Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. Besides, this study also discusses the challenges of designing and training deep neural networks.

This paper proposes a hierarchical deep neural network (HDNN) for diagnosing the faults on the Tennessee-Eastman process (TEP). The TEP process is a benchmark simulation model for evaluating process control and monitoring method. A supervisory deep neural network is trained to categorize the whole faults into a few groups. For each group of faults, a special deep neural network which is trained for the particular group is triggered for further diagnosis. The training and test data is generated from the Tennessee Eastman process simulation. The performance of the proposed method is evaluated and compared to single neural network (SNN) and duty-oriented hierarchical artificial neural network (DOHANN) methods. The results of experiment demonstrate that our method outperforms the SNN and DOHANN methods.

Recent years have seen increased research interest in replacing the computationally intensive Magnetic resonance (MR) image reconstruction process with deep neural networks. We claim in this paper that the traditional image reconstruction methods and deep learning (DL) are mutually complementary and can be combined to achieve better image reconstruction quality. To test this hypothesis, a hybrid DL image reconstruction method was proposed by combining a state-of-the-art deep learning network, namely a generative adversarial network with cycle loss (CycleGAN), with a traditional data reconstruction algorithm: Projection Onto Convex Set (POCS). The output of the first iteration’s training results of the CycleGAN was updated by POCS and used as the extra training data for the second training iteration of the CycleGAN. The method was validated using sub-sampled Magnetic resonance imaging data. Compared with other state-of-the-art, DL-based methods (e.g., U-Net, GAN, and RefineGAN) and a traditional method (compressed sensing), our method showed the best reconstruction results.

With extending applications of digital surveillance video, the fast browsing technique for surveillance video has become a hot spot in the domain. As one of the most important supporting technologies, region of interest (ROI) information coding is a necessary part of fast browsing framework for surveillance video. Therefore, a novel coding method of object region flags and successful application of object mapping flags for the scalable browsing of surveillance video are proposed in this paper. This object region flags coding method includes both intra-frame coding and inter-frame coding, wherein, the former eliminates the redundancy in spatial domain by improving the scanning mode of region flags, the latter eliminates the redundancy in temporal domain, which cuts coding cost obviously and provides essential supports for video synopsis browsing of surveillance video.

Arterial spin labeling perfusion MRI is a noninvasive technique for measuring quantitative cerebral blood flow (CBF), but the measurement is subject to a low signal-to-noise-ratio(SNR). Various post-processing methods have been proposed to denoise ASL MRI but only provide moderate improvement. Deep learning (DL) is an emerging technique that can learn the most representative signal from data without prior modeling which can be highly complex and analytically indescribable. The purpose of this study was to assess whether the record breaking performance of DL can be translated into ASL MRI denoising. We used convolutional neural network (CNN) to build the DL ASL denosing model (DL-ASL) to inherently consider the inter-voxel correlations. To better guide DL-ASL training, we incorporated prior knowledge about ASL MRI: the structural similarity between ASL CBF map and grey matter probability map. A relatively large sample data were used to train the model which was subsequently applied to a new set of data for testing. Experimental results showed that DL-ASL achieved state-of-the-art denoising performance for ASL MRI as compared to current routine methods in terms of higher SNR, keeping CBF quantification quality while shorten the acquisition time by 75%, and automatic partial volume correction.