Aiming to bypass the Lorentz force, this study analyzes Maxwell’s equations from the perspective of a receiver at rest. This approach is necessary because experimental results suggest that the general validity of the Lorentz force might be questionable in non-stationary cases. Calculations in the receiver’s rest frame are complicated and, thus, are rarely performed. In particular, the most important case is missing: namely, the solution of a Hertzian dipole moving in the rest frame of the receiver. The present article addresses this knowledge gap. First, this work demonstrates how the inhomogeneous wave equation can be derived and generically solved in the rest frame of the receiver. Subsequently, the solution for two uniformly moving point charges is derived, and the close connection between Maxwell’s equations and Weber electrodynamics is highlighted. The gained insights are then applied to compute the far-field solution of a moving Hertzian dipole in the receiver’s rest frame. The resulting solution is analyzed, and an explanation is presented regarding why an invariant and symmetric wave equation is possible for Weber electrodynamics and why the invariance could be the consequence of a quantum effect.

A new 3D morphable model (fitting) framework, based on inequality-constrained optimization and gradient correlation. An optimization-based method that does not require any training, and works robustly. Moreover, the method is capable of simultaneously fitting a 3DMM to multiple frames of a person. Finally, camera matrix can be injected.

In the face of recent cyber-attacks on critical national infrastructure and corporate networks, there is a significant growing concern about the security of defense and industrial robots on the Internet of Things (IoT). One cutting-edge technology to counter these threats of cybersecurity in the IoT ecosystem is the use of Artificial Intelligence (AI) to build complex AI algorithms to protect network systems and their devices. However, AI algorithms are vulnerable to adversarial examples. A method to produce adversarial examples to foolproof AI cybersecurity algorithms is presented.

Underwater acoustic (UWA) communication is an essential part of many civilian and military applications. For UWA sensor networks, the sensing information can only be interpreted meaningfully when the location of the sensor node is known. However, node localization is a challenging problem. Global Navigation Satellite Systems (GNSS), which are often used in terrestrial applications, do not work underwater. In this paper, we propose and investigate techniques for localization of a single-antenna UWA communication receiver with respect to one or more transmit antennas. These techniques are based on the matched field processing. Firstly, we demonstrate that a non-coherent ambiguity function (AF) allows significant improvement in the localization performance compared to the coherent AF previously used for this purpose, especially at high frequencies typically used in communication systems. Secondly, we propose a two-step (coarse-to-fine) localization technique. The second step provides refined spatial sampling of the AF in the vicinity of its maximum found on the coarse space grid covering an area of interest (in range and depth), computed at the first step. This technique allows high localization accuracy and reduction in complexity and memory storage, compared to the single step localization. Thirdly, we propose a joint refinement of the AF around several maxima to reduce outliers. For validation of the proposed techniques, we run numerical experiments in different UWA environments, with different parameters for the spatial sampling, number of transmit antennas and different accuracy for the estimation of the acoustic channel response.

In this paper, a novel pipeline for developing an end-toend masked/non-masked face detection method is proposed to improve the effectiveness of real-time surveillance systems at public places.

We show that in an optical waveguide with no material losses at wavelengths near the second-order Bragg condition, there exists two pairs of modes. One pair has identical propagation constants but have different attenuation coefficients, while a second pair with identical propagation constants (different from the first pair) and have different attenuation coefficients. The four attenuation coefficients may have either a positive value, representing power leaking out of a waveguide mode or a negative value, representing power from an external source leaking into a mode. Moreover, a mode with a positive (negative) attenuation before the second Bragg condition, has a negative (positive) attenuation after the second Bragg condition. Radiation near the second-order Bragg condition of a periodic waveguide typically occurs at an angle perpendicular or nearly perpendicular to the propagation direction of the waveguide because the scattering centers have a period equal to or close to the period of the longitudinal propagation constant of the mode. In this paper, stable numerical solutions for the modes of periodic dielectric structures are developed using Floquet-Bloch theory. One primary focus in this paper illustrates a unique method of analyzing such modes using eigenvectors forming a Hilbert space, allowing for expansion of arbitrary vectors and their derivatives used for calculations such as that involving group velocities. The dimension of the vector space is determined by the number of space harmonics used in the solution of the Floquet-Bloch equations. The accuracy of the numerical solutions is affected by the number of space harmonics; as that number is increased, the number of waveguide partitions must be increased to maintain a given accuracy.

This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. Our findings reveal notable correlations between different types of diversity, such as gender and institutional type, geography and topics, as well as topics and first author’s gender. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Finally, we analyse diversity initiatives that have been undertaken in other AI-related research communities to foster diversity, and conclude on a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The data collected in this work will be publicly available, ensuring strict personal data protection and governance rules.

The placement of a microphone at its correct position is crucial for automatic speech recognition (ASR) in a single-microphone distant speech recognition setup. Understandably, it is a practical problem that the performance degrades due to accidental displacement of the microphone from the correct position through routine use and maintenance. In this paper, an approach is formulated which makes it possible to determine the optimal position of the microphone, given the geometry of the room, the location of the speaker, and the inexact location of the microphone. The metric used to measure the correctness of the microphone position is the average character error rate (cer) of the ASR system at that position. The proposed approach uses limited available data which is usually the case when dealing with microphone position error analysis while considering room acoustics. A neural network classifier is trained using limited data to associate the location of the microphone with the corresponding class of cer values. The obtained prediction is used to trace the microphone back to the optimal position.

This paper studies the capacity of the two-user intensity-modulation/direct-detection (IM/DD) interference channel (IC), which is relevant in the context of multi-user optical wireless communications. Despite some known single-letter capacity characterizations for general discrete-memoryless ICs, a computable capacity expression for the IM/DD IC is missing. In this paper, we provide tight and easily computable inner and outer bounds for a general two-user IM/DD IC under peak and average optical intensity constraints. The bounds enable characterizing the asymptotic sum-rate capacity in the strong and weak interference regimes, as well as the generalized degrees of freedom (GDoF) in the symmetric case. Using the obtained bounds, the GDoF of the IM/DD IC is shown to have a ‘W’ shape similar to the Gaussian IC with power constraints. The obtained bounds are also evaluated numerically in different interference regimes to show their tightness, and used to study the performance of on-chip and indoor OWC systems.

With the rapid growth of electric vehicle (EV) penetration, uncoordinated charging of EVs is putting further stress on electrical power supply in urban residential areas. Potential of demand-side management and schedulable ability of EVs promote the demand-side management operator like residential load aggregator (RLA) to dispatch the electricity demand and supply of the residential quarter to enhance the economics and security of energy use. Considering the main uncertainties of EVs’ arrival time, departure time, and charging amount, a two-stage real-time optimal electricity dispatch strategy for residential quarter with EVs’ charging load is proposed in this paper. Firstly, a real-time optimal approach is proposed based on the receding horizon optimization process to decompose the day-ahead optimal period into sequential advanced optimization horizons. Secondly, considering the dual optimization goals of economics and security, a two-stage optimization procedure is proposed for the residential quarter’s electricity dispatch in the advanced optimization horizon. And on the basis of these models, solving timeline with detailed solving steps of the proposed two-stage real-time optimal electricity dispatch strategy is presented. Finally, case studies on the data of a real residential quarter located in Shanghai City demonstrate the validity and effectiveness of the proposed methodology.

6G communication networks must be highly dependable due to the huge number applications, services, and public infrastructures that will be dependant on it. However, the integration of vast number of technologies, ranging from physical layer up to the application layer, and diverse services ranging from entertainment to mission critical public services need investigation of the infrastructure from dependability point of view. We take four dependability concepts such as reliability, availability, safety and security, and discuss the dependability of 6G networks. Since intelligence and distribution of control functions and elements make the core of future networks, specific diligence is given federated learning and edge intelligence. This article, in summary, provides interesting insights into existing challenges and advocates future research through highlighting the most important research directions to make 6G dependable.

Autonomous driving is a anticipated technology in both research groups and industry. Naturalistic human driving behavior offers guidance for autonomous driving in terms of human driver reaction under certain scenario, ground truth of detection and path planning, statistics for functional safety and SOTIF, etc. How to efficiently obtain the target market naturalistic driving scenario for SAE L3 and above highly automated driving (HAD) systems is a major challenge on the road of their series production. Based on above demand, this paper uses Unmanned Aerial Vehicle (UAV) to collect traffic data from congested highways and expressways in China, conducts data preprocessing and quality evaluation, target vehicle recognition and tracking, and structured driving scenarios mining. It is expected to provide data support for HAD development in the Chinese market, including human driver behavior analysis, quantitative metric extraction, real driving test scenario generation, etc. We call this large-scale naturalistic traffic flow dataset as Aerial Dataset for China Congested Highway & Expressway (AD4CHE). It contains 5.12 hours aerial survey data of 53761 vehicles in 4 different cities in China, with a total driving mileage of 6540.7 km, 16099 lane changes and 3331 cut-ins. Some initial findings from AD4CHE are introduced. The dataset will be open sourced at https://auto.dji.com/cn.

Physiological response to physical exercise through analysis of cardiopulmonary measurements has been shown to be predictive of a variety of diseases. Nonetheless, the clinical use of exercise testing remains limited because interpretation of test results requires experience and specialized training. Additionally, until this work no methods have identified which dynamic gas exchange or heart rate responses influence an individual’s decision to start or stop physical activity. This research examines the use of advanced machine learning methods to predict completion of a test consisting of multiple exercise bouts by a group of healthy children and adolescents. All participants could complete the ten bouts at low or moderate-intensity work rates, however, when the bout work rates were high-intensity, 50% refused to begin the subsequent exercise bout before all ten bouts had been completed (task failure). We explored machine learning strategies to model the relationship between the physiological time series, the participant’s demographic variables, and the binary outcome variable indicating whether the participant completed the test. The best performing model, a generalized spectral additive model with functional and scalar covariates, achieved 93.6% classification accuracy and an F1 score of 93.5%. Additionally, functional analysis of variance testing showed that participants in the ’failed’ and ’success’ groups have significantly different functional means in three signals: heart rate, oxygen uptake rate, and carbon dioxide uptake rate. Overall, these results show the capability of functional data analysis with generalized spectral additive models to identify key differences in the exercise-induced responses of participants in multiple bout exercise testing.

This paper deals with a three-dimensional multiple input, multiple output (MIMO) passive coherent location (PCL) system in a scenario with multiple transmitters and receivers. The goal is to accurately estimate the location of a single observed target despite challenges such as undetected measurements and outliers due to non-line-of-sight effects. A volumetric search method is proposed to overcome outlier presence. This method, which divides the search region into cuboids, offers a fresh perspective on identifying and discarding outliers while selecting consistent measurements. The centroid of the selected cuboid provides an alternative accurate location estimate, which is particularly valuable when many outliers are present. We also introduce an iterative approach to outlier removal based on model-mismatch cost functions for benchmarking. The selected consistent measurements are used to feed a nonlinearly constrained least squares (NLCLS) algorithm explicitly designed for MIMO PCL systems. A unified mathematical framework is established for this NLCLS approach, which accounts for the nonlinear relationships among the optimization variables via constraints. Additionally, a simplified and efficient version of the NLCLS algorithm is proposed. This streamlined approach iteratively minimizes the model mismatch by using a single constraint, ensuring accurate estimation with reduced computational complexity. The proposed NLCLS method and its simplified version are compared against unconstrained counterparts, namely spherical interpolation and spherical intersection. The Cramer-Rao lower bound is derived for this application, and numerical experiments highlight the robustness of the cuboid-based volumetric search and the nonlinearly constrained algorithms.

A novel Local Iterative Physical Optics (LIPO) algorithm for electromagnetic scattering of layered dielectric media with rough surfaces has been developed to simulate the electromagnetic scattering of electrically large dielectric layers with relatively smooth surfaces variation. The LIPO algorithm iteratively corrects the equivalent local surface currents according to the conventional PO theorem to compensate for the electromagnetic field deviation across the dielectric layers’ surfaces. Numerical validation has been performed with one-layer water surface and two-layer soil surfaces to show the performance of the proposed LIPO algorithm. The experimental result shows that it takes only a few iterations for the algorithm to increase the surface current accuracy by more than three orders of magnitude, compared to the conventional PO theorem.

New power-intensive industries and electrification of existing industrial processes put pressure on electric distribution grids. To evaluate whether flexibility measures such as procuring congestion management services can be considered to defer grid reinforcements, distribution system operators (DSOs) first need to characterize the need for flexibility in their grids. While characterization and modelling of flexibility resources have been intensively studied over the last years, characterizing the need for flexibility from the DSO’s perspective has received much less attention. This paper proposes a methodology for quantitatively characterizing the need for flexibility in distribution grids based on time series load demand data. The methodology and its application to active distribution grid planning is demonstrated for a real, industrial grid area in Norway with a varied set of industries and plans for electrification. The benefits were demonstrated by comparing probabilistic metrics for flexibility needs with the traditional methods currently used for grid planning by DSOs, which provide much more limited information.

This letter presents a novel design for a 3D-printed circular waveguide dual-mode (CWDM) filter with a modified cavity shape. The modification leads to a wide spurious-free stopband, which is highly desirable for channel separation in waveguide contiguous output multiplexers (OMUXs) in satellite communication systems. The new resonant cavity design is a result of applying shape deformation to a basic circular cavity in order to move away and suppress parasitic modes. A fourth-order Ku-band channel filter with two transmission zeros is designed, fabricated by additive manufacturing (AM) in one piece and measured. In comparison with the state-of-the-art design of a stepped CWDM filter, an improvement of approximately 35% wider spurious-free range is achieved

Silicon carbide integrated circuits have been demonstrated to operate at high temperatures, such as ~460ºC at the Venus’ surface, for two months and for over a year at 500ºC. At these high temperatures, the SiC integrated circuits and sensors need to be packaged in quite different ways than those below 300ºC. In addition, to integrate more devices into the limited footprint of the high-temperature circuit board, 3D packaging is a notable advantage. In this work, 3D stacking of SiC chips using a gold wirebonding interconnect is investigated. The gold bonding wire is used due to its mechanical robustness and chemical inertness at high temperatures. Triple-stacked SiC chips are bonded to each other and to the gold contact pads on the alumina substrate with screen-printed gold pastes. The mechanical die shear test, wire pull tests, and interconnect electrical resistance tests are executed and analyzed before and after the 3D SiC chip packages are subject to a 600ºC thermal aging process in air for up to ten days. This 3D SiC chip packaging has promise for long-duration high temperatures (up to 600ºC) applications and may be potentially of use for applications such as Venus’s surface sensing and telemetry.