To overcome the shortage of traditional flight mechanism, we propose a type of anti-gravity engine based on General Relativity. Natural anti-gravity phenomenon is found under special spacetime background by analysis geodesic motion of spacecraft and vanishment of Riemann curvature locally, which demonstrates the feasibility of artificial anti-gravity with the aid of electromagnetic field. To implement artificial anti-gravity under Schwarzschild background, we choose current loop as axisymmetric field source and obtain exact solution of Einstein-Maxwell field equation using Killing symmetry and Ernst generation technique. An implementation with superconductor shield is given according to the Meissner effect, calculation implies that existing nuclear fusion engineering capability could meet the strength requirements of the field source.

A document by Jingyu Gao . Click on the document to view its contents.

social media became a fertile soil for various threats, extremism, and radicalization. This challenged policy-makers, researchers and practitioners. Preventing such extreme activities from happening becomes an ultimate priority at local and global scale. This paper introduces a new intertwine between radicalization and natural language processing capable of estimating the risk score of individuals based on their social media activities. The system uses a hybridized ERG22+ and VERA-ER model, which classifies individuals as high or low risk radicalization profile. The developed system was tested and validated on the Video Comments Threat Corpus dataset and Twitter pro-ISIS fanboys datasets where it achieves 95.1% and 64.9% accuracy, respectively.

Tuberculosis (TB) is still a serious public health concern across the world, causing 1.4 million deaths each year. However, there has been a scarcity of radiological interpretation skills in many TB-infected locations, which may cause poor diagnosis rates and poor patient outcomes. A cost-effective and efficient automated technique might help screening evaluations in underprivileged countries and provide early illness diagnosis. In this work, we proposed a deep ensemble learning framework that integrates multisource data of two deep learning-based techniques for the automated diagnosis of TB. The integrated model framework has been tested on two publicly available datasets and one private dataset. While both proposed deep learning-based automated detection systems have shown high accuracy and specificity compared to state-of-the-art, the en- semble method significantly improved prediction accuracy in detecting chest radiographs with active pulmonary TB from a multi-ethnic patient cohort. Extensive experiments were used to validate the methodology, and the results were superior to previous approaches, showing the method’s practicality for application in the real world. By integrating supervised prediction and unsupervised representation, the ensemble method accu- rately classified TB with the area under the receiver operating characteristic (AUROC) up to 0.98 using chest radiography outperforming the other tested classifiers and achieving state- of-the-art. The methodology and findings provide a viable route for more accurate and quicker TB detection, especially in low and middle-income nations.

In the AC surrounded Multi-terminal HVDC (AC-MTDC) grids, the substantial growth in Renewables-based Power Generation (RPG) out-turn high inertia into low or Zero-Inertia systems and are the near future of the transiting power systems. In this context, it necessitates the development of a Zero-Inertia AC-MTDC grid test system and appropriate control strategies for the satisfactory operation of the system. Thus, this paper develops the Zero-Inertia AC-MTDC grid while replacing the synchronous generators with the Inverter-Based Resources (IBRs). Besides, it utilizes the Grid Forming (GFM), Grid Supporting (GST), and Grid Following (GFL) control strategies for the Zero-Inertia AC-MTDC grids. The GFM and GST control modes are utilized to start up the system and improve the system’s frequency and dc voltage profiles in a decentralized manner during any imbalances in the grid. Moreover, the GST mode is achieved in this paper through a conventional fixed droop and proposed Modified-Operating Point-based droop (MOPD) control strategies for Voltage Source Converters (VSCs) connected to grid and wind farms in the AC-MTDC grids. The significance and validation of these control modes are perceived through the various case studies with a developed Zero-Inertia AC-MTDC test system and inertia AC-MTDC grid in the PSCAD/EMTDC simulation.

The goal of the metaverse is to provide a synchronized interoperability of real and virtual worlds across multiple domains and applications. The main challenge is to make the metaverse interface both immersive and responsive to user actions. In this paper, we propose a new concept called a distributed wearable smartphone, which provides a synergistic integration of head-mounted displays, wearable sensors and other smart devices to translate user context and actions across real and virtual metaverse domains. The proposed architecture is flexible and can be composed from any set of devices, depending on the target application. The key benefits of the proposed approach are the realistic 3D representation and the semi-virtual user experience, which can be extended to a wide range of different applications in industry, education, healthcare and entertainment.

The standard interpretation of Young’s double slit experiment is that the light behaves as waves before and after passing through the diaphragm of the double slit. There is one primary maximum intensity at the central position of the interference pattern. Recently, the photon chamber has been proposed and utilized to study the behavior of photons near the detecting screen. The experiments show that the light, near the screen, behaves as photons propagating along the straight-line trajectories that distribute as waves. Furthermore, the photon chamber placed near the double slit shows the non-interference pattern. blob:https://www.techrxiv.org/66ec822d-f054-4cc3-8b9e-8ff89d6481e2 In this article we show how the non-interference (non-wave) pattern evolves gradually to the interference pattern: (1) at 50 mm from the diaphragm of the double slit, the non-interference pattern shows and has two primary maximum intensities (Figure (a)); (2) at 150 mm, the two intensities of the non-interference pattern become wider and closer (Figure (b)); (3) at 200 mm, the two intensities of the non-interference pattern merge (Figure (c)); (4) at 400 mm, the non-interference pattern evolves to the interference pattern(Figure (d)). The interpretations of the optics wave and electromagnetic wave theories of the double slit experiments is incomplete. This phenomenon provides information for further theoretical development.

In this paper, a novel communication-efficient distributed stochastic algorithm (referred as CO-DSA) is proposed for solving large-scale consensus optimization problems. As compared to the existing relevant work where only a sublinear convergence rate is obtained for strongly convex and smooth objective functions, CO-DSA achieves a linear convergence rate even in the presence of an eventtriggered based communication-censoring strategy. Moreover, by properly setting the threshold function of the event-triggered communication scheme, CO-DSA maintains the same convergence rate as the algorithm without event-triggered communication. This means CO-DSA theoretically yields communication efficiency for free. Numerical experiments verify the theoretical findings and also show the excellent communication saving effect of CO-DSA in large distributed networks.

Explainable Artificial Intelligence (XAI) research focuses on effective explanation techniques to understand and build AI models with trust, reliability, safety, and fairness. Feature importance explanation summarizes feature contributions for end-users to make model decisions. However, XAI methods may produce varied summaries that lead to further analysis to evaluate the consistency across multiple XAI methods on the same model and data set. This paper defines metrics to measure the consistency of feature contribution explanation summaries under feature importance order and saliency map. Driven by these consistency metrics, we develop an XAI process oriented on the XAI criterion of feature importance, which performs a systematical selection of XAI techniques and evaluation of explanation consistency. We demonstrate the process development involving twelve XAI methods on three topics, including a search ranking system, code vulnerability detection and image classification. Our contribution is a practical and systematic process with defined consistency metrics to produce rigorous feature contribution explanations.

Assessing the Impact of Natural Gas and Hydrogen Blending in Integrated Energy System Modeling, submitted to the IEEE GM 2023 Abstract: This paper assesses the impact of natural gas and hydrogen blending in integrated energy system modeling based on a novel blended transport problem. The proposed formulation ensures that natural gas and hydrogen flow in the same direction and compliance with the maximum hydrogen blending rate, which cannot be guaranteed by a standard transport problem. Our case study shows that the gas flow formulation - and blending in particular - strongly influences investment decisions in the power, natural gas, and hydrogen sectors, which can ultimately lead to suboptimal infrastructure planning (represented in the model as ”regret” in the form of non-supplied hydrogen).

In the current paper, both concepts of redundancy and modularity as two aspects of reliability in the MMC are combined to suggest the optimum blocking voltage of the switch

Real Time embedded systems are highly complex due to interactions and interdependencies between various hardware/software units and policies of the processors with applications running on it. To deal with fluctuating workloads and subsequent tasks, smart adaptability of supply clock and voltage is required in order to optimize power without compromising on the performance. This is done using Dynamic Voltage and Frequency Scaling (DVFS) technique. An improved version of DVFS is proposed in this paper which treats it as a recurrent problem with an aim to capture the intricate dependencies amongst various factors influencing the operation. The authors have employed application independent- Radial Basis Neural Network to generate series of predicted frequencies for current workload of the processor, followed by seq2seq-LSTM based encoder decoder model using Attention to decide if the frequency generated by the Artificial Neural Network (ANN) model is optimum from power conservation point of view. The proposed model predicts the workload and then compares the predicted frequency to the critical value or deadline of the current task. The experiments were conducted on a single core processor on which a benchmark application was run, and promising prediction accuracy rates were obtained without incurring degradation of critical performance parameters.

According to the electromagnetic uniqueness theorem, the radiation behavior of an antenna under test (AUT) can be recovered from measurements of two tangential components of its radiated fields on an enclosing surface. In practice, measurements conducted in the radiating near-field (NF) of an AUT utilize probe antennas of finite size. Thus, instead of discrete field values, spatially blurred probe signals are acquired. The probe influence can be compensated, however, this commonly requires precise knowledge about the probe antenna. In this work, the concept of NF far-field (FF) transformations (NFFFTs) with full correction of the influence of unknown probe antennas is introduced. Two nonconvex and two convex formulations are presented and their relation to the similar task of phase retrieval is highlighted. Simulation and measurement results illustrate the validity of the concept, shed light on the required complexity of measurement setups, and illustrate limitations of the approach. Special attention is paid to the case of high practical relevance when AUT and probe are identical.

Stretch reflexes are crucial for performing accurate movements and correcting for unpredictable environments at low energy costs. Stretch reflexes are modulated by supraspinal structures via corticofugal pathways. Neural activity in these structures is difficult to observe directly, but characterization of reflex excitability during volitional movement can be used to study how these structures modulate reflexes and how neurological injuries impact this control. We have developed a novel protocol to quantify stretch reflex excitability during ballistic reaching. This novel method relies on a custom haptic device (NACT-3D) capable of applying high-velocity (> 270 °/s) joint perturbations in the plane of the arm while participants perform 3D reaching tasks in a large workspace. We assessed its performance on two control participants and four participants with chronic hemiparetic stroke. Participants reached ballistically from a near to a far target, with elbow extension perturbations applied in random catch trials. Perturbations were applied before movement, at movement onset, or at peak movement velocity. Stretch reflexes were elicited in the biceps muscle in the stroke cohort as measured by electromyographic (EMG) activity both before (pre phase) movement and near movement onset (early phase). Significant reflexive EMG was also seen in the pectoralis major in the pre motion phase. In the control cohort, no significant reflexive EMG was seen. This novel protocol can capture increased stretch reflex excitability in post-stroke individuals when compared to controls and quantify the modulation of stretch reflexes during movement.

In this paper, an efficient all-digital demodulator for multi-h CPM is proposed based on a low-complexity decision-directed synchronization algorithm. We derive the maximum-likelihood estimation of the carrier phase and timing errors and propose a reduced-complexity timing error discriminator with the linear phase approximation (LPA). Compared to the traditional synchronization method, it reduces about 2/3 of matched filter banks since only on-time matched filter banks are required. To demonstrate the stability of the proposed algorithm, the S-curve is derived theoretically and plotted with numerical simulation. The numerical results show that the LPA-based synchronization algorithm has no loss of bit error rate (BER) performance compared to the commonly used methods. Then, the LPA-based synchronization and the maximum-likelihood sequence detection for the three kinds of promising multi-h CPM demodulation are implemented on a Xilinx Kirtex-7 FPGA platform achieving the global clock rate of 200MHz. The BER of the overall receiver is tested on board, and the results show that its performance has an ignorable loss to the numerical simulation. The implementation overhead on FPGA is reduced by about 30% compared to the conventional method.

The characterization of unknown antennas under test (AUTs) from measurements of the radiated near field (NF) is commonly performed with known probe antennas. By accounting for the behavior of a given probe in the processing of the NF data, the obtained quantities of interest, e.g., the far-field (FF) behavior of the radiator, are free of biases and distortions caused by the probe sensor. We discuss an NF FF transformation (NFFFT) which can fully compensate the effect of the probe, while only requiring knowledge about its electrical size, position, and orientation. An iterative nonconvex technique based on alternating projections is discussed, as well as a convex approach utilizing bilinear forms is provided. Connections to the related problem of phase retrieval are highlighted. Simulation results showcase the validity of NFFFTs with full probe correction of unknown probes and shed some light on potential limitations.

We consider a set reconciliation setting in which two parties hold similar sets which they would like to reconcile In particular, we focus on set reconciliation based on invertible Bloom lookup tables (IBLTs), a probabilistic data structure inspired by Bloom filters but allowing for more complex operations. IBLT-based set reconciliation schemes have the advantage of exhibiting a low complexity, however, the schemes available in literature are known to be far from optimal in terms of communication complexity (overhead). The inefficiency of IBLT-based set reconciliation can be attributed to two facts. First, it requires an estimate of the cardinality of the set difference between the sets, which implies an increase in overhead. Second, in order to cope with errors in the aforementioned estimation of the cardinality of the set difference, IBLT schemes in literature make a worst-case assumption and oversize the data structures, thus further increasing the overhead. In this work, we present a novel IBLT-based set reconciliation protocol that does not require estimating the cardinality of the set difference. The scheme we propose relies on what we term multi-edge-type (MET) IBLTs. The simulation results shown in this paper show that the novel scheme outperforms previous IBLT-based approaches to set reconciliation

In recent years, extensive research has been conducted on Internet of Things (IoT). Wireless Smart Ubiquitous Network (Wi-SUN) has gained considerable attention as a wireless communication standard for IoT. Wi-SUN Field Area Network (Wi-SUN FAN) is a technical specification of Wi-SUN that can be implemented in both indoor and outdoor IoT communication infrastructure with multi-hop routing. Although Wi-SUN FAN version 1.0 (Wi-SUN FAN 1.0) has been standardized by IEEE 2857-2021, there have been no studies or reviews conducted on the transmission performance of Wi-SUN FAN 1.0 regarding transmission success rate and delay time using computer simulations and experimental evaluation environments involving actual devices. In this study, the specifications of the Wi-SUN FAN are reviewed, and the fundamental transmission performance, such as average transmission success rate and average delay time, is measured using computer simulation as reference data. An experimental evaluation environment involving actual devices is developed to validate the characteristics evaluated by computer simulation. The characteristics determined by the computer simulation and experimental evaluation environment are in good agreement. Using the validated simulator, we evaluate the transmission performance in the wireless IoT environment with one border router and 100 routers randomly arranged in a flat square field with 4,000 m on a side. The average transmission success rate is approximately 1 at 1.00 × 10–1 s–1 or less. Consequently, Wi-SUN FAN 1.0 can communicate with a higher transmission success rate even when transmitting frequent IoT-data, which is once every ten seconds. 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.