procedure for developing the diagnosis method for failure detection of elevator doors. The first step of the procedure is the motion analysis of the elevator doors by three-dimensional video monitoring. The signal from the dynamic distances between the elevator doors versus time is extracted by the image processing to measure the changing distance between elevator doors. The second step is modeling the signal curve by trapezoidal curves from the noised signal data. The third step is to train the classifiers to identify the motion of the elevator doors. Monte Carlo method is used to simulate and create normal and abnormal samples for training classifiers. The failure of detection of the elevator doors can be implemented by using three classifiers, such as K-Nearest Neighbor Classification, Support Vector Machine, and Binary Classification Tree for identification of the dynamical curves of the elevator doors. The results show Binary Classification Tree achieves the classification accuracy (99.28%) that is better than K-Nearest Neighbor and Support Vector Machine. The numbers of features influence the performance of Binary Classification Tree. The results show that the Binary Classification Tree with four features has the best accuracy, and the running speed is 0.51s. The accuracy of the three features has also reached 90.28%, but the running time is 0.24s. The result shows that there is a trade-off between accuracy and time.