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.

This study paper deals with various Cloud computing and deployment models provided by the Cloud. Cloud computing is the combination of two words, the term Cloud is just an Internet because with the internet we use different type of resources or services like storage, database, networking and server then the term computing is the data processing, storage over the Internet, so Cloud computing is basically the use of different types of services over the internet by individual or by the organization. We can say that the “Cloud computing” is a set of different types of hardware and software that work collectively to deliver many aspects of computing to the end user as online services. Cloud computing is an approach defined as internet based computing where virtual shared servers provide software, infrastructure, platform devices, and other resources and hosting to customers on a pay-as-use basis. These services are offered by shared network and other relevant IT resources on Private, Public, hybrid and community Cloud models.

This article proposes a contrastive learning-based scene classification method to extract features under occluison circumstance. Novel occlusion features analysis methods are proposed also.

As the demand grows to develop end-user trust in AI models, practitioners start to build and configure customized XAI (Explainable Artificial Intelligence) methods. The challenge is the lack of systematic evaluation of the newly proposed XAI method. As a result, it limits the confidence of XAI explanation in practice. In this paper, we follow a process of XAI method development and define two metrics in terms of consistency and efficiency in guiding the evaluation of trustworthy explanations. We demonstrate the development of a new XAI method in feature interactions called Mean-Centroid Preddiff, which analyzes and explains the feature importance order by a clustering algorithm. Following the process, we perform cross-validation on Mean-Centroid Preddiff with existing XAI methods. They show comparable consistency and gain in computation efficiency. The practice helps to adopt the core activities in the trustworthy evaluation of a new XAI method with rigorous cross-validation on consistency and efficiency.

We present a novel method for transferring human actions from a source to a target video with improved human motion smoothness and better image quality. Unlike the previous image-to-image pose transfer and synthesis work, which in actuality transfer information from an image to another image, we take consideration of temporal pose variations. Recent methods aiming at video-to-video human motion transfer suffer from the generated image quality. In contrast our proposed Video-to-video Action Transfer GAN (VAT-GAN) has achieved better image quality by employing a cascaded sequence of Action Transfer Blocks (ATB) with multi-resolution structure similarity (MR-SSIM) loss. The addition of MR-SSIM loss has resulted in improved generated image quality compared to the previously used ℓ1 based loss. To ensure temporal human motion smoothness, we make the ATBs aware of temporal pose variations of the target video. The proposed VAT-GAN is evaluated on an existing dataset proposed by Chan et al. [1]  as well as on new test videos downloaded from the internet consisting of unseen persons, actions and poses. The proposed VAT-GAN has achieved better visual and quantitative performance compared to the existing state of the art methods both in terms of image quality and human motion smoothness. The code and the results including synthesized videos will be made publicly available: https://github.com/MehwishG/VAT-GAN.

Advances in Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) are enabling remote health monitoring in everyday settings for early detection and prevention of chronic health problems. Such solutions can be used to augment a conventional physician-centered healthcare system. Stress, as one of the critical health problems, affects individuals adversely in terms of both physical and mental health. Prior studies on stress evaluation utilize a centralized cloud-based approach that combines data from each client for modeling. However, such a centralized approach raises data privacy concerns. To preserve privacy, decentralized federated learning has been proposed as a potential alternative framework. Nevertheless, existing federated learning algorithms have to deal with data heterogeneity; data skewness in each participant can significantly degrade the overall model performance. To tackle this challenge, we present a personalized, low-overhead clustered federated learning algorithm for stress-level recognition. The proposed algorithm outperforms two state-of-the-art baseline algorithms by providing over 7% and 12% increase in accuracy, respectively. The proposed algorithm also obtains a reduction of 37.5% and 9.6% in the training runtime compared to the two baseline algorithms. We also present a novel cold-start algorithm for new clients who join the trained system. Our results suggest that this cold-start algorithm is robust in terms of individual classification accuracy and total training time.