The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. Therefore, a smart monitoring system that detects and tracks the suspected COVID-19 infected persons may improve the clinicians decision-making and consequently limit the pandemic spread. This paper entails a new framework integrating the Machine Learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, Lung UltraSound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a Service (federated-MLaaS); (i) the distributed batch-MLaaS, which is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream-MLaaS installed into a hybrid fog/cloud environment for a short-term decision-making. Stream-MLaaS use a shared federated prediction model stored into the cloud; whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream-ML algorithms from the Python’s libraries.

Volume plasmon polariton (VPP), a high-k mode that arises due to the coupling between two even modes of adjacent layers of an hyperbolic metamaterial (HMM) configuration, is very difficult to be excited by using prism coupling technique due to huge wave-vector mismatch. In this work, we present a graphene-based HMM structure integrated with metal grating to facilitate excitation of VPP modes. A graphene HMM is composed of multilayer graphene super-lattice similar to metal-dielectric super-lattice structure. We report the analytical formulation of the dispersion relation and numerical results of the characteristics of the excited VPP modes for the proposed structure in the Terahertz region of the spectrum. The best achieved imaging resolution of our proposed structure is 15 nm when used as an infra-red imaging platform. As a sensing platform, a maximum sensitivity of 11,050 nm/RIU is achieved for this configuration. The tunability of the resonance wavelength with respect to the structural parameters of the device is also studied and confirmed. Such promising findings are expected to make the proposed structure with integrated excitation coupler a potential candidate for tunable sensor design for different nanophotonic applications, including imaging, and, biomedical and chemical sensing applications.

The harmonic state-space, the dynamic phasor, and the generalized dq modeling are three methods developed for linearization of ac power electronic systems. This paper reveals explicitly mathematical relationships between the three modeling methods in both time and frequency domain. Representations of linearized models in different reference frames and from time domain to frequency domain, as well as their transformations are elaborated step by step. Case studies on a three-phase voltage-source converter that is connected to an unbalanced grid verify the theoretical findings.

Offshore grids can have an important role in the transition of the energy system to sustainability. Although they require extensive infrastructure investments, they open up for the exploitation of a vast amount of additional resources and may be important to provide for part of the increasing electricity demand driven by sector coupling between the electricity, heat, and transport sectors. We show that the level of such sector coupling is decisive for the development of offshore grids. Performing energy system optimization in a model application of the Northern-central European energy system and the North Sea offshore grid towards 2050, we find that without sector coupling no offshore grid may develop, and that the higher the level of sector coupling, the higher the value of offshore grids. The electrification of the transport sector favors offshore grid development more than the electrification of the heat sector. Offshore grid infrastructure development can therefore not be discussed as a separate political topic, but seen in connection to sector coupling.

Sparse approximation is critical to the applications of signal or image processing, and it is conducive to estimate the sparse signals with the joint efforts of transformation analysis. In this study, a simultaneous Bayesian framework was extended for sparse approximation by structured shared support, and a simultaneous sparse learning algorithm of structured approximation (SSL-SA) is proposed with transformation analysis which leads to the feasible solutions more sensibly. Then the improvements of sparse Bayesian learning and iterative reweighting were embedded in the framework to achieve speedy convergence as well as high efficiency with robustness. Furthermore, the iterative optimization and transformation analysis were embedded in the overall learning process to obtain the relative optima for sparse approximation. Finally, compared to conventional reweighting algorithms for simultaneous sparse models with l1 and l2, simulation results present the preponderance of the proposed approach to solve the sparse structure and iterative redundancy in processing sparse signals. The fact indicates that proposed method will be effective to sparsely approximate the various signals and images, which does accurately analyse the target in optimal transformation. It is envisaged that the proposed model could be suitable for a wide range of data in sparse separation and signal denosing.

An alternative to conventional spacetime is rigoursly formulated for nonlocal electromagnetism using the general concept of fiber bundle superspace.

This letter describes how traditional Butterworth low pass filters can enhance the performance of the tracking differentiator introduced by Han by mitigating the effect of additive high frequency noise that corrupts the output measurement. The tracking differentiator obtains much of its utility from its realization in cascaded integral form. By combining the cascaded integral form realization of Butterworth low pass filters with its the noise rejection features one can design a tracking differentiator that is efficiently tuned to reject high frequency output noise.

This work validates cylindrical IE-GSTC by applying it to physical metasurfaces, i.e. metasurfaces defined by material properties and dimensions rather than by susceptibility tensor components. Previously reported IE-GSTC which was formulated for zero thickness GSTC discontinuity is extended to handle finite thickness of physical metasurfaces. A simple analytical approach is used to extract the bianisotropic susceptibility tensor of concentric, multilayered, magneto-dielectric shell. Plane wave scattering by a physical metasurface constructed of four segments of multilayered, magneto-dielectric metasurface scatterers is used as an example problem to validate cylindrical IEGSTC. A second example considers an opening on the cylindrical metasurface, confirming IE-GSTC can handle metasurfaces with openings. Good agreement is obtained between IE-GSTC results and full wave simulation results for both cases.

Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.

The key benefits of cloud services, such as low cost, access flexibility, and mobility, have attracted users worldwide to utilize the deep learning algorithms for developing computer vision tasks. Untrusted third parties maintain these cloud servers, and users are always concerned about sharing their confidential data with them. In this paper, we addressed these concerns for by developing SecureDL, a privacy-preserving image recognition model for encrypted data over cloud. Additionally, we proposed a block-based image encryption scheme to protect images' visual information. The scheme constitutes an order-preserving permutation ordered binary number system and pseudo-random matrices. The encryption scheme is proved to be secure in a probabilistic viewpoint and through various cryptographic attacks. Experiments are performed for several image recognition datasets, and the achieved recognition accuracy for encrypted data is close with non-encrypted data. SecureDL overcomes the storage, and computational overheads occurred in fully-homomorphic and multi-party computations based secure recognition schemes.

Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location and the extent of the cancer tissue, it is difficult to detect and dissect the tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that the radiomics shape features have the highest correlation with survival prediction. The proposed approaches were assessed on the Brain Tumor Segmentation (BraTS-2020) challenge dataset. The highest accuracy of image features with random forest regressor approach was 51.5\% for the training and 51.7\% for the validation dataset. The gradient boosting regressor with shape features gave an accuracy of 91.5\% and 62.1\% on training and validation datasets respectively. It is better than the BraTS 2020 survival prediction challenge winners on the training and validation datasets. Our work shows that handcrafted features exhibit a strong correlation with survival prediction. The consensus based regressor with gradient boosting and radiomics shape features is the best combination for survival prediction.

Broadband reliable communication is a competitive 5G technology for cognitive communication scenarios, but meanwhile introduces multiform interference to existing broadband transform domain communication system (TDCS) transmission. In order to facilitate the improvement of the anti-jamming performance for the coexistence of diverse interference and TDCS signals in wireless heterogeneous networks, it is important to separated and eliminate various interference to TDCS systems. In this paper, a novel sparse learning method-based cognitive transformation framework of interference separation is formulated for accurate interference recovery, which can be efficiently solved by iteratively learning the prior sparse probability distribution of the interference support. To further improve the separation accuracy and iterative convergence, the principal component analysis and Bayesian perspective in orthogonal base learning are exploited to singly recover the multiple interference and TDCS signals. Moreover, utilizing different sparsity states of spectrum analysis, the proposed novel interference separation algorithm is extended to simultaneous separation based on state evolving of approximation message passing, which iteratively learns the belief propagation posteriors and keeps shrunk by iterative shrinkage threshold. Simulation results demonstrate that the proposed methods are effective in separating and recovering the sparse diversities of interference to TDCS systems, and significantly outperform the state-of-the-art methods.

Traditional participatory systems modelling demands synchronous time from many experts and face-to-face interaction. This is not always feasible (e.g. recent pandemic) and can restrict which participants can be included. There are additional limitations in the effectiveness of physical paper-based modelling when handling large complex systems with numerous variables and links between them. The key challenge facing practitioners is then how we can retain the benefits of traditional participatory modelling whilst exploiting the advantages of new technologies? This paper contributes to development of an original systematic methodology based on Cybernetic principles. The proposed method, refers to 5X – standing for Expose, Explore, Exploit, Explain, and Expand, offers a fully virtual co-produced environment for better engagement of stakeholders, developing more confidence in systems mapping, and promote knowledge to other policy areas. A primary application of the proposed method in a real policy setting illustrates its capability in generating a shared policy understanding of complex Inclusive Economy system, where there is conflicting or dispersed knowledge about system structure, refining this understanding through online feedback channels, and transferring this understanding to wider policy and academic partners through mutual relationships.

In the span of a decade, innovations in cloud computing have led to a new understanding of computing to be used as a utility. Majority of cloud service providers are making the service better and competitive for end-user. Aside from the number of services introduced by these providers, users are feeling uneasy and are unaware of consequences while switching from one service to another. Internal architecture of the cloud makes it difficult for end-users to understand. To overcome this issue a new concept of multi-cloud has been introduced. In multi-cloud technology, we can use multiple clouds from different vendors without platform complexity. Hence summarized, Multi-cloud is the usage of autonomous cloud platforms with one interface which may clue to different administrative and implementation domains. This paper reviews the literature of recently presented solutions and architectures for multi-cloud platforms.

Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results. The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates state-of-the-art methods. The complexity of tasks under the challenge has grown from segmentation (Task1) to overall survival prediction (Task 2) to uncertainty prediction for classification (Task 3). The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1. The aim is to showcase a complete change of trends in automated brain tumor models. The paper also covers end to end joint models involving brain tumor segmentation and overall survival prediction. All the methods are probed, and parameters that affect performance are tabulated and analyzed.

A multilayer aperture antenna array in millimeter-wave band is presented in this article. The antenna array is based on glide-symmetric holey gap-waveguide technology combined with E-plane insertion gaps for a low-cost and low-loss design. The radiating part of the antenna array is formed by an array of sixteen aperture antennas, grouped in four sets of 2x2 antenna subarrays in E-plane configuration. The 2x2 subarrays are fed by a one-to-four corporate feeding network in E-plane with holey gap-waveguide technology. The antenna array has been manufactured with high precision stereolithography (SLA) and subsequent metal plating. This design procedure yields a low-cost and low-weight manufacturing process for functional prototypes. The complete array has been manufactured and measured, comparing its performance with the simulation results. Measurements show an input reflection coefficient below -10 dB which ranges from 68 GHz to 74 GHz. The measured radiation patterns suit adequately the defined ones in the design stage. Moreover, gain above 19 dBi in the entire operating frequency band is achieved with a 74.1% mean antenna efficiency.

The Internet of Things (IoT) is permeating our daily lives where it can provide data collection tools and important measurement to inform our decisions. In addition, they are continually generating massive amounts of data and exchanging essential messages over networks for further analysis. The promise of low communication latency, security enhancement and the efficient utilization of bandwidth leads to the new shift change from Mobile Cloud Computing (MCC) towards Mobile Edge Computing (MEC). In this study, we propose an advanced deep reinforcement resource allocation and securityaware data offloading model that considers the computation and radio resources of industrial IoT devices to guarantee that shared resources between multiple users are utilized in an efficient way. This model is formulated as an optimization problem with the goal of decreasing the consumption of energy and computation delay. This type of problem is NP-hard, due to the curseof-dimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. Additionally, an AES-based cryptographic approach is implemented as a security layer to satisfy data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead by up to 13.2% and 64.7% in comparison with full offloading and local execution while scaling well for large-scale devices.

IoT sensors are becoming increasingly important supplement to traditional monitoring systems, particularly for in-situ based monitoring. However, data collection based on IoT sensors are often plagued with missing values usually occurring as a result of sensor faults, network failures, drifts and other operational issues.