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.

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.

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.

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.

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.

The proliferation of distributed energy resources (DERs), located at the Distribution System Operator (DSO) level, bring new opportunities as well as new challenges to the operations within the grid, specifically, when it comes to the interaction with the Transmission System Operator (TSO). To enable interoperability, while ensuring higher flexibility and cost-efficiency, DSOs and the TSO need to be efficiently coordinated. Difficulties behind creating such TSO-DSO coordination include the combinatorial nature of the operational planning problem involved at the transmission level as well as the nonlinearity of AC power flow within both systems. These considerations significantly increase the complexity even under the deterministic setting. In this paper, a deterministic TSO-DSO operational planning coordination problem is considered and a novel decomposition and coordination approach is developed. Within the new method, the problem is decomposed into TSO and DSO subproblems, which are efficiently coordinated by updating Lagrangian multipliers. The nonlinearities at the TSO level caused by AC power flow constraints are resolved through a dynamic linearization while guaranteeing feasibility through “l1-proximal” terms. Numerical results based on the coordination of the 118-bus TSO system with up to 32 DSO 34-bus systems indicate that the method efficiently overcomes the computational difficulties of the problem.

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind-source separation methods, and helps enable in the wild EEG recordings to be performed.

This paper presents an iterative algorithm for image and video denoising which is based on fractional block-matching and transform domain filtering. We propose fractional motion estimation technique to find the most accurate similar blocks for each block of an image which improves sparsity enabling effective image denoising. By taking the advantage of blocks similarity and wavelet transform domain filtering along with weighted average function (WAF) in an iterative based manner, we achieve a higher level of sparsity and a better exploiting of blocks similarity redundancies of noisy images that increase the chance of preserving details and edges in the restored image. Since our algorithm is iterative, we can tradeoff between image denoising degree and computational complexity. In addition, we develop a video denoising algorithm based on the proposed image denoising algorithm. The simulation results of images and videos contaminated by additive white Gaussian noise demonstrate that our algorithm substantially achieves better denoising performance compared with previously published algorithms in terms of subjective and objective measures.

In this paper, a novel and non-invasive stator inter-turn short circuit (ITSC) online detection method is presented for an induction machine (IM), driven by a two-level voltage source inverter (2L-VSI) via finite control set model predictive control (FCS-MPC). The main idea of the proposed method is to utilize the controller itself as an observer: under the presence of a fault, the distribution of inverter switching states significantly deviates from the original balanced case. Therefore, by inspecting the inverter switching vectors, which are the outcomes of the FCS-MPC routine’s online optimization procedure, a stator fault can be detected efficiently. It is observed that both the zero-vector allocation over the complex plane and the allocation among the active vectors are influenced by the presence of a stator short-circuit fault. The proposed fault detection strategy introduces little to no extra burden for processor and memory. Experimental results verify the proposed method, and inter-turn short circuits of two and three turns are confidently detected and located for a 500 W, two-pole IM.

Human Activity Recognition (HAR) an important area of research in the light of enormous applications that it provides, such as health monitoring, sports, entertainment, efficient human computer interface, child care, education and many more. Use of Computer Vision for Human Activity Recognition has many limitations. The use of inertial sensors which include accelerometer and gyroscopic sensors for HAR is becoming the norm these days considering their benefits over traditional Computer Vision techniques. In this paper we have proposed a 1-dimensional Convolutions Neural Network which is inspired by two state-of-the art architectures proposed for image classifications; namely Inception Net and Dense Net. We have evaluated its performance on two different publicly available datasets for HAR. Precision, Recall, F1-measure and accuracies are reported.

The wide bandgap material, Gallium Nitride (GaN), has emerged as the dominant semiconductor material to implement high-electron mobility transistors (HEMTs) that form the basis of RF electronics. GaN is also an excellent material to realize photoconductive switches (PCSS) whose high-frequency performance could exceed that of RF HEMTs. In this paper, we numerically model the output characteristics of a GaN PCSS as a function of the input electrical and optical bias and the device dimensions. Importantly, we show that operating the GaN PCSS in the regime of negative differential mobility significantly benefits its high-frequency performance by compressing the temporal width of the output current pulse, while also enhancing its peak value. We find that when the optically excited carriers are generated in the middle of the active region, the bandwidth of the device is approximately 600 GHz, while delivering an output power exceeding 800 mW with a power gain greater than 35 dB. The output power increases to 1.5 W, and the power gain exceeds 40 dB with a near-terahertz bandwidth ( ≈ 800 GHz), as the laser source is moved closer to the anode. Finally, we elucidate that under high optical bias with significant electrostatic screening effects, the DC electric field across the device can be boosted to further enhance the performance of the GaN PCSS.

ResDock is a new method to improve the performance of protein-protein complex structure prediction. It utilizes shape complementarity of the protein surfaces to generate the conformation space. The use of an appropriate scoring function helps to select the feasible structures. An interplay between pose generation phase and scoring phase enhance the performance of the proposed ab initio technique.

This paper considers the power allocation for non-orthogonal multiple access (NOMA) users to enable using the successive interference cancellation (SIC) while providing reliable error performance. The derived closed-form expressions are applicable for arbitrary number of NOMA users each of which has a square or rectangular quadrature amplitude modulation (QAM) constellation with arbitrary order. The obtained numerical results show that power assignment process at the transmitter for the superposition process and at the receiver for the SIC process should be performed meticulously because the power difference between the weakest and strongest users can be tremendous when the number of users or the modulation orders increase. Moreover, the derived expressions can be used to reduce the computational complexity that is required to obtain the optimal power coefficients using brute force methods by significantly reducing the search space.

In this paper a brand new channel error detection and correction method is provided. Artificially adding symbols to the source binary sequence makes the binary sequence present a regularity. The receiver can use this rule to implement error detection and correction. Since many redundant symbols are added, a weighted probability model lossless coding method is proposed in this paper to remove redundant information, and the reasons why Markov chains and conditional probabilities are not feasible are given. It is proven that the method in this paper can reach the channel capacity when the code length approaches infinity. It is shown experimentally that when the code rate is 1/2 in the BIAWGN channel, the method in this paper is superior to the polar code and LDPC code.

Field-programmable gate arrays (FPGAs) are remarkably versatile. FPGAs are used in a wide variety of applications and industries where use of application-specific integrated circuits (ASICs) is less economically feasible. Despite the area, cost, and power challenges designers face when integrating FPGAs into devices, they provide significant security and performance benefits. Many of these benefits can be realized in client compute hardware such as laptops, tablets, and smartphones.

In this paper, a brand-new hash algorithm is provided. The length of the hash value can be customized. It is theoretically proved that the hash collision limit can be reached. The algorithm can be applied flexibly, and it can be applied to digital signatures, document verification and other fields in the future.

Recent studies show that physiological data can detect changes in mental effort, making way for the development of wearable sensors to monitor mental effort in school, work, and at home. We have yet to explore how such a device would work with a single participant over an extended time duration. We used a longitudinal case study design with ~38 hours of data to explore the efficacy of electrodermal activity, skin temperature, and heart rate for classifying mental effort. We utilized a 2-state Markov switching regression model to understand the efficacy of these physiological measures for predicting self-reported mental effort during logged activities. On average, a model with state-dependent relationships predicted within one unit of reported mental effort (training RMSE = 0.4, testing RMSE = 0.7). This automated sensing of mental effort can have applications in various domains including student engagement detection and cognitive state assessment in drivers, pilots, and caregivers.

This paper performs optimization of permanent magnet clutch shapes with the following objectives: maximum tangential attraction force and minimum volume of utilized materials. To form optimal shapes, the points on the clutch surface are chosen as optimization parameters. The optimization is carried out using Ārtap framework in connection with COMSOL software, where the 3D model of the clutch has been created. After the optimization, the tangential attraction force has increased by 13% and the volume of the clutch has been reduced by 24%.