Cognitive unmanned aerial vehicles (CUAVs) play a vital role in next-generation wireless networks as they assist in massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) services. This study focuses on multiple CUAV-enabled networks, wherein CUAVs are paired with each other. We analyze the data rate, energy efficiency, and latency of such networks by applying the finite information block length theory, wherein mMTC and URLLC information use a non-orthogonal multiple access technique. Furthermore, we formulate an optimization problem to maximize the energy efficiency of paired CUAV devices by jointly optimizing the transmission power of the mMTC and URLLC information to satisfy the latency requirement. The numerical results indicate that our proposed multiple-CUAV-enabled scheme enhances the network performance of CUAV devices in terms of energy efficiency and latency better than the existing scheme.

This work discusses the capability of tailoring sensitivity of an optical-fiber-based Fabry–Perot interferometric (FPI) sensor to changes in refractive index (volume) and formation of a layer on the sensor surface (adlayer). A simple single-layer approach shows some disadvantages, especially when refractive index (n) sensing is considered, e.g., there is no shift of the interference pattern in the wavelength domain. The considered FPI sensor is based on two transparent thin films deposited on a single-mode optical fiber’s end face. As the first layer, a high-n titanium oxide (TiO2) was chosen. We show that addition of a second layer of lower n results in the blueshift of spectral pattern with external n (next). Moreover, when an additional layer, e.g., biological one, is formed, a redshift of the pattern appears, what is in contrary to the shift induced by the increase of next. Numerical analysis as well as experiments show that a wide range of materials (with different n and thickness) can be applied as the second layer, influencing both volume and adlayer sensitivities. We have found that when the second layer thickness is tailored to obtain a well spectrally defined pattern, high n contrast between the layers increases the volume sensitivity, while for the moderate n contrast the adlayer sensitivity of the FPI can be enhanced. The proposed approach shows large tuning capability towards desired application, as well as can be easily introduced to large-scale sensor manufacturing.

The goal of this work is to examine radar absorbing material (RAM)reflectivity (R)through a frequency (f) range from 0.5 GHz to 40 GHz. Variation of nonmagnetic, homogeneous, and non-dispersive RAMR is reformed to facilitate calculations. Effect of dielectric parameters (E and u) on R for one layer-metal-packed RAM with two different thicknesses are given. Electromagnetic interference shielding effectiveness (EMISE)reflection loss (R), absorption loss (A), and internal loss (M)for one and two layers located between two semi-infinite free space media are derived. Attenuation (a)variations with f, e, u, and layer thickness for single and double layer are given in graphical form.

The increasing demand in data rates and new emerging applications require that the future generation of satellite networks will be designed at sub-THz frequencies with ultra-broad bandwidth. At such frequencies, the satellite channel is impaired by the highly frequency dependent absorption of the signal by the gaseous atmospheric media besides the thermal noise from the sky, the ground, receiver circuit and molecular absorption noise thus, making it a highly frequency-selective channel. In this work, we derive a theoretical upper bound on the channel capacity of a LEO satellite communication link by optimizing the power spectral density (PSD) of the transmitted signal considering the physicla aspects of THz channel. We obtain a closed form expression of optimum transmitted signal PSD that maximizes the channel capacity of LEO satellite link as the elevation angle changes with the transmitted power held constant.

The paper is about the modeling attacks in self-driving vehicle, so to consider the faults and attacks prior to designing such a system.

Special Relativity (SR) is the cornerstone for modern physics and for variety of applications. SR is based on two postulations, the principle of relativity and the principle of constancy of the propagation speed of light wave. However, up to now, the constancy of the propagation speed of light wave has not been experimentally confirmed yet. Recently we have shown that light propagate as particles even in “pure” wave experiments. In this article we propose a doable experiment to test the second postulation for photons as shown below. If the experiment shows a negative result, i.e., the constancy of the speed of light as photons holds, then we, the first time, confirm experimentally the second postulation of SR. If the experiment shows a positive result, i.e., the propagation speed of photons is not constant, then, the second postulate of SR is disproved. Either negative or positive results would be profoundly significant.

The droop controllers of inverter-based resources (IBRs) can be adjustable by grid operators to facilitate regulation services. Considering the increasing integration of IBRs at power distribution level systems like microgrids, cyber security is becoming a major concern. This paper investigates the data-driven destabilizing attack and robust defense strategy based on adversarial deep reinforcement learning for inverter-based microgrids. Firstly, the full-order high-fidelity model and reduced-order small-signal model of typical inverter-based microgrids are recapitulated. Then the destabilizing attack on the droop control gains is analyzed, which reveals its impact on system small-signal stability. Finally, the attack and defense problems are formulated as Markov decision process (MDP) and adversarial MDP (AMDP). The problems are solved by twin delayed deep deterministic policy gradient (TD3) algorithm to find the least effort attack path of the system and obtain the corresponding robust defense strategy. The simulation studies are conducted in an inverter-based microgrid system with 4 IBRs and IEEE 123-bus system with 10 IBRs to evaluate the proposed method.

Federal Energy Regulatory Commission (FERC) Orders 841 and 2222 have recommended that distributed energy resources (DERs) should participate in energy and reserve markets; therefore, a mechanism needs to be developed to facilitate DERs’ participation at the distribution level. Although the available reserve from a single distribution system may not be sufficient for tertiary frequency regulation, stacked and coordinated contributions from several distribution systems can enable them participate in tertiary frequency regulation at scale. This paper proposes a deep reinforcement learning (DRL)- based approach for optimization of requested aggregated reserves by system operators among the clusters of DERs. The cooptimization of cost of reserve, distribution network loss, and voltage regulation of the feeders are considered while optimizing the reserves among participating DERs. The proposed framework adopts deep deterministic policy gradient (DDPG), which is an algorithm based on an actor-critic method. The effectiveness of the proposed method for allocating reserves among DERs is demonstrated through case studies on a modified IEEE 34-node distribution system.

We present a new machine learning algorithm, Latent Similarity, and use it to predict subject (endo)phenotypes from fMRI data. fMRI can be used to predict dysfunctional mental states. In addition, endophenotypes are known to be predictive of disease status, and are of interest in developmental studies. However, fMRI studies often suffer from small cohort size and high feature dimensionality, making reproducible prediction challenging. The innovation of our algorithm is to combine a kernel similarity function with metric learning to increase the robustness of prediction. Our algorithm becomes robust by utilizing the N squared connections between the N subjects in the cohort, rather than the features of the N subjects themselves. We identify important functional connections in the default mode and uncategorized functional networks for predicting age, sex, and intelligence. We also find that only a few connections contain most of the information required for any predictive task. We believe that our algorithm can be beneficial in small sample size, high noise, high dimensionality settings.

The increasing length of the subsurface pipe causes overlapping, accumulating, and sometimes the old pipe layout is not available. Consequently, accidents, damages, time delay and financial losses are occurred during construction of new structures or expanding the roadway or installing of new pipes. Therefore, the depth, diameter, material of the existing pipe as well as the map of the pipe are indispensable to know for appropriate construction planning and development work. Using this algorithm and genetic algorithm optimization, the radius of the field pipe was assessed with 83, 67 and 89% accuracy, the depth was estimated with 95, 95 and 98% accuracy considering the effect of the pipe radius. The effect of the pipe radius should be considered to calculate the pipe depth with higher accuracy. The material of the field pipe was successfully determined using the evaluated relative permittivity of the pipe. A 3D map of the field pipe was developed by applying the tracing algorithm and linear regression to the estimated depth.

Strip-map data from the TerraSAR-X satellite and laser altimeter data from ICESat-2 acquired over polar regions to capture information on sea ice.

In this work, using investigation of odd order OAM modes in the two-dimensional MMI waveguides, a novel integrated optical OAM device is introduced to do mode conversion between beams carrying OAM with opposite topological charges. The proposed convertor is passive and the input and output ports can be swapped. The ability to produce high purity output mode (94% and 82% for OAM modes with l=±1 and l=±3, respectively), reciprocal behavior and compatibility with silicon on insulator technology are the remarkable features that make the proposed passive convertor utilizable in many classical and quantum communication systems.

A novel algorithm for on-board satellite hyperspectral data compression is proposed. Computational complexity is targetted with comparable compression performance with state-ot-the-art on-board satellite hyperspectral data compression methods.

During the last years, interoperability has become one of the main challenges within blockchain research as blockchains increasingly require integration between each other. Indeed, blockchains work by design in silos of information as interoperability is not a native feature. The main efforts in the field target permissionless blockchains, such as Bitcoin and Ethereum. However, interoperability in permissioned blockchains remains as an almost untouched area of work as they introduce additional requirements focusing on privacy and identity. Although there are some interoperability solutions for permissioned blockchains, they are either high-level design proposals not providing concrete implementations or focus on interoperability issues between business applications and blockchain platforms. In this paper we propose a gateway-based platform-to-platform interoperability solution for permissioned blockchains, which comprises a detailed solution design and a reference implementation. The proposal was assessed through the development of a social security case scenario, based on the reference implementation and using two blockchain platforms: Hyperledger Fabric and Corda.

Recent advances in integrated circuits and micromachining have enabled the integration of battery-powered micro-actuators in miniaturized drug delivery systems. However, the power/energy management system that treats current overloading remains sub-optimal. Overloading not only deteriorates the actuators’ long-term performance but also attenuates battery capacity. In this work, we are proposing a simple yet powerful solution to manage current overloading to maximize battery-powered system life-time. The proposed solution consists of a digitally programmable soft starter and DC-DC converter that can dynamically balance the trade-off between inrush current amplitude and motor starting speed as well as maximally minimize the continuous current draw from a battery. Our experimental results show that the proposed soft starter alone can enhance the battery capacity by 18% and together with the DC-DC converter, they can increase the drug delivery cycles by 33% without sacrificing the system’s output performance.

This article has been accepted for publication in IEEE/OSA Journal of Lightwave Technology. Citation information: DOI 10.1109/JLT.2022.3198649 Benefiting from the development of image sensors and the popularity of light-emitting diode (LED) lighting technology, visible light positioning (VLP) technology based on image sensors has ushered in vigorous development and broad prospects, which can provide low-cost and high-accuracy position service. However, the existing approaches require dense LEDs or sensors such as gyroscopes to assist positioning, which limits the area and lower the accuracy of positioning because of the errors from imperfect sensors. In this paper, we propose a simultaneous localization and calibration VLP method based on double coplanar circular LED lights aiming to get rid of the dependence on additional sensors and dense LED transmitters. By the pinhole camera model and the perspective projection of circle, our proposed method extends the available position area and relaxes the required quantity of LED to two. The experiment result showed that our system has a mean 3D positioning accuracy of 7.91cm, a mean angle error of less than 1.6°, and an average latency of 182ms on mobile devices.

The direct array factor synthesis method is extended in this paper. Two parallel current sheets are used for synthesizing array factors with non mirror-symmetrical radiation patterns in the two sides of the sources.  Analysis shows that the synthesized current distributions are affected by the distance between the two current sheets.  Numerical results demonstrated that the method is efficient for synthesizing large scale antenna arrays with non mirror-symmetrical footprints.

The COVID-19 pandemic has led to an unprecedented change in transportation, including shared mobility services. This study attempted to identify the user group of ride-share services by leveraging daily ride-sharing trip data for the year of 2020 associated with other socio-demographic and built-environment attributes of Chicago, Illinois. The study employed K-means clustering for user group segmentation. Results show: i) the cluster with the largest share of census tracts generate lowest average trips which is clearly an impact of the pandemic; ii) The high-income cluster generates short trip and coupled with high population, land-use, and employment density; iii) The low-income cluster generates longer trips coupled with diversity of land-use mx, employment and population density. Results of this study provide insights for policymakers and ride- sharing operators to ensure access to the services among the population irrespective of spatial diversity.