The Internet of Medical Things (IoMT) is a brand new technology of combining medical devices and other wireless devices to access to the healthcare management systems. This article has sought the possibilities of aiding the current Corona Virus Disease 2019 (COVID-19) pandemic by implementing machine learning algorithms while offering emotional treatment suggestion to the doctors and patients. The cognitive model with respect to IoMT is best suited to this pandemic as every person is to be connected and monitored through a cognitive network. However, this COVID-19 pandemic still remain some challenges about emotional solicitude for infants and young children, elderly, and mentally ill persons during pandemic. Confronting these challenges, this article proposes an emotion-aware and intelligent IoMT system, which contains information sharing, information supervision, patients tracking, data gathering and analysis, healthcare, etc. Intelligent IoMT devices are connected to collect multimodal data of patients in a surveillance environments. The latest data and inputs from official websites and reports are tested for further investigation and analysis of the emotion analysis. The proposed novel IoMT platform enables remote health monitoring and decision-making about the emotion, therefore greatly contribute convenient and continuous emotion-aware healthcare services during COVID-19 pandemic. Experimental results on some emotion data indicate that the proposed framework achieves significant advantage when compared with the some mainstream models. The proposed cognition-based dynamic technology is an effective solution way for accommodating a big number of devices and this COVID-19 pandemic application. The controversy and future development trend are also discussed.

Recently, several lightweight methods have been proposed to implement single-image super-resolution (SISR) on resource-constrained devices. However, these methods primarily focus on simplifying network structures without the full utilization of shallow features. The fact remains that shallow features encompass crucial details for the super-resolution task, including edges, textures, and colors. Therefore, developing a novel architecture that can effectively integrate features from different levels and capitalize on their mutual complementarity is necessary. We first analyze the relationship between multi-stage features and the restoration tasks in a classic lightweight SR method. Based on these observations, we propose an Omni-Stage Feature Fusion (OSFF) architecture, which incorporates Original Image Stacked Initialisation, Shallow Feature Global Connection, and Multi-Receptive Field Dynamic Fusion. An Attention-Enhanced Feature Distillation module is also designed to enhance the model performance. Finally, leveraging these contributions, we construct an Omni-Stage Feature Fusion Network (OSFFNet). Through extensive experiments on various benchmark datasets, the proposed model outperforms state-of-the-art methods. Notably, it achieves a 0.26dB PSNR improvement over the second-best method for x2 SR on the Urban100 dataset.

Modern data centers host diverse hyper text transfer protocol (HTTP)-based services, which employ persistent transmission control protocol (TCP) connections to send HTTP requests and responses. However, the ON/OFF pattern of HTTP traffic disturbs the increase of TCP congestion window, potentially triggering packet loss at the beginning of ON period. Furthermore, the transmission performance becomes worse due to severe congestion in the concurrent transfer of HTTP response. In this paper, we provide the first extensive study to investigate the root cause of performance degradation of highly concurrent HTTP connections in data center network. We further present the design and implementation of TCP-TRIM, which employs probe packets to smooth the aggressive increase of congestion window in persistent TCP connection and leverages congestion detection and control at end-host to limit the growth of switch queue length under highly concurrent TCP connections. The experimental results of at-scale simulations and real implementations demonstrate that TCP-TRIM reduces the completion time of HTTP response by up to 80%, while introducing little deployment overhead only at the end hosts.

Currently, the security of the Internet of Things (IoT) has aroused great concern. Face detection under arbitrary occlusion has become a key problem affecting social security. This paper designs a novel face occlusion recognition framework in the security scene of IOT, which is used to detect some crime behaviors. Our designed framework utilizes the gradient and shape cues in a deep learning model, and it has been demonstrated to be robust for its superiority to detect faces with severe occlusion. Our contributions contain three main aspects: Firstly, we present a new algorithm based on energy function for face detection; Secondly, we use the CNN models to create deep features of occluded face; Finally, to check whether the detected face is occluded, novel sparse classification model with deep learning scheme is constructed. Statistical results demonstrate that, compared with the state of the arts, our algorithm is superior in both accuracy and robustness. Our designed head detection algorithm can achieve 98.89% accuracy rate even though there are various types of severe occlusions in faces, and our designed occlusion verification scheme can achieve 97.25% accuracy rate, at a speed of 10 frames per second.

Digital twin technology has recently gathered pace in engineering communities as it allows for the convergence of the real structure and its digital counterpart. 3D point cloud data is a more effective way to describe the real world and to reconstruct the digital counterpart than the conventional 2D images or 360-degree images. Large-scale, e.g., city-scale digital twins, typically collect point cloud data via internet-of-things (IoT) devices and transmit it over wireless networks. However, the existing wireless transmission technology can not carry real-time point cloud transmission for digital twin reconstruction due to mass data volume, high processing overheads, and low delay-tolerance. We propose a novel artificial intelligence (AI) powered end-to-end framework, termed AIRec, for efficient digital twin communication from point cloud compression, wireless channel coding, and digital twin reconstruction. AIRec adopts the encoder-decoder architecture. In the encoder, a novel importance-aware pooling scheme is designed to adaptively select important points with learnable thresholds to reduce the transmission volume. We also design a novel noise-aware joint source and channel coding is proposed to adaptively adjust the transmission strategy based on SNR and map the features to error-resilient channel symbols for wireless transmission to achieve a good tradeoff between the transmission rate and reconstruction quality. The decoder can accurately reconstruct the digital twins from the received symbols. Extensive experiments of typical datasets and comparison with baselines show that we achieve a good reconstruction quality under 24× compression ratio.