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Rolling element bearing is a critical component in rotating machinery that reduces the friction between moving pairs. Bearing fault diagnosis is always considered as a research hotspot in the field of prognostics and health management, especially with the application of deep learning. Deep learning, such as a convolutional neural network (CNN), can extract features automatically compared with traditional methods. However, the construction of the CNN model and the training process still need a lot of prior knowledge, and it takes a lot of time to build an optimal model to achieve a high classification accuracy. In addition, great challenges of universal applicability exist when different input forms (e.g., different sampling lengths or signal forms) are considered. This paper presents a universal bearing fault diagnosis model transferred from a well-known Alexnet model, and only the last fully connected layer needs to be replaced, which could reduce prior knowledge and extra time in establishing a new model. Accordingly, it is necessary to convert a raw acceleration signal to a uniform-sized time-frequency image, even when these data have different sizes. Furthermore, standardized images created by eight time-frequency analysis methods are applied to validate the effectiveness of the proposed method in two case studies. The results indicate that this method can be applied in bearing fault diagnosis, and t-SNE helps to understand the process of feature extraction and condition classification. INDEX TERMS Bearing fault diagnosis, deep learning, time-frequency analysis, visualization technology.

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Copyright protection for digital multimedia has become a research hotspot in recent years. As an efficient solution, the digital watermarking scheme has emerged at the right moment. In this article, a highly robust and hybrid watermarking method is proposed. The discrete wavelet transform (DWT) and all phase discrete cosine biorthogonal transform (APDCBT) presented in recent years as well as the singular value decomposition (SVD) are adopted in this method to insert and recover the watermark. To enhance the watermark imperceptibility, the direct current (DC) coefficients after block-based APDCBT in high frequency sub-bands (LH and HL) are modified by using the watermark. Compared with the conventional SVD-based watermarking method and another watermarking technique, the watermarked images obtained by the proposed method have higher image quality. In addition, the proposed method achieves high robustness in resisting various image processing attacks.

Screen touch gesture has been shown to be a promising modality for touch-based active authentication of users of mobile devices. In this paper, we present an approach for active user authentication using screen touch gestures by building linear and kernelized dictionaries based on sparse representations and associated classifiers. Experiments using a new dataset collected by us as well as two other publicly available screen touch datasets show that the dictionary-based classification method compares favorably to those published in the literature. Experiments done using data collected in three different sessions corresponding to different environmental conditions show a drop in performance when the training and test data come from different sessions. This suggests a need for applying domain adaptation methods to further improve the performance of the classifiers.

Currently, the watermark capacity of most self-recovery fragile image watermarking schemes is fixed. That means for smooth regions and texture regions, the length of watermark information is always the same. However, it is impractical since more recovery information is needed for the recovery of texture regions. In this paper, a self-recovery fragile image watermarking with variable watermark capacity is proposed. Based on the characteristic of singular value decomposition (SVD), a new block classification method is introduced. The image blocks are classified into smooth blocks and texture blocks. For smooth blocks, the average pixel values are adopted as the recovery information to recover the tampered blocks, while for texture blocks, the quantized and coded DCT coefficients are adopted as the recovery information. After encrypted by binary pseudo-random sequence, the recovery watermark of each block is embedded into its mapping block. In the detection side, the three-level detection mechanism is applied to detect and locate the tampered regions. The experimental results prove that the proposed method achieves good tamper detection results, and the recovered image has better image quality than other self-recovery fragile watermarking methods.

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Power line detection plays an important role in an automated UAV-based electricity inspection system, which is crucial for real-time motion planning and navigation along power lines. Previous methods which adopt traditional filters and gradients may fail to capture complete power lines due to noisy backgrounds. To overcome this, we develop an accurate power line detection method using convolutional and structured features. Specifically, we first build a convolutional neural network to obtain hierarchical responses from each layer. Simultaneously, the rich feature maps are integrated to produce a fusion output, then we extract the structured information including length, width, orientation and area from the coarsest feature map. Finally, we combine the fusion output with structured information to get a result with clear background. The proposed method fully exploits multiscale and structured prior information to conduct both accurate and efficient detection. In addition, we release two power line datasets due to the scarcity in the public domain. The method is evaluated on the well-annotated power line datasets and achieves competitive performance compared with state-of-the-art methods.