Limits of phase locked loop (PLL) are necessary to make sure the transient converter frequency bounded in a safety range. This leads to the grid-connected voltage source converter (VSC) with limits of PLL as one switched system. Stability of switched dynamic system is fully discussed in this paper. One conservative analytical stable region of the unbounded autonomous system is derived by Lyapunov method. It is proved that when bounds are touched, any trajectory of the studied switched system deviates from these bounds at two fixed switching Points. Moreover, the switched system energy is non-increasing when trajectories lie inside the derived analytical stable region. Based on these properties of switched system, it is proved that to ensure the globally asymptotically stability, the fixed switching Points should be placed inside the derived stable region of the unbounded autonomous system. Finally, stabilization method is proposed via placement of the fixed switching Points so that the globally asymptotically stability is ensured.

The Satellite Application Facility on Climate Monitoring (CM-SAF) Spectral Resolved Irradiance (SRI) and National Renewable Energy Laboratory National Solar Radiation Database Spectral on Demand (NSRDB-S) satellite-based spectral irradiance products are tested here against benchmark data and models at seven ground stations: one in Spain for CM-SAF SRI and six in North America for NSRDB-S. Benchmarks include WISER spectroradiometers, spectra modeled from SolarSIM-G measurements and the SMARTS radiative code with two alternate input sources: AErosol RObotic NETwork (AERONET) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. The satellite products are tested in terms of their ability to estimate photovoltaic (PV) spectral effects for six PV module technologies. The spectra are also compared directly under clear-sky conditions. Both CM-SAF SRI and NSRDB-S outperform the simple benchmark of neglecting spectral effects in terms of predicting instantaneous spectral mismatch factors, but only CM-SAF SRI generally does better at predicting long-term spectral derate factors. The clear-sky results reveal systematic differences between NSRDB-S and benchmark spectra, likely due to the NSRDB-S treatment of aerosols. Meanwhile, the mean SMARTS spectra with AERONET and MERRA-2 inputs are in good agreement, showing promise for the use of MERRA-2 as input to clear-sky models.

The lunar surface has a stable luminosity. To use the Moon as a calibration standard, the Robotic Lunar Observatory (ROLO) program models the integration of the radiance of the entire lunar surface. However, the albedos of the mare and the highlands are very different. The modeling based on the lunar global irradiance/reflected radiance is bound to result in higher uncertainty. In contrast, if the local calibration of the lunar surface is adopted, the lunar complex topography effect cannot be ignored. This paper presents a new model for quantifying multiple reflections of radiation between terrains (MRRT). The relationship between the bidirectional reflectance factor (BRF) of the observed pixel and the true microtopography reflectance is established, which shows that the BRF is mainly influenced by the true topography reflectance, the terrain undulation, the incident irradiance on the topography surface, and the masking in the observation direction. The new model applied on the lunar surface obtains clearer terrain details. The inversion reflectance of the Chang’e-3 landing area is closer to the reflectance measured in situ, and the reflectance curves of the Apollo 16 landing area are almost consistent under different illumination observation geometries. This shows that the MRRT model can effectively eliminate the topographic effect. Compared with the ROLO model, the MRRT model does not restrict the specific selection, so it can select a region with a uniform material distribution, small albedo difference, and low topography undulation to establish the lunar surface radiometric calibration field with the advantage of providing stable radiation characteristics.

This paper presents, a mutual coupling reduction technique between a very closely spaced (1.8 mm) two-element microstrip based MIMO antennas using a modified inverted-fork shaped decoupling (m-IFSD) structure. The m-IFSD consists of an inverted fork and a cross shaped structure with a shorted via. The combined effect of inverted-fork and shorted cross shaped structure results in mutual coupling reduction below −35 dB between adjacent antenna elements. The decoupling technique is analyzed using an approximate transmission-line model and field distribution. Further, the two-element MIMO antenna design is extended to 8-element MIMO configuration to improve the MIMO diversity. To verify the proposed isolation technique a two-element MIMO antenna prototype is fabricated and measured. The proposed MIMO antenna exhibits a low mutual coupling (< −35 dB) with good impedance matching (< −10 dB) at 5.45 GHz. The MIMO antenna provided a total active reflection coefficient (TARC) less than −10 dB and envelop correlation coefficient (ECC) (for isotropic propagation scenario) less than 0.5. Finally, the ECC of the proposed MIMO antenna system is analyzed for a realistic Gaussian/Uniform propagation scenario for various incidence angles and angular spreads to better understand the effect of the mutual coupling reduction technique.

In this work we propose a new cooperation strategy between connected autonomous vehicles in on-ramps merging scenarios and we implement the cut-in risk indicator (CRI) to investigate the safety effect of the proposed strategy. The new cooperation strategy considers a pair of vehicles approaching an on-ramp. The strategy then makes decisions on the target speeds/accelerations of both vehicles, possible lane changing, and a dynamic decision-making approach in order to reduce the risk during the cut-in manoeuvre. In this work, the CRI was first used to assess the risk during the merging manoeuvre. For this purpose, scenarios with penetration rates of autonomous vehicles from 20% to 100%, with step of 10%, both connected and non-connected autonomous vehicles were evaluated. As a result, on average a 35% reduction of the cut-in risk manoeuvres in connected autonomous vehicles compared to non-connected autonomous vehicles is obtained. It is shown through the analysis of probability density functions characterising the CRI distribution that the reduction is not homogeneous across all indicator values, but depends on the penetration rate and the severity of the manoeuvre.

This is a preprint of the paper, “Artificial Intelligence Techniques for Next-Generation Massive Satellite Networks”.

In a worldwide health crisis as exigent as COVID-19, there has become a pressing need for rapid, reliable diagnostics. Currently, popular testing methods such as reverse transcription polymerase chain reaction (RT-PCR) can have high false negative rates. Consequently, COVID-19 patients are not accurately identified nor treated quickly enough to prevent transmission of the virus. However, the recent rise of medical CT data has presented promising avenues, since CT manifestations contain key characteristics indicative of COVID-19. This study aimed to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. First, the dataset utilized in this study was derived from three major sources, comprising a total of 17,698 chest CT slices across 923 patient cases. Image preprocessing algorithms were then developed to reduce noise by excluding irrelevant features. Transfer learning was also implemented with the EfficientNetB7 pre-trained model to provide a backbone architecture and save computational resources. Lastly, several explainability techniques were leveraged to qualitatively validate model performance by localizing infected regions and highlighting fine-grained pixel details. The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958. Explainability measures showed that the model correctly distinguished between relevant, critical features pertaining to COVID-19 chest CT images and normal controls. Deep learning frameworks provide efficient, human-interpretable COVID-19 diagnostics that could complement radiologist decisions or serve as an alternative screening tool. Future endeavors may provide insight into infection severity, patient risk stratification, and prognosis.

This paper considers the problem of motion coordination for a multi-agent network whose goal is to track a large-scale multi-target system in a region populated by dynamic obstacles. We first characterize a density path which corresponds to the expected evolution of the macroscopic state of the multi-target system, which is represented by the probability density function (PDF) of a time-varying Gaussian mixture (GM). We compute this density path by using an adaptive optimal control method which accounts for the distribution of the (possibly moving) obstacles over the environment described by a time-varying obstacle map function. We show that each target of the multi-target system can find microscopic inputs that can collectively realize the density path while guaranteeing obstacle avoidance at all times. Subsequently, we propose a Voronoi distributed motion coordination algorithm which determines the individual control inputs of each agent of the multi-agent network so that the latter can track the multi-target system while avoiding collisions with obstacles and their teammates. The proposed algorithm relies on a distributed move-to-centroid control law in which the density over the Voronoi cell of each agent is determined by the  estimated macroscopic state evolution of the multi-target system. Finally, simulation results are presented to showcase the effectiveness of our proposed approach.

We consider a class of Nash games with nonconvex coupling constraints where we leverage the theory of tangent cones to define a novel notion of local equilibrium: Clarke’s local generalized Nash equilibrium (CL-GNE). Our first technical contribution is to show the stability of these equilibria on a specific local subset of the original feasible set. As a second contribution, we show that the proposed notion of local equilibrium can be equivalently formulated as the solution of a quasi-variational inequality, remarkably, with equal Lagrange multipliers. Next, we define conditions for the existence and uniqueness of the CL-GNE. To compute such an equilibrium, we propose two discrete-time distributed dynamics, or fixed-point iterations. Our third technical contribution is to  prove convergence under (strongly) monotone assumptions on the pseudo-gradient mapping of the game. Finally, we apply our theoretical results to a competitive version of the optimal power flow control problem. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The Internet of Things (IoT) is a transformative technology marking the beginning of a new era where physical and digital worlds are integrated by connecting a plethora of uniquely identifiable smart objects. Although the Internet of terrestrial things (IoTT) has been at the center of our IoT perception, it has been recently extended to different environments, such as the Internet of underWater things (IoWT), the Internet of Biomedical things (IoBT), and Internet of underGround things (IoGT). Even though radio frequency (RF) based wireless networks are regarded as the default means of connectivity, they are not always the best option due to the limited spectrum, interference limitations caused by the ever-increasing number of devices, and severe propagation loss in transmission mediums other than air. As a remedy, optical wireless communication (OWC) technologies can complement, replace, or co-exist with audio and radio wave-based wireless systems to improve overall network performance. To this aim, this paper reveals the full potential of OWC-based IoT networks by providing a top-down survey of four main IoT domains: IoTT, IoBT, and IoGT. Each domain is covered by a dedicated and self-contained section that starts with a comparative analysis, explains how OWC can be hybridized with existing wireless technologies, points out potential OWC applications fitting best the related IoT domain, and discusses open communication and networking research problems. More importantly, instead of presenting a visionary OWC-IoT framework, the survey discloses that OWC-IoT has become a reality by emphasizing ongoing proof-of-concept prototyping efforts and available commercial off-the-shelf (COTS) OWC-IoT products.

Free-space optical communication (FSOC) systems require precise pointing, acquisition, and tracking to send and receive optical beams for effective operation. In a lens-assisted beam steering (LABS) system, light is steered by controlling the emission location in a focal plane. The emitted light is directed into the scene by a lens, like a camera but operating in reverse. In this paper, we demonstrate a novel free-space optical communication link using a micro-opto-electro-mechanical-system (MOEMS) based photonic integrated circuit (PIC) for LABS between multiple receive locations. The MOEMS PIC operates via selective electrical actuation of an array of small grating switches (30 μm x 30 μm footprint, 100 μm pitch). Data rates up to 10 Gbps and a 3 dB optical bandwidth covering the infrared C and L bands (1530 nm to 1625 nm) are measured over a 1 m free-space link distance. Eye diagrams indicate a quality communication link for data rates up to 10 Gbps and bit-error-rates < 10-10 are measured for on-off keying (OOK) modulation. A measured beam profile is propagated into the far-field via simulation, and used to calculate link budgets for example CubeSat crosslink and downlink scenarios. Link budget calculations indicate potential > 1 Gbps CubeSat FSOC crosslinks for link lengths > 1000 km and 1 W of input optical power, using identical 90 mm transmit and receive apertures and a commercially-available fiber-coupled InGaAs avalanche photodiode (APD) receive detector.

Electrification of higher torque density applications in the transportation sector and robotics sector are primarily met with permanent magnet synchronous machines (PMSMs), attributing to their unique properties. However, wide operating temperatures, field weakening controls, and low-quality magnets, lead to uniform demagnetization of rotor magnets. Demagnetization in torque control applications reduce the effective output torque as the output torque is proportional to rotor flux level. Detection of such rotor demagnetization is essential and only second to few key faults from the system perspective. A simple and common approach to detect uniform demagnetization in a sensored-FOC PMSM drive system in torque control mode is discussed in this paper with experimental results emulating uniform demagnetization effect. The false positive nature in the proposed algorithm under a position sensor offset error is discussed, followed by a disambiguation strategy, which are primary contributions of this paper. Analysis, simulation and experimental results are presented to validate the proposed disambiguation strategy.

Segmentation of breast masses in digital mammograms is very challenging due to its complexity. The recent U-shaped encoder-decoder networks achieved remarkable performance in medical image segmentation. However, these networks have some limitations: a) The multi-scale context information is required to accurately segment mass but is not effectively extracted and utilized. b) The global context information is often ignored by the skip connection. To overcome these limitations and achieve better segmentation, we propose an Enhanced U-shaped Network (EU-Net). The proposed EU-Net comprises of 3 novel components: 1) dense block, which is employed in the encoder and the decoder in place of convolutional layers to achieve the multi-scale features. 2) Multi-Scale Feature Extraction and Fusion, which is used in the junction between the encoder and the decoder for further extracting and fusing the multi-scale context information. 3) Skip Connection Reconstruction, which is inserted between the encoder and the decoder at each stage, to redesign the skip connection and emphasize the global context information. Extensive experimental results under different settings show that the proposed EU-Net achieves superior performances than the previous state-of-the-art segmentation models, and other existing approaches on IN-Breast and CBIS-DDSM mammogram datasets. The generalization ability of the proposed EU-Net is evidenced through crossdataset and ternary dataset evaluation performance. In the ternary dataset evaluation, the model is trained and evaluated on the UDIAT breast ultrasound dataset without finetuning. The EU-Net achieves higher generalization performance in both evaluation experiments. These experiments collectively indicate the efficiency and high generalization ability of the proposed EU-Net.

This paper proposes a novel framework for modular multilevel converter (MMC) current harmonic emission estimation from medium voltage (MV) charging stations. The framework is enlightened by wave decomposition, exploiting an analytical derivation of the emission, without a time-domain model. Accordingly, voltage harmonic distribution in grids can be estimated with a frequency-domain model, using the estimated voltage-dependent current emission in the iterations. Compared to conventional time-domain simulation, such method not only enables analytical harmonic estimation integrated with a large load-flow model, but also benefits from the potential of distributed computation. A 10.5kV grid-tied single-phase half-bridge MMC simulation case on PLECS has been implemented and investigated for comparison. Corresponding results revealed that the estimated harmonic series kept the absolute average difference to the PLECS model lower than 0.02% and  a correlation ratio higher than 99.990% with the simulated ones. The effectiveness of the proposed estimation framework is successfully verified.

The highly dynamic nature of cognitive radio (CR) systems and their stringent latency requirements pose a major challenge in the realization of efficient intelligent transport systems (ITS). In this paper, we investigate relay selection and opportunistic spectrum access in conjunction with blockchain technology in a secure manner. Specifically, we propose a cross-layer method for secure relay selection, where secondary relays (SRs) are granted access to available spectrum bands based on the balance of their respective virtual wallets. These virtual wallets, which are built based on the SRs’ secrecy capacity and their behavior in the network, are the predominant factors that allow SRs to participate in an auction model. To quantify the trustworthiness of the SRs, we formulate a mathematical framework to evaluate the trust value of each SR, which is then leveraged for rewarding or penalizing the SR. Furthermore, we develop an offline blockchain framework to store the information of participating relays and make it available for future operations, in order to detect reputable and non-reputable relays in the presence of multiple eavesdroppers. The stored reputations of the participating relays are used to develop a self-learning algorithm to exclude the non-reputable relays from the selection group. Finally, we present thorough numerical results to demonstrate the superiority of the proposed system in terms of security, credibility, and integrity.

In the foreseeable Intelligent Transportation System (ITS), Intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge Computing (MEC) helps overcome this limitation effectively. In this paper, we investigate the vehicle and edge server collaborative computation offloading problem by jointly optimizing the partial offloading ratio and resource allocation ratio.

In this letter, we present a two-stage pipeline for robust network intrusion detection. First, we implement an extreme gradient boosting (XGBoost) model to perform supervised intrusion detection, and leverage the SHapley Additive exPlanation (SHAP) framework to devise explanations of our model. In the second stage, we use these explanations to train an auto-encoder to distinguish between previously seen and unseen attacks. Experiments conducted on the NSL-KDD dataset show that our solution is able to accurately detect new attacks encountered during testing, while its overall performance is comparable to numerous state-of-the-art works from the cybersecurity literature.

Optimization tools for long-term grid planning considering flexibility resources require aggregated flexibility models that are not too computationally demanding or complex. Still, they should capture the operational benefits of flexibility sufficiently accurately for planning purposes. This paper investigates the sufficiency of an aggregated flexibility model for planning tools by comparing it against a detailed flexibility model. Two different constraint formulations, namely based on recovery period and temporal proximity were tested to account for post activation dynamics of flexibility resources. The results show that the recovery period based formulation results in excessive demand reduction. The proximity constraint formulation on the other hand results in realistic activation of flexibility resources which represents an improvement over the base formulation without constraints for post activation dynamics.