Cyber-physical systems comprise multiple interdependent networks supporting each other. This paper considers a combination of power grid and computer network as a typical instance of the interdependent networks. A node in an interdependent network fails if it cannot receive the required volume of support from the nodes in the other interdependent network. Meanwhile, each node supporting the other network is required to receive a sufficient volume of intra-network flow named “support flow”, which corresponds to the flow of electric power in the power grid and the flow of control messages in the computer network. Each sink node of the support flow must terminate a sufficient volume of support flow generated at source nodes in the same network in order to achieve the support of the other network. This means that the support flow is one of the essential elements to make the interdependent networks operable. The conditions related to the support flow increases the possibility of a small initial node failure invoking a cascading failure with a wide range. This paper proposes a robust design method for the interdependent networks to maximize the robustness against the cascading failures while assuring satisfaction of the constraints on the support flow. The method can derive the interdependency to minimize the impact of the cascading failures induced from a given set of possible initial node failures when the configuration of the respective interdependent networks is specified beforehand. The robust design problem is formulated using a mixed integer linear programming model and two kinds of metaheuristic methods are proposed to solve the problem efficiently even if a great number of initial node failures are specified. Several characteristics of the risk of the cascading failures are revealed from the results of the robust design. Simulation experiments demonstrate the effectiveness of the proposed metaheuristic methods.

A study was made of ways to minimize the delay time of signals from sensors for which it is necessary to convert an alternating output voltage into a direct voltage proportional to it. It is shown that this delay can be reduced by optimizing the parameters of the low-pass filters, taking into account the given suppression of variable signals in the stopband. The circuits of Chebyshev and Butterworth filters from the first to the sixth order are investigated, their comparison with each other in terms of the delay of the main signal and the efficiency of suppression of its harmonics outside the operating frequency range is given. The results of modeling the amplitude-frequency and transient characteristics of a low-pass filter in the Micro-Cap 12 environment for the case of its use in a transformer angle sensor are presented.

This paper proposes a self-supervised latent space clustering algorithm, called the Deep Latent Space Clustering, for the detection of stealthy false data injection attacks (FDIAs) in smart grids against state estimation algorithms. The stealthy FDI model considered is based on the accurate AC state estimation and is able to bypass conventional bad data detection (BDD) algorithms with ease. The key element of the detection model is a stacked autoencoder network that first undergoes a carefully designed two-step finetuning process, following which a trainable clustering head is stacked on top of the finetuned encoder and the final network is further trained to achieve a clean clustering of the data into benign and compromised samples without labelled supervision. To test the efficacy and scalability of the detection model, it is tested on the standard IEEE 14 bus, 118 bus and 300 bus test systems. The self-supervised clustering model is compared to several supervised, semi-supervised and unsupervised algorithms proposed in the literature for detection of FDI on the aforementioned test cases and has been found to perform at par with the state of the art among them.

Federated learning (FL) has recently emerged as a novel technique for training shared machine learning models in a distributed fashion while preserving data privacy. However, the application of FL in wireless networks poses a unique challenge on the mobile users (MUs)’ battery lifetime. In this paper, we aim to apply reconfigurable intelligent surface (RIS)-aided wireless power transfer to facilitate sustainable FL-based wireless networks. Our objective is to minimize the total transmit power of participating MUs by jointly optimizing the transmission time, power control, and the RIS’s phase shifts. Numerical results demonstrate that the total transmit power is minimized while satisfying the requirements of both minimum harvested energy and transmission data rate.

With the fast expansion of the power grid and increasing complexity due to modern equipment, power flow models with non-convexity and long computing time are not suitable for network calculation and optimization problems. Therefore, this paper proposes a linearized branch flow model (LBF) considering line shunts (LBFS). The strength of LBF lies in its linear mathematical structure, and hence the convex nature, which is primarily achieved by regarding the apparent power flow as the branch current magnitude. Moreover, the calculation accuracy in nodal voltage magnitude is significantly improved by appropriately modeling line shunt admittances and network equipment like transformers, shunt capacitors and distributed generators (DG). We show the application scope of LBFS by controlling the network voltages through a two-stage stochastic optimization Volt/VAr control (VVC) problem considering DG output uncertainty. Since LBFS results in a linear VVC program, the global solution is guaranteed. Simulations show that VVC framework can optimally dispatch the discrete control devices, viz. substation transformers and shunt capacitors, and also optimize the decision rules for real time reactive power control of DGs. Besides, the computing efficiency is significantly improved compared to traditional VVC methods.

An adaptive frequency sampling (AFS) strategy is proposed in conjunction with characteristic basis function method (CBFM) to investigate the problem of wide-band scattering from objects buried in layered media. Conventionally, the CBFM is implemented in the method of moments (MoM) formulation to reduce the solution time at a single frequency. However, wide-band analysis of the above problem is still time consuming when a large number of frequency samples are employed, together with uniform sampling. To mitigate this issue and speed up the process, we propose the AFS algorithm, which selects the frequency samples via an iterative process involving the error-estimates and in turn guarantees the convergence of the wide-band solution process. These samples are used as inputs to the vector fitting (VF) algorithm, which obtains a rational model and subsequently derives the scattered fields in the frequency range of interest efficiently. Numerical results are included to demonstrate that the number of required samples is significantly reduced without compromising the accuracy of the solution.

In transient-state multi-parametric MRI sequences such as MR-STAT, MR Fingerprinting, or Hybrid-state imaging, the flip angle pattern of the RF excitation varies over the sequence. This gives considerable freedom to choose an optimal pattern of flip angles. For pragmatic reasons, most optimization methodologies choose for a single-voxel approach, i.e. without taking the spatial encoding scheme into account. Particularly in MR-STAT, the context of spatial encoding is important. So we present a methodology, called BLAKJac, that is sufficiently fast to optimize a sequence in the context of a predetermined phase-encoding pattern. Based on MR-STAT acquisitions and reconstructions, we show that sequences optimized using BLAKJac lead to better results than conventional single-voxel optimized sequences. In addition, BLAKJac provides analytical tools that give insights in the performance of the sequence at very limited computation time. Our experiments are based on MR-STAT, but the theory is equally valid for other transient-state multi-parametric methods.

This paper develops the frequency controller for a battery energy storage system (BESS) to facilitate a smooth island transition of a hydro-powered microgrid during an unplanned grid outage. The proposed frequency controller uses a PI-based droop structure. The analytical expression of the controller parameter tuning is derived that accounts for the limitations in the power response of a hydro generator and the desired frequency performance criteria set by the microgrid operator. The effectiveness of the frequency controller tuning was verified in Simulink using phasor simulations. Results show that the proposed PI-based droop outperforms the classical proportional droop control in order to fulfil the frequency quality requirement of the microgrid without over-dimensioning the size of the storage capacity.

This is a preprint version submitted to review for publication on the IEEE Transactions on Biomedical Engineering (TBME). In this study we investigated the estimation method of cross-bicoherence to infer quadratic phase coupling (QPC) in interaction networks based on empirical Bayes estimation. Nonlinear interaction, ubiquitous in a wide range of biomedical fields, often makes it challenging to apply conventional parametric approaches. While cross-bicoherence has been recognized as a key statistic of QPC, prior studies have not elucidated how reliable the estimation can be for realistic time series. This work demonstrates that cross-bicoherence estimated by the proposed method effectively detects QPC, with illustrative simulation studies of both typical QPC and neural mass models of cortical columns.

Many algorithms have been proposed to solve the clustering problem. However, most of them lack a proper strategy to maintain a good balance between exploration and exploitation to prevent premature convergence. Multi-Trial Vector-based Differential Evolution (MTDE) is an improved differential evolution (DE) algorithm that is done by combining three strategies and distributing the population between these strategies to avoid getting stuck at a local optimum. In addition, it records inferior solutions to share information about visited regions with solutions of the next generations. In this paper, an Improved version of the Multi-Trial Vector-based Differential Evolution (IMTDE) algorithm is proposed and adapted for clustering data. The purpose here is to enhance the balance between the exploration and exploitation mechanisms in MTDE by employing Gaussian crossover and modifying the sub-population distribution between the strategies. To evaluate the performance of the proposed clustering, 19 datasets with different dimensions, shapes, and sizes were employed. The obtained results of IMTDE demonstrate improvement in MTDE performance by an average of 12%. Our comparative study with state-of-the-art algorithms demonstrates the superiority of IMTDE in most of these datasets because of the effective search strategies and the sharing of previous experiences in generating more promising solutions. Source code is available on Github: https://github.com/parhamhadikhani/IMTDE-Clustering.

In this paper quantum switches, qswitches, are introduced to generate a superposition of resistor networks. Measuring the qswitches results in one resistor network with specific values for the electrical variables; then using the probabilities for each network with the currents and voltages, expectation values are calculated. Finally a control signal, or feedback, is used to change the qswitches probability amplitudes.

This paper concentrates on cell-free massive multiple-input and multiple-output (MIMO) network with variable-resolution analog-to-digital converters (ADCs). In such architecture, all ADCs equipping at any access point (AP) can use arbitrary bit resolution to realize adaptive quantization and reduce power consumption. Under this circumstance, we first introduce a channel estimator based on linear minimum mean-square error (LMMSE) theory. On this basis, intra-AP and inter-AP bit allocation problems are investigated to maximize channel estimation quality subject to the total number of quantization bits. By leveraging on the statistical properties of the estimated channels and estimation errors, we then derive the theoretical expressions of the achievable uplink spectral efficiency (SE) for maximal ratio combining (MRC) and minimum mean-square error (MMSE) combining, respectively. Furthermore, to maximize the sum SE under the constraint of total ADC quantization bits, we also investigate intra-AP and inter-AP bit allocation problems for both single-user and multi-user scenarios. Finally, simulation results confirm that our theoretical analyses are correct and accurate. In addition, we resort to numerical results to achieve some new insights and verify the advantages and conclusions pertinent to the proposed bit allocation techniques.

Despite many advances in human activity recognition, most existing works are conducted with supervision. Supervised methods rely on labeled training data. However, obtaining labeled data is difficult, costly, and time-consuming. In this paper, we introduce an automatic multi-objective particle swarm optimization clustering based on Gaussian mutation and game theory (MOPGMGT) to tackle the problem of human activity discovery fully unsupervised and map the multi-objective clustering problem to game theory to get the best optimal solution. The proposed algorithm does not require any prior knowledge of the number of activities to be discovered and can find the optimal number. Multi-objective optimization problems typically cannot have a single optimal solution. To solve this issue, Nash Equilibrium (NE) is applied to the pareto front to choose the best solution. NE does not just look for the best solution, but tries to optimize the final solution by considering the effect of choosing each of the solutions as the best solution on the other solutions and one with the best impact is chosen. Moreover, a Gaussian mutation is applied on the pareto front to avoid premature convergence. Experiments on five challenging datasets demonstrate that the proposed approach is the most efficient to achieve better accuracy in human activity discovery and also can determine the optimal number of clusters.

UAVs can be used as aerial relays to provide communication services in remote uncovered areas or dense environments with occasional high capacity demands. However, due to the low power of Internet-of-Things (IoT) devices, UAV-based IoT applications, such as precision agriculture and environment monitoring, may experience high shadowing or equipment failure, which degrades the communications’ quality between IoT devices and their gateways. To tackle this issue, we consider the long range (LoRa) communication technology. Specifically, we investigate the performance of LoRA-enabled aerial communications, where a LoRa gateway communicates with a distant IoT device through the assistance of an amplify-and-forward aerial relay. Under the assumption of shadowed Rician fading channels, we characterize at first the end-to-end LoRa communication link. Then, we derive an exact symbol error rate expression for the underlying system model. Finally, numerical results are presented to corroborate the efficacy of our derived expressions and provide valuable insights into the error performance of LoRa-enabled aerial networks.

Coronavirus (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most infected people will experience moderate to severe respiratory illnesses and recover without the need for specialized treatment. Some, on the other hand, will become quite ill and seek medical care. Older people and those with poor health conditions such as cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness. Anyone can get sick with COVID-19 and get very sick or die at any age. Pneumonia is also an infection in one or both lungs caused by bacteria, viruses, or fungi. The infection leads to inflammation in the air sacs of the lungs, which are called alveoli. The alveoli get clogged with fluid or pus, making breathing difficult. So, for detecting the COVID-19 and Pneumonia in now a days become costly and time taking process especially for common people and radiologists. This paper introduces AUTO- ML process to detect the diseasesc(COVID or Pneumonia) inside the human lung.

In this work, a non-orthogonal multiple access NOMA) relying on a two-way relaying (TWR) approach is investigated to promote more efficient use of the available spectrum. Specifically, a NOMA-enabled half-duplex decode-and-forward relay node is used to simultaneously exchange data between two cellular users and a base station (BS), while assuming that the direct links between the two users and the BS are unavailable or negligible. In our analysis, the ergodic capacity is evaluated, in which high signal-to-noise ratio approximations, as well as upper bounds are derived owing to the intractability related to the exact analysis. The results reveal the superiority of the proposed TWRNOMA scheme compared with the traditional one-way relaying (OWR-NOMA) scheme. Additionally, simulation results show that selecting a relay station (RS) closer to the BS can achieve a higher ergodic sum capacity or the system than selecting a far RS.

Abstract—In this contribution a novel dielectric radio frequency (RF) applicator cell with an open aperture exceeding the beam waist for terahertz (THz) transmission is presented. Dielectric heating equations are shown that explain the relation of losses and temperature of the material under test. Next to fundamental considerations, we are presenting 3D-EM simulations and measurement results of a manufactured RF applicator cell that shows excellent properties within the frequency range from 10MHz to 1 GHz.

Cybersecurity researchers always ignore the “people” behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— “How to solve cyberspace security issues via social engineering?” We briefly review existing frameworks for social engineering attacks and pro?pose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four “guidelines” for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field.