This paper proposes a prior driven semi-supervised ViT-GAN called RC-ViTGAN for recoloring images while retaining color harmonization and semantic rationality. The encoder of RC-ViTGAN is based on the vision transformer to avoid the locality of convolutional networks, which facilitates the extraction of global information from images. Besides, we release an RC500 dataset, which is the largest publicly accessible and pioneering dataset for recolorization, providing convenience for subsequent studies. In addition, we present a novel semi-supervised training strategy, including a prior-driven self-supervised initialization method using contrastive learning. The proposed training strategy leverages massive amounts of unlabeled and pseudo-labeled data, addressing the shortage of labeled data in re-colorization. Code and dataset are available at https://github.com/tsz12/RC-ViTGAN.git.

Unsupervised domain adaptation (UDA) methods have achieved significant success in the cross-modality medical image segmentation tasks. However, due to the inherent properties of medical images, i.e., the large distribution gap between different modalities and the low intensity contrast between different categories of organ structures, it is challenging for current UDA methods to perform well on the boundary regions. In this paper, we propose a boundary contrast domain adaptation framework for cross-modality medical image segmentation. Concretely, we consider the same category prototypes as positive samples to reduce category-level distribution differences between domains, and boundary features as negative samples to improve the discriminative ability of ambiguous boundary regions. And we construct mixed samples by bidirectional cross-domain cutmix for self-training to further reduce the domain gap. Moreover, we dynamically assign weights to different parts of the pseudo-labels to prevent model degradation. Experimental results show our proposed method outperforms the state-of-the-art methods.

For computer-aided diagnosis(CAD) to be successful, automatic segmentation needs to be reliable and efficient. Semi-supervised segmentation (SSL) techniques make extensive use of unlabeled data to address the issue of the high acquisition cost of medically labeled data. However, different anatomical regions and boundaries in medical images may exhibit similar gray-level features. The discrimination of similar regions and the geometrical limitations on boundaries are disregarded by current semi-supervised algorithms for segmenting medical images. In this work, we propose a framework for multi-task pixel-level representation learning that is led by certainty pixels. Specifically, we concentrate on the task of segmentation prediction as the primary task and shape-aware level set representation as a collaborative task to enforce local boundary constraints on unlabeled data. We construct dual decoders to obtain predictions and uncertainty maps from different perspectives, which can enhance the capacity to distinguish similar regions. In addition, we introduce certainty pixels to guide the computation of pixel-level contrastive loss to strengthen the correlation between pixels. Finally, experiments on two open datasets demonstrate that our strategy outperforms current approaches. The code will be released at https://github.com/yqimou/SAMT-PCL.

Oral diseases have a significant impact on human health, often going unnoticed in their early stages. Deep learning, a promising field in artificial intelligence, has shown remarkable success in various domains, especially dentistry. This paper aims to provide an overview of recent research on deep learning applications in dentistry, with a focus on dental imaging. Deep learning algorithms perform well in difficult tasks such as image segmentation and recognition, enabling accurate identification of oral conditions and abnormalities. Integration of deep learning with other oral health data offers a holistic understanding of the relationship between oral and systemic health. However, there are still many challenges that need to be addressed.

Internet of Medical Things (IoMT) refers to applying Internet of Things (IoT) into the medical field. The IoMT enables a medical system to connect various smart devices, such as wearable sensors, medical examination instruments, and hospital assets, for establishing an information platform. These smart devices act as the basic nodes in an IoMT system, collecting or generating health data and transmitting the data to the server for further processing and analysis. Physicians apply health data to make better medical decisions. Recently, the IoMT has been widely applied in many areas, including smart hospital, remote health monitoring, disease diagnosis, and infectious disease tracking. In this review, we investigated the IoMT from its concept and theory to its deployment domains, adopted technologies, and applications. We provided theoretical explanations with various examples and more than one hundred representative references. We also presented a cutting-edge discussion for the challenges and directions of the IoMT. We hoped that this systematic review would be beneficial to readers of all levels and backgrounds, including industry beginners, medical institution administrators, policy makers, and experienced researchers.

Malaria is a life-threatening disease, which mainly occurs in developing countries and regions with poor sanitary conditions. Early diagnosis of malaria will effectively decrease the death rate. In this article, we develop an automatic malaria disease diagnosis framework integrating blockchain-enabled cloud–edge computing and deep learning. The diagnosis task is divided into malaria parasite segmentation from blood smear images and classification of parasite species and stages. To meet the massive demand for deep learning training, we design a diagnosis pipeline that is deployed in a cloud–edge paradigm to utilize both local and remote resources. At edge nodes, preprocessed data sets are classified by U-Net in a supervised approach to generate coarse probability maps. Then, the normalized images and generated probability maps are uploaded to the cloud server. At the cloud, the uploaded probability maps are used to weakly supervise the stacked dilated U-Net (SDU-Net) to segment infected cells. Further classifications of malaria parasites species and stages are conducted by a pretrained MobileNet V1. The blockchain technology is adopted during the data transmission process. The diagnosis results will be sent back to the original local hospital immediately through the cloud. Our framework improves the diagnosis accuracy and eases the burden of deep learning training. Evaluation on real data collection MP-IDB demonstrated the effectiveness of our method.

The rapid development of Internet of Things has led more realization of digital twins (DT), such as healthcare, smart homes, virtual reality, etc., gesture recognition is a fundamental component of DT. Its implementation can provide users with personalized services or improved human-computer interaction, such as smart home control, in-car interaction, etc., most of existing gesture recognition methods are based on vision or wearable device. However, the vision-based methods face the problem of privacy breach, whereas the wearable-based methods may bring inconvenience to users. With the wide deployment of Wi-Fi networks, lots of consumer devices are widely accessible in people’s homes. Motivated by the fact that Wi-Fi signal propagation can be affected by human motion, the opportunity to use Wi-Fi signals for gesture recognition can be further explored. However, the challenge is that the received Wi-Fi signal shows great differences when the same person performs the same gesture in different environments or different person performs the same gesture in the same environment. Therefore, the signal alignment across different domain needs to be solved. In this paper, we propose a gesture recognition system named Phase-Attention-based-Conv-CSI (PAC-CSI), which consists of two modules: data processing and gesture recognition. In the data processing module, we eliminate random phase noise in channel state information (CSI) and perform phase calibration. In the gesture recognition module, we feed the processed phase sequence into a lightweight deep neural network for gesture recognition. PAC-CSI can obtain the gesture category in about 200ms, which can meets the real-time requirements of DT. The gesture recognition accuracy of our proposed system in a single domain is 99.46%, and its performance across new locations, orientations, users, and environments is 98.77%, 98.90%, 97.54%, and 96.47%, respectively.

Abstract Digital image noise may be introduced during acquisition, transmission, or processing and affects readability and image processing effectiveness. The accuracy of established image processing techniques, such as segmentation, recognition, and edge detection, is adversely impacted by noise. There exists an extensive body of work which focuses on circumventing such issues through digital image enhancement and noise reduction, but this work is limited by a number of constraints including the application of non-adaptive parameters, potential loss of edge detail information, and (with supervised approaches) a requirement for clean, labeled, training data. This paper, developed on the principle of Noise2Void, presents a new unsupervised learning approach incorporating a pseudo-siamese network. Our method enables image denoising without the need for clean images or paired noise images, instead requiring only noise images. Two independent branches of the network utilize different filling strategies, namely zero filling and adjacent pixel filling. Then, the network employs a loss function to improve the similarity of the results in the two branches. We also modify the Efficient Channel Attention module to extract more diverse features and improve performance on the basis of global average pooling. Experimental results show that compared with traditional methods, the pseudo-siamese network has a greater improvement on the ADNI dataset in terms of quantitative and qualitative evaluation. Our method therefore has practical utility in cases where clean images are difficult to obtain.

Tuberculosis is a very deadly disease, with more than half of all tuberculosis cases dead in countries and regions with relatively poor health care resources. Fortunately, the disease is curable, and early diagnosis and medication can go a long way toward curing TB patients. Unfortunately, traditional methods of TB diagnosis rely on specialist doctors, which is lacking in areas with high TB mortality rates. Diagnostic methods based on artificial intelligence technology are one of the solutions to this problem. We propose a Deep Transferred EfficientNet with SVM (DTE-SVM), which replaces the pre-trained EfficientNet classification layer with an SVM classifier and achieves auspicious performance on a small dataset. After ten runs of 10-fold Cross-Validation, the DTE-SVM has a sensitivity of 93.89±1.96, a specificity of 95.35±1.31, a precision of 95.30±1.24, an accuracy of 94.62±1.00, and an F1-score of 94.62±1.00. In addition, our study conducted ablation studies on the effect of the SVM classifier on model performance and briefly discussed the results.

As one of the most important artificial intelligence-enabled industrial applications, fault diagnosis is vital in the safe, stable, and reliable operation of the equipment. Many existing deep learning-based fault diagnosis methods assume that the distribution of training data is the same as that of testing data, which is almost impossible in practical industrial applications. In addition, most of these fault diagnosis methods are generally memory-intensive and computationally expensive. A compressed unsupervised deep domain adaption model-based fault diagnosis method is proposed to overcome the abovementioned two issues. First, a standard unsupervised domain adaption model is designed to extract the features of training data and testing data, respectively. Then, the maximum mean discrepancy term is introduced to minimize the discrepancy between the extracted features of them. Next, the standard model is compressed through iteratively pruning the redundant convolutional channels. Finally, the obtained compressed model is applied to diagnose faults. The performance of the proposed method is verified on the Case Western Reserve University bearing dataset. Experimental results show that the compressed model can significantly reduce the memory occupation, computational cost, and inference time compared with the standard model, but still achieve comparable or even better accuracy on ten transfer diagnostic tasks.

Radio frequency identification technology has been widely used to verify the presence of items in many applications such as warehouse management and supply chain logistics. In these applications, the challenge of how to timely identify the missing tags (namely tag searching or missing tag identification) is a key focus. Existing missing tag identification solutions have not achieved their full potentials because collision slots have not been well explored. In this paper, we propose an approach named collision resolving based missing tag identification (CR-MTI) to break through the performance bottleneck of existing missing tag identification protocols. In CR-MTI, multiple tags are allowed to respond with different binary strings in a collision slot. Then, the reader can verify them together by using the bit tracking technology and particularly designed string, thereby significantly improve the time efficiency. CR-MTI also reduces the number of messages transmitted by the reader using customized coding. We further explore the optimal parameter settings to maximize the performance of our proposed CR-MTI. Extensive simulation results show that our proposed CR-MTI outperforms prior art in terms of time efficiency, total executive time and communication complexity.

How to obtain the information from massive tags is a key focus of RFID applications. The occurrence of collisions leads to problems such as reduced identification efficiency in RFID networks. To tackle such challenges, most tag collision arbitration protocols focus on scheduling tag identification with collision avoidance. However, how to effectively identify tags in collisions to improve identification efficiency has not been well explored. In this paper, we propose a group query allocation method to divide the string space into mutually disjoint subsets which contains several strings. Each string can be viewed as a full ID or partial ID of a tag. When multiple string from a subset are sent simultaneously, the reader can identify all of them in a time slot. Based on the group query allocation method, a segment detection based characteristic group query tree (SD-CGQT) protocol is presented for fast tag identification by significantly reducing the collision slots and transmitted bits. Numerous experimental results verify the superiority of the proposed SD-CGQT, compared to prior arts in system efficiency, total identification time, communication complexity and energy consumption.

With the increasing importance of data security, privacy protection has gradually risen to a strategic position, especially IoT data privacy protection. The concern for data security has become a national strategy. The discovery of potential risks of privacy data is of great significance, such as the risk of data privacy leakage, data security vulnerabilities, etc. In this article, starting from the privacy data protection mechanism in the Industrial Internet of Things (IIoT) scenario, we proposed a method based on generative adversarial imitation learning (GAIL) to discover the privacy data security risks in IIoT by training privacy protection agents using a large amount of expert data on privacy protection. Finally, our proposed method is validated by relevant simulation experiments, and the results show that our proposed method has wide generalizability and reliability to obtain the maximum payoff of the agents and thus, reduce the risk of data security leakage.

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.

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.

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.

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.

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.

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.

Aortic stent has been widely used in restoring vascular stenosis and assisting patients with cardiovascular disease. The effective visualization of aortic stent is considered to be critical to ensure the effectiveness and functions of the aortic stent in clinical practice. Volume rendering with ray casting has been used as an effective approach to enable the effective visualization of aortic stent. The volume rendering relies on the transfer function that converts the medical images into optical attributes including color and transparency. This article proposes a new transfer function, namely, the multi-dimensional transfer function, to provide additional transparency value of a voxel. The proposed approach using the additional transparency value effectively assists the distinguishing of tissues that have the same CT value. The transparency values are simultaneously determined by gray threshold and gray change threshold, which can recognize the unnecessary structures such as bones transparent. A series of experimental results demonstrate that the situation of aorta stent of a patient can be directly observed, and the angle of view can be switched arbitrarily. The proposed method provides a new way for the operation of a virtual endoscopy to reach the place of blood vessels that a traditional endoscopy fails to reach.

Software paradigm is a driving force for the evolution of software technology. With the continuous improvement in the current cloud computing and the Internet environment, software will develop further into Cloudware, which is emerging as a new software paradigm. This paper defines the concept of Cloudware, and discusses it in the context of software paradigm. Then, based on a loosely coupled von Neumann computing model, we propose a new method of constructing a Cloudware PaaS system which can directly deploy software into the cloud without any modification. By using micro-service architecture, we can achieve high performance, scalable deployment, faults tolerance and flexible configuration. Finally, we evaluate this method by carrying out an interactive delay experiment that directly focuses on users' experience, which shows the effectiveness of our method.