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Forming a stable autonomous vehicle group is extremely challenging in a highway scene that has several entrances and exits. Existing studies focus on centralized autonomous vehicle groups with leading nodes. Such groups suffer from unbalanced computing tasks, asymmetric information, and weak stability. This article introduces a side chain consensus-based decentralized autonomous vehicle group formation method in a highway scene. First, we side chain consensus to describe states of autonomous vehicles. Then, we give decentralized autonomous vehicle group formation and maintenance methods based on side chain consensus. Finally, we conduct simulations to evaluate the quality of side chain consensus and stability of vehicle groups, which shows that our method has better properties in the balance of computing tasks, information symmetry, and stability than existing methods. [ABSTRACT FROM AUTHOR]

Oral cancer has been one of the most mortal diseases in the world, and accurate and timely treatment will efficiently improve the survival and cure rate of the patients. However, the traditional diagnosis ways by the clinicians could be laborious and easily misdiagnosed, and oral cancer usually with different morphological features, which makes it challenging to achieve the high accuracy classification automatically. To address this challenge, in this article, we propose an intelligent multisampling tensor model to achieve oral cancer and cyst classification from magnetic resonance imaging (MRI). Specially, our approach first encodes the input image by four simple sampling operations, which enables the model to learn more regional, global, influential, and correlative features, and then a representation fusion strategy is adopted to fuse those extracted representations. Afterward, those are contracted by sequences of matrix product states, which map the input representation into high dimensional space to conduct a classifier operation. Finally, we evaluate our proposed method on oral MRI cancer data, and the experimental result demonstrates that our approach could achieve competitive classification results. [ABSTRACT FROM AUTHOR]

The planet Earth is being affected by a series of wildfires, which have been steadily increasing over the last two decades. Forests have undergone deforestation due to natural forest fires and forest fires caused by man. These events are occurring on a global scale, and in Brazil, these wildfires are having an extreme impact on the Amazon forest as well as other forest biomes. This article proposes a novel lightweight convolutional neural network (CNN) model for wildfire detection through RGB images. This new method presents more advantages than the other methods used for the same task. Our CNN architecture can be used with aerial images from unmanned aerial vehicles and from video surveillance systems, combined with edge computing devices for image processing with a CNN. The proposed system is able to send wildfire alerts. The images do not have to be sent to a cloud computer as they can be processed in an edge device. However, it sends a string of alerts whenever a wildfire is detected. The evaluation of our proposed method showed that it required about 30 ms for the classification time, per image, and achieved an accuracy of 98.97% and an $F1$ -score of 95.77%, which is a very promising result. [ABSTRACT FROM AUTHOR]

Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation. [ABSTRACT FROM AUTHOR]

At present, the application of Electroencephalogram (EEG) signal classification to human intention-behavior prediction has become a hot topic in the brain computer interface (BCI) research field. In recent studies, the introduction of convolutional neural networks (CNN) has contributed to substantial improvements in the EEG signal classification performance. However, there is still a key challenge with the existing CNN-based EEG signal classification methods, the accuracy of them is not very satisfying. This is because most of the existing methods only utilize the feature maps in the last layer of CNN for EEG signal classification, which might miss some local and detailed information for accurate classification. To address this challenge, this paper proposes a multi-scale CNN model-based EEG signal classification method. In this method, first, the EEG signals are preprocessed and converted to time-frequency images using the short-time Fourier Transform (STFT) technique. Then, a multi-scale CNN model is designed for EEG signal classification, which takes the converted time-frequency image as the input. Especially, in the designed multi-scale CNN model, both the local and global information is taken into consideration. The performance of the proposed method is verified on the benchmark data set 2b used in the BCI contest IV. The experimental results show that the average accuracy of the proposed method is 73.9 percent, which improves the classification accuracy of 10.4, 5.5, 16.2 percent compared with the traditional methods including artificial neural network, support vector machine, and stacked auto-encoder. [ABSTRACT FROM AUTHOR]

In this article, we present a deep learning approach to sketch-based shape retrieval that incorporates a few novel techniques to improve the quality of the retrieval results. First, to address the problem of scarcity of training sketch data, we present a sketch augmentation method that more closely mimics human sketches compared to simple image transformation. Our method generates more sketches from the existing training data by (i) removing a stroke, (ii) adjusting a stroke, and (iii) rotating the sketch. As such, we generate a large number of sketch samples for training our neural network. Second, we obtain the 2D renderings of each 3D model in the shape database by determining the view positions that best depict the 3D shape: i.e., avoiding self-occlusion, showing the most salient features, and following how a human would normally sketch the model. We use a convolutional neural network (CNN) to learn the best viewing positions of each 3D model and generates their 2D images for the next step. Third, our method uses a cross-domain learning strategy based on two Siamese CNNs that pair up sketches and the 2D shape images. A joint Bayesian measure is used to measure the output similarity from these CNNs to maximize inter-class similarity and minimize intra-class similarity. Extensive experiments show that our proposed approach comprehensively outperforms many existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technology has been widely introduced to the existing medical system. An eHealth system based on IoT devices has gained widespread popularity. In this article, we propose an IoT eHealth framework to provide an autonomous solution for patients with interventional cardiovascular diseases. In this framework, wearable sensors are used to collect a patient's health data, which is daily monitored by a remote doctor. When the monitoring data is abnormal, the remote doctor will ask for image acquisition of the patient's cardiovascular internal conditions. We leverage edge computing to classify these training images by the local base classifier; thereafter, pseudo-labels are generated according to its output. Moreover, a deep segmentation network is leveraged for the segmentation of stent structs in intravascular optical coherence tomography and intravenous ultrasound images of patients. The experimental results demonstrate that remote and local doctors perform real-time visual communication to complete telesurgery. In the experiments, we adopt the U-net backbone with a pretrained SeResNet34 as the encoder to segment the stent structs. Meanwhile, a series of comparative experiments have been conducted to demonstrate the effectiveness of our method based on accuracy, sensitivity, Jaccard, and dice. [ABSTRACT FROM AUTHOR]

Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shapes in clinic. Thus, an automatic segmentation method for IVOCT lumen contour is necessary to reduce the doctors’ workload while ensuring diagnostic accuracy. In this paper, we proposed a deep residual segmentation network of multi-scale feature fusion based on attention mechanism (RSM-Network, Residual Squeezed Multi-Scale Network) to segment the lumen contour in IVOCT images. Firstly, three different data augmentation methods including mirror level turnover, rotation and vertical flip are considered to expand the training set. Then in the proposed RSM-Network, U-Net is contained as the main body, considering its characteristic of accepting input images with any sizes. Meanwhile, the combination of residual network and attention mechanism is applied to improve the ability of global feature extraction and solve the vanishing gradient problem. Moreover, the pyramid feature extraction structure is introduced to enhance the learning ability for multi-scale features. Finally, in order to increase the matching degree between the actual output and expected output, the cross entropy loss function is also used. A series of metrics are presented to evaluate the performance of our proposed network and the experimental results demonstrate that the proposed RSM-Network can learn the contour details better, contributing to strong robustness and accuracy for IVOCT lumen contour segmentation. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis (FDD) is crucial for stable, reliable, and safe operation of industrial equipment. In recent years, deep learning models have been widely used in data-driven FDD methods because of their automatic feature learning capability. In general, these models are trained on historical sensor data, and therefore, it is very difficult to meet the real-time requirement of online FDD applications. Since transfer learning can solve different but similar problems in the target domain efficiently and effectively with the knowledge learned from the source domain, this paper proposes an online fault diagnosis method based on a deep transfer convolutional neural network (TCNN) framework. The TCNN framework is made up of an online CNN based on LeNet-5 and several offline CNNs with a shallow structure. First, time-domain signal data are converted into images that contain abundant fault information and are suitable as the input of CNN. Then, the online CNN is constructed to automatically extract representative features from the converted images and classify faults. Finally, in order to improve the real-time performance of the online CNN, several offline CNNs are also constructed and pretrained on related data sets. By directly transferring the shallow layers of the trained offline CNNs to the online CNN, the online CNN can significantly improve the real-time performance and successfully address the issue of achieving the desired diagnostic accuracy within limited training time. The proposed method is validated on two bearing data sets and one pump data set, respectively. The prediction accuracy of the proposed method using three data sets are 99.88%, 99.13%, and 99.98%, respectively. The experimental results also indicate that the improvement of accuracy is 19.21% for the motor bearing case, 29.82% for the rolling mill bearing case, and 33.26% for the pump case during the early stage of learning. [ABSTRACT FROM AUTHOR]