Emerging compute-intensive and latency-sensitive vehicular applications are expected to be deployed at the edge instead of the cloud to shorten the network latency. Mobile fog nodes carried by moving vehicles have been proposed to complement the stationary fog nodes co-located with base stations to handle the spatio-temporal variations of the demand in a cost-efficient way. Existing works on capacity planning for such vehicular fog computing (VFC) scenarios assume that the vehicular traffic follows certain spatio-temporal patterns, which may change in different seasons, and create capacity plans accordingly. In other words, they consider long-term capacity planning, leaving the adaptation to temporary changes or unexpected variations out of scope. In this work, we propose an integer linear programming (ILP) based framework to optimize the routing strategy of vehicular fog nodes (VFNs) in order to maximize the profit received by the service provider, taking into account the quality of service (QoS) received by the users and service level agreement (SLA) of various applications. To adapt to the temporal variations in demand, we predict the traffic flow and resource consumption from the users with feedback from service evaluation. To reduce the computational time and enable parallel processing, we create the capacity plan in two steps, namely global planning and regional planning. Through simulations, we show that the proposed solution achieves an 85% higher profit and a 20% higher service rate compared to the strategy where the VFNs randomly travel and serve the surrounding users without demand prediction. It achieves similar network latency compared to the strategy using only stationary fog nodes, but with a higher cost-efficiency. We also evaluate the impacts of number of VFNs, cost parameters, and regional size on the capacity plan. We find that a high number of VFNs, a small regional size, a high penalty cost, and low traveling and rental costs will lead to a high service rate; while a large regional size and low traveling, rental, and penalty costs will result in a high profit.

This paper presents a novel technique for improving the performance of a modal filter by using an electromagnetic absorber. The study considers a two-conductor modal filter based on a microstrip transmission line. The authors analyzed the effect of the absorber on the MF performance in suppressing conducted and radiated interferences (in the near and far fields). The frequency characteristics of the device were then redefined into the time characteristics in the Advanced Design System. As an excitation, the authors used an ultra-wideband interference pulse of 155 ps (at 0.5 level) and then employed N-norms to estimate and characterize the decomposed pulses. The results show that the use of the absorber can significantly improve the attenuation of the ultra-wideband interference. It was experimentally demonstrated that the value of N1 decreased by a factor of 4.31, N2 – by a factor of 6.52, N5 – by a factor of 1.87. In terms of radiated interference in the near field, the improvement in attenuation of each norm achieved at least 2.81 times. The analysis of the characteristics in the far field showed that the use of the electromagnetic absorber allows reducing the transmission coefficient on average by 2.25 times at a distance of 1 m. To validate the measurement results, the electrodynamic simulation of the modal filter characteristics in the frequency range up to 6 GHz was carried out and showed full agreement with the experimental results.

Systems with non-Hermitian Hamiltonians, specially those exhibiting parity-time (PT ) symmetry, are of particular interest as they describe physical open systems with  alanced loss into and gain from the environment. In this context, the study of eigenvalues location and the  corresponding phase transitions as a function of the degree of non-Hermiticity  is analized here using automatic control tools, particularly the Root-Locus technique. This provides insight on the behavior of these systems, allowing to generalize results for higher order ones. The proposed ideas are analyzed in detail for a class of coupled resonant circuit chains.

The combination of multiple-input multiple-output (MIMO) and intelligent reflecting surfaces (IRSs) is foreseen as a key enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system with the aim of maximizing the system sum-rate for every channel realization. The first one is a novel contextual bandit (CB) approach with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is properly met. Both proposals perform remarkably better than state-of-the-art heuristic methods in high multi-user interference scenarios.

Mobile communication networks are experiencing an important evolution, with the emerging deployment of 5G networks and the successive decay in the usage of previous generations in next years. In parallel, policies promoting an ecological transition gain social impact and economic interest. In the telecommunications market, the shift between two dominant generations could be an important chance to introduce renewable energy sources in an attempt to greening the sector. This work analyses the current situation and provides insight on the possibilities to incorporate renewable energy supplies, concretely photovoltaics (as it seems to be the most promising among clean electric sources), also comparing the characteristics of standard facilities in two different European countries as Spain and Poland.

Precise force sensing is essential for the mechanical characterization and robotic micromanipulation of biological targets. In this work, a high-resolution MEMS capacitive force sensor was proposed for measuring ultralow multiphysics. A bionic swallow structure that contained multiple feathered comb arrays was designed for reducing chip dimension and eliminating undesirable mechanical cross-coupling effect. The comb structure was optimized for maximum sensitivity, linearity, and compact chip size. Utilizing a novel interconnection configuration, interferences derived from parasitic capacitance and electrostatic forces exerted negligible effects on the sensor output. The proposed bionic force sensor was fabricated following a simple three-mask process and integrated with ASIC readouts. Its measuring sensitivity was 7.151 fF/nm, 0.529 aF/nN, and 4.247 pF/g for displacement, force, and inclination measurements, respectively. The proposed sensor had a large measurement range of 1000.00 nm and 13.83 µN with a high linearity of 0.9998. The 1-σ resolution was 0.0328 nm and 0.4436 nN, and the noise floor resolution was 0.0044 nm √? and 0.0597 nN/ √? for displacement and force measurements, respectively. The bias stability of Allan deviance was 0.0050 nm and 0.0678 nN at an integration time of 0.65 s. The proposed bionic swallow sensor exhibited considerable improvement over existing capacitive sensors and feasibility for ultralow multiphysics measurement in biomedical applications.

we propose a novel QRS detection algorithm based on a new Permutation Entropy (PE) method that we developed and referred to as the Adaptive Improved Permutation Entropy (AIPE) method. The parameters of the AIPE method are determined based on the specific signal properties. Implementing the AIPE method leads to prominently preserving the QRS complex and eliminating noises of the ECG signal without smoothing the ECG signal.

Temporary overvoltage (TOV) is becoming one challenge for the integration of significant power electronics resources into a power system. To assess the risk of TOV in a Multi-Infeed Power Electronic System (MIPES), a worst-case estimation approach is proposed to assess TOV risk. The paper starts with analyzing the mechanism of TOV, which reveals that TOV is dominated by excessive reactive power resulting from control lagging of converters. Then, the principle of approximating the TOV for a Voltage Source Converter (VSC) connecting to an infinite bus is introduced within the context of the RMS model. After that, a methodology based on the concept of generalized short-circuit ratio for overvoltage (gSCR-TOV) is introduced to quantify the risk of TOV in a multi-bus system. The intent of this concept is to permit screening to identify those nodes at greatest need for further EMT assessment. To localize these nodes, a concept of weighted sensitivity matrix is further proposed to pinpoint the bus subject to highest TOV in a MIPES. Finally, case studies of EMT time-domain simulations are carried out and verifies the effectiveness of the proposed methodologies.

Process optimization and control can become challenging when the measurements are affected by irregular noise. Classical approaches utilize Gaussian method like Kalman filtering to alleviate the sensory noise. However, many industries involve skewed noise in their processes. While the closed skew normal (CSN) distribution generalizes a Gaussian distribution with additional parameters, its dimension increases during recursive estimation, making it impractical. Even though there exist some techniques for the solution, they are typically either too complicated or inaccurate for the higher dimensional problems. This study proposes a novel online optimization scheme to reduce the dimensionality of a CSN distribution while considering the properties of the complete empirical distribution. Since the objective function used during the optimization step considers the geometry of the metric space, the proposed scheme achieves the higher accuracy without sacrificing computational efficiency. After finding the reliable combination of objective function and optimizer, the proposed filter is applied to two real-time pilot-scale experiments. The results indicate that it is beneficial for recursive state estimation in the presence of skewed noise.

With increasing human dependence on electricity and increasing energy demand, electrical infrastructure has emerged to be one of the most critical services. On the other hand, climate-change driven extreme events progressively increase in frequency and intensity. This is one of the main reasons for making the power grid more resilient to extreme events during which uninterrupted power supply is crucial in keeping the consequences of the extreme event limited. The first step towards making the power grid more resilient is to evaluate the probabilities of failure for all assets at risk from the extreme events. Thus, this paper presents the a methodology to calibrate fragility curves. The strength of the proposed approach is its ability to calibrate the fragility curves utilizing limited data, which is the most common constraint in carrying out such analyses. This paper describes the calibration of transmission tower fragility curves for Puerto Rico utilizing only damage reports for hurricane Maria. That, combined with Puerto Rico’s wind modeling & geographic information system, this study also calibrates fragility curves for transmission towers made with four different construction materials.

In this paper, we provide a comprehensive and systematic review and analysis of the recent progress in DL methods for omnidirectional vision. There are some previous surveys in the literature. However, some of them are focused on the specific vision tasks, especially room layout reconstruction, 3D scene geometry recovery. Moreover, some provide limited reviews of omnidirectional video streaming methods. Unlike existing surveys, this paper highlights the importance of deep learning and probe the recent advances for omnidirectional vision, both methodically and comprehensively. To the best of our knowledge, this is the first survey to comprehensively review and analyze the DL methods for omnidirectional vision. The main contributions of this paper to the community are five folds: (I) We comprehensively review and analyze the DL methods for omnidirectional vision, including the omnidirectional imaging principle, representation learning, datasets, a taxonomy, and applications, to highlight the differences and difficulties with the 2D planner image data. (II) We conduct an analytical study of recent trends of DL for omnidirectional vision, both hierarchically and structurally. Moreover, we offer insights into the discussion and challenge of each category. (III) We summarize the latest novel learning strategies and potential applications for omnidirectional vision. (I) We provide insightful discussions of the challenges and open problems yet to be solved and propose the potential future directions to spur more in-depth research by the community. (II) We create an open-source repository that provides a taxonomy of all the mentioned works and code links, and hope it can shed light on future research. The organization of this paper is structured as follows. In Sec.2, we introduce the imaging principle of ODI, convolution methods for omnidirectional vision, and some representative datasets. In Sec.3, we review and analyze the existing DL approaches for various tasks and provide taxonomies to categorize the relevant papers. Sec.4 covers novel learning paradigms for the tasks in omnidirectional vision, e.g., unsupervised learning, transfer learning, and reinforcement learning. Sec.5 then scrutinizes the applications, followed by Sec. 6, where we discuss open problems and future directions. Finally, we conclude this paper in Sec. 7. Furthermore, we summarize most, if not all but representative, works (over 200 papers) in the last five years, which were published in the top-tier conferences and journals in computer vision/graphics and machine learning.

In this paper, the problem of passive direction finding is addressed using an acoustic vector sensor array (AVS), which may be deployed either in free space or near a reflecting boundary. Building upon the $4 \times 1$ vector field measured by an AVS, the particle-velocity coarray augmentation (PVCA) is proposed to admit the underdetermined direction finding using the spatial difference coarray derived from the vectorization of the array covariance matrix. Unlike the widely used spatial coarray Toeplitz recovery technique, the PVCA is applicable to arbitrary array geometries and imposes no reduction of the spatial difference coarray aperture. For the array located at or near a reflecting boundary, the PVCA allows resolving up to $13$ sources, while for the array located in free space, the PVCA can identify $9$ sources at most. By applying to the systematically designed nonuniform arrays, such as coprime arrays and nested arrays, the PVCA can be coupled with the spatial smoothing technique to get the number of resolvable sources multiplied. Finally, the efficacy of the PVCA is verified by numerical simulations.

The dielectric slab waveguide (DSW) has been the subject of much analysis in the past decades, with many important applications in high-speed high-bandwidth electromagnetic propagation; however, nearly all analysis of the DSW’s stochastic propagation loss $\alpha$ (dB/cm), associated with random surface roughness of its sidewalls, has revolved around the transverse-electric (TE) mode of operation. This work derives analytical expressions for $\alpha$ in the transverse-magnetic (TM) mode, and correlates it against numerical experiments in the method of finite-difference time-domain (FDTD) in both two-dimensional (2D) and three-dimensional (3D) space. In this work, we introduce the novel \textit{singular effective impedance} (SEI) formulation for normalization of the TM mode $\alpha$ and verify its accuracy through FDTD which shows near 0\% mean-error in both TM and TE modes. We perform several stochastic numerical experiments in 3D FDTD, where the DSW’s width is set to be much larger than its height, and show that $\alpha$ correlates quite well against the proposed 2D analytical model and 2D FDTD. Finally, analysis of FDTD-computed data reveals that existing analytical models of $\alpha$ may be missing higher-order interaction terms involving correlation length $L_c$ (m) of the surface roughness.

The cooperation between Unmanned Aerial Vehicles (UAVs) and ground Mobile Edge Computing (MEC) servers in processing tasks is becoming one of the main research trends of MEC networks. Despite the advantages of UAV-assisted MEC, it is restricted by the limited battery capacity and sensitive energy consumption of UAVs. Unlike the previous works where UAVs are allowed to either process tasks locally or offload them to ground MEC servers, in this paper, we propose a multi-hop task routing solution for Internet of Things (IoT) networks in which a UAV can also relay to another UAV with better connection to a ground MEC server. Furthermore, the UAV can make benefit of existing Intelligent Reflective Surfaces (IRSs) to further improve tasks offloading and reduce energy consumption. We show that the problem of minimizing the total energy of UAVs is NP-hard, and we propose a graph-based heuristic solution to solve it. Simulation results show that the proposed graph-based solution outperforms the traditional no-relaying scheme, especially when IRSs are deployed. Furthermore, a Convolutional Neural Network (CNN) is devised to reduce the delay of finding the decisions for the UAVs at the centralized coordinator. Simulations show that the CNN achieves very close energy consumption performance and a remarkable reduction in execution time compared to the graph-based heuristic solution.

In this article, an exponential tapered Vivaldi antenna is designed. A ZIM (Zero Index Metamaterial) is introduced into the antenna to enhance its gain in whole of the ultrawideband. The proposed structure is covering the ultrawideband of 3.1-10.6 GHz as specified by FCC. The antenna has a bandwidth of 8.2GHz for the range 2.93- 11.13GHz. After introduction of metamaterial in the antenna, the gain of the antenna is enhanced in whole of the ultrawideband whereas maximum enhancement in the gain is 2.22dB at 10.6GHz. The proposed antenna also has low cross polarization less than -30dB for all the frequencies.

Synthetic data is an indispensable supplement to the difficult-to-acquire real data in order to meet the substantial demand by machine learning based systems. Data playing the key role in machine learning models, its objective and maintainable quality metrics are vital for quality assurance of the whole system. This paper introduces a systematic and domain-neutral methodology based on formalized scenario variation and experimental digital twins for the generation of synthetic data. The methodology uses human-readable scenarios and semantically meaningful parameter variations to describe possible entities, actions and events to be simulated, whereas experimental digital twins bring the scenarios to life by the integration of various domains of a system such as mechanics, sensors, actuators and communication under one platform that can be simulated as a whole. The scenario description and digital twin simulation is carried out iteratively to derive the optimal distribution of synthetic data. Thus scenarios and experimentable digital twins can together serve as mediums to systematically cover diverse application scenarios, test dangerous situations and find faults within a system.

Inverse dynamics from motion capture is the most common technique for acquiring biomechanical kinetic data. However, this method is time-intensive, limited to a gait laboratory setting, and requires a large array of reflective markers to be attached to the body. A practical alternative must be developed to provide biomechanical information to high-bandwidth prosthesis control systems to enable predictive controllers. In this study, we applied deep learning to build dynamical system models capable of accurately estimating and predicting prosthetic ankle torque from inverse dynamics using only six input signals. We performed a hyperparameter optimization protocol that automatically selected the model architectures and learning parameters that resulted in the most accurate predictions. We show that the trained deep neural networks predict ankle torques one sample into the future with an average RMSE of 0.04±0.02 Nm/kg, corresponding to 2.9±1.6% of the ankle torque’s dynamic range. Comparatively, a manually-derived analytical regression model predicted ankle torques with a RMSE of 0.35±0.53 Nm/kg, corresponding to 26.6±40.9% of the ankle torque’s dynamic range. In addition, the deep neural networks predicted ankle torque values half a gait cycle into the future with an average drop in performance of 1.7% of the ankle torque’s dynamic range. This application of deep learning provides an avenue towards the development of predictive control systems for powered limbs aimed at optimizing prosthetic ankle torque.

It is necessary to control contact force through modulation of joint stiffness in addition to the position of our limb when manipulating an object. This is achieved by contracting the agonist muscles in an appropriate magnitude, as well as, balancing it with contraction of the antagonist muscles. Here we develop a decoding technique that estimates both the position and torque of a joint of the limb in interaction with an environment based on activities of the agonist-antagonistic muscle pairs using electromyography in real time. The long short-term memory (LSTM) network that is capable of learning time series of a long-time span with varying time lags is employed as the core processor of the proposed technique. We tested both the unidirectional LSTM network and bidirectional LSTM network. A validation was conducted on the wrist joint moving along a given trajectory under resistance generated by a robot. The decoding approach provided an agreement of greater than 93% in kinetics (i.e. torque) estimation and an agreement of greater than 83% in kinematics (i.e. angle) estimation, between the actual and estimated variables, during interactions with an environment. We found no significant differences in performance between the unidirectional LSTM and bidirectional LSTM as the learning device of the proposed decoding method.