Dynamical Analysis for High-Order Delayed Hopfield Neural Networks with Impulses

The global exponential stability and uniform stability of the equilibrium point for high-order delayed Hopfield neural networks with impulses are studied. By utilizing Lyapunov functional method, the quality of negative definite matrix, and the linear matrix inequality approach, some new stability criteria for such system are derived. The results are related to the size of delays and impulses. Two examples are also given to illustrate the effectiveness of our results

On the Performance Trade-off of Distributed Integrated Sensing and Communication Networks

In this letter, we analyze the performance trade-off in distributed integrated sensing and communication (ISAC) networks. Specifically, with the aid of stochastic geometry theory, we derive the probability of detection of that of the coverage given user number. Based on the analytical derivations, we provide a quantitative description of the performance limits and the performance trade-off between sensing and communication in a distributed ISAC network under the given transmit power and bandwidth budget. Extensive simulations are conducted and the numerical results validate the accuracy of our derivations

Medical Image Registration Framework Using Multiscale Edge Information

AbstractEfficient multiscale deformable registration frameworks are proposed by combining edge preserving scale space (EPSS) with the free form deformation (FFD) for registration of medical images, where multiscale edge information can be used for optimizing the registration process. EPSS which is derived from the total variation model with the L1 norm (TV-L1) can provide useful spatial edge information for mutual information (MI) based registration. At each scale in registration process, the selected edges and contours are sufficiently strong to drive the deformation using the FFD grid, and then the deformation fields can be gained by a coarse to fine manner. In our deformable registration framework, two ways are proposed for implementing this idea. The experiments on clinical images including PETCT and CT-CBCT show accuracy and robustness when compared to traditional method for medical imaging system

Is Blockchain for Internet of Medical Things a Panacea for COVID-19 Pandemic?

The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of the general public and the healthcare system of the society. As a promising approach to address the emerging challenges caused by the epidemic of infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed in hospitals, clinics, and healthcare centers can save the diagnosis time and improve the efficiency of medical resources though privacy and security concerns of IoMT stall the wide adoption. In order to tackle the privacy, security, and interoperability issues of IoMT, we propose a framework of blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In this paper, we review the benefits of this architecture and illustrate the opportunities brought by blockchain-enabled IoMT. We also provide use cases of blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including the prevention of infectious diseases, location sharing and contact tracing, and the supply chain of injectable medicines. We also outline future work in this area.Comment: 15 pages, 8 figure

The physio-biochemical characterization reflected different calcium utilization efficiency between the sensitive and tolerant peanut accessions under calcium deficiency

Peanut yield in southern China is usually limited by calcium deficiency in soil. Most previous studies have found that small-seed varieties showed higher tolerance than large-seed varieties (e.g. Virginia type) under calcium deficiency, however, our preliminary research found that sensitive varieties also existed in small-seed counterparts. Few studies have been conducted to characterize low-calcium tolerance among small-seed germplasms with genetic diversity, and the differences in physiological characteristics between sensitive and tolerant varieties has not been reported yet. Thus, in order to better understand such differences, the current study firstly collected and characterized a diversity germplasm panel consisting of 50 small-seed peanut genotypes via a 2-year field trial, followed by the physiological characterization in sensitive (HN032) and tolerant (HN035) peanut genotypes under calcium deficiency. As a result, the adverse effects brought by calcium deficiency on calcium uptake and distribution in HN032 was much larger than HN035. In details, calcium uptake in the aboveground part (leaves and stems) was reduced by 16.17% and 33.66%, while in the underground part (roots and pods), it was reduced by 13.69% and 68.09% under calcium deficiency for HN035 and HN032, respectively; The calcium distribution rate in the pods of HN035 was 2.74 times higher than HN032. The utilization efficiency of calcium in the pods of HN035 was 1.68 and 1.37 times than that of HN032 under calcium deficiency and sufficiency, respectively. In addition, under calcium deficiency conditions, the activities of antioxidant enzymes SOD, POD, and CAT, as well as the MDA content, were significantly increased in the leaves of HN032, peanut yield was significantly reduced by 22.75%. However, there were no significant changes in the activities of antioxidant enzymes, MDA content, and peanut yield in HN035. Therefore, higher calcium absorption and utilization efficiency may be the key factors maintaining peanut yield in calcium-deficient conditions for tolerant genotypes. This study lays a solid foundation for selecting low-calcium tolerant varieties in future peanut breeding

Securing internet of medical things with friendly-jamming schemes

The Internet of Medical Things (IoMT)-enabled e-healthcare can complement traditional medical treatments in a flexible and convenient manner. However, security and privacy become the main concerns of IoMT due to the limited computational capability, memory space and energy constraint of medical sensors, leading to the in-feasibility for conventional cryptographic approaches, which are often computationally-complicated. In contrast to cryptographic approaches, friendly jamming (Fri-jam) schemes will not cause extra computing cost to medical sensors, thereby becoming potential countermeasures to ensure security of IoMT. In this paper, we present a study on using Fri-jam schemes in IoMT. We first analyze the data security in IoMT and discuss the challenges. We then propose using Fri-jam schemes to protect the confidential medical data of patients collected by medical sensors from being eavesdropped. We also discuss the integration of Fri-jam schemes with various communication technologies, including beamforming, Simultaneous Wireless Information and Power Transfer (SWIPT) and full duplexity. Moreover, we present two case studies of Fri-jam schemes in IoMT. The results of these two case studies indicate that the Fri-jam method will significantly decrease the eavesdropping risk while leading to no significant influence on legitimate transmission

OF-MSRN: Optical Flow-Auxiliary Multi-Task Regression Network for Direct Quantitative Measurement, Segmentation and Motion Estimation

Comprehensively analyzing the carotid artery is critically significant to diagnosing and treating cardiovascular diseases. The object of this work is to simultaneously achieve direct quantitative measurement and automated segmentation of the lumen diameter and intima-media thickness as well as the motion estimation of the carotid wall. No work has simultaneously achieved the comprehensive analysis of carotid artery due to three intractable challenges: 1) Tiny intima-media is more challenging to measure and segment; 2) Artifact generated by radial motion restrict the accuracy of measurement and segmentation; 3) Occlusions on diseased carotid walls generate dynamic complexity and indeterminacy. In this paper, we propose a novel optical flow-auxiliary multi-task regression network named OF-MSRN to overcome these challenges. We concatenate multi-scale features to a regression network to simultaneously achieve measurement and segmentation, which makes full use of the potential correlation between the two tasks. More importantly, we creatively explore an optical flow auxiliary module to take advantage of the co-promotion of segmentation and motion estimation to overcome the restrictions of the radial motion. Besides, we evaluate consistency between forward and backward optical flow to improve the accuracy of motion estimation of the diseased carotid wall. Extensive experiments on US sequences of 101 patients demonstrate the superior performance of OF-MSRN on the comprehensive analysis of the carotid artery by utilizing the dual optimization of the optical flow auxiliary module

Axial Attention Convolutional Neural Network for Brain Tumor Segmentation with Multi-Modality MRI Scans

Accurately identifying tumors from MRI scans is of the utmost importance for clinical diagnostics and when making plans regarding brain tumor treatment. However, manual segmentation is a challenging and time-consuming process in practice and exhibits a high degree of variability between doctors. Therefore, an axial attention brain tumor segmentation network was established in this paper, automatically segmenting tumor subregions from multi-modality MRIs. The axial attention mechanism was employed to capture richer semantic information, which makes it easier for models to provide local–global contextual information by incorporating local and global feature representations while simplifying the computational complexity. The deep supervision mechanism is employed to avoid vanishing gradients and guide the AABTS-Net to generate better feature representations. The hybrid loss is employed in the model to handle the class imbalance of the dataset. Furthermore, we conduct comprehensive experiments on the BraTS 2019 and 2020 datasets. The proposed AABTS-Net shows greater robustness and accuracy, which signifies that the model can be employed in clinical practice and provides a new avenue for medical image segmentation systems