In the last years, temporal metamaterials have been exploited as a novel platform for conceiving several electromagnetic and optical devices based on the anomalous scattering response achieved at a single or multiple sudden change of their medium properties, which however is difficult to carry out in a realistic scenario In this letter we investigate on the possibility to emulate the scattering response of a temporal metamaterial without acting on the medium properties, but on the effective refractive index and wave impedance perceived by the wave during the propagation within a guiding structure rather than the actual material, by acting on its boundaries. The work presents in closed form the scattering coefficients achieved when the boundary properties are suddenly modified for inducing an effective temporal interface. In this framework, temporally controlled metasurfaces can be used to implement the proposed concept, giving an easier path also to the design and realization of novel devices at microwave and optical frequencies.

A Digital Twin is defined as a digital replica of a real entity in the physical world. In this study, the Digital Twin simulation is developed for connected and automated vehicles (CAVs) by leveraging the Unity game engine. A Digital Twin simulation architecture is proposed, which contains the physical world and the digital world. Particularly, the digital world consists of three layers, where the Unity game objects are built to simulate the “hardware”, the Unity scripting API are used to simulate the “software”, and external tools (e.g., SUMO, MATLAB, python, and/or AWS) are leveraged to enhance the simulation functionalities. A case study of personazlied adaptive cruise control (P-ACC) is conducted to showcase the effectiveness of the proposed Digital Twin simulation, where the ACC system can be designed to satisfy each driver’s preference with the help of cloud computing.

Smart contract-based methods are used to implement the blockchain applications. While smart contracts have separate pre-deployed steps, which are suitable for applications that are deployed once and invoked many times. However, there are smart contracts that are used only one time (the disposable smart contract) or few times. Pre-deployment requires an additional step and additional transactions, which bring burdens (such as longer waiting time and more transaction fees) to users. In this paper, we propose a new Lifecycle model of smart contracts, which allows combining the pre-deployment with function invocations. This facilitates the usage of the disposable smart contract, as users are only required to send one transaction to perform both the pre-deployment and the function invocation. Together with the smart contract separation, it also allows participants to customize their special smart contracts at the request time. At last, we verify the proposed model and it shows the potential to save additional burden and to facilitate the usage of the smart contract.

In cross-chain scenarios, there are different blockchains, which need to cooperate. Cooperation among different blockchains is done by smart contracts that work together to complete cross-chain tasks. When numerous cooperative smart contracts are involved, smart contracts form a complex interaction network, which makes it difficult to evaluate the cooperation. It needs a common model to quantitatively analyze the cross-chain cooperation of associated smart contracts. In this paper, we model the cooperation among smart contracts as conditions and their corresponding actions, the condition-trigger model. Then we propose the method to calculate the cooperation probabilities by the graph weight. As the edge weight lacks the information of interaction probabilities, we introduce the dimension of the edge weight to calculate the probabilities. Finally, we verify the proposed condition-trigger model and its different types. It demonstrates that our proposed methods can effectively analyze the cross-chain cooperation among smart contracts.

Free-space optical (FSO) systems are able to offer the high data-rate, secure, and cost-efficient communication links required for applications such as wireless front- and backhauling for 5G and 6G communication networks. Despite the substantial advancement of FSO systems over the past decades, the requirement of a line-of-sight connection between transmitter and receiver remains a key limiting factor for their deployment. In this paper, we discuss the potential role of intelligent reflecting surfaces (IRSs) as a solution to relax this requirement. We present an overview of existing optical IRS technologies; compare optical IRSs with radio-frequency IRSs and optical relays; and identify various open problems for future research on IRS-assisted FSO communications.

For multiprimary displays that have four or more primaries, a color may be reproduced using multiple alternative control vectors. We provide a complete characterization of the Metameric Control Set (MCS), i.e., the set of control vectors that reproduce a given color on the display. Specifically, we show that MCS is a convex polytope whose vertices are control vectors obtained from (parallelepiped) tilings of the gamut, i.e., the range of colors that the display can produce. The mathematical framework that we develop: (a) characterizes gamut tilings in terms of fundamental building blocks called facet spans, (b) establishes that the vertices of the MCS are fully characterized by the tilings of the gamut, and (c) introduces a methodology for the efficient enumeration of gamut tilings. The framework reveals the fundamental inter-relations between the geometry of the MCS and the geometry of the gamut developed in a companion Part I paper, and provides insight into alternative strategies for color control. Our characterization of tilings and the strategy for their enumeration also advance knowledge in geometry, providing new approaches and computational results for the enumeration of tilings for a broad class of zonotopes in R3.

Initializing weights are important for fast convergence and performance improvement of Artificial Neural Network models. This study proposes a heuristic method to initialize weights for Neural Network with Fibonacci sequence. Experiments have been carried out with different network structures and datasets and results have been compared with other initialization techniques such as Zero, Random, Xavier and He. It has been observed that for small sized datasets, Fibonacci initialization technique reports 94% of test accuracy which is better than Random (85%) and close to Xavier (93%) and He (96%) initialization methods. Also, for medium sized dataset, we have noted that performance of Fibonacci weight initialization method is comparable with the same for Random, Xavier and He initialization techniques.

Radar-based precipitation nowcasting refers to predicting rain for a short period of time using radar reflectivity images. For dynamic nowcasting, motion fields can be extrapolated using an approximate and localized reduced-order model. Motion field estimation based on traditional Horn-Schunck (HS) algorithm suffers from over-smoothing at discontinuities in non-rigid and dissolving texture present in precipitation nowcasting products. An attempt to preserve the discontinuities using an L1 norm formulation in HS led to the use of Total Variation L1 norm (TVL1). In this paper, we propose a radar-based precipitation nowcasting model with TVL1-based estimation of motion field and Sparse Identification of Non-linear Dynamics (SINDy)-based estimation of non-linear dynamics. TVL1 is effective in preserving the edges especially in the case of the eye of typhoons and squall lines while estimating motion vectors. SINDy captures the non-linear dynamics and generates the subsequent update values for the motion field based on a reduced-order representation. Finally, the SINDy-generated ensemble of motion field is used along with the radar reflectivity image for generating precipitation nowcasts. We evaluated the effectiveness of TVL1 in preserving edges while capturing the motion field from non-rigid surfaces. The performance of the proposed TVL1-SINDy model in nowcasting weather events such as Typhoons and Squall lines are evaluated using performance metrics such as Mean Absolute Error (MAE), and Critical Success Index (CSI). Experimental results show that the proposed nowcasting system demonstrates better performance compared to the benchmark nowcasting models with lower MAE, higher CSI at higher lead times.

The National Institute of Standards and Technology defines the fundamental characteristics of cloud computing as: on-demand computing, offered via the network, using pooled resources, with rapid elastic scaling and metered charging. The rapid dynamic allocation and release of resources on demand to meet heterogeneous computing needs is particularly challenging for data centres, which process a huge amount of data characterised by its high volume, velocity, variety and veracity (4Vs model). Data centres seek to regulate this by monitoring and adaptation, typically reacting to service failures after the fact. We present a real cloud test bed with the capabilities of proactively monitoring and gathering cloud resource information for making predictions and forecasts. This contrasts with the state-of-the-art reactive monitoring of cloud data centres. We argue that the behavioural patterns and Key Performance Indicators (KPIs) characterizing virtualized servers, networks, and database applications can best be studied and analysed with predictive models. Specifically, we applied the Boosted Decision Tree machine learning algorithm in making future predictions on the KPIs of a cloud server and virtual infrastructure network, yielding an R-Square of 0.9991 at a 0.2 learning rate. This predictive framework is beneficial for making short- and long-term predictions for cloud resources.

Hyperspectral image (HSI) classification using convolutional neural network requires a lot of training samples, which is not always available. Consequently, decreases the classification accuracy due to the overfitting problem. Many studies have been conducted to solve the issue; however, they failed to solve it entirely. Therefore, we proposed a new approach to classify HSI with few training samples using a convolutional neural network in that context. The proposed approach employed an extended morphological profile cube (EMPC) to extract rich spectral-spatial features and then used a 3D densely connected network for classification. Besides, we used sparse principal component analysis to reduce the high spectral dimension of HSI. Experiments results on Indian Pines (IP) and University of Pavia (UP) datasets proved the efficiency of the proposed approach. It increased the OA by 2.61% - 13% and the Kappa coefficient by 2.68% - 15:51% on IP dataset and increased the OA by 0.17% - 11% and the Kappa coefficient by 0.23% - 19% on UP dataset, which is superior to some state-of-art methods.

Cell-free massive MIMO systems consist of many distributed access points with simple components that jointly serve the users. In millimeter wave bands, only a limited set of predetermined beams can be supported. In a network that consolidates these technologies, downlink analog beam selection stands as a challenging task for the network sum-rate maximization. Low-cost digital filters can improve the network sum-rate further. In this work, we propose low-cost joint designs of analog beam selection and digital filters. The pro-posed joint designs achieve significantly higher sum-rates than the disjoint design benchmark. Supervised machine learning (ML) algorithms can efficiently approximate the input-output mapping functions of the beam selection decisions of the joint designs with low computational complexities. Since the training of ML algorithms is performed off-line, we pro-pose a well-constructed joint design that combines multiple initializations, iterations, and selection features, as well as beam conflict control, i.e., the same beam cannot be used for multiple users. The numerical results indicate that ML algorithms can retain 99-100% of the original sum-rate results achieved by the proposed well-constructed designs.

In this article, we investigate mechanism of torque ripple generation by time and space harmonic magnetic fields in permanent magnet synchronous motors to obtain advanced motor designs. The general expression between the torque ripples and harmonic air-gap flux densities in the motor is derived by using Maxwell stress tensor. Both the numerical and experimental verifications of this expression are carried out. Then, the major harmonic magnetic fields that produce the torque ripples are specified and the differences between the surface and interior permanent magnet synchronous motors are investigated. According to these investigations, the shape of the rotor surface of an interior permanent magnet motor is optimized. It is clarified that specific harmonic components of the torque ripples in interior permanent magnet synchronous motors can be reduced to be nearly zero by optimizing the rotor surface shape.

Face Recognition is one of the most common biometric strategies which has gained popularity because of the accuracy and security. This paper presents the implementation of a Convolution Neural Network architecture for door automation. This model is devised to overcome the disadvantages of a traditional door system and other methods such as door automation using Bluetooth, figure prints, passwords, or retinal scans. It allows the authorized people to gain access to the house by face recognition. The proposed system makes use of convolution neural network architectures and RaspberryPi. The ResNet architecture [6] is used to implement face recognition and runs on RaspberryPi. The images of the residents of the house will be used to train the model. If the person is a resident of the house, the face will be recognized and the lock will open, else it will be recognized as a human and an alarm will ring and an email alert consisting of the image of the person in front of the door will be sent to the owner. It has numerous advantages as it is user-friendly especially for senior citizens, lesser maintenance, does not require the residents to carry the keys and reduces the threat of robbery.

The black-box impedance of the voltage source converters (VSCs) can be directly identified at the converter terminal without access to its internal control details, which greatly facilitates the converter-grid interactions. However, since the limited impedance data amount in practical industrial applications, the existing impedance identification methods cannot accurately capture characteristics of the impedance model at various operating scenarios, which is the indicators of the VSCs system stability at the changing profiles of renewables and loads. In this paper, a transfer learning based impedance identification is proposed to fill this research gap. This method can significantly reduce the required data amount used in impedance identification so that the black-box impedance-based stability method could be applied for the practical industrial application. The comparison results confirm the accuracy of the impedance model obtained by this transfer learning based impedance identification method.

Here we demonstrate a two-dimensional β-gallium oxide-based high electron mobility transistor (HEMT) comprising of a finite gap—access region gap (L_ARG ) in Ohmic-contact access regions with record transconductance linearity. Apart from limiting two-dimensional electron gas (2DEG) density n_s dependency on gate voltage, higher saturation current is estimated for the proposed design. Since the access regions length directly affects the Capacitance of the device and resultant switching applications. In this work, the effect of the gate-source and gate-drain length on device linearity is performed using Atlas-2D simulations. C-V characteristics of the proposed device are explained based on the physical explanation and validated using appropriate models. The higher values of transconductance g_m and current gain cut-off frequency f_T on a large span of operating voltages ensure improved transistor performance for low-noise amplification and RF application and are reported for the first time.

Haptic feedback is critical in a broad range of human-machine/computer-interaction applications. However, the high cost and low portability/wearability of haptic devices remain unresolved issues, severely limiting the adoption of this otherwise promising technology. Electrotactile interfaces have the advantage of being more portable and wearable due to their reduced actuators’ size, as well as their lower power consumption and manufacturing cost. The applications of electrotactile feedback have been explored in human-computer interaction and human-machine-interaction for facilitating hand-based interactions in applications such as prosthetics, virtual reality, robotic teleoperation, surface haptics, portable devices, and rehabilitation. This paper presents a technological overview of electrotactile feedback, as well a systematic review and meta-analysis of its applications for hand-based interactions. We discuss the different electrotactile systems according to the type of application. We also discuss over a quantitative congregation of the findings, to offer a high-level overview into the state-of-art and suggest future directions. Electrotactile feedback systems showed increased portability/wearability, and they were successful in rendering and/or augmenting most tactile sensations, eliciting perceptual processes, and improving performance in many scenarios. However, knowledge gaps (e.g., embodiment), technical (e.g., recurrent calibration, electrodes’ durability) and methodological (e.g., sample size) drawbacks were detected, which should be addressed in future studies.

Information technology (IT) companies implement multi-dimensional policy plans that include procedures, sub-plans, and instructions to outline their business scopes, targets, and communications. This work outlined the IT policy implementation plan designed by an imaginary company with a random name called Northcentral Cloud Consulting Firm (NCCF), containing proposed IT policies, milestones and roadmaps, control framework, stakeholder responsibilities, knowledge transfer plan, and leadership roles. As NCCF’s major customers seek data-driven solutions in cloud computing, the NCCF IT policy plan provides various data policies, including security and proper usage of machine learning services. The plan offers a detailed roadmap of its financial, geographical, and reputational expansion within three years. The IT policy plan also compromises an IT risk management, contingency, and emergency communication plan, mainly for protecting data and business continuity. Stakeholder responsibilities are incorporated into the IT policy plan, as NCCF considers any engagement with its customers as a collaborative effort in which both parties have and share several responsibilities.

In federated edge computing networks, edge computing (EC) nodes from multiple service providers (SPs) are installed to serve the same customer base and each SP intends to maximize their economic utilities. Therefore, to address research problems like EC node placement, job request allocation, and load balancing, we need to involve some economic aspects along with network engineering aspects. This implies that we need to propose novel system models and formulate problems in a significantly different way than the existing ones. Thus, we are motivated to discuss about the primary aspects of these new problem formulations and propose a novel economic and game-theoretic model for load balancing among federated EC nodes in this paper. In this game formulation, instead of focusing on latency minimization, under-loaded EC nodes intend to maximize their economic utilities by receiving any extra workload and incentives from their overloaded neighbors while satisfying the expected latency target. Furthermore, we design a centralized control mechanism, tailor-made for ultra-reliable and low latency (uRLL) applications, for implementing this load balancing framework by incorporating artificial intelligence (AI)-enhanced traffic prediction algorithms.