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Millimeter wave (mmWave) radar is an attractive solution for non-contact vital sign monitoring due to its characteristics of robustness against noise, interference, and high-range resolutions. However, due to the attenuation of mmWave signal strength with distance, most previous methods require users to be relatively close to the radar (

This study combines comprehensive analyses of design parameters with encapsulation solutions of Direct Copper Bond (DCB) substrates used in high voltage power converter modules, focusing on partial discharge (PD) activity under varying pressures for aerospace applications. A detailed PD investigation on triple junction points as the main culprit of the failure in power electronics was conducted, and the design and material aspects through experimental and simulation analysis were examined. PD initiation and extracted information are presented for three common pad geometries on two types of substrates (AlN and G-10/FR4). The results reveal better performance of the AlN that has higher permittivity, i.e., 20-30% and 10-20% higher PD inception voltage (PDIV) than G-10/FR4 at ambient and low pressures, respectively, and 40-65% lower PDIV at low pressures due to the higher rate of gas ionization compared to the ambient pressure. The study also explores the impact of pad geometric design such as corner smoothness and spacing on electric field intensity, with an introduced pressure- and temperature-dependent ionization model coupled with 3D finite element model to quantify the impact of the pressure on the gas ionization and PD initiation. Furthermore, an extensive experimental and numerical study of PD on liquid and gel encapsulated DBC substrates at ambient and low pressures was also conducted. Two silicone gels (Dowsil $^{\text {TM}}~3$ -4170 and Sylgard $^{\text {TM}}~527$ ) and one refrigerant dielectric liquid (fluorinert FC40) were investigated as encapsulating dielectrics for high voltage applications. The results reveal that different silicone gels, despite similar dielectric properties, can exhibit drastically different PD resistance due to intrinsic bulk properties and self-healing capabilities, outperforming liquid-based encapsulants. This highlights the significance of dielectric fluid and gel insulations characteristics to the performance of HV modules for aerospace applications.

PCB layouts with different polygon shapes and insulation distances were prepared and their surface flashover voltage subjected to different ramping rates were measured at different temperatures and different low gas pressures for emulated high-altitude conditions. The dependence of the surface flashover voltage on effects of gas pressure, surface insulation distance, temperature, and polygon shape was studied. Modulation efforts which include tailoring the local surface conductivity and local topography modification were performed to increase the flashover of PCB, and the related mechanism was studied. The results showed that the voltage ramping rate plays an important role in determining flashover voltage. The flashover voltage is lower when the sample is subjected to a rapid voltage ramping rate than with a slow ramping rate. This phenomenon is more prominent towards ambient pressure. The increase in temperature results in a decrease in flashover voltage at 100 kPa, while at 20 kPa and 10 kPa, the influence of temperature on flashover becomes less significant. Through-holes designed in the PCB have a positive role in increasing flashover voltage at lower pressures. However, at 100 kPa, the holes no longer contribute to any higher flashover voltage. Modification of local surface conductivity has no contribution in increasing the flashover voltage, while a surface coating with a surface conductivity of $10^{-10}$ S dramatically decreases the flashover voltage. The work presented in this paper provides a reference for the design and modification of PCB layouts for use in future aerospace hybrid propulsion systems.

Owing to the rapid development of unmanned aerial vehicle (UAV) technology and various photogrammetric software, UAV photogrammetry projects are becoming increasingly automated. However, marking ground control points (GCPs) in current UAV surveys still generally needs to be manually completed, which brings the problem of inefficiency and human error. Based on the characteristics of UAV photogrammetry, a novel type of circular coded target with its identification and decoding algorithm is proposed to realize an automatic and accurate approach for marking GCPs. UAV survey experiments validate the feasibility of the proposed method, which has comparative advantages in efficiency, robustness, and accuracy over traditional targets. Additionally, we conducted experiments to discuss the effects of projection size and viewing angle, number of coded bits, and environmental conditions on the proposed method. The results show that it can achieve robust identification and accurate positioning even under challenging conditions, and a smaller number of coded bits is recommended for better robustness.

The use of artificial intelligence and machine learning is recognized as the key enabler for 5G mobile networks which would allow service providers to tackle the network complexity and ensure security, reliability and allocation of the necessary resources to their customers in a dynamic, robust and trustworthy way. Dependability of the future generation networks on accurate and timely performance of its artificial intelligence components means that disturbance in the functionality of these components may have negative impact on the entire network. As a result, there is an increasing concern about the vulnerability of intelligent machine learning driven frameworks to adversarial effects. In this study, we evaluate various adversarial example generation attacks against multiple artificial intelligence and machine learning models which can potentially be deployed in future 5G networks. First, we describe multiple use cases for which attacks on machine learning components are conceivable including the models employed and the data used for their training. After that, attack algorithms, their implementations and adjustments to the target models are summarised. Finally, the attacks implemented for the aforementioned use cases are evaluated based on deterioration of the objective functions optimised by the target models.

Landslide risk assessment is an important component of the landslide research field. For the problem of landslide assessment indicators, we utilize the TOPSIS-Entropy method to assess the risk situation of landslide occurrences, which is easy to obtain directly from sensor data. By using the TOPSIS-Entropy method in landslide datasets, the instability margins of landslide risk are obtained, reflecting the current instability probability of the landslide body. For the landslide prediction issue, deep neural networks are used to predict the corresponding landslide instability margins (LIMs). Attention mechanism-based (Attn) temporal convolutional networks (TCN) connected with recurrent neural network (RNN) models for landslide risk prediction are proposed, including TCN-Attn-RNN and RNN-Attn-TCN, which both use an encoder–decoder architecture. The encoder in the first model uses the temporal convolutional network (TCN), and the decoder uses a neural network with an RNN architecture, including long short-term memory (LSTM) networks, gated recurrent units (GRUs), and their derivative algorithms. In the second model, the encoder uses a neural network with an RNN architecture, and the decoder uses a TCN. Combining the TOPSIS-Entropy method with TCN-Attn-RNN and RNN-Attn-TCN, reliable prediction models of landslide risk are proposed. By building a landslide simulation platform, we obtained landslide data. Compared to their counterparts, the proposed prediction models of landslide risk instability margins have better predictive effects.

In this paper, we proposed a cross-shaped interconnected receiver–transmitter (RT) metasurface (MS) that was employed for a high-aperture-efficiency, high-gain, circularly polarized Fabry–Pérot (FP) antenna. The RT-MS unit cell consists of upper-layer cross-shaped patch and lower-layer squared patch that are connected by two metal-probes that cross the sandwiched middle metal-plane. The bottom patch receives electromagnetic wave, functioning as receiver, and transfers it to the top cross-shaped patch passing through the two mental probes. Through tuning the relative locations of the two probes, equal amplitude and 90° phase difference can be obtained, thereby achieving linear to circular polarization conversion performance, indicating that the MS has the ability of tuning transmission coefficients independently. Meanwhile, the MS is designed to present high reflectivity that high-gain property is obtained when forming a FP cavity. Then, an off-center coaxial patch antenna is applied as feeder due to its stable radiation performance. To further enhance the performance of the source antenna, an AMC structure is adopted to improving the radiation gain, efficiency and radiation directivity by suppressing surface wave loss. Meanwhile, the profile was also reduced. The linearly polarized wave emitting from the feeder multi-reflected in the FP cavity formed by MS superstrate and AMC structure, and then transmit it in phase to the space, thus a high-gain and CP radiation is achieved as final. The measurements demonstrate that the proposed RT-MS-based antenna perform a maximum gain of 18.7dBic at 9.4GHz with aperture efficiency of 76.7%, and a circular polarization bandwidth within 9.2-9.7GHz, while, the antenna owns a compact size of only $2.85\lambda _{0}\times 2.85\lambda _{0}$ . Thus, a novel RT-MS-based FP antenna exhibit a good radiation performance that can be a candidate in applying to communication and point-to-point links.

This study designed a sub-pixel precision edge detecting algorithm to enhance contour smoothness. First, the coordinate value of RGB pixel is projected on the space line of R=G=B to obtain gray image. Then, pixel edges are located using a Canny detector. Next, the edge width is thinned to a single pixel using a morphological thinning operation. Finally, sub-pixel-level smooth contours are extracted by interpolation. In this sub-pixel level contour extraction process, a Single-Pixel-Multi-Point interpolation method was developed to enhance edge smoothness and obtain high precision in edge estimation. This method divides edges in a $3\times 3$ pixels block into nine arrangement modes. According to the arrangement of the eight neighborhoods of a centered edge pixel, different locations of interpolated sub-pixel points are calculated by interpolation with Bezier curves. For symmetrically arranged linear edge pixels, this method can be used to determine the exact contour. Experimental results showed that the proposed algorithm can improve the smoothness of image edge contour. As the curvature of the edge increases, the maximum systematic error will increase. For the edge pixel centered in the $3\times 3$ pixels block and two pixels located at the corner of one side, the max systematic error is 0.5 pixel. For two edge pixels aligned in a single row or column with one located at a corner, the max systematic error is 0.25 pixel.

Based on the receding horizon principle, cost function of nonlinear model predictive control (NMPC) becomes an accumulating type to extend prediction horizon. A nonlinear model predictive speed control (NMPSC) with prediction horizon self-tuning method is proposed in this paper, and applied into a permanent magnet synchronous motor (PMSM) rotor position control system with inner-loop of speed. The prediction horizon is improved as a positive integral discrete-time variable which needs to be adjusted according to operating states in each sampling period. Control performances such as rotor position and speed integral of time-weighted absolute value of errors (ITAEs), delay time and calculation burden are compared with the conventional control strategy, and advantages including ITAEs, delay time, weighting factor sensitivities and servo stiffness are obtained. The effectiveness and correctness are verified by simulation and experimental results.

Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation. In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics. On this basis, a scenario generation method for multiple wind farms is proposed. For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCH-t) model is proposed to capture the time-series characteristics of wind power generation. For the dependence structure, a time-varying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms. Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated. The effectiveness of the scenarios is evaluated in 3 aspects. The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences.

Structural optimization has always been the main solution for increasing the stability of flexible screens. The bending method used to test a flexible display screen is an important factor in assessing the functionality and practicability of the electronic device. The bending mode of the display screen impacts the screen service life. In this study, 2D simulation models with different bending radii, bending directions, and bending times were constructed in ABAQUS and compared to determine the effects on the flexible screen from changing the stress value, the location of the stress concentration area and the deformation state of the screen under U- and water-drop-shaped bending. The simulation results show that U-shaped bending severely strains the junction between the bent and unbent areas of a model, whereas water-drop-shaped bending can avoid such problems. In addition, the results of multiple bending experiments were used to determine an appropriate reserved length of r=2.1~2.4 mm for a model under water-drop-shaped bending.

Electric power steering system (EPS) with traditional power supply can't provide enough power for heavy-duty vehicle to turn at low speed. To solve the problem, a novel EPS with hybrid power supply was constructed. In this paper, firstly, mathematical model of the EPS with hybrid power supply was established including model of hybrid power supply system and basic model of EPS. Then the power mode recognition was obtained according to the certain threshold of steering resistance torque, state of charge (SOC) of super-capacitor and output voltage of the vehicle power supply, and the corresponding finite automaton model was built. The EPS system with hybrid power supply was regarded as a hybrid system due to continuous event in a single mode and discrete event during mode switching process, thus the multi-mode switching control strategy was proposed. Taken the different characteristics of hybrid power supply in different power modes into account, the corresponding local controllers were designed which were consisted of fuzzy-PID controller, active disturbance rejection controller (ADRC) and sliding mode controller. Besides, the fuzzy supervisory controller was also designed to ensure the stability of the hybrid system during mode switching process. The simulation results show that the local controllers can achieve fast and accurate track of target current in each power mode, and the fuzzy supervisor can effectively ensure the stability of the switching process, which demonstrates the feasibility of the control strategy.

Experiment is an important method to study the thermal and flow performance. Nevertheless, only limited information, such as local temperature and pressure, can be obtained through detection machines. Based on deep learning, a general, useful and flexible reconstruction model is proposed to reconstruct global flow field in two-dimension domain with the limited information exploiting from experiments as input information. Besides, the corresponding performance parameters Nu and $f $ are extracted from generated fields. To validate the feasible, stability and accuracy of the framework, the micro channels with nano fluids are taken as validation case. First of all, the comparison between reconstructed fields and original fields are presented. It shows that reconstructed fields are almost the same as original ones and extracted performance parameters also have high precision. Next, the effects of train size, measuring uncertainty and measuring layouts are considered in sensitivity analysis. Higher train size and smaller measuring uncertainty are advantageous to the reconstruction results. Measuring layout has little influence on reconstruction performance and at least 7 local measuring points are enough.

In fifth generation and beyond Internet of things (5G-IoT), the buffer-aided relaying network provide an efficient way to maintain the coverage area and enhance the edge user's transmission experience. However, the effect of non-ideal factor to the buffer-aided relaying network, such as the signaling overhead, have not been investigated. In this paper, we propose a practical hybrid bit-level network coding (BNC) for the buffer-aided relaying network while taking the unreliable transmission, limited relay-buffer size and signaling overhead into consideration. Afterward, we derive its concise closed-form expressions and the associated upper bound expressions for the throughput, queuing delay and overflow probability. Monte-Carlo simulation proves the validness of our analysis. Simulation results also demonstrate that compared to existing studies, our proposal can enhance the system energy efficiency (EE) performance, and buffer size has a positive effect on the EE performance.

Aiming at the low frequency oscillation and voltage drop caused by the wind-thermal bundled system transmitted to the load center across AC and DC lines, an adaptive sliding mode gain control method based on the structure decentralized control theory is proposed. On the basis of this method, a rotor-side adaptive terminal sliding mode controller for doubly-fed induction generator based on synchronous rotating coordinate system is designed. Based on RTDS, a real-time simulation platform for transient stability control of “wind-thermal bundled” transmission system was developed, and real-time simulation experiments were performed. Experimental results prove that the proposed method in this paper can effectively suppresses the frequency and voltage fluctuations of the system, significantly improve the transient stability of the system and enhance the robustness of the system during grid faults.

The benign and malignant (BM) classification of breast masses based on mammography is a key step in the diagnosis of early breast cancer and an effective way to improve the survival rate of patients. Nevertheless, due to the differences in size, shape and texture of breast masses and the visual similarity between masses of the same category, it is difficult to obtain a robust classification model using conventional deep learning methods. To address this problem, we proposed a Multi-Tasking U-shaped Network (MT-UNet), which contains three key ideas: 1) the U-shaped classification architecture constructed can well adapt to the heterogeneity of breast masses; 2) the combination of the proposed truncated normalization method and adaptive histogram equalization method can enhance the contrast of image; 3) training with label smoothing method can alleviate the problem of convergence difficulty caused by insufficient training data. The performance of the proposed scheme is evaluated on the public dataset of DDSM and INbreast. On the DDSM dataset, the Area Under Curve (AUC) and accuracy (ACC) reached 0.9963 and 0.9817, respectively. On the INbreast dataset, the AUC and ACC reached 0.9767 and 0.9391, respectively. Experimental results show that the proposed method can obtain a competitive performance.

Large-scale wind farms (WFs) generally consist of hundreds of wind turbines (WTs), and the WFs simulation model construction would be complex and even impossible if we develop each individual WT in detail. Therefore, the WFs equivalent simulation model with required accuracy is essential to be developed to explore the WFs operation characteristics. This article proposes an equivalent method for large-scale WFs using incremental clustering and key parameters optimization. Firstly, to acquire more comprehensive and distinguishable representations of WTs operation characteristics, the time series of WT active power, reactive power, voltage and current are selected as the multi-view clustering indicator (CI). Then, considering the computer memory pressures encountered by traditional clustering algorithms in dealing with large-scale WFs, a novel clustering algorithm namely multi-view incremental transfer fuzzy C-means (MVIT-FCM) is proposed, and this algorithm can process the WTs clustering problems without requiring to consider the scale of the WFs. Finally, to further increase the equivalent accuracy of the WFs equivalent simulation model, key parameters in the equivalent model are found using Sobol' criterion and then optimized using the designed Q-learning based non-dominated sorting genetic algorithm II (NSGA-II). To verify the effectiveness of the proposed method, the modified WFs system in China is utilized for case study, and the performance of the proposed model is compared with several state-of-the-art models. Simulation results show that the equivalent accuracy of the proposed model is higher when comparing with other models. Also, the proposed model has the advantage of processing the WFs equivalent problems with any scales.

In this paper, we investigate a relay system with multi-pair single-antenna devices, where massive antenna array (also known as massive multiple-input-multiple-output (MIMO) technology) is deployed at the relay with low-complexity maximum-ratio combining/maximum-ratio transmission precoding scheme. By combining massive MIMO and relay cooperation, the performance of relay-assisted communication systems is predicted to be dramatically improved in many areas, e.g., spectral efficiency, energy efficiency, inter-user interference cancelation, and so on, by exploring the abundant spatial degree of freedom of massive MIMO. However, a large number of antennas employed at relay node inevitably leads to antenna correlation because of small antenna spacing subjected to the limited size of the relay node, which may affect the system performance and has not yet been compressively studied. Thus, we focus on analyzing the effect of antenna correlation on the asymptotic performance of system ergodic rate by using a general channel correlation model. First, by means of the deterministic equivalent technology, the analytical expression of the ergodic rate with arbitrary channel correlations is derived, which presents the quantitative relations among system parameters, i.e., the numbers of relay antennas and device-pairs, the channel spatial correlations at the relay's receiver side and the transmitter side, and the transmit powers of the source devices and the relay. By using a general scaling model for system parameters with respect to the relay antenna number, the asymptotic performance of the ergodic rate under spatially correlated channel and the corresponding scaling laws of the system parameters are obtained, which provides useful guidelines to trade off the transmit power and the ergodic rate as well as the number of the served device-pairs. It is further revealed that the influence degrees of the antenna correlations at the relay's receiver and the transmitter on the ergodic rate is nearly the same. Finally, the analytical conclusions are justified through the numerical simulations.

In this paper, a novel method to design a multifunctional metasurface (MS) is proposed and demonstrated. Inspired by shared aperture technology, the proposed MS is integrated with both radiation and low backward scattering performance. Each MS unit cell consists of two parts, polarization conversion surface part and radiation part. Different from traditional MS design, the proposed two parts are integrated together, and thus, bi-functions are realized in one surface. To acquire radiation performance, the feeding structure is added to each radiation in part by introducing an antenna design concept. The peak gain of the MS attains 18.1 dBi and main-beam patterns are all along the normal direction. To obtain low backward scattering performance, the polarization of an incident electromagnetic wave is transformed by the reflected MS. The reflected wave will have a 180° phase difference through rotating the polarization conversion surface structure. Thus, two different types of MS units are arranged in chessboard array to achieve reflection suppression. The radar cross section reduction band is from 3.0 to 4.3 GHz with peak values of 7.2 and 16.8 dB. Both the simulated and experimental results prove that our method offers a feasible strategy for the multifunctional MS design which can lead to many exciting applications in different frequency domains.

Numerical simulation of three-layer layered electromagnetic waves is key problem for nondestructive testing of ground penetrating radar (GPR) pavement. In this paper, the difference iterative scheme of three-dimensional first-order symplectic partitioned Rung-Kutta is derived, which is applied to pavement detection of ground penetrating radar by using Higdon ABC boundary condition. Incident waves are considered as line sources. The accuracy and efficiency of the proposed algorithm are verified by the traditional 3D-FDTD algorithm. The results indicate that the accuracy and efficiency between the two methods are consistent. Unlike the traditional 3D-FDTD algorithm, the CPU time of the proposed method is reduced by 30%. The diseases location of the pavement structure is directly reflected by the numerical simulation result of the proposed method. This provides a three-dimensional symplectic partitioned Rung-Kutta algorithm, which can be applied to the forward simulation of GPR. It provides a three-dimensional symplectic partitioned Rung-Kutta algorithm, which can be applied to the forward simulation of GPR. The accurate electromagnetic response information of the target can be obtained by the proposed method.

The deep convolutional neural network has strong representative ability, which can learn latent information repeatedly from signal samples and improve the accuracy of automatic modulation classification (AMC). In this paper, a novel compressive convolutional neural network (CCNN) is proposed for AMC, where different constellation images, i.e., regular constellation images (RCs) and contrast enhanced grid constellation images (CGCs), are generated as network inputs from received signals. Moreover, a compressive loss constraint is proposed to train the CCNN, which aims at capturing high-dimensional features for modulation classification. Additionally, CCNN utilizes intra-class compactness and inter-class separability to enhance the classification and robustness performance for the different orders of modulations. The simulation results demonstrate that CCNN displays superior classification and robustness performance than existing AMC methods.

In Global Navigation Satellite System (GNSS) meteorology, improved understanding of weighted mean temperature (Tm) variation and estimation is imperative because Tm is crucial to the quantification of precipitable water, which is an important parameter in numerical weather prediction systems. The commonly used methods for determining Tm use the relationship between surface temperature Ts and Tm, and use blind models developed from atmospheric reanalysis products. Since the Ts recorded in the sensors or the sample points of the reanalysis data sets are usually not at the same height with the GNSS station, it is always necessary to vertically adjust the estimated Tm, which requires Tm lapse rate. In this study, the globally distributed radiosonde data were collected to calculate the Tm lapse rate, and the relationship between the Tm lapse rate and location, seasonal changes was shown in detail. To verify the importance of the Tm lapse rate in Tm calculation, the Tm lapse rate obtained in this study was applied in the Global pressure and temperature 2 wet (GPT2w) model. Numerical results show that the root mean square error (RMSE) of Tm estimated with the consideration of Tm lapse rate is improved by 0.64 K in average. Moreover, the impact of Tm on GNSS-PWV was analyzed, showing that the improvement of RMSEPWV and RMSEPWV/PWV are 0.05 mm and 0.23%, respectively.

Different from traditional communication systems, information-centric Internet of things (IC-IoT) as a novel smart paradigm needs to guarantee end-to-end connectivity for rapidly growing smart devices. How to meet the demands of massive connection has become a key problem for IC-IoT. Sparse code multiple access (SCMA) as a code-domain non-orthogonal multiple access (NOMA) technique has been intensively investigated. With SCMA, each user employs different sub-carrier frequencies for transmission, and different users can share the same sub-carrier frequency via superposition coding. The spectrum efficiency is thus improved. However, designing large-scale codebook sets is still an open problem for SCMA. In this paper, a new lattice-based codebook design method is proposed via mother constellation optimization. First, the optimization problem of the mother constellation is formulated. Through analysis, the problem can be converted into two sub-problems. Accordingly, we first use the lattice theory to find a constellation containing infinite points with large coding gain. After that, we search for a boundary that contains a set of points via spherical packing. Such an approach enables us to obtain a real constellation satisfying a power saving criterion. Secondly, the mother constellation with large power variance is obtained from the real multi-dimensional constellation. Finally, lattice-based codebooks are generated by combining the mother constellation and the mapping matrices with constellation rotation. Simulations demonstrate that the designed codebooks have improved bit error rate (BER) performance with large codebook size, especially at high signal to noise ratio (SNR).

In this paper, we investigate the effects of residual hardware impairments (RHIs), channels estimation errors (CEEs) and imperfect successive interference cancellation (ipSIC) on the cooperative non-orthogonal multiple access (NOMA) system over Nakagami-m channels, where the amplify-and-forward (AF) relay can harvest energy from the source. The exact expressions for outage probability and ergodic sum rate are derived in closed-form. In addition, the asymptotic outage analyses in the high signal-to-noise (SNR) regime are carried out. The results show that the outage probability exists an error floor due to the existence of CEEs and compared with RHIs, CEEs have a more serious impact on the system outage performance. The close simulation results of Monte Carlo verify the accuracy of our theoretical derivation. Finally, the performance of energy efficiency is examined with RHIs, CEEs and ipSIC.

We propose a method of computational temporal ghost imaging over a single optical fiber using simple optical intensity detection instead of coherent detection. The transfer function of a temporal ghost imaging system over a single optical fiber is derived, which is a function of the total fiber dispersion and the power density spectrum of the light source. In the simulations, we find that the bandwidth of the transfer function will decrease rapidly along with the increase of fiber length or light source bandwidth, resulting in a significantly distorted ghost image. To improve the image quality, we first calculate the transfer function by approximating the optical spectrum of the light source based on intensity detection as its power density spectrum. Then, we use an optimized Fourier deconvolution to compute a high-quality image by deconvoluting the measured transfer function from the spectrum of the distorted image. Our experiments achieve single-arm ghost images of a 5-Gb/s nonreturn-to-zero pulse object over a 200-km fiber, which have almost the same quality as the two-arm ones.

The utilization rate of power equipment plays a decisive role in the economic operation of power utilities. By determining the reasonable range of life cycle utilization rate of the distribution network equipment, it is of great significance to the management of the distribution network equipment. In this paper, the reasonable range of the life cycle utilization rate of distribution network equipment is determined. The life cycle utilization rate of distribution network equipment depends on the burden rate, load rate, and life expectancy rate, whose reasonable values are analyzed and exemplified, respectively. The optimal model of the burden rate in different conditions is established. The different load characteristic curves are also given by sorting out the load data. The calculation method of the life expectance rate is presented in this paper. The reasonable range of the life cycle utilization rate is finally obtained by defining the boundary condition of its composition. By setting the reasonable range of life cycle utilization rate of the distribution network equipment, power utilities can improve efficiency.

The radiation performance of orbital angular momentum (OAM) vortex waves (OAMVWs) generated by reflective metasurface is comprehensively investigated. According to different incident sources, the issue is classified into two cases, namely, the plane-wave incidence and the focused incidence. In both cases, the radiation performance deteriorates with the increasing of topological charge l. Moreover, the largest l that can be generated is limited by the scale of the metasurface. In addition, a detailed analysis reveals that phase quantization affects the lobe levels of OAMVWs but has no influence on the divergence angle of the main lobe. Nevertheless, for the case of plane-wave incidence, 1-bit phase quantization method is not able to generate OAMVWs in normal direction and will cause grating lobe when the beam scans. Meanwhile, the grating lobe carries OAM with opposite l against the main lobe. Finally, a focused-feed metasurface based on 1-bit phase quantization is simulated and measured. The simulation and measurement results show that OAMVW with topological charge l = 1 is successfully generated and detected, validating the effectiveness of phase quantization method in OAMVW generation. It is worth to point out that phase quantization is highly promising in electronic-controlled reconfigurable OAMVW generation.

Electromagnetic slip coupling (EMSC), like other contactless transmission devices, has lots of advantages over contact transmissions such as clutch function, continuous speed regulation, and no mechanical wear. Nevertheless, the EMSC has inherent slip energy especially under the large-slip condition, which leads to low efficiency. A novel energy-regenerative EMSC was developed including wound-type EMSC and the energy-regenerative apparatus for slip energy recovery. To improve recovery efficiency of the EMSC, the field-oriented control strategy was proposed. In this case, the mathematical model of EMSC under synchronously rotating coordinate system was constructed and verified by experiment. Since direct-axis component and quadrature-axis component of current in three-phase windings of the EMSC are coupled, the feed forward decoupling control approach was employed. Space vector pulse width modulation technique was utilized to eliminate harmonic components of current in three-phase windings. Simulations and experiments were carried out to validate the proposed control strategy. The results showed that the voltage and current in three-phase windings were almost in-phase, the current only contained few of harmonic components, and slip energy of the EMSC decreased by 82.5%. In conclusion, the field-oriented control strategy is conducive to recycle the slip energy of the EMSC efficiently.

This paper proposes an optimal coordinated method for electric vehicles (EVs) participating in frequency regulation (FR) under different power system operation states (PSOSs). In the proposed methodology, the FR power of EVs and generators is coordinated with different optimization objectives for power system secure and economic operations. When a power system operates in normal state, the minimum FR cost is used as an optimization objective considering the battery degradation cost. In the abnormal state, the minimum frequency restoring time is considered in the optimization objective. Based on the optimized results in different scenarios, the output power coordinated control rule between EVs and generators is drawn. Simulations on an interconnected two-area power system have validated the superiority of the proposed optimized coordinated control strategy.

This paper investigates the physical-layer security of uplink non-orthogonal multiple access (NOMA) in the cellular Internet of Things (IoT) with invoking stochastic geometry. Poisson cluster process-based model is applied to characterize the NOMA uplink transmission scenario, where IoT terminals are located around the serving base station. Considering the severe interference brought by a large number of IoT terminals, inter-cell interference is also taken into account. To enhance the physical-layer security of uplink NOMA transmission with limited overhead increment at IoT terminals, the base stations not only receive the signals from IoT terminals but also keep emitting jamming signals all the time to degrade the performance of any potential eavesdroppers. In order to characterize the physical-layer security performances, we derive expressions of coverage probability and secrecy outage probability. To obtain further insights, network-wide secrecy throughput (NST) and network-wide secrecy energy efficiency (NSEE) are analyzed. It is demonstrated that the security performance can be improved by the proposed full-duplex base station jamming scheme at the cost of reliable performance. The analytical and simulation results show the effects of BS intensity and jamming power on network performances. We also verify that NST and NSEE can be significantly enhanced by our proposed scheme. Using these results, the security of confidential information transmitted by low-complexity IoT terminals can be protected from overhearing.

With the explosive growth of smart devices and development of wireless technology, numerous new applications such as Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), autonomous driving and intelligent manufactory enter our daily life and put stringent requirements on the current communications technologies. Boosted by multimedia applications and Internetof-Things (IoT), immersive communications is considered to bring a novel vision on the development of advanced communications systems and networks. Nevertheless, to fully explore the immersive experience and applications, ultrareliable, low latency and high data rate communications systems are required. However, the conventional access network can hardly accommodate such stringent requirements, due to limited capacity and long latency on the backhaul links. Novel approaches that bring various network functions and contents to the network edge, i.e., mobile edge computing and caching, are promising to tackle the aforementioned challenges.

A high-linearity directly-modulated analog photonic link (APL) with simultaneous suppression of the even-order intermodulation distortion, third-order intermodulation distortion (IMD3), and cross-modulation distortion (XMD) is proposed and experimentally demonstrated by the corporation of push-pull structure and an adaptive compensation algorithm. In the proposed directly-modulated APL system, the push-pull structure is introduced to effectively eliminate all the even-order nonlinear distortions, including the second-order intermodulation distortion and the second-order harmonic distortions. To further simultaneously suppress the IMD3 and XMD, an adaptive compensation algorithm is designed and adopted at the receiver. The experimental results show that the second-order spurious-free dynamic range and the third-order spurious-free dynamic range is improved by 19.8 and 12.4 dB, respectively, corresponding to 7.15% error vector magnitude performance improvement for a 120 Mb/s 64QAM-OFDM RF signal transmission when the input RF power is 13 dBm.

In this paper, the effective rate of multiple-input single-output (MISO) systems over independent and identically (i. i. d.) distributed κ-μ shadowed fading channels is studied under delay constraints. We derive an analytical expression for the effective rate of MISO systems over κ-μ shadowed fading channels. The approximate analysis method is further invoked to solve the infinite series problem. Based on the moment matching method, a closed-form expression for the effective rate is obtained. In addition, to capture insights into the effects of the model and fading parameters on the effective rate of the systems, the asymptotic effective rates in the high- and low-signal-to-noise ratio (SNR) regimes are obtained. The analytical results are compared with Monte Carlo simulations, which validate the correctness of the theoretical analysis.

The explosive growth of mobile date traffic and ubiquitous mobile services cause an high energy consumption in mobile devices with limited energy supplies, which has become a bottleneck for deploying device-to-device (D2D) communication. Simultaneous wireless information and power transfer (SWIPT), which enables mobile devices to harvest energy from the radio frequency signals, has emerged as a promising solution to improve the energy efficiency (EE) performance. In this paper, we address joint power control and spectrum resource allocation problem in SWIPT-based energy-harvesting D2D underlay networks. First, we formulate joint optimization problem as a 2-D matching between D2D pairs and cellular user equipments (CUEs), and propose a preference establishment algorithm based on Dinkelbach method and Lagrange dual decomposition. Second, we propose an energy-efficient stable matching algorithm by exploring the Gale-Shapley algorithm, which is able to maximize the EE performance of D2D pairs and the amount of energy harvested by CUEs simultaneously. Third, we provide in-depth theoretical analysis of the proposed matching algorithm in terms of stability, optimality, and complexity. Simulation results demonstrate that the proposed algorithm can bring significant EE performance gains compared with some heuristic algorithms.