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This paper studies interval coordination problems for multiagent systems with antagonistic interactions. For strongly connected signed networks, it is shown that when the intersection of intervals imposed by agents is nonempty: (1) the multiagent system achieves bipartite consensus with structurally balanced network; (2) all agents’ states must converge to 0, if the signed network is structurally unbalanced. We establish the consensus conditions for bipartite consensus and zero-value consensus by employing the Gauge Transformation and robust analysis of signed networks. When the signed networks are strongly connected and the intersection of intervals is empty, the system reaches an asymptotically stable and unique equilibrium. Moreover, the equilibrium states are only decided by the network structure and interval constraints, and are not related to initial agents’ states. Associating the equilibrium of dynamics with the solution of a system of nonlinear equations, we obtain the uniqueness, stability and continuity of equilibria. Finally, numerical simulations are presented to illustrate the theoretical results.

The deployment of renewable energy in power systems may raise serious voltage instabilities. Electric vehicles (EVs), owing to their mobility and flexibility characteristics, can provide various ancillary services including active and reactive power. However, the distributed control of EVs under such scenarios is a complex decision-making problem with enormous dynamics and uncertainties. Most existing literature employs model-based approaches to formulate the active and reactive power control problems, which require full models and are time-consuming. This paper proposes a multi-agent reinforcement learning method featuring actor-critic networks and a parameter sharing framework to solve the EVs coordinated active and reactive power control problem towards both demand-side response and voltage regulations. The proposed method can further enhance the learning stability and scalability with privacy perseverance via the location marginal prices. Simulation results based on a modified IEEE 15-bus network are developed to validate its effectiveness in providing system charging and voltage regulation services.

The continuous deployment of distributed energy sources and increase in the adoption of Electric Vehicles (EVs) require smart charging algorithms. Existing EV chargers offer limited flexibility and controllability, and do not fully consider factors (such as, EV user waiting time and length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions and charging cost. In this paper, a Neuro-Fuzzy-PSO based novel and advanced smart charge controller is proposed which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (PV) output and battery state of health. A rule based Fuzzy controller becomes complex as the number of inputs to the controller increases. Also, it becomes difficult to achieve an optimum operation due to conflicting nature of control requirements. To optimize the controller response, Particle Swarm Optimization (PSO) technique is proposed to provide a global optimum solution based on a pre-defined cost function and to address the implementation complexity PSO is combined with neural network. The proposed Neuro-Fuzzy-PSO control algorithm meets EV user requirements, work within technical constraints and is simple to implement in real-time (and requires less processing time). Simulation using MATLAB and experimental results using dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller.

To further preserve the data privacy of federated learning (FL), we propose a differentially private FL (DPFL) scheme with misaligned power allocation (MPA-DPFL). Unlike most existing over-the-air FL studies, in MPA-DPFL, the gradients are aggregated through over-the-air computation (Aircomp) but do not need to be aligned in the transmission. Therefore, MPA-DPFL can avoid the problem that the signal-to-noise ratio (SNR) of the system is limited by the device with the worst channel condition. We formulate an optimization problem to minimize the optimality gap of MPA-DPFL while guaranteeing a certain degree of privacy protection. Additionally, we demonstrate that the MPA-DPFL is more suitable than the DPFL with aligned power allocation (APA-DPFL) when the channel condition of a device in the system is lower than a threshold. The analytical results are validated through simulation.

The escalating demand on Electric Vehicles (EVs) has enhanced the necessity of adequate charging infrastructure, especially in residential areas. This paper proposes a smart charging approach for off-board Electric Vehicles (EVs) chargers in Home-Energy-Hub (HEH) applications along with DC sources such as Photovoltaic (PV) and Battery Storage (BS). The proposed method facilitates smart charging and discharging of EVs to obtain both Vehicle-to-X (V2X) and X-to-Vehicle (X2V) operations focusing on the domestic applications integrated with renewable and storage elements. Furthermore, the optimal State-of-Charge (SOC) profiles for BS and EV in the HEHs system is defined by the extended Bellman-Ford-Moor Algorithm (BFMA). This modified BFMA utilizes the forecasted data such as solar irradiation, electricity tariff, and power consumption to gain economic benefits in HEHs with respect to user and EV requirements. Moreover, the plugging time, duration and initial/final SOC are fluctuating at each connection due to the stochastic nature of EV conditions and user settings. This study presents a laboratory implementation of two-level Hierarchical Energy Management System (HEMS) for HEHs with plug-in electric vehicles. In fact, the primary level includes power converters controller, while the proposed algorithm is implemented in the secondary level. Finally, the simulation and experimental results confirm the effectiveness of the proposed analysis regarding the interaction of HEHs and power grid with EVs behavior.

This letter investigates a virtual reality (VR) delivery system, where the original VR contents requested by all users are stored at the macro base station (MBS). To reduce latency, MBS can either transmit the original VR data or the computed VR data to multiple users aided by an intelligent reflecting surface (IRS) to prevent attacks from an eavesdropper with imperfect channel state information (CSI). We jointly optimize the transmission policies, MBS transmit power, IRS phase shift and computing frequency to minimize the latency over all users subject to security constraint. Numerical results validate the robustness of our proposed algorithm.

In this paper, we present the computation-efficient resource allocation (RA) schemes for millimeter-wave mobile edge computing (mmWave-MEC) system with the aid of reconfigurable intelligent surface (RIS), which is used to assist the uplink communication from the users to the base station (BS). By means of the theoretical analysis, the achievable rate and computation efficiency (CE) are derived. Then, the optimization problem for the CE maximization under the constraints of the minimum rate, maximum power consumption and local CPU frequency is formulated, where the joint design of the hybrid beamforming at the BS and the passive beamforming at the RIS as well as the local resource allocation of each user is carried out. An effective iterative algorithm based on the penalized inexact block coordinate descent (BCD) method is proposed to obtain the computation-efficient RA scheme. Next, a low-complexity suboptimal RA scheme based on the BCD method is proposed, and corresponding algorithm is presented. Simulation results show that the proposed schemes are effective, and high CE can be attained. Moreover, the second scheme can achieve the CE performance close to the first scheme but with lower complexity. Besides, it is effective to deploy the RIS scheme in mmWave-MEC system, which can strike a balance between the CE and energy consumption when compared to the conventional relay schemes. 10.13039/501100001809-Natural Science Foundation of China (Grant Number: 62031017, 61971220 and 61971221); Open Research Fund of State Key Laboratory of MillimeterWaves of Southeast University (Grant Number: K202215).

The DC power system, due to its convenience of conversion, integration, and use, is getting immense attention in the field of power transmission and distribution. It is superior to traditional AC systems in terms of efficiency, reliability, and control simplicity as well. A DC circuit breaker is one of the important elements of any DC power system. It is a sophisticated technology designed to break DC current only. The breaking of a DC current is always challenging compared to the breaking of an AC current, as DC current does not have natural zero crossing points like AC current has. Moreover, DC current breaking becomes more critical when the current is inductive as energy stored in the network inductance opposes instantaneous current breaking. Hence, this energy needs to be absorbed and dissipated as heat during the current breaking operation, which is exactly what is done in the traditional DC circuit breaker topologies. This paper introduces a new topology for DC circuit breakers with a mechanism to reuse this stored energy instead of dissipating it. The mechanism is analogous to regenerative braking in electric drive systems and can enhance the overall system efficiency. The proposed scheme was analyzed through rigorous computer simulation and was experimentally validated.

A fiber ring laser based on a side-polish fiber Mach-Zehnder interferometer (MZI) for effective improving refractive index (RI) and torsion sensing is proposed and investigated. The side-polished fiber MZI sensor can not only enhance the evanescent wave but also break circular symmetry of optical fiber face section, so it can be used for the surrounding RI and torsion sensing. A spectrum 3 dB bandwidth of less than 0.15 nm has been achieved which makes the fiber ring laser torsion sensing system to have higher measurement resolution. Experiment results show that the RI and torsion sensitivity of proposed sensor is dependent on the polish depth: the thicker the polish depth, the higher the sensitivity. For a sensor with side polish depth of 47 μ m, the measured RI sensitivity reaches-81.36 nm/RIU, the torsion sensitivity is as high as-0.019nm/O, the sensitivity converted to torsional rate is-0.267 nm/(rad. m-1).

Wireless networks have been widely utilized in industries, where wireless links are challenged by the severe nonstationary Rician fading channel, which requires online link quality estimation to support high-quality wireless services. However, most traditional Rician estimation approaches are designed for channel measurements and work only with nonmodulated symbols. Then, the online Rician estimation usually requires a priori aiding pilots or known modulation order to cancel the modulation interference. This article proposes a nondata-Aided method with redundant Gaussian mixture model (GMM). The convergence paradigm of GMM with redundant subcomponents has been analyzed, guided by which the redundant subcomponents can be iteratively discriminated to approach the global optimization. By further adopting the constellation constraint, the probability to identify the redundant subcomponent is significantly increased. As a result, accurate estimation of the Rician parameters can be achieved without additional overhead. Experiments illustrate not only the feasibility but also the near-optimal accuracy.

An intelligent reflecting surface (IRS) is proposed to enhance the physical layer security in the Rician fading channel wherein the angular direction of the eavesdropper (ED) is aligned with a legitimate user. A two-phase communication system under active attacks and passive eavesdropping is considered in this scenario. The base station avoids direct transmission to the attacked user in the first phase, whereas other users cooperate in forwarding signals to the attacked user in the second phase, with the help of IRS and energy harvesting technology. Under the occurrence of active attacks, an outage-constrained beamforming design problem is investigated under the statistical cascaded channel error model, which is solved by using the Bernstein-type inequality. An average secrecy rate maximization problem for the passive eavesdropping is formulated, which is then addressed by a low-complexity algorithm. The numerical results of this study reveal that the negative effect of the ED's channel error is larger than that of the legitimate user.

In this paper, we study the transmission design for reconfigurable intelligent surface (RIS)-aided multiuser communication networks. Different from most of the existing contributions, we consider long-term CSI-based transmission design, where both the beamforming vectors at the base station (BS) and the phase shifts at the RIS are designed based on long-term CSI, which can significantly reduce the channel estimation overhead. Due to the lack of explicit ergodic data rate expression, we propose a novel deep deterministic policy gradient (DDPG) based algorithm to solve the optimization problem, which was trained by using the channel vectors generated in an offline manner. Simulation results demonstrate that the achievable net throughput is higher than that achieved by the conventional instantaneous-CSI based scheme when taking the channel estimation overhead into account.

Long Range Wide Area Network (LoRaWAN) is one of the most promising IoT technologies that are widely adopted in low-power wide-area networks (LPWAN). LoRaWAN faces scalability issues due to a large number of nodes connected to the same gateway and sharing the same channel. Therefore, LoRa networks seek to achieve two main objectives: successful delivery rate and efficient energy consumption. This paper proposes a novel game theoretic framework for LoRaWAN named Best Equal LoRa (BE-LoRa), to jointly optimize the packet delivery ratio and the energy efficiency (bit/Joule). The utility function of LoRa node is defined as the ratio of the throughput to the transmit power. LoRa nodes act as rational users (players) which seek to maximize their utility. The aim of the BE-LoRa algorithm is to maximize the utility of LoRa nodes while maintaining the same signal-to-interference-and-noise-ratio (SINR) for each SF. The power allocation algorithm is implemented at the network server, which leads to an optimum SINR, spreading factors (SFs) and transmission power settings of all nodes. Numerical and simulation results show that the proposed BE-LoRa power allocation algorithm has a significant improvement in packet delivery ratio and energy efficiency as compared to the Adaptive Data Rate (ADR) algorithm of legacy LoRaWAN. For instance, in very dense networks (624 nodes), BE-LoRa can improve the delivery ratio by 17.44% and reduce power consumed by 46% compared with LoRaWAN ADR.

International audience; Use of unmanned aerial vehicles (UAVs) to provide on-demand communications has been receiving growing interest, especially for use in remote and hard-to-reach areas. Also, the use of light-emitting diode-based lighting in UAVs has opened opportunities for data transmission through visible-light communications. To manage multiuser interference while avoiding complex handover procedures, we consider the use of zero forcing (ZF) pre-coding. Since the performance of ZF pre-coding depends on the correlation between channel gains of users, we propose in this paper to reduce it by means of location optimization of UAVs. More specifically, we use particle swarm optimization with the objective of maximizing the overall achievable network throughput. Furthermore, to relax the optimization requirements at UAVs, we investigate the case when the optimization is performed at a specific rate under different mobility conditions.

This paper presents a cloud energy storage (CES) architecture for reducing energy costs for residential microgrid users. The former of this article concentrates on identifying an appropriate battery technology from various battery technologies with the aid of a simulation study. The later part addresses the economic feasibility of the storage architecture with three different scenarios namely grid connected energy storage, distributed energy storage (DES) and CES. The performance of the proposed architecture has been evaluated by considering five residential users with suitable battery technology identified from the former part of the study. For the purpose of the analysis, PV and load profiles including seasonal effects and grid price were taken from IIT Mumbai, India and IEX portal, respectively. In addition, this article also examines the impact of increased number of users with CES. The value of this study is that the proposed CES architecture is capable of reducing the cost of electricity experienced by the user by 11.37% as compared to DES. With this, CES operator’s revenue can be increased by 6.70% in summer and 16.97% in winter in the case of fixed number of users. Finally, based on the analysis and simulation results, this paper recommends CES with Li-ion battery technology for residential application.

Precise timing among wireless sensor nodes is a key enabling technology for time-sensitive industrial wireless sensor networks (WSNs). However, the accuracy of timing is degraded by manufacturing tolerance, aging of crystal oscillators, and communication delays. This article develops a framework of packet-coupled oscillators (PkCOs) to characterize the dynamics of communication and time synchronization of clocks in WSNs. The nonidentical clock is derived to describe the embedded clock's behavior accurately. A proportional–integral (PI) packet coupling scheme is proposed for synchronizing networked embedded clocks, while, scheduling wireless Sync packets to different slots for transmission. It also possesses the feature of automatically eliminating the effects of unknown processing delay, which further improves the synchronization performance. The rigorous theoretical analysis of PI-based PkCOs is presented via studying a closed-loop time synchronization system. The performance of PI-based PkCOs is evaluated on a hardware testbed of IEEE 802.15.4 WSN. The experimental results show that the precision of the proportional-integral PkCOs protocol is as high as 60 $\mu s$ (i.e., 2 ticks) for 32.768 kHz crystal oscillator-based clocks.

The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers’ preferences are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios, but also mitigates congestion during specific hours while respecting EV drivers’ preferences. Moreover, the simulation results revealed the significant impact of EV drivers' preferences on the optimal solutions and cost/revenue of the stakeholders.

This article proposes a fuzzy logic-based energy-management system (FEMS) for a grid-connected microgrid with renewable energy sources (RESs) and energy storage system (ESS). The objectives of the FEMS are reducing the average peak load (APL) and operating cost through arbitrage operation of the ESS. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state of charge of ESS, the power difference between load and RES, and electricity market price. The effectiveness of the fuzzy logic greatly depends on the membership functions (MFs). The fuzzy MFs of the FEMS are optimized offline using a Pareto-based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy-logic controller. A comparison with other control strategies with similar objectives is carried out at a simulation level. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, U.K.

A new U-shape panda polarization-maintaining fiber (PPMF) based microfiber interferometer coated with graphene oxide (GO) film was proposed and experimentally demonstrated for relative humidity (RH) sensing. Experimental results show that the U-shape sensor has refractive index (RI) sensitivity of 1692.5 nm/RIU in the RI range of 1.33 when the diameter of the taper waist is 10.08 μm. The surface of the U-shape sensor was then modified chemically and coated with a thin layer of GO film (59.64 nm) for RH detection and the sensitivity is proportional to RH: as RH increases from 30% to 98%, the sensitivity increases from 0.111 to 0.361 nm/%RH and the response time is 0.28 s. In addition, the cross sensitivity to temperature, stability, reproducibility, and response/recovery time of the RH sensor were studied in detail. The proposed U-shape fiber RH sensor has advantage of high sensitivity, good reproducibility and fast response (0.28 s), which has potential application in areas requiring dynamic measurement of RH variations such as industrial product fabrication process control and breath state monitoring.

International audience; Visible light communications (VLC) (VLC) has been introduced as a complementary wireless technology that can be widely used in industrial indoor environments where automated guided vehicles aim to ease and accelerate logistics. Despite its advantages, there is one significant drawback of using an indoor vehicular VLC (V-VLC) network that is there is a high handover outage duration. In line-of-sight VLC links, such handovers are frequently due to mobility, shadowing, and obstacles. In this paper, we propose a frequency diversity and link aggregation solution, which is a novel technique in Data link layer to tackle handover challenge in indoor V-VLC networks. We have developed a smallscale prototype and experimentally evaluated its performance for a variety of scenarios and compared the results with other handover techniques. We also assessed the configuration options in more detail, in particular focusing on different network traffic types and various address resolution protocol intervals. The measurement results demonstrate the advantages of our approach for lowoutage duration handovers in V-VLC. The proposed idea is able to decrease the handover outage duration in a two-dimensional network to about 0.2 s, which is considerably lower compared to previous solutions.

Layered or hybrid optical orthogonal frequency division multiplexing (OFDM) has been proposed for use in optical communications due to its excellent spectral and power efficiencies, especially in visible light communications (VLC). However, most of the current works concentrate on transmitter and receiver design as well as the quality of service in communication networks. In this paper, we propose a spectrum-efficient dimmable triple-layer hybrid optical OFDM (DTH-OFDM) scheme to tackle the illumination requirements, considering different practical indoor VLC scenarios from low illumination to high illumination intensities. In the proposed DTH-OFDM scheme, the required dimming level is achieved by jointly adjusting the dimming factors and direct current bias. We investigate the comprehensive performance analysis of the proposed DTH-OFDM in detail, including probability density function, bit error rate (BER), spectral and energy efficiencies. In addition, a joint dimming control and optimal power allocation problem for DTH-OFDM is formulated and solved using convex optimization under the constraints of light emitting diode (LED) nonlinearity, dimming target and communications reliability. Numerical results show that, the proposed DTH-OFDM can offer continuous and arbitrary dimming target with higher spectral efficiency and lower BER compared with its counterparts, as well as an enhanced tolerance to the LED nonlinearity.

In industrial internet of things (IIoT), ultra-reliable and low-latency communication (URLLC) is proposed to guarantee the requirement of real-time wireless control systems in worst case, so as to maintain the system working in all cases. However, it is extremely challenging to maintain URLLC throughout the whole control process due to the scarcity of wireless resource. This paper develops an autonomous device-to-device (D2D) communication scheme by jointly considering reliability in URLLC and control requirement. In the proposed scheme, we consider the actual control requirement, i.e., control convergence rate, into communication design, where we find that it can be converted into a constraint on communication reliability. Then, the communication reliability constraint comes from control aspect, instead of URLLC, which leads to that the system does not need to guarantee worst case in URLLC. Second, the sensors autonomously decide whether to be activated with optimal probabilities to participate in the control process, which can maintain the communication reliability requirement with significantly less resource consumption. Simulation results show remarkable performance gain of our method. For instance, compared with fixed activation probability 40% only considering URLLC, the average power consumption of the proposed method can be reduced by at most about 100%.

In unmanned aerial vehicle (UAV)-assisted data collection system, UAVs can be deployed to charge ground terminals (GTs) via wireless power transfer (WPT) and collect data from them via wireless information transmission (WIT). In this letter, we aim to minimize the time required by a UAV via jointly optimizing the trajectory of the UAV and the transmission scheduling for all the GTs. This problem is formulated as a mixed integer nonlinear programming (MINLP) which are difficult to address in general. To this end, we develop an iterative algorithm based on binary search and successive convex optimization (SCO) to solve it. The simulation shows that our proposed solution outperforms the benchmark algorithms.

In this letter, we analyze the secrecy performance of a reconfigurable intelligent surface (RIS)-aided communication system with spatially random unmanned aerial vehicles (UAVs) acting as eavesdroppers. We consider the scenarios where the base station (BS) is equipped with single and multiple antennas.The signal-to-noise ratios (SNRs) of the legitimate user and the eavesdroppers are derived analytically and approximated through a computationally effective method. The ergodic secrecy capacity is approximated and derived in closed-form expressions.Simulation results validate the accuracy of the analytical and approximate expressions and show the security-enhanced effect of the deployment of the RIS., Comment: Accepted by IEEE Wireless Communications Letters (WCL)

In this paper, we investigate the design of robust and secure transmission in intelligent reflecting surface (IRS) aided wireless communication systems. In particular, a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers, where the artificial noise (AN) is transmitted to enhance the security performance. Besides, we assume that the cascaded AP-IRS-user channels are imperfect due to the channel estimation error. To minimize the transmit power, the beamforming vector at the transmitter, the AN covariance matrix, and the IRS phase shifts are jointly optimized subject to the outage rate probability constraints under the statistical cascaded channel state information (CSI) error model. To handle the resulting non-convex optimization problem, we first approximate the outage rate probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on alternating optimization, the penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power compared to other benchmark schemes.

Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs' scheduling becomes more prominent. In this article, a new approach to energy hubs' scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach.

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere with wireless signal propagation, that manifested as unique patterns for activity recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carries substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in a certain speed and scale, recent works commonly have a moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep-learning model that caters to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data.

In this letter, a low-complexity receiver (Rx) is proposed for hybrid asymmetrically clipped optical orthogonal frequency division multiplexing (HACO-OFDM). Owing to the special time-domain property of HACO-OFDM, overlaid asymmetrically clipped OFDM (ACO-OFDM) and pulse-amplitude-modulated discrete multitoned (PAM-DMT) signals can be distinguished in the time domain to reduce its computational complexity. Besides, the near-optimal optical power allocation is further applied to optimize the proposed system performance. Theoretical analysis and simulation results demonstrate that, the proposed Rx can achieve nearly the same bit error rate (BER) performance as the BER-optimal iterative Rx but with an effective complexity reduction, thus demonstrating its application potential in high-speed optical communication systems.

Press-pack insulated gate bipolar transistors (PP-IGBTs) are commonly connected in series and stacked together with heatsinks using an exterior clamping fixture in order to achieve high-voltage dc-link levels. A suitable contact area between the clamping fixture and the device is essential to ensuring optimal PP-IGBT thermomechanical performance, especially for the first and last devices in a stack. In this study, the effects of the clamping area on collector deformation, temperature, and stress distributions are investigated by means of the finite-element method (FEM). Moreover, this article analyzes the influence of heatsink thickness to maximize the stress evenness of the terminal PP-IGBT and reduce the overall length of the stack system. The results indicate that the collector lid is prone to warpage due to thermal expansion, which results in a decrease in the effective contact area between component layers. As the contact resistance increases, the chips accumulate considerable heat. Increasing the clamping area at this point can adequately compensate for the warp deformation and can also improve the stress uniformity of the chips. Finally, an experiment making use of stress-sensitive film has been carried out to verify the developed FEM models.

In the above article [1] , the references in Tables V – IX were incorrectly assigned. The corrected tables are provided here.

The electrically sensing characteristics of a liquid-crystal (LC)-infiltrated polarization-maintaining photonic crystal fiber (PCF) have been studied. The small holes on the end face of the fiber collapse and two large holes remain open by controlling discharging-time, -current, and -position of a fiber splicer, then LC is selectively infiltrated into the two large holes, which can not only save LC but also make the welding between the LC-infiltrated polarization-maintaining PCF and single-mode fiber much easier. A new method to weld the two fibers is proposed by filling and volatilizing ethanol to make LC a few millimeters away from the end face which can improve the sensing system stability and prevent the discharge of a fiber splicer from destroying LC molecules. A Sagnac interferometer is set up by embedding the LC-infiltrated polarization-maintaining PCF in a fiber loop and then its electroresponse characteristics are studied. The refractive-index distribution of the LC-infiltrated polarization-maintaining PCF varies with electric voltage due to the variable index of LC, which makes it possible to detect voltage. Three voltage ranges are discussed by the different dips and the sensitivity is improved with voltage increasing. The high sensitivity is up to 3.49 nm/V with the tuning range of 7 nm as voltage changes from 149.67 to 151.61 V. The Sagnac interferometer embedded with an LC-infiltrated polarization-maintaining PCF can be utilized as a voltage sensor, electro-optical modulator, or filter.

Graphene, a two-dimensional monatomic layer of carbon material, has demonstrated as a good candidate for applications of ultrafast photodetectors, transistors, transparent electrodes, and biosensors. Recently, many studies have shown that using metallic deep gratings could enhance the absorptance of graphene of 2.3% up to 80% in the near infrared region for applications in photon detection. This paper presents utilizing a nanograting structure, namely, a compound metallic grating could greatly enhance the absorptance of graphene up to 98% and widen its spectral bandwidth to 0.6 μm, which are greater than those of previous work. The study also showed that the absorptance spectrum is insensitive to angles of incidence. Furthermore, the proposed graphene-covered compound grating might bring a lot of benefits for graphene designs-based optical and optoelectronic devices.

With the increase of the number of electric vehicles (EVs), it is of vital importance to develop the efficient and effective charging scheduling schemes for all the EVs. In this article, we aim to maximize the social welfare of all the EVs, charging stations (CSs) and power plant (PP), by taking into account the changing demand of each EV, the changing price, the capacity and the congestion balance between different CSs. To this end, two efficient scheduling algorithms, i.e., Centralized Charging Strategy (CCS) and Distributed Charging Strategy (DCS) are proposed. CCS has a slightly better performance than the DCS, as it takes all the information and make the decision in the central control unit. On the other hand, DCS dose not require the private information from EVs and can make decentralized decision. Extensive simulation are conducted to verify the effectiveness of the proposed algorithms, in terms of the performance, congestion balance, and computing complexity.

In this paper, an unmanned aerial vehicle (UAV)-aided wireless emergence communication system is studied, where an UAV is deployed to support ground user equipments (UEs) for emergence communications. We aim to maximize the number of the UEs served, the fairness, and the overall uplink data rate via optimizing the trajectory of UAV and the transmission power of UEs. We propose a Deep Q-Network (DQN) based algorithm, which involves the well-known Deep Neural Network (DNN) and Q-Learning, to solve the UAV trajectory problem. Then, based on the optimized UAV trajectory, we further propose a successive convex approximation (SCA) based algorithm to tackle the power control problem for each UE. Numerical simulations demonstrate that the proposed DQN based algorithm can achieve considerable performance gain over the existing benchmark algorithms in terms of fairness, the number of UEs served and overall uplink data rate via optimizing UAV’s trajectory and power optimization.

Rapidly growing distributed renewable networks make an increasing demand on various types of power converters to feed different loads. Power converters with constant power load are one typical configuration that can degrade the stability of the power conversion system due to the negative impedance characteristic. This paper presents a nonlinear analysis method using the developed complete-cycle solution matrix method by transforming the original linear time-variant system into a summation of segmented linear time-invariant systems. Thus, the stability of the nonlinear system can be studied using a series of the corresponding state transition matrix and saltation matrix. As this derived matrix contains all the comprehensive information relating to the system’s stability, the influence of the constant power load to system’s fast-timescale stability in both continuous conduction mode and the discontinuous conduction mode can be fully investigated and analyzed. The phenomena of fast-timescale instability around switching frequency for power converters with a constant power load are observed and investigated numerically. Finally, experimental results have proven the analysis and verified the effectiveness of the developed method.

Losses in light-emitting-diode (LED) driver cause increasing temperature and shorten their lifespan. Therefore, improving the efficiency of LED drivers not only saves energy but also is indispensable to increase their lifespan. In this article, a new LED driver topology is proposed to improve the performance of valley switching by decreasing the mosfet switching losses. The proposed topology is designed in a way that the mosfet works at the significantly lower switching and conduction losses in compared with conventional LED drivers. It elaborates how the proposed topology also improves the overall efficiency by decreasing power losses in other main elements of the driver, including inductance and diode. In addition, a new valley switching implementation is introduced for the new converter, which decreases the cost and dimension of the LED drivers. The experimental results confirm the high efficient operation of the proposed LED driver by reaching the efficiency up to 97% at a wide range of operating voltage.

Chemical doping is widely used in the electronic devices. In p-type semiconductor thin films, oxygen doping fills the hole traps and increases hole concentrations, improving the performance of the organic field-effect transistors (OFETs). Due to the low ionization potential for p-type semiconductors, the superfluous holes induced by the oxygen doping degrades the OFETs off-state leakage performance. On the other hand, for p-type semiconductors with high ionization potential (up to 5.5-6.0 eV), the limited oxidation of oxygen is hard to achieve satisfactory doping concentrations to fill the trap states. This refers to the well-known intrinsic incompatibility between the oxygen doping and high-performance OFETs. Herein, a novel strategy is introduced to overcome the incompatibility and achieve high-performance OFETs by using the structural polymer electret. That is, moderate hole concentrations induced by low-pressure (30 Pa) oxygen plasma fill the hole traps within semiconductor. And the built-in field resulted from polymer electret accumulates the holes inside semiconductor near the semiconductor/electret interface, thus improving the OFETs performance. Using a model organic semiconductor with high ionization potential-2,7-didodecyl[1]benzothieno [3,2-b][1]benzothiophene (C12-BTBT) as an example, the high-performance OFETs with field-effect mobility ( $\mu _{FET}$ ) of 3.5 cm2V−1 s−1, subthreshold-swing ( SS ) of 110 mV decade−1, on-off ratio of 104, and widely-tunable threshold voltage ( ${V} _{t}$ ) are realized at a low voltage below 2 V in the open air.

International audience; This paper proposes inter-cell interference mitiga-tion in a dimming-aware way for multi-cell visible-light communication networks, through efficient time-scheduling, scaling, and coordination of non-orthogonal multiple access (NOMA) transmissions at the access points. By this method, users are grouped and served in different time slots depending on whether they are at the center or at the edge of the cell. This reduces the number of NOMA users per time slot, which decreases the network computational complexity, by reducing the average number of successive interference cancellation steps. Comparison of the proposed scheme with the classical NOMA over different dimming constraints shows an improvement of up to 39% and 37% in the average sum-rate and fairness, respectively, for a 30% duty cycle transmission in a 4-cell scenario with 8 users.

In a power system with high penetration of renewable power sources, gas-fired units can be considered as a back-up option to improve the balance between generation and consumption in short-term scheduling. Therefore, closer coordination between power and natural gas systems is anticipated. This article presents a novel hybrid information gap decision theory (IGDT)-stochastic cooptimization problem for integrating electricity and natural gas networks to minimize total operation cost with the penetration of wind energy. The proposed model considers not only the uncertainties regarding electrical load demand and wind power output, but also the uncertainties of gas load demands for the residential consumers. The uncertainties of electric load and wind power are handled through a scenario-based approach, and residential gas load uncertainty is handled via IGDT approach with no need for the probability density function. The introduced hybrid model enables the system operator to consider the advantages of both approaches simultaneously. The impact of gas load uncertainty associated with the residential consumers is more significant on the power dispatch of gas-fired plants and power system operation cost since residential gas load demands are prior than gas load demands of gas-fired units. The proposed framework is a bilevel problem that can be reduced to a one-level problem. Also, it can be solved by the implementation of a simple concept without the need for Karush–Kuhn–Tucker conditions. Moreover, emerging flexible energy sources such as the power to gas technology and demand response program are considered in the proposed model for increasing the wind power dispatch, decreasing the total operation cost of the integrated network as well as reducing the effect of system uncertainties on the total operating cost. Numerical results indicate the applicability and effectiveness of the proposed model under different working conditions.

In this paper, we study the impact of correlation on the bit error rate (BER) and the channel capacity of a free-space optical (FSO) multiple-input-multiple-output (MIMO) system employing optical space shift keying (OSSK) over a fading channel. In order to study a practical correlated channel, we consider the effect of channel correlation due to both small-and large-scale eddies and show that the use of OSSK over correlated FSO channel can lead to an improved system performance with increasing correlation level of upto 0.9. In this work, we first develop an analytical framework for different performance metrics of the OSSK multiple-input single-output system with correlation and then extend our investigation by proposing an asymptotically accurate mathematical framework for MIMO. We also validate all the analytical results using MATLAB simulations. Finally, we develop an experimental setup of FSO with two correlated links to study the throughput and latency of the links at different turbulence levels.

Reliable information about the active loads in the energy system allows for effective and optimized energy management. An important aspect of intelligent energy monitoring system is load disaggregation. The proliferation of direct current (dc) loads has spurred the increasing research interest in extra low voltage (ELV) dc grids. Artificial intelligence, such as deep learning algorithms of stacked recurrent neural network (RNN), improved results on a variety of regression and classification tasks. This paper proposes a 1-D convolutional stacked long short-term memory RNN technique for the bottom-up approach in load disaggregation using single sensor multiple loads ELV dc picogrids. This eliminates the requirement for communication and intelligence on every load in the grid. The proposed technique was applied on two different dc picogrids to test the algorithm's robustness. The proposed technique produced excellent result of over 98% accuracy for smart loads and over 99% accuracy for dumb loads in ELV dc picogrid.

This paper proposed a grid connected solar Photovoltaic (PV) Systems with a new voltage balancing converter suitable for Neutral-Point-Clamped (NPC) Multilevel Inverter (MLI). The switched-capacitors used in the proposed converter is able to balance the DC link capacitor voltage effectively by using proper switching states. The proposed balancing converter can be extended to any higher levels and it can boost the DC input voltage to a higher voltage levels without using any magnetic components. This feature allows the converter to operate with the boosting capability of the input voltage to the desired output voltage while ensuring the self-balancing. In this paper the proposed converter is used for a grid connected solar PV system with NPC multilevel inverter, which is controlled using vector control scheme. The proposed grid connected solar PV system with associated controllers and maximum power point tracking (MPPT) is implemented in Matlab/SimPowerSystem and experimentally validated using dSPACE system and designed converters. The simulation and experimental results show that the proposed topology can effectively balance the DC link voltage, extract maximum power from PV module and inject power to the grid under varying solar irradiances with very good steady state and dynamic performances.

Intelligent Transportation System (ITS) is one of the main systems which have been developed to achieve safe traffic and efficient transportation. It enables the vehicles to establish connections with other road entities and infrastructure\ud units using Vehicle-to-Everything (V2X) communications. As a consequence, all road entities become exposed to either internal or external attacks. Internal attacks cannot be detected by traditional security schemes. In this paper, a recommendation based trust model for V2X communications is proposed to defend against internal attacks. Four types of malicious attacks are analysed. In addition, we conduct various experiments with different percentage of malicious nodes to measure the performance of the proposed model. In comparison with the existing model, the proposed model shows an improvement in the network throughput and the detection rate for all types of considered malicious behaviors. Our model improves the Packet Dropping Rate (PDR) with 36% when the percentage of malicious nodes is around 87.5%.

A simple optic-fiber Fabry-Perot (FP) sensing technique was proposed and experimentally investigated by using polymer material to connect the ends of two singlemode fibers. Four different polymer materials (benzocyclobutene (BCB), UV88 (Relentless, China), Loctite3525 (HenKel, Germany), and NOA68 (Norland, USA)) filling the FP cavity were used to comparatively study the sensing performance of temperature, strain and refractive index. The result shows that the FP sensor with BCB has excellent repeatability with good linear response to temperature in a wide range from room temperature to 250 °C, which is much larger than that of other three materials (2)) and the best repeatability among the four polymer materials.

Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management.

A mid-infrared tunable diode laser absorption spectroscopy (TDLAS) gas sensor based on hollow waveguide (HWG) gas cell for real-time exhaled carbon dioxide (eCO2) detection is reported. A $2.73~\mu \text{m}$ distributed feedback (DFB) laser was used to target a strong CO2 absorption line, and wavelength modulation spectroscopy (WMS) with the second harmonic (WMS- $2f$ ) was used to retrieve the CO2 concentration with high sensitivity. The influence of different parameters, including coupling configuration of HWG, laser-to-HWG and HWG-to-detector coupling alignment on the stability of the HWG sensor is systematically studied. The HWG eCO2 sensor showed a fast response time of 2.7s, detection limit of 17 ppmv, and measurement precision of 20.9 ppmv with a 0.54 s temporal resolution. The eCO2 concentrations changed in breath cycles were measured in real time. The Allan variance indicated that the detection limit can reach 1.7 ppmv, corresponding to a detection sensitivity of $1.3\times 10^{\mathbf {-8}}$ cm−1 Hz−1/2, as the integration time increases to 26 s. This work demonstrates the performance characteristics and merits of HWG eCO2 sensor for exhaled breath analysis and potential detection for other exhaled gases.

In this paper, a robust fault estimation approach is proposed for multi-input and multi-output nonlinear dynamic systems on the basis of back propagation neural networks. The augmented system approach, input-to-state stability theory, linear matrix inequality optimization, and neural network training/learning are integrated so that a robust simultaneous estimate of system states and actuator faults are achieved. The proposed approaches are finally applied to a 4.8 MW wind turbine benchmark system, and the effectiveness is well demonstrated.

Publisher Copyright: CCBY Mobile edge computing (MEC) has been proposed as a promising solution, which enables the content processing at the edges of the network helping to significantly improve the quality of experience (QoE) of end users. In this article, we aim to utilize the MEC facilities integrated with time-varying renewable energy resources for charging/discharging scheduling known as green scheduling of on-move electric vehicles (EVs) in a geographical wide area comprising of multiple charging stations (CSs). In the proposed system, the charging/discharging demands and the contextual information of EVs are first transmitted to nearby edge servers. With instantaneous electricity load/pricing and the availability of renewable energy at nearby CSs collected by aggregators, a weighted social-welfare maximization problem is then solved at the edges using greedy-based algorithms to choose the best CS for the EV's service. From the system point of view, our results reveal that compared to cloud-based scheme, the proposed MEC-assisted EVs scheduling system significantly improves the complexity burden, boosts the satisfaction (QoE) of EVs' drivers by localizing the traffic at nearby CSs, and further helps to efficiently utilize the renewable energy across CSs. Furthermore, our greedy-based algorithm, which utilizes the internal updating heuristics, outperforms some baseline solutions in terms of social welfare and power grid ancillary services.

This paper proposes a guidance scheme to achieve an autonomous precision landing on Mars and proposes a practical fixed-time stabilization theorem to analyze the robustness of the guidance. The proposed guidance is mainly based on the fixed-time stabilization method, and it can achieve the precision landing within a pre-defined time. This property enables the proposed guidance to outperform the finite-time stabilization technique which cannot handle uncertainties well and whose convergence time is dependent on initial states. Compared with the existing fixed-time stabilization theorem, the proposed practical fixed-time stabilization theorem can achieve a shorter convergence time and cope with unknown disturbances. When the Mars landing guidance is designed by this proposed theorem, the upper bound of the landing time and the maximum landing error subject to unknown disturbances can be calculated in advance. Theoretical proofs and Monte Carlo simulation results confirm the effectiveness of the proposed theorem and the proposed guidance. Furthermore, the efficacy of the proposed guidance with thrust limitations is also demonstrated by testing of 50 cases with a range of initial positions and velocities.

We propose an optical power domain non-orthogonal multiple access (OPD-NOMA) scheme for visible light communications. OPD-NOMA superposes user messages in the optical power domain based on a light-emitting diode (LED) array. The maximum driven current for respective circuits is reduced compared to that of conventional NOMA, in which the LED-array-module is driven by a single circuit. OPD-NOMA reduces the gain and bandwidth requirements for the driver circuit of light source. The nonlinear power-current response of LED largely restricts its usable dynamic range and thus the transmit power. In OPD-NOMA, signals with lower powers suffer from reduced nonlinear distortion. The experimental results show that, OPD-NOMA offers improved transmission performance compared to conventional NOMA using the same driver circuit, since it can make a better use of the linear dynamic range of the LED’s power-current response.