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A frequency-hopping optical stealth communication scheme based on a supercontinuum spectrum laser source is proposed and demonstrated in a proof-of-principle experiment. In this system, the frequency bands of the wavelength selective switch (WSS) turn off in the public channel and the WSS select in the stealth channel are consistent. Hence, the supercontinuum spectrum signal light can be embedded in the spectral gap of different wavelength-division multiplexed (WDM) signals to achieve the effect of concealment. By selecting different frequency bands to access the public channel at different times based on a WSS, providing huge flexibility and additional security. Attributing to the narrower frequency band occupied by the public signal, the colossal convenience of the frequency hopping of the stealth signal carrier can be guaranteed. In order to measure the security of the proposed scheme, the physical layer security of the system using the interception probability as an index is investigated.

Underwater Wireless Sensor Networks (UWSNs) have emerged as a critical component within underwater distributed perception applications. Due to the external factors, including ocean currents and undulating seabed topography, the UWSNs topology that changes dynamically over time cannot ensure optimal service performance. This paper proposes a balancing energy consumption and localization through coverage (BECLC), which adjusts the position of drifting underwater nodes through improved virtual forces, and achieves improvements in node energy consumption as well as localization accuracy. Firstly, the UWSNs coverage problem is modeled as a virtual force problem involving underwater nodes, underwater relay nodes, and obstacles, which can guide densely or sparsely dispersed underwater nodes to suitable locations under the action of joint virtual forces. Next, considering that the underwater relay node needs to consume more energy in the process of forwarding more data, the non-uniform deployment combined with hierarchy routing is proposed to reduce the coverage holes caused by node energy exhaustion. Finally, with regards to the drifted coordinates and noisy ranging of uncertain measurements, the theoretical localization performance of underwater target is evaluated with use of Cramer-rao lower bound as a benchmark. The experimental results show that proposed method not only enhances the coverage rates, but also prolongs the network surviving time and improves the localization accuracy in UWSNs.

This work investigates and analyzes collaborative beamforming for a Swarm-enabled distributed sensing project. It investigates the use of a three-dimensional, randomly populated, and uniformly-distributed array in three configurations: spherical, cylindrical, and cubical. These topologies uniquely act like a practical bound to contain the elements in swarm-type applications. In addition, these topologies provide mathematical simplicity toward understanding the fundamental research problem of both surface and underwater swarm-based UUV sensor networks, and their constraints to implementing a physical system in a volumetric setting. Therefore, statistical, ensemble, mean-valued average beampatterns scanned at the meridian elevation plane are analyzed in closed form using a large population of one million elements densely populated amongst geometrical bounds. This large density profile applies the law of large numbers in which numerical beampatterns converge to their expected (mean) patterns. Faithful agreement of the solution is shown to validate the distributed array pattern behavior. Finally, additional simulations are provided in this work using a cylindrical manifold that comprises ten, fifty, and one hundred isotropic radiators to determine the feasibility of a small element population.

With the development of artificial intelligence technology, time series classification has attracted greater attention. Various methods have been considered for using deep learning models to perform the task. Training such models, however, requires a large amount of high-quality labeled samples, which may not be available due to the expensive cost of collection. Therefore, we proposed a novel framework named Convolutional Neural Network based Stepwise Improving Generative Adversarial Network (SIGAN-CNN) to solve this problem by generating more samples from the original distribution. We designed the structure of the generator and discriminator of SIGAN to be suitable for time series data. We also explored a method for extracting trend information from time series data to obtain trend samples for stepwise training. Therefore, the generator can fit the original time series data with subtle variations. More importantly, SIGAN can improve the diversity of the generated samples and the stability of the training process to achieve a higher quality of the generated samples. The generated samples are then combined with CNN based time series data classification methods to improve the classification performance of time series data. Especially, the combination of SIGAN and Multi-scale Attention Convolutional Neural Network (MACNN) is suitable for this task. We conducted a comprehensive evaluation of 8 standard datasets from various domains. The results demonstrate that SIGAN-MACNN achieves the best performance and outperforms the other state-of-the-art methods by a large margin. Therefore, SIGAN-MACNN offers an effective solution for addressing the time series classification task of small sample size.

Ship detection holds a pivotal position in intelligent maritime transportation systems, ensuring the stable development of maritime engineering. However, the complex marine environment and practical deployment requirements pose numerous challenges to ship detection tasks. To address these limitations, this paper proposes a robust and real-time ship object detection method based on enhanced CNN (RRSD). Firstly, an efficient feature extraction module (EFEM) is proposed, which introduces a cross-stage stacked enhanced multi-head self-attention to reduce the computational complexity of the model while enhancing the network’s feature extraction capability. Next, a feature fusion network (FFM) is introduced to enhance the feature representation capability of small objects by effectively aggregating features of different scales in the backbone. To focus on the attention regions in the image, designing a lightweight enhanced attention module (EAM) to improve the model’s inference speed and classification ability. Additionally, by utilizing data augmentation techniques without any additional cost, the overall performance of the model is intuitively and efficiently improved. The proposed method is extensively experimented on a self-built ship detection dataset (SDD2024) through qualitative and quantitative experiments. The experimental results indicate that RRSD achieves a detection performance of 88.01mAP50 (mean average precision at an intersection over unionof 50) and 7.8ms, significantly outperforming object detection models of the same scale, and achieving a good trade-off between detection accuracy and speed. Furthermore, robustness experiments and embedded deployment tests demonstrate its good detection accuracy, fast speed, and satisfactory robustness in ship detection tasks, which basically meet the practical requirements of deployment and implementation.

As a key sensor, radar plays an important role in obtaining war information. However, radar will be affected by the deteriorating electromagnetic environment on the battlefield. Therefore, it is necessary to carry out electromagnetic interference effect experiments to improve the anti-interference ability. In the electromagnetic interference experiment of the radar TR component, the signal output from the signal generator passes through the power amplifier, the transmitting antenna and the receiving antenna, and then is applied to the TR component. In the experiment, to obtain a certain size of interference signal acting on the TR component, it is often necessary to manually adjust the output signal strength of the signal generator repeatedly through human experience. The experimental process is complicated, and the experimental error is large. Therefore, it will make the experiment more convenient and accurate to achieve the desired interference signal size on the TR component by accurately predicting the output signal strength value of the signal generator, which has important practical significance. This paper proposes an IGWO-SVR based signal generator output signal strength prediction model, which includes Improved Grey Wolf Optimizer (IGWO) and Support Vector Regression (SVR) algorithms. IGWO is a new swarm optimization algorithm proposed in this paper. By improving the convergence factor a and the final position of $\omega $ wolf, IGWO solves the problems that the traditional GWO algorithm easily falls into local optimum and the convergence speed is slow. IGWO is used to optimize two hyperparameters of SVR (penalty coefficient C and kernel parameter $\gamma $ ). SVR is used to predict the output signal strength value of the signal generator. To prove the validity of the IGWO-SVR, comparison experiments are made between the IGWO-SVR and 20 other models. The real data obtained from the experiments of electromagnetic interference effect by irradiation method are selected as the experimental data. Mean Absolute Error (MAE), Mean Squared Error (MSE), and Fitting Degree R Squared $(R^{2})$ are used to evaluate the overall performance of the models. Through comparative experiments, the MAE of the IGWO-SVR model is 1.1481, MSE is 2.6679, $R^{2}$ is 0.9430, and its performance in various evaluation indexes is better than other models.

To validate gravity recovery and climate experiment (GRACE)/ GRACE follow-on (GRACE-FO) gravity solutions, we compare satellite altimetry observations of Caspian Sea level (CSL) change with CSL estimates from satellite gravity from April 2002 to December 2020. We use GRACE/GRACE-FO Release 6 GSM fields [spherical harmonics (SH)] from the three processing centers [Center for Space Research (CSR), Jet Propulsion Laboratory (JPL), and Geoscience Research Center (GFZ)] and three mascon solutions from CSR, JPL, and Goddard Space Flight Center (GSFC). CSL change is a regional scale signal that should be reasonably well resolved by satellite gravity measurements, but spatial leakage and other corrections are still required. We computed an average of smoothed SH solutions and those that are both smoothed and decorrelation filtered (to remove north–south stripe noise). Averaging mitigates attenuating effects of decorrelation filtering on the north–south oriented Caspian Sea Signal. After spatial leakage, terrestrial water storage, and steric corrections, most GRACE/GRACE-FO CSL estimates agree remarkably well with the altimetry series over a range of time scales as measured by trend and seasonal components and at other frequencies. The linear CSL trend from altimetry is −7.55 ± 0.17 cm/yr, while GRACE/GRACE-FO values range from −7.30 ± 0.17 to −8.66 ± 0.20 cm/yr. Annual amplitudes from altimetry are (17.75 ± 1.28 cm) with GRACE/GRACE-FO values in the range 17.05 ± 1.49 to 19.16 ± 1.55 cm, with good phase agreement. The GSFC mascon solution shows substantially smaller annual amplitude (11.62 ± 1.04 cm) than others. We found no bias between GRACE and GRACE-FO, but GRACE-FO shows larger root-mean-square differences from altimetry. Among the three standard SH solutions, those from CSR show the best agreement with altimetry.

To address challenges such as missed detection and low recognition rates in multi-target detection within coal mining excavation scenes, attributed to factors like low illumination, high dust and fog, multi-target occlusion and large target scale spans, we propose a multi-target detection method based on LDSI-YOLOv8 specifically designed for such environments. Firstly, the method improves image clarity and reduces the difficulty of effective feature extraction through restricted histogram equalization. Moreover, by using depth-wise convolutional layer with large kernels and dilation-wise residual module with a two-step method, it enhances the extraction and fusion abilities of multi-scale target features. In addition, the relationship between feature maps is utilized to recall occluded features, which enhances the detection of occluded targets. Finally, the idea of fusing auxiliary borders and focusing on the shape and scale of the bounding box makes the bounding box regression faster and more accurate, further enhancing the detection ability for multi-targets. The proposed network is trained and evaluated on a dataset of excavation scenes derived from real mining production videos. The experimental results demonstrate that the proposed LDSI-YOLOv8 detection algorithm is capable of overcoming harsh environments and improving the detection accuracy of occluded and multi-scale targets. With an average detection accuracy of 91.4%, which is 4.3% higher than the original YOLOv8 algorithm, it reduces the number of parameters by 12.2%. Furthermore, it achieves a detection speed of 88.2 FPS, meeting the requirements of real-time detection.

Intramedullary nailing is frequently utilized in the treatment of femoral shaft fractures. However, an incorrect entry point will result in poor reduction of the shattered bones. Furthermore, intramedullary nails frequently encounter obstacles during insertion. The goal of this study contains two parts: 1) determining the ideal entry point and drill-hole shape for a given patient with its well-matched intramedullary nail (IMN), and 2) planning an appropriate insertion path for the IMN. Computed tomography scans from patients’ femurs and their well-matched IMNs are obtained to generate three-dimensional models and centerlines. A centerline self-growing strategy that combines four evaluating factors is employed to determine optimal centerline segments and align the IMN with the medullary cavity. For the first objective, the position of entry point under the optimal registration is computed. The drill-hole shape comprises all the intersections between the IMN and the femur near the entry point at 5 different depth positions. For the second objective, a visualized planning path including a series of the positions and postures is established through optimal registration. A local coordinate system is defined at the endpoint of the IMN. The transformation parameters between adjacent positions are computed for intra-operative guidance. The method was verified using the four experiments: entry point evaluation, path feasibility, path effectiveness, and IMN insertion guidance. During the whole insertion process, the maximum distance between the IMN centerline and the center point of the narrowest position of the medullary cavity is 0.673 mm, which meets clinical requirements. The IMN insertion guidance experiment demonstrates that the determined path is appropriate. In the path effectiveness experiment, the IMN can be successfully inserted into the medullary cavity, the upper and lower broken bones fit tightly without displacement, and the average time taken to plan an insertion path using this method is 9.37 min. The experimental results show that when the IMN is inserted according to the determined entry point and drill-hole, the shattered bone cracks fit closely. Additionally, the IMN can be successfully inserted without obstacles following the planned path which can minimize the damage to intramedullary tissues.

In this paper, a microfiber knot resonator (MKR) was encapsulated in polydimethylsiloxane (PDMS), and its capability to sense temperature was experimentally evaluated. A very high sensitivity of -12 nm/°C was recorded for the MKR device, with a microfiber diameter of 2μm and a ring diameter of 95 μm. However, light-loss was significant in the small MKR. We attribute this to the low ratio of ring-length to microfiber-diameter. A stable temperature-sensing performance and sensitivity of negative 8.48 nm/°C were obtained for the MKRs with a microfiber diameter of 3 μm and a ring diameter of 3 mm. A MKR device with a non-uniform microfiber (in diameter) was also fabricated and studied. This compact and miniature temperature-probe will be a promising candidate for wearable devices.

Dissipative Kerr solitons (DKS)s offer an integrated platform for broad spectrum coverage, which is suitable for the high sensitivity spectroscopy. However, the large linespacing of the soliton comb limits the spectroscopy detection resolution. We overcome this limitation by modulating the soliton comb lines through an external EOM with a tunable RF source. By locking the pump laser to a reference cavity, the linewidth of the pump laser was compressed from $1.13\:\mathrm{MHz}$ to $72\:\mathrm{kHz}$, leading to the silimar linewidth of comb lines and showing a frequency resolution of hundreds of $kHz$. Based on the scanned microcomb, we measured the absorption spectra of a gas cell and the high-Q optical modes in the microcavity, with a fast scanning speed of $\mathrm{16\,\,\mathrm{GHz/\mu s}}$. The scanning comb allows the spectroscopy measurement to cover the whole comb mode spectrum over THz bandwidth. Our setup holds great potential for simple, robust spectroscopy measurement.

Field grading within the dry-type bushing insulation presents a major challenge for DC applications due to the highly temperature-dependent conductivity of the epoxy resin insulation. An electric field regulation method based on the epoxy/graphene oxide (EP/GO) nanocomposites with the reduced temperature coefficient of conductivity is proposed and investigated in this paper. DC conductivity of EP/GO nanocomposites with different loadings of 0, 0.05, 0.1 and 0.5 wt% at various temperature are investigated. Trap level distributions are characterized by the isothermal discharge current (IDC) method to clarify the conduction mechanism. Obtained results show that more shallower traps are introduced and the thermal activation energies of the nanocomposites are reduced by the GO nanoparticles, indicating that the temperature coefficient of conductivity of the nanocomposites is reduced. The improvement of electric field distribution within the bushing insulation under operating voltage is verified when the EP/GO nanocomposite with an appropriate content is employed, based on an established ±800 kV valve-side bushing for a converter transformer. However, more defects will be introduced by the GO fillers with excessive content (0.5 wt%), resulting in an obvious reduction of the DC breakdown strength.

Computational strategies play a vital role in the prediction of adverse drug events (ADEs) owing to their low cost and increased efficiency. In this study, we used the strengths of the Jaccard and Adamic-Adar indices to build feature fusion-based predictive network models (FFPNMs) with three different machine learning (ML) methods respectively to predict drug-ADE associations. Our FFPNM with the logistic regression (LR) model improved to an area under the receiver operating characteristic curve (AUROC) value of 0.849, while the corresponding AUROC values for the pharmacological network model (PNM) and model based on similarity measures were 0.824 and 0.821, respectively. FFPNM with random forest (RF) is the best model among them with an AUROC value of 0.856, and the performance of FFPNM with SVM is close to that of FFPNM with RF and higher than that of FFPNM with LR. In these models, the bipartite network consisted of 152 drugs and 633 ADEs, which were obtained from the FDA Adverse Event Reporting System (FAERS) 2010 dataset. To better evaluate the performance of FFPNMs, we performed model predictions by different network consisting of 1177 drugs and 97 ADEs which were from the data of the first 120 days of FAERS 2004. FFPNM with RF achieved the best predictive result with AUROC value of 0.913. The results show that FFPNMs with ML methods, specially RF, have a superior prediction performance and robustness using only the topology features of the drug-ADE network. From our findings, the optimal, concise, and efficient models as computational methods for drug-ADE association predictions, were revealed. Source codes of this paper are available on https://github.com/Coderljl/FFPNM.

In this paper, a three-dimensional (3-D) printed spherical antenna with bandwidth enhancement under the operation of TM101 and TM211 modes is presented. The antenna is started from a metallized spherical cavity with a rectangular feed waveguide. Then, some slots are suitably introduced on its sidewall. With the help of these introduced slots, two resonant modes (TM101 and TM211) can be excited simultaneously and merged with each other, resulting in a wideband radiation characteristic with two resonances. An antenna prototype operating at X and Ku bands is designed and implemented as proof of concept. The antenna is additively manufactured by incorporating polymer-based stereolithography and electroless copper plating techniques. Measured results show that the antenna not only owns a wide impedance bandwidth of 40.9% from 9.77 to 14.81 GHz, but also maintains stable radiation patterns over the operating band. Besides, the measured average gain is as high as 10.1 dBi. As the measured results are in good agreement with the simulated ones, a simple and effective design of 3-D printed wideband spherical antenna with high radiation performances is verified.

A tunable photonic microwave frequency up-conversion scheme using opto-electronic oscillator (OEO) based on a monolithic integrated three-section laser without any electro-optic modulator (EOM) or filter is proposed. By adjusting the monolithic integrated three-section laser to operate in the period-one (P1) dynamical state, the signal carrier frequency up-converted from the intermediate frequency (IF) to the frequency at the difference and the summation between the oscillation frequency of OEO and IF can be realized. The tunability of the up-converted frequency can also be greatly guaranteed just by altering the control currents of the monolithic integrated three-section laser induced P1 frequency. In this paper, an IF of 2 GHz with 10 Msymbol/s 16-QAM signals is successfully up-converted to 16 GHz and 20 GHz simultaneously. The up-converted frequencies can be tuned from 16 GHz/20 GHz to 19 GHz/23 GHz, and the error vector magnitudes (EVMs) of all those up-converted signals satisfy the limitation in satellite communication.

The operating temperature of the magnetic coupler in wireless power transfer (WPT) systems for electric vehicles (EVs) determines the system reliability and service life. This paper studies the modeling and analysis of temperature characteristics of a general magnetic coupler. Firstly, a thermal circuit model is established to show the heat transfer of the magnetic coupler in the air. Secondly, the heating mechanism of the coil, core and aluminum (Al) shield plate is studied. The thermal power of each component is calculated, and the temperature distribution is qualitatively given based on the proposed thermal circuit model. Then, the temperature distribution of each component is simulated, and the results are consistent with the theoretical analysis. Finally, a 6.6 kW WPT prototype is set up, and the temperature of each component is measured after 30 minutes of operation. The temperature of the coil, 34.6 °C, is the highest among that of the coil, core, Al plate and coil base. The agreement between experimental results and the theoretical simulation results shows that the thermal field simulation can accurately predict the temperature of the magnetic coupler by reasonably setting the thermal power addition method.

In this paper, a security screening imaging method for walking human with single channel synthetic aperture radar (SAR) technology is proposed. According to the theory of SAR imaging, the imaging response for each single echo of the three dimensional (3D) moving target is analyzed. Then, the relationships between the accurate degree of imaging result and radar system parameters are presented with the global human walking model, which can be summarized as: positive correlations with radar angular velocity and rotation radius, negative correlations with aperture time and motion velocity, and uncertainty with radar initial angle. Hence, the short synthetic aperture time and high-resolution of terahertz (THz) radar makes it particularly suitable for walking human security imaging. Meanwhile, the main Doppler frequency shift of walking human imaging is corrected by the back-projection (BP) algorithm modified with the estimated velocity detected by the range profile. Besides, the numerical simulation experiments of point targets are provided to verify the conclusion of moving target imaging response. Furthermore, the experiments of walking human are processed with different radar system parameters to demonstrate the performance and effectiveness of our method.

A deep transfer learning (TL)-based comprehensive eye diagram analysis and diagnosis scheme that can output essential eye diagram parameters, estimate fiber link length, calculate Q-factor, and diagnose device imperfection-induced impairments is proposed. TL can be used to extract system information and optical signal characteristics contained in eye diagrams and apply the learned knowledge and extracted features obtained from source tasks to related target tasks. As a source task, the proposed method estimates the transmission distance of a fiber link using convolutional neural network (CNN)-based eye diagram recognition. The feature extraction layers of the CNN are transferred to six target tasks involving the recognition of cross percentage, levels “0” and “1,” eye height and width, and Q-factor. Using TL reduces the total training times for on-off keying (OOK) and pulse amplitude modulation (PAM4) formats by > 95% and 60%, respectively. We also investigated six common PAM4 impairments caused by transmitter imperfection by setting the impairment category identification as source task and the impairment-degree diagnoses as target tasks. The TL methods consistently outperformed non-TL methods, with higher accuracies and significantly reduced training times. The proposed impairment diagnosis technique should be useful in impairment healing and fault correction.

A machine learning (ML)-based multifunctional optical spectrum analysis technique is proposed to perform not only the conventional analysis functions but also the extended analysis functions, including center wavelength detection, optical signal-to-noise (OSNR) calculation, bandwidth recognition, as well as spectral distortion diagnosis. We have investigated four widely used ML algorithms, including support vector machine (SVM), artificial neural network, k-nearest neighbors, and decision tree. First, the wavelengths, OSNRs, and bandwidths of optical signals are processed by four ML methods based on the spectral data. The good performance and fast processing speed are obtained, especially for SVM, achieving the optimal accuracy (100%) and the least test time. For the need of the practical application, we also investigate the more complicated case, where wavelength, OSNR, and bandwidth are variable simultaneously so that the ML should analyze these three parameters comprehensively. Even in this case, the overall accuracy is still larger than 99.1%. In addition, the extended analysis functions are also studied to diagnose the spectral distortion caused by the cascaded filtering effect and off-center filtering effect. The number of cascaded filters and the offsets of filter shift and laser drift can be effectively estimated by the SVM with high average accuracy and low standard deviation, which are useful for failure detection and distortion recovery. This technique has the potential to be applied in the optical spectrum analyzer to implement the multifunctional spectrum analysis or in the optical performance monitor to execute the spectral distortion diagnosis.

With the increasing capacity of new energy in the power system, new energy cannot provide support for the system frequency directly. This characteristic of new energy affects the frequency stability of the power system. Therefore the control strategy of a virtual synchronous generator (VSG) is proposed to improve the frequency stability of the system. An adaptive virtual inertia control strategy based on an improved bang-bang control strategy for a micro-grid is presented. On one hand, it can make full use of the variability of virtual inertia to reduce dynamic frequency deviation. On the other hand, the steady-state interval of frequency and the steady-state inertia are set to improve the system frequency stability. Then the stability analysis of the value range of the virtual inertia is performed by the small signal model of the VSG for the micro-grid. Meanwhile, the ranges of virtual inertia and steady-state inertia are determined. Finally, Matlab/Simulink is applied to accomplish simulation experiments to compare various virtual inertia control strategies. The results indicate the effectiveness of the proposed strategy.

Accumulation of pollution particles on outdoor insulators is the premise of pollution flashover accidents in power systems. In this paper, according to the actual operating polymer insulators, the wind velocity, the concentration of pollution particles, the diameter of pollution particles, and the hanging angle of insulator were considered to investigate the accumulation characteristic of pollution particles by using FLUENT software. The pressure nephogram of polymer insulator, the velocity vector of air flow field, movement trajectory of pollution particles, and collision rate were obtained. It is found that the pressure of the insulator surface on the windward side is bigger than that on the leeward side, and the pressure difference increases with increasing the wind velocity. As the increase in particle diameter, the collision rate between pollution particles and insulator surface shows the increasing tendency. When the diameter of pollution particles is small, the easier the particles follow the airflow to bypass the insulator surface without colliding with it. However, due to the influence of gravity force and inertia, the collision rate of pollution particles increases significantly with the increase in particle diameter. Under the strong wind velocity, the air swirls are generated on the leeward side of the insulator surface, causing the collision rate of the pollution particles increases slowly with the increase in the wind velocity. While the concentration of pollution particle increases, the collision number between insulator surface and pollution particles shows the increasing tendency at different wind velocity. When the particle diameter is in range of 1 to 8 μm, with increasing the hanging angle of “V”type insulator from 0° to 60°, the collision rate of pollution particles shows the increasing tendency. When the particle diameter is 9 to 50 μm, the collision rate of particles shows the increasing tendency when the insulator angle is lower than 30°, but shows the decreasing tendency from 30°to 60°. As the humidity increases, the water vapor in the air prevents the movement of pollution particles, resulting in a decrease in the collision rate. With increasing the pollution particles diameter, the influence of air humidity on the collision rate of pollution particles is gradually weakened.

Electric field optimization is a critical technique to ensure the compact structure and reliable operation for AC gas insulated power apparatus. To obtain a more uniform electric field distribution, this paper proposes the concept of the d-SFGM insulator, i.e., applying the surface functionally graded material (SFGM) with a designed thickness (d) distribution of high permittivity along the insulator surface. The magnetron sputtering technique was used to fabricate the d-uniform insulator and the d-SFGM insulator by depositing BaTiO3 layers. The theoretical analysis indicates that the electric field distribution is optimized using the d-SFGM insulator, followed by the d-uniform insulator. The finite element simulations were employed to investigate the electric field distributions and dielectric losses of different insulators. The results show that for the case of the d-uniform insulator, a higher permittivity or larger thickness of the sputtering layer results in a more uniform electric field distribution. With the application of the d-SFGM insulator, the maximum electric field strength and dielectric loss are both significantly reduced compared to the d-uniform insulator. Finally, experimental tests were conducted to further verify the feasibility of SFGM. The AC flashover voltage of the d-SFGM insulator is much higher than those of the conventional insulator and the d-uniform insulators. Therefore, the d-SFGM insulator has great potential for improving the reliability of the AC gas insulated power apparatus.

This paper presents a comprehensive study on a new family of slotted hemispherical resonators and their applications in waveguide bandpass filters (BPFs) to extend the spurious-free stopbands. The proposed scheme focuses on suppressing unwanted spurious modes in the air-filled hemispherical resonators by adding functional slots to the metallic cavity shells. The spurious TM2m1 and TE101 modes in the hemispherical resonators are effectively suppressed by interrupting the surface current with slots, without significantly degrading the unloaded quality factor of the fundamental TM101 mode. Resonant modes in the slotted hemispherical resonators are analyzed. Design, fabrication, and measurement of X-band proof-of-concept waveguide BPFs based on slotted hemispherical resonators are expounded. Each filter is monolithically prototyped by employing metallic additive manufacturing technology. The filters demonstrate good passband performance-average insertion loss: 0.6-1.1 dB, frequency shift: 0.9%-1%, and return loss: >20 dB, and more significantly extended stopbands with spurious-free frequency ratios of >1.8:1 and stopband rejections over 36 dB.

In this paper, a third-order waveguide bandpass filter (BPF) based on slotted spherical resonators with a wide spurious-free stopband is presented. The resonator consists of a spherical cavity with slots opened at the top and bottom. Compared with a non-slotted spherical resonator, the slotted resonator suppresses the two spurious modes (TM211 and TE101) whilst maintaining the fundamental TM101 mode. The unloaded quality factor of the TM101 mode is not significantly degraded. This is achieved by interrupting surface current and radiating the unwanted spurious modes with the slots. The BPF is designed at a center frequency of 10 GHz with a fractional bandwidth of 1%. Two filter prototypes are fabricated, one using metal-based selective laser melting (SLM) and the other by polymer-based stereolithography apparatus (SLA) techniques. The slots also facilitate the copper electroplating process for the SLA-printed filter. The measured results show that the average passband insertion losses of the SLM- and SLA-printed filters are 0.33 and 0.2 dB, respectively. The corresponding passband return losses are better than 22 and 20 dB. The filters demonstrate excellent passband performance and wide spurious-free stopbands up to 16 GHz with stopband rejections of over 20 dB.

Electric field distortion caused by the DC conductivity variation and space charge accumulation under temperature gradient in the cross-linked polyethylene (XLPE) insulation is the most serious problem in HVDC cable application. In this paper, three different voltage stabilizers, Bis(4-fluorophenyl)-methanon, Bis(4-hydroxyphenyl) ketone, and Bis[4-(dimethylamino)phenyl] diketone, denoted as A, B, and C, respectively, were mechanically dispersed into the XLPE material with an addition of 0.5 wt%, aiming at improving the DC conductivity characteristics and space charge behaviors of the XLPE insulation at different temperature. The DC conductivity under different temperature and the electric field was tested by a three-electrode system. Meanwhile, space charge properties at 30 °C and 90 °C were investigated using PEA method, and the trap level distribution was estimated by the surface potential decay (SPD) process. The experimental results indicate that all the three voltage stabilizers have improved the DC conductivity characteristics of the XLPE, especially the voltage stabilizers B and C. In addition, the XLPEs modified by the voltage-stabilizers B and C with higher deep trap level demonstrate an excellent ability on space charge suppression at different temperatures. It is concluded that owing to the excellent voltage stabilizing effect, the voltage stabilizers B and C show much potential in HVDC cable application.

The existing distance and similarity measures of hesitant fuzzy linguistic term sets (HFLTSs) only cover the difference of linguistic terms but have no consideration of the difference between the numbers of linguistic terms. Thus, the concept of hesitance degree of HFLTSs is introduced to describe the hesitant degree among several linguistic terms in each HFLTS during the decision-makers' evaluating process. Considering the hesitance degree of HFLTSs, several novel distance and similarity measures of HFLTSs are developed, and their properties are discussed. Afterward, a novel hesitant fuzzy linguistic-based on the novel similarity measures and the Boolean matrix is developed to classify the objects with hesitant fuzzy linguistic information and a numerical example of the automobile recommendation is given to illustrate the performance of our developed clustering method. The results indicate that our developed clustering method can fully express the original evaluation information and has less computational effects on clustering results than the previous method.

In the coherent long-reach passive optical networks (LRPON), it is crucial to propose cost-effective digital signal processing (DSP) technologies to reduce the overall complexity and power consumption. This paper has proposed a low-complexity chromatic dispersion (CD) estimation scheme based on deep neural networks (DNN) and the error vector magnitude (EVM). To add comparisons, the performances of CD estimation schemes using other two well-known machine learning algorithms including the k-nearest neighbor (KNN) and the decision tree (DT) have also been investigated. The simulation results show that the proposed CD estimation scheme is effective in the coherent LRPON with the quadrature phase shift keying (QPSK) and 16-ary quadrature amplitude modulation (QAM) systems at 14Gbaud rate, 28Gbaud rate and 56Gbaud rate. The comprehensive performances of the DNN outperform those of the KNN and the DT. The mean estimation error of the DNN is less than 20ps/nm within the 100 km access distance in the 28Gbaud QPSK/16QAM systems. What's more, compared with classical methods using the CD scanning and frequent domain equalizers (FDE), the computation complexity of the proposed CD estimation scheme based on the DNN-EVM has been respectively reduced by 72.3 times, 86.7 times and 2.8 times about the amount of multipliers, adders and comparators.

Optical image is a kind of important data for communication because it is a two or three -dimensions data set to express communication information such as geographical signal, medical signal, remote sensing signal, etc. Thus, how to express the optical image properly is critical for the communication analytics. A new display method for optical image is described which is derived from the concept-Grayscale Standard Display Function (GSDF) which has been defined in DICOM, a medical image standard. The method analysis GSDF based on neural network processing which is different to DICOM. And the training data are from a self-assembly Equipment in NIM which is a traceable optical display equipment. Thus, the method has common usage for all optical image display, exceeding medical image. Furthermore, it is suitable for standardization because of the traceability.

The Internet of Things (IoT) is an important part of the new generation of information technology. It utilizes many sensor nodes to collect data and monitor the environment and can be applied in various fields. However, because the energy of sensor nodes is limited and the batteries cannot be replaced, the lifetimes of the sensor nodes are limited by the energy of their batteries. Only a reasonable method for saving energy can reduce the energy loss of the nodes in the communication process. Many studies have found that batteries have the property of battery-friendliness; that is, if they continue working, their energy drops rapidly. However, if they rest after working for a period of time, the batteries' energy can be partially restored. In this paper, an effective relay selection scheme for prolonging the lifetimes of nodes is designed based on the battery-friendly nature of the battery. In the proposed battery-friendly relay selection (BFRS) scheme, the relay node is selected based on the weighted synthesis value of the recovered energy, its distance to the sink and the residual energy. A forwarding node that has recovered more energy and is closer to the sink is more likely to be selected as the relay node because of its larger weight. Hence, the BFRS-based routing scheme can improve the lifetime by recovering more energy. Through extensive experiments, we demonstrate that our BFRS scheme outperforms previously proposed schemes. In a network with 500 nodes, compared with the shortest routing algorithm, the network lifetime is increased by 51.26%, and the amount of transferred data is increased by 42.93%.

Oil-paper insulation will inevitably endure impulse voltage with a superimposed dc voltage (superimposed voltage) because of introduced lightning or switching, which can induce charge accumulation and trigger flashover during the operation of a converter transformer. Temperature rise is also a nonnegligible factor that can significantly influence the performances of oil-paper insulation. This paper aims to reveal the effects of ambient temperature and superimposed voltage on surface charge and flashover behaviors in an oil-paper insulation system. Surface potential decay (SPD) experiments were performed under superimposed voltage with various polarity and amplitude combinations and at the temperature ranging from 20 °C to 80 °C. The results show that the initial surface potential and decay rate increase as the amplitude of the impulse voltage increases at 20 °C. The initial surface potential declines, and the process of SPD is accelerated at higher temperatures because of the increased concentration of shallow traps. Subsequently, flashover tests were conducted under superimposed voltage. It is found that flashover occurs after applying superimposed voltages even with a rather low dc voltage. In addition, the dc voltage component is primarily responsible for flashover. Weibull results also indicate that it is prone to trigger flashover at higher temperatures, because thermal excitation helps more carriers to escape from deep traps and participate in the process of flashover. Accordingly, the insulation characteristics of oil-paper system can be significantly influenced by superimposed voltage and temperature, which should draw great concern among the power grid.

This paper provides a comprehensive presentation for the RF design and implementation of novel millimeter-wave waveguide bandpass filters (BPFs) that are composed of compact hemispherical resonators. The proposed hemispherical resonator features a high unload quality factor and a volume 50% smaller than a spherical one and more significantly is much less degenerate than the latter due to its structural asymmetry. With proper geometrical configurations, the hemispherical resonator BPF can perform significantly improved out-of-band rejections compared with the spherical resonator BPFs of the same orders. The second-and fourth-order waveguide-fed BPFs are designed at X and Ka bands with flexible inter-resonator coupling geometries. The proof-of-concept Ka-band filters are monolithically prototyped with a high-temperature-resistant ceramic-filled photosensitive resin by using a fast and low-cost stereolithography (SLA)-based additive manufacturing technique. A proprietary electroless nickel/copper/silver plating process is reported for surface metallization of the utilized commercially available resin. The Ka-band filters demonstrate in the passbands small insertion losses (0.43-1 dB), good return losses (mostly >10-17 dB), and small frequency shifts (0.01%-0.47%), which validates excellent fabrication accuracy and reliability of the SLA printing and the metal plating. Characterization and quantification of surface morphology for SLA-printed samples are performed by employing contact profilometry and scanning electron microscopy.

In the social-collaboration scenario, the social-collaboration tasks need to be completed/coordinated by multiple people. For example, in the field of open-source software development, most software developments require multiple developers to collaborate with each other. With the increase in the number and variety of social-collaboration tasks, it is difficult for individuals to discover social-collaboration tasks that they can participate. If we can help match the social-collaboration tasks with appropriate users, the quality and speed of these tasks will be improved, thus social organizations (e.g., companies, teams, and research institutions) and individuals can improve productivity, which is very significant. However, most related work in recommending individuals to participate in social-collaboration tasks mainly focus on individual's data features/characteristics (e.g., personal behaviors and attributes), and little work is based on the social collaborative data features/characteristics within the individual's participation in the social-collaboration tasks, such as the types of social-collaboration tasks that individuals participate, collaborative content, collaborative behavior, collaborative intensity, and so on. This paper proposes a universal recommendation method based on personal social-collaboration preferences, which is used to recommend the social-collaboration tasks that individuals can participate. The characteristics of social-collaboration data contain a large number of individuals' preferences for social-collaboration, which helps improve recommendation performance. We perform a large number of experiments to verify the effectiveness of our proposed method, based on our collected real-world data sets in the open source software development services (Bugzilla and Github). Experimental results show that the proposed algorithm can be well applied to the social-collaboration task recommendation.

The emergence of many new computing applications, such as Internet of Vehicles (IoV) and smart homes, has been made possible by the large pool of cloud resources and services. However, the cloud computing paradigm is unable to meet the requirements of delay-sensitive business applications, such as low latency, mobility support, and location awareness. In this context, Mobile Edge Computing (MEC) is introduced to improve the quality of experience (QoE) by bringing cloud resources and services closer to the user by leveraging available resources in the edge networks. However, the performance of MEC is dynamic in nature due to its location awareness, mobility and proximity. As a result, an effective mechanism is needed for providing efficient dynamic service maintenance for edge services. In this paper, we propose applying the Skyline Graph Model and employing the Directed Acyclic Graph theory to store and update mobile edge services. Specifically, the Skyline Graph (SG) algorithm is designed to solve the insertion, deletion, updating and searching of mobile edge services to achieve efficient maintenance for edge services. Comprehensive experiments are conducted on both real-world web services and simulated datasets to evaluate the effectiveness and efficiency of our approaches. The results show that our algorithms can achieve significantly better performance and robustness than the baseline algorithm.

Visible light communication (VLC) can provide short-range optical wireless communication together with illumination using LED lightings. Since VLC channel is an open wireless channel, physical-layer security is desirable in order to hide secret information from unauthorized receivers, without reliance on upper-layer cryptographic techniques. In this paper, a secure coding scheme based on polar codes is proposed to simultaneously achieve physical-layer security and transmission reliability for indoor VLC systems under Wyner's wiretap model. An indoor VLC degraded wiretap channel model and an indoor VLC undegraded wiretap model are analyzed to estimate the corresponding secrecy capacity with typical indoor VLC scenarios considering the requirements of illumination and reliable transmission. Then, a physical-layer secure coding scheme and an enhanced scheme are designed to provide securely reliable communication without sacrificing the illumination functionality. Numerical results show that for VLC degraded wiretap channel, the proposed coding scheme can reduce the bit error rate (BER) of main channel to nearly 0 and increase that of wiretap channel to 0.5. Therefore, the mutual information between the sender and the eavesdropper and the BER of the legitimate receiver are both decreased to nearly 0. Both the security and reliability of VLC are guaranteed by the proposed coding scheme. And the practical secrecy rate reaches 85.96% at some positions when codeword length is 2 048 bits. Furthermore, an enhanced coding scheme with a feedback channel is proposed to handle the case that the wiretap channel is not degraded with respect to the main channel. With the feedback channel, the wiretap channel can be equivalently degraded and the security can be increased gradually with the increment of the degradation level of the wiretap channel. Meanwhile, the secrecy capacities can be asymptotically achieved for indoor VLC systems.

Linearly homomorphic signature schemes allow the performance of linear computations on authenticated data. They are important primitives for many applications, such as electronic voting, smart grids, electronic health records, and so on. Proxy signature schemes allow an original signer to delegate his/her signing power to a proxy signer, so that the proxy signer can sign on behalf of the original signer. Therefore, a signature scheme offering both of the above signatures' properties is very desirable. In this paper, we construct the first linearly homomorphic proxy signature scheme, so the proxy signer can produce a linearly homomorphic signature on behalf of the original signer. The scheme is provably secure in the random oracle model. Moreover, the length of signature is short and constant. Linearly homomorphic proxy signature scheme can be used in applications, such as electronic business and cloud computing.

Covert channels represent unforeseen communication methods that exploit authorized overt communication as the carrier medium for covert messages. Covert channels can be a secure and effective means of transmitting confidential information hidden in overt traffic. For covert timing channel, the covert message is usually modulated into inter-packet delays (IPDs) of legitimate traffic, which is not suitable for voice over LTE (VoLTE) since the IPDs of VoLTE traffic are fixed to lose the possibility of being modulated. For this reason, we propose a covert channel via adjusting silence periods, which modulates covert message by the postponing or extending silence periods in VoLTE traffic. To keep the robustness, we employ the Gray code to encode the covert message to reduce the impact of packet loss. Moreover, the proposed covert channel enables the tradeoff between the robustness and voice quality which is an important performance indicator for VoLTE. The experiment results show that the proposed covert channel is undetectable by statistical tests and outperforms the other covert channels based on IPDs in terms of robustness.

Research into combined adaptive filters has been attracting considerable attention; the performance of which is generally superior to that of the individual component filters. Formulating an adaptive filtering algorithm to handle a range of cases can be very difficult, particularly in complicated scenarios. This paper applies a robust convex combination method to the problem of acoustic echo cancellation. According to the minimum mean square error criterion, we first derive the optimal combination factor for a combination adaptive filter with two component filters. Next, we provide an approximation method to the inaccessible optimal combination factor. Finally, we extend this approximated factor to a robust one so that it is applicable even in situations with strong a ambient impulsive noise (IN) and abrupt changes in the echo channel. To do this, we non-linearly transfer the approximated combination factor so that this combination factor exhibits less transient time during the adaptation process of the combination factor and a fewer fluctuations in its steady state. Moreover, we detect the presence of the strong ambient IN and force the combined system output mainly contributed by one of the two component adaptive filters which is less sensitive to the the strong IN. We adopted computer simulations combining two affine projection sign algorithms with different step sizes as an example. Simulation results show that the proposed method is superior to comparable algorithms in terms of the normalized mean squared deviation and echo return loss enhancement.

Due to the imbalanced distribution of business data, missing user features, and many other reasons, directly using big data techniques on realistic business data tends to deviate from the business goals. It is difficult to model the insurance business data by classification algorithms, such as logistic regression and support vector machine (SVM). In this paper, we exploit a heuristic bootstrap sampling approach combined with the ensemble learning algorithm on the large-scale insurance business data mining, and propose an ensemble random forest algorithm that uses the parallel computing capability and memorycache mechanism optimized by Spark. We collected the insurance business data from China Life Insurance Company to analyze the potential customers using the proposed algorithm. We use F-Measure and G-mean to evaluate the performance of the algorithm. Experiment result shows that the ensemble random forest algorithm outperformed SVM and other classification algorithms in both performance and accuracy within the imbalanced data, and it is useful for improving the accuracy of product marketing compared to the traditional artificial approach.

A novel amplified feedback DFB laser with extended modulation bandwidth is experimentally demonstrated in this paper. All the three sections of laser cavity, including DFB section, phase section, and amplified feedback section, have the same active layer, which can avoid the butt-joint regrowth process. The gratings in both DFB section and amplified feedback section are fabricated by the reconstruction equivalent chirp technique, which can significantly decrease the difficulties in realizing precise grating structure. An enhanced −3 dB bandwidth of 19 GHz is achieved by means of a normal low-frequency wafer. Modulation linearity and relative intensity noise characteristics are experimentally investigated. 20 MSymbol/s 32-QAM signal with 19 GHz carrier is transmitted via 25-km radio-over-fiber link using the as-fabricated directly modulated DFB laser, and the average error vector magnitude of the whole link is 3.91%.