Optical Coherence Tomography (OCT) is an emerging imaging tool that is now widely adopted in various medical settings such as cardiology and ophthalmology and is emerging in dentistry. In OCT, light of low coherence is used for image capturing which results in the introduction of speckle noise. Specifically, a degraded signal-to-noise ratio accentuates ambiguity in feature-extraction and contributes to the introduction of artefacts. This ultimately impacts its clinical utility where clear diagnostic detail is essential. In this work a concentrated-unsupervised deep learning denoising framework for OCT images is proposed, incorporating attention gate encoders. Attention gates are utilized to ensure focus on denoising the foreground and to ‘hard-threshold’ the background. Training data was created by processing the image with state-of-the-art denoisers (BM3D and NLM, etc.) to emphasize only essential data removal. The proposed framework was analysed quantitatively and visually, in comparison against state-of-the-art denoising algorithms. The experimental results show that the approaches verifiably remove speckle noise and achieves superior quality to well-known denoisers. The method improved the PSNR by 29.6 dB, CNR by 11.5 dB and, ENL by 1196.6 dB compared to original image and state-of-the-art denoisers.
The use of seismic waves to explore the subsurface underlying the ground is a widely used method in the oil industry, since different kinds of the rocks and mediums have different reflection rate of the seismic waves, so the amplitude of the reflected waves can unraveling the geological structure and lithologic character of a certain area under the ground, but the management and processing of seismic wave data often affects the efficiency of oil exploration and development. Different kinds of the seismic data bulk are always mixed and hard to be classified manually. This paper presents a classification model for four main types of seismic data, and proposed a classification method based on Mel-spectrum. An accuracy of 98.32% was achieved using pre-trained ResNet34 with transfer learning method. The accuracy is further improved compared with the pure fourier transformation method widely used in previous studies. Meanwhile, the transfer learning method and fine-tune strategy to train the neural network by training the first N-1 layers of the network separately and then train the fully connected layers further improves the training efficiency. Our model can also be seen as an efficient data quality control scheme for oil exploration and development. Meanwhile, our method is future-proofed, for further improvement of the seismic data processing quality control system, according to the spectrum characteristics, this model can be further extended into a error data classification model, reduces the workload of the bulk data management.
Autism spectrum disorder (ASD) is an incurable neurodevelopmental disorder with a wide range of clinical symptoms that mainly include social and communication deficits. Unfortunately, there is still no effective method for ASD diagnosis. Recently, researchers have presented a number of machine learning methods for ASD identification based on multi-site data, and these methods have achieved remarkable results. However, multi-site data is directly used, ignoring the heterogeneity between different sites. To address this issue, we propose a low-rank domain adaptive method with inter-class difference constraint (LRDAIC) for multi-site ASD identification based on resting-state functional magnetic resonance imaging (rs-fMRI). Firstly, we treat one site as the target domain and the remaining sites as the source domains. Then, data from these domains is transformed into a common space while considering inter-class difference, and the inter-class difference constraint term is further introduced to maximize the distance between different classes to enhance data discrimination ability. Moreover, each class of data from each source domain is linearly represented by all the data of the corresponding class from the target domain in this space. Finally, we evaluate the performance of our method on the basis of the ABIDE1 dataset, and the results demonstrate that our method is superior to several state-of −the-art low-rank domain adaptation methods.
Constructing visual encoding model is an important way to understand the human visual system. Data-driven encoding models learn features by directly fitting neural data, providing novel insights for visual encoding. Prediction accuracy is an important performance goal of visual encoding. The GaborNet visual encoding (GaborNet-VE) model has both good expressiveness and interpretability, while its prediction performance still needs to be improved. In this work, we proposed an improved GaborNet-VE model by adding data augmentation and nonlinear mapping module. The experimental results show that the improved GaborNet-VE model achieves higher prediction accuracy in V2, V3, V4 and LO. Our research provides a new framework for the data-driven encoding model.
Using communication networks and processing units in physical infrastructures has led to a new generation of systems which is known as Cyber Physical Systems (CPSs). These systems have several applications in different fields such as transportation networks, industrial control processes, water distribution systems, power systems, and etc. As it is shown in the literatures, CPSs are vulnerable to cyber-attack and physical attack in their communication channels and physical infrastructures, respectively. In this study, we will propose an observer which is used to detect false data injection attack in the turbine governor and load frequency controllers systems in the power systems. Then, a bank of attack isolation observers (AIOs) are proposed for identifying the location of the attacks in the generator buses. The numerical simulations on the IEEE 39-bus system show the feasibility and effectiveness of the proposed approach.
