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Face Presentation Attack Detection (PAD) is critical for enhancing the security of facial recognition systems against sophisticated attacks. This study explores the use of general Image Quality Assessment (IQA) methods in face PAD, offering an alternative strategy that deviates from traditional, face-specific PAD techniques. Our evaluation of eight widely-used IQA methods across four PAD databases is structured around three distinct experimental protocols. Preliminary findings indicate that the general IQA methods are not fully effective in differentiating between genuine and attack samples, highlighting the need for modification. Nonetheless, a notable enhancement in performance is observed following the re-training of these methods using PAD datasets, bringing their effectiveness in line with that of advanced traditional PAD methods. This study provides evidence for the potential of general IQA in bolstering the resilience of face recognition systems against presentation attacks.

This study demonstrates the first work that achieves accurate modeling of Hydrogen plasmatreated (H-treated) p-GaN gate devices with the ASM-GaN model, facilitating simulations for applications in monolithic integrated circuit (IC) design. The workflow for ASM-GaN model parameter extraction and optimization using IC-CAP is proposed. The I-V characteristics of both Enhancement / Depletion (E/D) mode devices are modeled and fitted. The impact of device capacitance on the dynamic properties of monolithic IC is investigated through the ASM model. The results demonstrate that Cds, Cgd, and Cgs have different effects on the monolithic logic circuit performances. The high-level fitting of experimental data and circuit simulation of Inverter, NAND, and Comparator circuits proves the credibility of the modeling workflow and device capacitance modulation. This work provides a method to speed up the GaN monolithic IC design by accurate modeling with fast parameter extraction workflow regardless of the fabrication process. The reliable prediction of the circuit’s dynamic performance will lay the foundation for designing and scaling up the GaN monolithic IC application.

In intersatellite laser communications, the centroiding accuracy of a laser spot is crucial for maintaining steady communication links. However, the systematic error introduced by discrete sampling restricts further improvement of centroiding accuracy when choosing algorithms that are widely used in engineering. Additionally, the ultrahigh computational complexity and multiple-step iterations of the Gaussian fitting (GF) algorithm are unsuitable for real-time implementation, even though the algorithm can achieve the highest centroiding accuracy. In this study, we propose a laser spot centroiding algorithm based on template correlation to simultaneously satisfy the requirements of real-time performance and accuracy. The proposed algorithm evaluates the central location of a laser spot by obtaining the index of the maximum Pearson correlation coefficient (PCC). Simulations performed under different conditions reveal that the proposed algorithm is robust against the interference of background noise and the bad pixels. Moreover, experimental verification is performed based on the implementation on a Field-Programmable Gate Array (FPGA) in real-time, meanwhile its accuracy is on the same level as that of the GF algorithm and better than those of other widely-used algorithms. Therefore, the proposed algorithm is suitable for accurate real-time locating of laser spots in engineering applications of the intersatellite laser communications.

The fingerprint positioning has achieved remarkable results in indoor localization tasks, but the method usually relies on a large amount of fingerprint data to build a fingerprint database, and the amount and diversity of fingerprint data will directly affect the effectiveness of fingerprint positioning. Since fingerprint acquisition is limited and disturbed by space and time, it consumes a lot of labor and time costs to collect fingerprint data in the localization environment, and wireless fingerprint data is time-sensitive and environment-dependent, and changes in the localization environment will reduce the usability of the existing fingerprint database. The complex and repetitive fingerprint acquisition work seriously affects the feasibility of practical deployment of fingerprint positioning systems in the positioning environment. Therefore, the study of low-cost wireless fingerprint database construction methods has become an inevitable part of promoting the widespread deployment of indoor fingerprint positioning systems. In this paper, we introduce the traditional data augmentation-based approach and the advanced machine learning model-based approach, systematically presenting the underlying models and algorithms of both. The former reviews the application of two traditional data enhancement methods, namely channel propagation models and interpolation or regression, to the construction of low-cost wireless fingerprint databases, while the latter taps into techniques for reducing the cost of fingerprint database construction by combining generative adversarial networks and small-sample learning models with the indoor localization domain. Finally, we discuss the current challenges and future research directions for achieving high-performance indoor localization based on low-cost wireless fingerprint databases, and suggest some useful research guidelines.

In this work, dual-gate enhancement-mode (E-mode) device based NAND circuit (DG-NAND) and the NAND block with double E-mode devices (DD-NAND) are developed and fabricated based on the GaN MIS-HEMTs (metal-insulator-semiconductor-high-electron-mobility-transistors) platform. The DG-NAND circuit has an area of 0.118 mm2 with the probe pad, which is 24% lower than the area of the DD-NAND circuit. Both static and dynamic experimental results validate the advantages of performance improvement of NAND circuits designed by dual-gate technology at an input voltage of 9 V. This paper demonstrates the design potential of dual-gate NAND in an all-GaN MIS-HEMTs platform through compact design.

SiC MOSFETs devices with double-trench dominate the market due to their low on-resistance. However, studies on its temperature-dependent properties are not comprehensive. This work uses fast I-V and static I-V techniques to explore the location of electrons trapped in the device under moderate gate stress. Threshold voltage instability ( ${\mathrm {V}}_{\mathrm {TH}}$ hysteresis and $\Delta{\mathrm {V}}_{\mathrm {TH}}$ ) and on-resistance degradation ( $\Delta{\mathrm {R}}_{\mathrm {ON}}$ ) are used to characterize oxide trapping. Although the observation method is different, it can be found that the ${\mathrm {V}}_{\mathrm {TH}}$ instability and ${\mathrm {R}}_{\mathrm {ON}}$ degradation increase linearly with logarithmic time over a wide time range from $100~\mu{\mathrm {s}}$ to 104 s, suggesting that the direct tunneling mechanism dominates the electrons trapping in the oxide near the SiO2/SiC interface. The interface trap density is $3.8\times 10^{12}$ cm−2 $\cdot$ eV−1. In addition, a negative temperature dependence is shown in the test, and the fitting parameter $\gamma$ from 0.16 to 0.18 indicated that these traps are concentrated in the oxide layer. These traps’ energy level at 0.68 eV below the conduction band was obtained in the recovery phase through the Arrhenius plot.

Tunable near-infrared sources are highly demanded due to their various applications in biomedicine, printing, barcode technology, and optical storage. In this paper, widely central phase-matching wavelength tunable and high efficiency are demonstrated in theoretically and experimentally, pumped by a compact all-polarization-maintaining (PM) 9-character cavity fiber laser operating near-infrared (785 nm) based on a multiperiod grating. The wavelength tuned at about 18.2 nm as the grating period changed 1 $\mu \text{m}$ . A wide spectral tuning of 42 nm (from 776 nm to 818 nm) and high efficiency about 12.3% have been obtained. Besides, we discovered that the non-central second harmonic can also be generated from the second harmonic generation caused by strong phase mismatch, and the sum frequency generation (SFG) on account of the nonlinearity of the crystal. The result shows that the central and off-central phases match harmonics to form continuous broadband at some temperatures and grating periods, which can be widely exploited for wavelength conversion and tunable channel output.

The modeling of low earth orbit (LEO) satellite channel depends on its Doppler power spectrum. Due to satellite during transit, diversity and dynamic channel scene, the modeling of Doppler power spectrum has two serious problems: one is that the shape of the Doppler power spectrum will vary with the change of scenes and time, but the use of the existing traditional Doppler power spectrum models is difficult to accurately describe them. The other one is that the amount of measured data used for modeling is too large to handle, and data aliasing is easy to occur between different scenes, which will make it difficult to ensure the accuracy of model parameter fitting. In this paper, a two-side truncated asymmetric Doppler power spectrum model is proposed to universally describe the Doppler power spectrum during satellite transit. In addition, the atlas fingerprint method clustering is adopted to realize the classification of the measured data samples of Doppler power spectrum in multiple scenes, and the data with the strongest representation ability in each scene is selected to fit the model parameters. The simulation results show that the proposed model is in good agreement with the measured data. Therefore, parameter fitting using the proposed method can improve the accuracy of the model, so as to better describe the characteristics of LEO satellite channel fading in the frequency domain.

In this paper, the GaN-based MIS-HEMTs with Si3N4 single-layer passivation, Al2O3/SiNx bilayer passivation, and ZrO2/SiNx bilayer passivation are demonstrated. High-k dielectrics are adopted as the passivation layer on MIS-HEMTs to suppress the shallow traps on the GaN surface. Besides, high permittivity dielectrics passivated MIS-HEMTs also show an improved breakdown voltage characteristic, and that is explained by 2-D simulation analysis. The fabricated devices with high-k dielectrics/SiNx bilayer passivation exhibit higher power properties than the devices with plasma enhanced chemical vapor deposition-SiNx single layer passivation, including smaller current collapse and higher breakdown voltage. The Al2O3/SiNx passivated MIS-HEMTs exhibit a breakdown voltage of 1092 V, and the dynamic Ron is only 1.14 times the static Ron after off-state VDS stress of 150 V. On the other hand, the ZrO2/SiNx passivated MIS-HEMTs exhibit a higher breakdown voltage of 1207 V, and the dynamic Ron is 1.25 times the static Ron after off-state VDS stress of 150 V.

With the increasing demand of location-based services, channel state information (CSI) has attracted great interest because of the fine-grained information it provides. In this paper, we propose an original network structure, which exploits both the local information and global information in CSI amplitude for fingerprint localization. First, we validate the correlation between adjacent subcarriers and introduce the position-dependent local feature (PDL-feature). Next, local connection based deep neural network (LC-DNN) is designed to improve positioning performance by extracting and exploiting the correlation between adjacent subcarriers for indoor localization. LC-DNN consists of locally-connected layer and fully-connected layer. In the locally-connected layer, the variation of CSI amplitude in local frequency range is extracted and spliced for rich information. The frequency range and the times of extraction are determined by receptive field length and step size respectively. In the fully-connected layer, not only global features of CSI amplitude are further extracted, but also the function between features and position coordinates is obtained. Experiments are conducted to validate the effectiveness of LC-DNN and investigate the influence of hyper parameters on localization. Moreover, the positioning performance of LC-DNN is compared with four methods based on deep neural networks (DNNs). Results show that LC-DNN performs well in positioning accuracy and stability, with the mean error of 0.78m.

Secure Manhattan distance computation allows two parties to privately compute the Manhattan distance of their points, which is important and has broad applications. In this paper, we present a protocol for secure Manhattan distance computation based on phase shift operation. Because of quantum mechanics' physical principle, our protocol can also resist outside attacks and don't leak any private information of participants.

With the development of intelligent manufacturing and the customized product demand of customers, manufacturing enterprises are urgently required to carry out high-efficiency, high-quality, flexibility and low-cost manufacturing to enhance the competitiveness of enterprises. Intelligent job shop scheduling problem is the core decision of intelligent manufacturing production management. Many-objective job shop scheduling algorithms can effectively solve this problem. However, existing optimization algorithms cannot effectively solve many-objective flexible job shop scheduling problem. This paper establishes the many-objective job shop intelligent scheduling model with complex constraints, and proposes an improved intelligent decision optimization algorithm named NSGA-III-APEV based on NSGA-III. This algorithm uses the penalty-based boundary intersection distance that takes into account both convergence and diversity simultaneously to define the distance between the population individual and the reference vector in the association operation. This paper exploits the penalty-based boundary intersection distance-based elimination mechanism to preserve individuals and reduce the computational cost in the individual preservation strategy. Meanwhile, the adaptive mutation strategy based on consanguinity is employed in genetic operators. The presented method effectively improves the convergence and diversity of the population. Finally, NSGA-III-APEV with other algorithms was compared through benchmarks. Experimental results demonstrated the effectiveness and superiority of the improved method. The feasibility of the improved method in solving the many-objective flexible job shop scheduling problem are verified by engineering examples.

Feature-based visual simultaneous localization and mapping (SLAM) is an effective localization approach for robots in unknown environments. Classic handcrafted features perform well in 2D image matching tasks. However, in the tracking task of SLAM, the region at the edge of the object in the image is often unstable because of the lack of spatial information. In this paper, we refer to the features at the edge of the object as edge-features and propose an effective method to process the edge-features in SLAM named Edge-Feature Razor (EF-Razor) for the above problems. EF-Razor first uses the semantics provided by the object detection YOLOv3 to distinguish edge-features. Through additional constraints on edge-features matching in the tracking process, EF-Razor can effectively reduce the impact of unstable features on the SLAM system. Then, EF-Razor adjusts the information matrix to increase the system's trust in the filtered features. This will make the calculation result of the bundle adjustment more stable. In order to evaluate the proposed method, we integrate EF-Razor to ORB-SLAM2 and perform experiments. The comparison results based on public datasets show the proposed method could effectively reduce the absolute trajectory error by 7%.

Indoor positioning and navigation is an essential field of location-based service. Non-line of sight (NLOS) error restricts the accuracy of indoor positioning. Many researchers have studied the localization problem in indoor NLOS environments, but there is still a problem that NLOS error cannot be mitigated in unknown areas. To solve the above problems, this paper proposes a method of constructing virtual base stations in unknown areas (UA-VBS), and presents the corresponding positioning algorithm to calculate the location of the user equipment. Firstly, the base stations are selected and the initial positioning is carried out. Then, multiple virtual base stations are constructed according to the user equipment positions in the first three steps. The LOS base stations and virtual base stations participate in the TDOA calculation together, and calculate the base stations’ combination with the minimum residual and the corresponding positioning result. Finally, the pedestrian dead reckoning fusion weight is updated by the residual value, and the accurate positioning result in NLOS environment is obtained. Simulation and experimental results show that the proposed algorithm has high positioning accuracy and stability in NLOS environment.

Despite that the accuracy and efficiency of stereo matching technology have significantly improved in the past decades, the issue of edge-blurring remains a challenge to most of the existing approaches. In this paper, we propose a minimum spanning tree (MST) based stereo matching method by using the image edge and segmentation optimization to preserve the image boundary. We first exploit a fast disparity range estimation method by combining the Surf and Akaze feature points to improve the computational efficiency. Second, we utilize the image edges and brightness information to generate a self-adaptive weight function, which is able to significantly improve the accuracy of MST aggregating in the regions of complex texture and boundaries with similar color distribution. Third, we employ the image segmentation to extract the invalid regions of the estimated disparity map, and propose a post-processing scheme to refine the disparity result. Finally, we run our method on several Middlebury and KITTI datasets. The comparison results between our method and other state-of-the-art approaches demonstrate that the proposed method has high accuracy for disparity computation and is especially robust to the edge-blurring.

Searchable encryption (SE) is a positive way to protect users sensitive data in cloud computing setting, while preserving search ability on the server side, i.e., it allows the server to search encrypted data without leaking information about the plaintext data. In this paper, a multi-client universal circuit-based full-blind quantum computation (FBQC) model is proposed. In order to meet the requirements of multiclient accessing or computing encrypted cloud data, all clients with limited quantum ability outsource the key generation to a trusted key center and upload their encrypted data to the data center. Considering the feasibility of physical implementation, all quantum gates in the circuit are replaced with the combination of π/8 rotation operator set {Rz(π/4), Ry(π/4), CRz(π/4), CRy(π/4), CCRz(π/4), CCRy(π/4)}. In addition, the data center is only allowed to perform one π/8 rotation operator each time, but does not know the structure of the circuit (i.e., quantum computation), soit can guarantee the blindness of computation. Then, through combining this multi-client FBQC model and Grover searching algorithm, we continue to propose a quantum searchable encryption scheme for cloud data. It solves the problem of multi-client access mode under searchable encryption in the cloud environment, and has the ability to resist against some quantum attacks. To better demonstrate our scheme, an example of our scheme to search on encrypted 2-qubit state is given in detail. Furthermore, the security of our scheme is analysed from two aspects: external attacks and internal attacks, and the result indicates that it can resist against such kinds of attacks and also guarantee the blindness of data and computation.

This paper presents a GaN based synchronous buck DC-DC converter, which monolithically integrates gate drivers and a half-bridge power stage in a 3-μm enhancement-mode (E-mode) GaN-on-Si process. The fabricated synchronous converter with integrated gate drivers is based on E-mode GaN MIS-HFETs (metal-insulator-semiconductor heterojunction-field-effect-transistors), which have a large gate swing of 10 V due to the insertion of 20 nm high-k gate insulator Al2O3. At 100 kHz, the proposed DC-DC integrated circuits (ICs) exhibit good thermal stability at high temperatures up to 250 °C for 25 V down conversion. Furthermore, four different designs (asynchronous and synchronous) including converters with external drivers were systematically evaluated at different input voltages and duty cycles, the GaN-based DC-DC converters with integrated gate drivers exhibit small voltage overshoots and oscillations due to reduced parasitic inductance and chip size. These results validate the advantages of monolithic, lateral integration of half-bridge GaN ICs with gate drivers for high temperature power converters.

With the increasing popularity of location-based services, channel state information (CSI) has received widespread attention in positioning due to the fine-grained information it provides. However, the raw amplitudes of CSI are especially sensitive to noise and easily effected by the frequency-selective fading. As the expanding of test area, positioning is disturbed by random noise and insufficient resolution of features severely. In this paper, we propose Cluster-Mapping (C-Map), an adaptive pre-processing system for CSI amplitude-based fingerprint localization. This system models positioning as a classification problem, an adaptive processing mechanism is constructed according to the characteristics of CSI amplitude. C-Map mainly contains two parts: dynamic denoising and feature enhancement. In the dynamic denoising, effective features are screened out by clustering iteratively, without specifying parameters according to the experimental environment. In the feature enhancement, polynomial fitting with regularization is used to reduce jagged fluctuations, then the trend of variation over 30 sub-carriers are described by combining derivation with nonlinear kernel function. Extensive experiments are conducted in typical environments to verify the superior performance of C-Map for the preprocessing of CSI amplitude. Compared with the combination of mean and multidimensional scaling (MDS), the average positioning error of C-Map is reduced 28.1% in comprehensive indoor environment, and 19% in garage. Furthermore, compared with the combination of DBSCAN and principal component analysis (PCA), the average positioning error of C-Map is reduced 33.1% in comprehensive indoor environment, and 28% in garage.

The paper introduces a 24-channel air-trench isolated Silica-based VOA array. The air trenches thermally insulate the VOAs with etched-free waveguide technologies. Reactive ion etching and wet etching were used at different channel locations to suppress thermal crosstalk and reduce power consumption. When thermal crosstalk is declined by 3 dB for the straight waveguide, the power consumption was decreased from 195 to 95 mW because the lightwave radiation can be scattered by the air trench in the dark state. This crosstalk is improved by 9 dB for the curved waveguide. The polarization dependent loss falls down to 0.28 dB at 15 dB attenuation since the air trench and etched-free waveguide also releases the stress.

Despite progress made in the accuracy and robustness of optical flow in past years, the problem of over-segmentation and the blurring of image edge and motion boundary caused by the illumination change, complex texture, large displacement, and motion occlusion still remain. Recently, we developed a guided filtering scheme for flow field estimation, which is implemented as an add-on optimal operation during the coarse-to-fine optical flow computation. In this paper, we first review the research progress in optical flow computation and discuss limitations of the currently popular median filtering heuristic for a flow field optimization. We then introduce a general formulation of the guided filtering and provide the detailed illustration. Furthermore, we explore the potential of the guided filtering optimization for the flow field estimation under the coarse-to-fine computing scheme. Finally, we modify some typical and state-of-the-art optical flow methods by applying the proposed guided filtering operation to the baseline models, and test the performances of the basic and developed models through the Middlebury, MPI-Sintel, and KITTI data. The experimental results demonstrate that the guided filtering scheme is able to preserve the image edges and motion boundaries, and to improve the accuracy and robustness of optical flow estimation.

Heterogeneous cloud radio access networks (H-CRANs), proposed to boost both spectral and energy efficiency while reducing the signaling overhead, have been regarded as a promising paradigm for fifth-generation wireless communication systems. To reduce the network power consumption, in this paper, we propose a joint remote radio head (RRH) activation and outage constrained coordinated beamforming (CoBF) algorithm for massive multiple-input multiple-output H-CRANs. Considering the imperfect channel state information and power consumption of fronthaul links and individual transmission power limitations at the RRHs, the downlink network power minimization problem subject to the constraints of specified outage probabilities at each macro user equipment (MUE) and each RRH user equipment (RUE) is reformulated. For a given RRH activation set, we first derive a conservative convex approximation for the outage constraints of RUEs by using semidefinite relaxation and an extended Bernstein-type inequality, while a closed-form expression is obtained for the outage constraints of MUEs. Then, we reformulate the nonconvex problem into a semidefinite program. Moreover, we propose a low-complexity algorithm to perform the joint optimization of the RRH activation and robust CoBF by using the group sparse beamforming method through the weighted 11/12 norm reformulation, where the group sparsity patterns of beamformers are used to guide the RRHs that can be switched off. Simulation results demonstrate that the proposed algorithm can significantly reduce the network power consumption by 28% in the low signalto-interference-plus noise ratio scenario. In addition, the algorithm can approach the system performance of the exhaustive search algorithm while having a much lower computational complexity.

The dynamic response of the pitch angle slows down when the pitch actuator of wind turbines fails, and the fault leads to fluctuations in the generator speed and in the output power. According to identification and analytical theories, a fault-tolerant control combined with fault estimation is proposed to solve this problem. The simplified two-order transform function of the pitch actuator is transformed into the identification equation by means of the Euler transformation method. Next, the time-varying natural frequency and the damping ratio of pitch actuators are regarded as gradual change and abrupt change parameters; these two parameters are estimated using the proposed sliding data window least squares-based iterative (SDW-LSI) identification algorithm. The sliding data window length is adjusted when changes in the system parameters are detected. Then, according to analytical theory, a compensation equation is derived in the pitch actuator, and the estimated values are fed back to the compensation module to adjust the relations before and after fault occurrences in the pitch actuator to eliminate the effects caused by the pitch actuator failure. Finally, the feasibility of the SDW-LSI algorithm is validated by choosing the fault of high air content in the hydraulic oil.

We present a new mechanism to realize broadband transmission (BT) at near infrared in a dual-layer metallic-silver-grating structure with a silicon nitride dielectric spacer. The physical mechanism is ascribed to the partial degeneracy between the first-order magnetic polariton (MP1) mode supported in the thin dielectric spacer and the Fabry-Pérot-like resonant modes supported in the gratings' slits. With parameters optimized, high transmittance up to 85% with full-width-at-half-maximum bandwidth of 64% near 1.9 μm can be realized for TM-polarized light. Moreover, the BT can be maintained in a large range of oblique incident angles from 0° to 30°. In another word, the BT is capable of not only collimated beams but also noncollimated incident lights. Moreover, the high-transmission window can be tuned in the IR spectrum region by modulating the metallic gratings' thicknesses.

In this paper, we predict an unexpected perfect optical absorption phenomenon of oblique-incident transverse magnetic polarized light in a metallic grating situated in asymmetric surroundings. Two physical processes result in the anomalous absorption: One is the total internal reflection at the grating's interface; another is the Fabry-Pérot-like resonance supported in the grating's slits. For small filling factor grating (f 0.9), anomalous absorption is realizable only if the total internal reflection occurs. The influences of structural parameters, such as grating thickness, slits material on the anomalous absorption are investigated. Based on these properties, a plasmonic absorption device with a designed absorption line shape is proposed.