Search

This paper focuses on communication-efficient federated learning problem, and develops a novel distributed quantized gradient approach, which is characterized by adaptive communications of the quantized gradients. Specifically, the federated learning builds upon the server-worker infrastructure, where the workers calculate local gradients and upload them to the server; then the server obtain the global gradient by aggregating all the local gradients and utilizes it to update the model parameter. The key idea to save communications from the worker to the server is to quantize gradients as well as skip less informative quantized gradient communications by reusing previous gradients. Quantizing and skipping result in ‘lazy’ worker-server communications, which justifies the term Lazily Aggregated Quantized (LAQ) gradient. Theoretically, the LAQ algorithm achieves the same linear convergence as the gradient descent in the strongly convex case, while effecting major savings in the communication in terms of transmitted bits and communication rounds. Empirically, extensive experiments using realistic data corroborate a significant communication reduction compared with state-of-the-art gradient- and stochastic gradient-based algorithms. [ABSTRACT FROM AUTHOR]

Recently, many researches on convolution neural network (CNN) based in-loop filters have been proposed to improve coding efficiency. However, most existing CNN based filters tend to train and deploy multiple networks for various quantization parameters (QP) and frame types (FT), which drastically increases resources in training these models and the memory burdens for video codec. In this paper, we propose a novel variable CNN (VCNN) based in-loop filter for VVC, which can effectively handle the compressed videos with different QPs and FTs via a single model. Specifically, an efficient and flexible attention module is developed to recalibrate features according to QPs or FTs. Then we embed the module into the residual block so that these informative features can be continuously utilized in the residual learning process. To minimize the information loss in the learning process of the entire network, we utilize a residual feature aggregation module (RFA) for more efficient feature extraction. Based on it, an efficient network architecture VCNN is designed that can not only effectively reduce compression artifacts, but also can be adaptive to various QPs and FTs. To address training data imbalance on various QPs and FTs and improve the robustness of the model, a focal mean square error loss function is employed to train the proposed network. Then we integrate the VCNN into VVC as an additional tool of in-loop filters after the deblocking filter. Extensive experimental results show that our VCNN approach obtains on average 3.63%, 4.36%, 4.23%, 3.56% under all intra, low-delay P, low-delay, and random access configurations, respectively, which is even better than QP-Separate models. [ABSTRACT FROM AUTHOR]

Traditional open-switch fault diagnosis methods suffer from poor rapidity or robustness. To solve this issue, a new differential current observer-based fault diagnosis method is proposed in this article. With the designed differential observer, fault symptoms (residuals) can be generated and adopted for fault diagnosis easily. Considering that the residuals are sensitive to motor operating condition in the conventional model-based method due to model error, an adaptive fault detection threshold is designed. As a result, the false detection and missed detection caused by the change of working condition can be avoided, and stronger robustness against speed, load, and parameter variations can be achieved with superior rapidity compared with existing methods. Finally, the rapidity and robustness of the proposed fault diagnosis method are verified sufficiently through experimental results. [ABSTRACT FROM AUTHOR]

This article proposes a new position estimation method based on an unsaturated flux linkage model for planar switched reluctance motors (PSRMs). The bipolar-voltage-injection method is used to measure the flux linkage, and it is found that ripples caused by iron loss and aluminum eddy current with different amplitudes appear at the troughs of the flux curve. The effect of the losses on the flux linkage measurement is revealed in this article, and the loss compensation models are built based on the analysis. The improved flux linkage model is proposed with the loss compensation models. Then, the position estimation method is given with the improved flux linkage model. Flux linkage measurement result shows that the model with loss compensation has higher accuracy than the conventional model. Initial and planar-motion position estimation experiments are carried out for the PSRM. The initial position estimation error is from −0.4 to 0.4 mm. The planar-motion position estimation errors are from −1.1 to 1.1 mm in the ${X}$ -axis and from −1.2 to 1.0 mm in the ${Y}$ -axis, and the effectiveness of the proposed flux linkage model is verified. [ABSTRACT FROM AUTHOR]

Relativistic backward wave oscillator (RBWO) is a promising high-power microwave (HPM) generator. In past decades, many efforts have been devoted to improving its practicality, in which the repetitively operating RBWO has drawn much attention. However, two bottlenecks once limited its development. One is the generation of massive anode plasmas or even damage of the collector, and the other is the RF breakdown of electrodynamic structures. This article describes an RBWO which can operate repetitively with 3-GW microwave power by mitigating these two problems. First, a titanium collector, which shows better performance in resisting electron beam bombardment than the conventional stainless steel collector, is adopted. Meanwhile, water-cooling technology is applied to expedite heat dissipation of the collector. Second, since our investigations demonstrate that an anodic mechanism, not the conventional cathodic mechanism, is the main cause for RF breakdown of electrodynamic structures under strong guiding magnetic field, the titanium material is also used to manufacture the electrodynamic structures of the RBWO in order to increase its power capacity. The advantage of the titanium RBWO is verified in experiments. The application of these techniques, as well as some important surface treatment techniques such as polishing, ultrasonic washing, and vacuum baking, leads to the stable repetitive operation of the RBWO with 3-GW output power and 25-ns microwave duration under a repetition rate of 50 Hz. The successive operation time of the device is up to 20 s. [ABSTRACT FROM AUTHOR]

Open-phase fault is a common failure in permanent magnet synchronous motor (PMSM), which would degrade motor performance and increase its loss due to the unbalanced phase currents. However, conventional fault-tolerant strategies suffer from the tracking problem of sinusoidal current references. In this paper, different from previous fault-tolerant strategies, a new fault-tolerant method for open-phase PMSM is proposed by designing a novel transformation matrix for current/voltage references. With the new transformation matrix, the voltage/current references in the d–q frame can be transformed to the phase voltage/current references of the two remaining phases. With the proposed method, open-phase fault-tolerant implementation can be much easier (proportional-integral controller is enough) and its performance can be much higher. Besides, as the tolerant method is designed based on the four-leg inverter, stronger fault-tolerance capability can be obtained compared with the three-phase inverter-based method. Finally, the experimental results confirm the validity of the proposed fault-tolerant method. [ABSTRACT FROM AUTHOR]

Due to the ironless structure of the toroidal winding in the tubular linear voice coil motor (TLVCM), the winding thickness is one of the critical parameters that have an impact on air-gap flux density, and hence affects the copper loss at rated thrust. This paper investigates and determines the thickness of the ironless toroidal winding of the TLVCM for the purpose of a minimum copper loss. Magnetic equivalent circuit (MEC) method is used to analyze the air-gap flux density with respect to winding thickness, and the corresponding copper loss at the rated thrust is assessed by considering the slot fill factor. The optimal solution is obtained according to the U-shaped curve of the copper loss. Moreover, the finite-element analysis is combined to validate the results of MEC method, and a good agreement is reached. Two prototypes of TLVCMs having similar slot fill factor are made, one of which is with the optimal winding thickness. The expected current at the rated thrust is obtained by a test platform of static thrust, so as to obtain the corresponding copper loss for each prototype. The experimental results show that the TLVCM with optimal winding thickness features a lower copper loss. Hence, the attempt to minimize the copper loss by designing the thickness of the ironless winding is highly effective. [ABSTRACT FROM AUTHOR]

In this paper, an improved tubular Halbach surface-mounted permanent magnet (HSPM) linear oscillating actuators (LOAs) with end ferromagnetic pole pieces is proposed. Generally, the end magnet rings are exposed to air due to the inherent opening structure of LOA, resulting in a rather larger reluctance of end air gap and a lower air-gap flux density. Therefore, the analysis of the end air gap is of great significance for LOA performance. Considering this fact, the ferromagnetic pole pieces are added at both the ends of the support tube of LOA, trying to direct the magnetic fields in desired paths and enhance the electromagnetic performance of LOA. Two HSPM LOAs with ferromagnetic and without ferromagnetic pole pieces are simulated, built, and tested. The results show that the electromagnetic performance of the proposed design is superior to the original. Moreover, its cost and volume are approximately unchanged. The proposed design plays a significant role in enhancing LOA performance and efficiency. [ABSTRACT FROM AUTHOR]

Single-image super-resolution (SR) reconstruction via sparse representation has recently attracted broad interest. It is known that a low-resolution (LR) image is susceptible to noise or blur due to the degradation of the observed image, which would lead to a poor SR performance. In this paper, we propose a novel robust edge-preserving smoothing SR (REPS-SR) method in the framework of sparse representation. An EPS regularization term is designed based on gradient-domain-guided filtering to preserve image edges and reduce noise in the reconstructed image. Furthermore, a smoothing-aware factor adaptively determined by the estimation of the noise level of LR images without manual interference is presented to obtain an optimal balance between the data fidelity term and the proposed EPS regularization term. An iterative shrinkage algorithm is used to obtain the SR image results for LR images. The proposed adaptive smoothing-aware scheme makes our method robust to different levels of noise. Experimental results indicate that the proposed method can preserve image edges and reduce noise and outperforms the current state-of-the-art methods for noisy images. [ABSTRACT FROM AUTHOR]

Akin to human transfer of experiences, transfer learning as a subfield of machine learning adapts knowledge acquired in one domain to a new domain. The authors systematically investigate how this concept might be applied to the study of users on emerging Web platforms, proposing a transfer learning–based approach, TraNet. [ABSTRACT FROM AUTHOR]

Radio frequency (RF) breakdown can result in pulse shortening and severely limits the performance of high-power microwave (HPM) generators. In this paper, the initial stage of RF breakdown in HPM generators with a strong external guiding magnetic field has been carefully analyzed. It is thought that the initial breakdown process involves electron emission induced by an intense local field, electron acceleration in the electric field, and electron bombardment on the structure’s surfaces. Strong external guiding magnetic fields play a key role in the RF breakdown process of HPM generators. They concentrate the impact energy and make it more destructive. To suppress RF breakdown, feasible solutions include lowering the local field strength, decreasing the energy of the field-emitted electrons obtained from the RF field, reducing the deposited energy density, and improving the material’s resistance to electron bombardment. [ABSTRACT FROM AUTHOR]

Metallic cathodes can be used to generate intense relativistic electron beams for high-power microwave devices. Many previous papers have demonstrated that metallic cathodes usually have a limited lifetime. In this paper, three metallic cathodes, namely, stainless steel cathode, copper cathode, and aluminum cathode, are researched with a repetitive-operation accelerator and an X-band relativistic backward-wave oscillator. The cathode surface morphologies are observed with a scanning electron microscope before and after lifetime test. The experimental results demonstrate that the stainless steel cathode has a very limited lifetime ( <10^3 pulses), the copper cathode has a moderate lifetime ( \sim 10^4 pulses), and the aluminum cathode has a surprisingly long lifetime (at least 10^5 pulses). The different lifetimes of the three cathodes may originate from the different situations of regeneration of microprotrusions on the cathode surface. The large number of regenerated microprotrusions which possess large microscopic field enhancement factors supports the long lifetime of the aluminum cathode. [ABSTRACT FROM PUBLISHER]

A linear voice coil motor (VCM) with forward winding for linear vibro-impact systems is proposed in this paper. Generally, only increasing the air-gap flux density of VCM, which ignores its bad thrust characteristic condition, is limited to obtain a higher thrust density, due to its intermittent duty and reciprocating linear motion. Thus, the analysis on the thrust characteristic is of momentous significance for VCM design. Considering this fact, we try to adjust the thrust characteristic to obtain the maximum thrust density. Magnetic-circuit analysis method is utilized to analyze the thrust characteristic along with a given total working stroke. Besides, finite-element analysis is also used in the selection of parameters. A series of prototype VCMs for experiments is made. The results from both theoretical simulations and practical experiments show increase of thrust density and decrease of thrust ripple compared with the original design, and prove the superiority of the proposed structure. [ABSTRACT FROM PUBLISHER]

With a lot of enhanced coding tools introduced, High Efficiency Video Coding (HEVC) achieves significant improvement in coding efficiency at the cost of increased computational complexity. To efficiently reduce the encoding time of HEVC, a wavefront-based high parallel (WHP) solution integrating novel data-level and task-level methods is proposed in this paper. On data level, optimal single-instruction-multiple-data algorithms are designed for the enhanced coding tools, i.e., replacing the multiplication in motion compensation by add and shift operations with reduced instruction cycles, removing the transpose in transform via realignment of coefficients, and minimizing the memory access in sum of absolute difference/sum of squared differences calculation by fully reusing the registers. On task level, a novel inter-frame wavefront (IFW) method is developed by effectively decreasing the dependence of wavefront parallel processing (WPP). In addition, a coding tree block level parallelism analysis method is presented to prove the superior of IFW method compared with other HEVC representative parallel methods. Besides, a three-level thread management scheme is proposed to best exploit the parallelism of IFW method and achieve corresponding encoding speedup. Extensive experimental results show that, the overall WHP solution can bring up to $57.65\times $ , $65.55\times $ , and $88.17\times $ speedup for HEVC encoding of Wide Video Graphics Array, 720p and 1080p standard test sequences, while maintaining the same coding performance as with WPP. The proposed solution is also applied in several leading video companies in China, providing HEVC video service for more than 1.3 million users everyday. [ABSTRACT FROM PUBLISHER]

Zeno runs, where infinitely many actions occur within finite time, may arise in Timed Automata models. Zeno runs are not feasible in reality and must be pruned during system verification. Thus it is necessary to check whether a run is Zeno or not so as to avoid presenting Zeno runs as counterexamples during model checking. Existing approaches on non-Zenoness checking include either introducing an additional clock in the Timed Automata models or additional accepting states in the zone graphs. In addition, there are approaches proposed for alternative timed modeling languages, which could be generalized to Timed Automata. In this work, we investigate the problem of non-Zenoness checking in the context of model checking LTL properties, not only evaluating and comparing existing approaches but also proposing a new method. To have a systematic evaluation, we develop a software toolkit to support multiple non-Zenoness checking algorithms. The experimental results show the effectiveness of our newly proposed algorithm, and demonstrate the strengths and weaknesses of different approaches. [ABSTRACT FROM PUBLISHER]

Some factors that influence the emission uniformity of explosive emission cathodes (EECs) in foilless diodes such as the guiding magnetic field strength and the rise rate of electric field have been researched in former literatures. This paper is concentrated on another factor that has been overlooked previously, i.e., the defects with dimensions of tens of micrometers on cathode surfaces, especially at blade edges. It is shown that these defects will significantly worsen the emission uniformity of EECs by introducing extraordinarily large microscopic field enhancement factors, and thus they should be eliminated. The micrometer-scale irregularities, however, should be maintained to provide effective emission micropoints. Meanwhile, the outer edge of an annular cathode blade should be rounded off to improve the emission uniformity. After being disposed like this, the emission uniformity of annular graphite cathodes in a foilless diode is obviously improved, which leads to an increase of energy efficiency by more than 20% by broadening the microwave pulse duration under a power level of 2.8 GW for an X-band relativistic backward wave oscillator. [ABSTRACT FROM PUBLISHER]

Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed. [ABSTRACT FROM AUTHOR]

In the first part of this paper, we derive a source model describing the relationship between the rate, distortion, and quantization steps of the dead-zone plus uniform threshold scalar quantizers with nearly uniform reconstruction quantizers for generalized Gaussian distribution. This source model consists of rate-quantization, distortion-quantization (D-Q), and distortion-rate (D-R) models. In this part, we first rigorously confirm the accuracy of the proposed source model by comparing the calculated results with the coding data of JM 16.0. Efficient parameter estimation strategies are then developed to better employ this source model in our two-pass rate control method for H.264 variable bit rate coding. Based on our D-Q and D-R models, the proposed method is of high stability, low complexity and is easy to implement. Extensive experiments demonstrate that the proposed method achieves: 1) average peak signal-to-noise ratio variance of only 0.0658 dB, compared to 1.8758 dB of JM 16.0's method, with an average rate control error of 1.95% and 2) significant improvement in smoothing the video quality compared with the latest two-pass rate control method. [ABSTRACT FROM AUTHOR]

This paper provides a systematic rate-distortion (R-D) analysis of the dead-zone plus uniform threshold scalar quantization (DZ+UTSQ) with nearly uniform reconstruction quantization (NURQ) for generalized Gaussian distribution (GGD), which consists of two aspects: R-D performance analysis and R-D modeling. In R-D performance analysis, we first derive the preliminary constraint of optimum entropy-constrained DZ+UTSQ/NURQ for GGD, under which the property of the GGD distortion-rate (D-R) function is elucidated. Then for the GGD source of actual transform coefficients, the refined constraint and precise conditions of optimum DZ+UTSQ/NURQ are rigorously deduced in the real coding bit rate range, and efficient DZ+UTSQ/NURQ design criteria are proposed to reasonably simplify the utilization of effective quantizers in practice. In R-D modeling, inspired by R-D performance analysis, the D-R function is first developed, followed by the novel rate-quantization (R-Q) and distortion-quantization (D-Q) models derived using analytical and heuristic methods. The D-R, R-Q, and D-Q models form the source model describing the relationship between the rate, distortion, and quantization steps. One application of the proposed source model is the effective two-pass VBR coding algorithm design on an encoder of H.264/AVC reference software, which achieves constant video quality and desirable rate control accuracy. [ABSTRACT FROM AUTHOR]

As part of video transmission, video quality assessment plays an important role in improving the user’s visual quality and reducing transmission bitrate. However, the existing assessment schemes for the tile-based transmission mechanism cannot accurately predict the video quality. In this paper, we propose a tile-based panoramic video quality assessment method to optimize the transmission strategy for tile-based streaming. Considering the distortion of panoramic video projection, the inverse of gnomonic projection and bilinear interpolation are utilized to convert the planar tiles to sphere. Video Multimethod Assessment Fusion (VMAF) is applied to obtain the tile quality score. Besides, the user’s head movement and eye movement are combined to generate a tile weight map, and generate the final score of the panoramic video through weighted averaging. In addition, considering the impact of the quality fluctuation of the tiles within field of view, the tile quality loss model is established from multiple dimensions including the encoding parameter, viewport position and video content. Experimental results show that there is a high correlation between the predicted results and actual subjective scores, under two video quality assessment datasets. Furthermore, we integrate the method into the panoramic video transmission system. Compared with the tile-based adaptive rate algorithm, the proposed method degrades the video quality by only 3%, while reducing the bandwidth consumption by 16.99%. [ABSTRACT FROM AUTHOR]

This paper gives a numerical model to deal with the thermal effect of field-induced thermal-assisted current passing through the microprotrusion on the cathode surface of a planar high-voltage vacuum diode. The model of protrusion is based on a one-dimensional truncated cone, whose physical and thermal properties have been assumed temperature dependent. The influences of space charge effect and Nottingham effect are included. According to the simulations, the delay times of explosive electron emission of cathodes made of different materials are given and compared with the experimental results, which seems to be in agreement. [ABSTRACT FROM AUTHOR]

This paper describes design and implementation of an end-to-end system which implements digital television (DTV) multicasting to the home over broadband IP access network. We have developed a DTV-IP gateway for introducing DTV programs from broadcasting network into IF network with special considerations of the challenges, which consist of different service models and different protocols when deploying DTV services between the traditional satellite, cable or terrestrial network and the broadband IP network. According to different application scenario, the DTV-IP gateway can output two types of streams: CBR (constant bit-rate) transport stream (TS) over IP and VBR (variable bit-rate) TS over II' Moreover, we have proposed a method to auto-regenerate the standard CBR TS from VBR TS over IP for the standard set-top-box (STB) with consideration of timing synchronization. This end-to-end system has been verified to work well in the broadband IP access network environment. [ABSTRACT FROM AUTHOR]

Gardner's timing method for BPSK or QPSK is actually one of the timing methods using the Nyquist sidebands. However, it requires at least two samples per symbol; and it is more suitable for two-level modulations. This paper presents another timing synchronization method that is suitable for ATSC DTV receivers, in which 8-VSB modulation is used. The correlation of both arms of the VSB signal is exploited. The S-curve of the proposed method is derived. Derivation shows the proposed method is also using the Nyquist sidebands. It can estimate the timing error even at symbol-rate. Simulations show the proposed method has good performance even in multi-path conditions. [ABSTRACT FROM AUTHOR]

Investigates the behavior of space charge in two types of Polyethylene(PE)/ionomer blends by measuring the charge distributions and conduction characteristics. Materials for experimental procedures of the space charge in two types of polyethylene/ionomer; Charge distributions of polyethylene/ionomer blends; Experimental results from obtained charge distributions in PE and ionomers; Comparison of polyethylene and ionomer blends; Conclusions.

In this article, the iterative learning control (ILC) problem is investigated for a class of stochastic time-varying systems with variable pass lengths. The randomness of the pass lengths is described by the recursive interval Gaussian distribution, and a modified iteration-average operator is developed to construct the novel ILC scheme for overcoming the limitation of conventional ILC algorithms that every pass must end in a fixed time of duration throughout the repetition. The proposed ILC approach works effectively to guarantee the boundedness of the tracking errors, which is demonstrated by a practical case study on a type of robot manipulator with two joints. [ABSTRACT FROM AUTHOR]

The effect of the local electric field on the radial oscillation of the electron beam in the low-magnetic-field foilless diode is studied theoretically and experimentally. The results obtained with different methods show good coherence. The radial oscillation amplitude of the electron beam affected by the local electric field depends on the electric field intensity and the phases of electrons entering and leaving the local electric field region. It is found that the enhanced radial electric field near the drift tube head results in the change of the radial oscillation amplitude with the anode–cathode gap. Meanwhile, a method to effectively suppress the radial oscillation is proposed. Optimizing the anode–cathode gap and the tilt angle of the drift tube head can effectively adjust the matching state of the electron phase and the local electric field, and thus the transverse momentum of the electron beam can be greatly reduced due to the negative effect of the radial electric field. [ABSTRACT FROM AUTHOR]

H∞ control can guarantee the performance of a nominal ICPT system. However, the dynamic performance may suffer degradation when the parameter deviates from its nominal value. Thus, to enhance the overall dynamic performance of the ICPT system under parameter perturbations, an H∞ control method with a selected weighting function considering parameter perturbations is proposed in this article. First, a generalized state-space model is established to analyze the influence of parameter perturbations on the open-loop system in detail. After that, an uncertain model is established via frequency-domain analysis, generating a numerical solution of a weighting function used to describe the parameter perturbations. Then, the generalized plant with uncertainty is obtained and transferred into a standard H∞ optimal problem via small gain theory. Based on the proposed controller, the dynamic performance and robustness of the closed-loop system are analyzed and verified theoretically. Finally, simulation and experimental results further verify that the proposed H∞ control method can make the closed-loop system obtain good dynamic performance and strong robustness against parameter perturbations. The settling time is less than 6.8 ms without any overshoot when the system suffers parameter perturbations. Besides, the controller can maintain good dynamic performance in the startup and reference tracking experiments. [ABSTRACT FROM AUTHOR]

The soft switching of power components in inductive power transfer systems can reduce switching loss and switching stress and decrease electromagnetic interference. However, the realization of soft switching in an inductive power transfer system may change the system's constant voltage and constant current output characteristics. For this reason, in this article, a zero-voltage switching technology based on a full-bridge inverter that is decoupled from the output characteristics of the system is proposed and validated. First, the basic conditions for the full-bridge inverter to achieve zero voltage switching are derived by analyzing the working mode of the full-bridge inverter circuit. Then, the constant current, constant voltage output, and zero phase angle input conditions of a first-order/second-order/third-order compensation structure are studied using the high-order two-port network cascading rule. On this basis, a parameter adjustment strategy for an arbitrary compensation structure is proposed to achieve zero voltage switching without affecting the output. Then analytical verification of the constant current output characteristics of a double-sided inductor-capacitor-capacitor compensation structure is performed as an example. Finally, a MATLAB/Simulink model and a set of 3.3 kW prototypes are established to verify the correctness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Objective quality estimation of media content plays a vital role in a wide range of applications. Though numerous metrics exist for 2D images and videos, similar metrics are missing for 3D point clouds with unstructured and non-uniformly distributed points. In this paper, we propose ${\sf GraphSIM}$ GraphSIM —a metric to accurately and quantitatively predict the human perception of point cloud with superimposed geometry and color impairments. Human vision system is more sensitive to the high spatial-frequency components (e.g., contours and edges), and weighs local structural variations more than individual point intensities. Motivated by this fact, we use graph signal gradient as a quality index to evaluate point cloud distortions. Specifically, we first extract geometric keypoints by resampling the reference point cloud geometry information to form an object skeleton. Then, we construct local graphs centered at these keypoints for both reference and distorted point clouds. Next, we compute three moments of color gradients between centered keypoint and all other points in the same local graph for local significance similarity feature. Finally, we obtain similarity index by pooling the local graph significance across all color channels and averaging across all graphs. We evaluate ${\sf GraphSIM}$ GraphSIM on two large and independent point cloud assessment datasets that involve a wide range of impairments (e.g., re-sampling, compression, and additive noise). ${\sf GraphSIM}$ GraphSIM provides state-of-the-art performance for all distortions with noticeable gains in predicting the subjective mean opinion score (MOS) in comparison with point-wise distance-based metrics adopted in standardized reference software. Ablation studies further show that ${\sf GraphSIM}$ GraphSIM can be generalized to various scenarios with consistent performance by adjusting its key modules and parameters. Models and associated materials will be made available at https://njuvision.github.io/GraphSIM or http://smt.sjtu.edu.cn/papers/GraphSIM. [ABSTRACT FROM AUTHOR]

Most existing random access schemes for machine-type communications (MTC) simply adopt a uniform preamble selection distribution, irrespective of the underlying device activity distributions. Hence, they may yield unsatisfactory access efficiency. In this paper, we model device activities for MTC as multiple Bernoulli random variables following an arbitrary multivariate Bernoulli distribution which can reflect both dependent and independent device activities. Then, we optimize preamble selection and access barring for random access in MTC according to the underlying joint device activity distribution. Specifically, we investigate three cases of the joint device activity distribution, i.e., the cases of perfect, imperfect, and unknown joint device activity distributions, and formulate the average, worst-case average, and sample average throughput maximization problems, respectively. The problems in the three cases are challenging nonconvex problems. In the case of perfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain stationary points of the original problem and an approximate problem, respectively. In the case of imperfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain a Karush-Kuhn-Tucker (KKT) point of an equivalent problem and a stationary point of an approximate problem, respectively. Finally, in the case of unknown joint device activity distribution, we develop an iterative algorithm to obtain a stationary point. The proposed solutions are widely applicable and outperform existing solutions for dependent and independent device activities. [ABSTRACT FROM AUTHOR]

Accurate estimation of mutual inductance in wireless power transfer (WPT) system is a prerequisite for accurate identification of load parameters, which is critical in system constant output and efficiency tracking control. In this article, a general load decoupling method for WPT system considering soft-start process and battery initial voltage is proposed. Moreover, a double-sided LCC topology is used to explain how to calculate the boundary conditions of load decoupling. Based on the proposed load decoupling method, a mutual inductance identification method is proposed for WPT system with double-sided LCC topology. Within the frequency range recommended, the unique solution of mutual inductance can be obtained by solving high-ordered equivalent impedance equation. In this case, there is no need to discuss the situation for multiple solutions. Based on the WPT prototype, the effectiveness of the proposed load decoupling method is verified, and parameter sensitivity of the identification method is analyzed in detail. Finally, the proposed method is experimentally verified. Results show that high identification accuracy can be achieved even when the coils are misaligned. Compared with the existing identification methods, the proposed method does not need any additional switches and auxiliary circuits, which reduces the complexity and system cost effectively. [ABSTRACT FROM AUTHOR]

We study the problem of two-stage sequential coding (TSSC), which is an extension of sequential coding of correlated sources. Let $X$ and $Y$ be dependent random variables. The network contains two encoders and two decoders: 1) a $Y$ encoder with input $Y$ ; 2) an $X$ encoder with inputs $X$ and $Y$ ; 3) a $Y$ decoder that reconstructs $Y$ ; and 4) an $X$ decoder that reconstructs $X$ . The first stage is traditional sequential coding, where the $Y$ encoder describes $Y$ to both the $X$ decoder and $Y$ decoder, and the $X$ encoder describes $X$ and $Y$ to the $X$ decoder. At the second stage, the $Y$ encoder refines the description of $Y$ , and the $X$ encoder refines the description of $X$ . The TSSC model is a theoretical abstraction of scalable video coding; here, $Y$ and $X$ represent successive frames of a video sequence, and the two stages together give an embedded description that allows the video to be decoded at two distinct rates. We give an inner bound on the rate distortion region for this TSSC model. The tight bound on the rate distortion region is derived when $Y$ must be reconstructed losslessly (in the usual Shannon sense) in the second stage. We also study the minimum total rate of the TSSC model and show that the minimum total rate of one-stage sequential coding cannot be achieved at both stages for jointly Gaussian sources. This theoretical result can shed light on the rate-distortion performance behavior of scalable video coding widely noted by practitioners. [ABSTRACT FROM AUTHOR]

For a drag-free satellite, one core technology required to achieve the mission requirements is drag-free control. The purpose of control is to offset the interference such as sunlight pressure, gravitational gradients of Earth and Moon, nonspherical perturbation, etc. Currently, the drag-free controller design is mainly aimed at the full frequency band. This will inevitably mix the control signal into the scientific measurement signal, thereby interfering with the processing of scientific data. Besides, the control at the full frequency band will increase fuel consumption. To solve the above problems, this article presented the controller design method for a drag-free satellite with scientific measurement constraints, which covers motion model establishing, coordinate axis decomposition, frequency separation control strategy formulation, H∞ mixed sensitivity controller design, controller stability analysis, and performance results. [ABSTRACT FROM AUTHOR]

As the impedance of the foilless diode for a relativistic backward wave oscillator and the transmission line of the high-current accelerator are mismatching, a method is brought forward to study the voltage and current transient process of the foilless diode by analyzing the voltage and current wave reflection and superposition. In addition, by theoretical analysis, particle-in-cell numerical simulations and experiment based on the SINUS-881 accelerator, the effects of periodic oscillations of growth rate in the high-voltage pulse's rising edge applying to the diode from the Tesla-type accelerator and the time when the cathode begins to emit on transient process are discussed. In addition, the characteristics of rising edges of voltage and current are meticulously discerned. [ABSTRACT FROM AUTHOR]

Experimental study on high-power subterahertz waves generated by an overmoded surface wave oscillator (SWO) is carried out in this paper. With accurate fabrication and assembly, the frequency obtained is 0.148 THz as measured by the cut-off waveguide and subharmonic heterodyne methods, and the output power is about 5 MW with the far-field measurement method at a diode voltage of 300 kV, beam current of 2.3 kA, and guiding magnetic field of 5 T. The frequency of the device remains fairly stable when the voltage of the diode changes from 200 to 330 kV. This is a typical feature of the SWO, and is verified by this experiment. The start-up time of the device is as small as 1 ns, which is suitable for use of the device as a short-pulse accelerator with a compact structure. [ABSTRACT FROM AUTHOR]

Foreground detection is an important task in computer vision applications. In this paper, we present an efficient foreground detection method based on a robust linear regression model. First, a novel framework is proposed where foreground detection has been cast as an outlier signal estimation problem in a linear regression model. We regularize this problem by imposing a so-called fused sparsity constraint, which encourages both sparsity and smoothness of vector coefficients, on the outlier signal. Second, we convert this outlier signal estimation problem into an equivalent Fused Lasso problem, and then use existing solutions to obtain an optimized solution. Third, a new foreground detection method is presented to apply this new model to the 2-D image domain by merging the results from different vectorizations. Experiments on a set of challenging sequences show that the proposed method is not only superior to many state-of-the-art techniques, but also robust to noise. [ABSTRACT FROM PUBLISHER]

This paper presents a dual camera system for high spatiotemporal resolution (HSTR) video acquisition, where one camera shoots a video with high spatial resolution and low frame rate (HSR-LFR) and another one captures a low spatial resolution and high frame rate (LSR-HFR) video. Our main goal is to combine videos from LSR-HFR and HSR-LFR cameras to create an HSTR video. We propose an end-to-end learning framework, AWnet, mainly consisting of a FlowNet and a FusionNet that learn an adaptive weighting function in pixel domain to combine inputs in a frame recurrent fashion. To improve the reconstruction quality for cameras used in reality, we also introduce noise regularization under the same framework. Our method has demonstrated noticeable performance gains in terms of both objective PSNR measurement in simulation with different publicly available video and light-field datasets and subjective evaluation with real data captured by dual iPhone 7 and Grasshopper3 cameras. Ablation studies are further conducted to investigate and explore various aspects, such as reference structure, camera parallax, exposure time, etc) of our system to fully understand its capability for potential applications. [ABSTRACT FROM AUTHOR]

Remote sensing images obtained from onboard linear array cameras suffer from geometric disturbances in the presence of attitude jitter from a satellite platform. Thus, platform jitter estimation is essential for improving satellite stability and enhancing remote sensing image quality. In this article, a novel integration framework is designed to estimate attitude fluctuation, which combines multispectral advanced spaceborne thermal emission and reflection/short-wave-infrared (ASTER/SWIR) data and blurred star images from onboard optical sensors. First, a multilevel XGBoost algorithm is applied to learn the nonlinear relationship between the star blurring and instantaneous jitter displacement. An image registration method and a deconvolutional process are then introduced to remove the satellite jitter from ASTER/SWIR data. Next, an augmented $H$$_\infty$ filter is proposed to fuse and estimate the satellite jitter from two algorithms. Simulating experiment results indicate that the jitter estimation error of the proposed integration framework is reduced by 40%. Compared with the existing jitter estimation methods only based on remote sensing image processing, our strategy has better robustness and accuracy, especially on extreme ground scenes. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) tumor is the most common primary brain malignant tumor. The precise identification of GBM tumors is very important for diagnosis and treatment. Hyperspectral imaging is a fast, noncontact, accurate, and safe modern medical detection technology, which is expected to be a new tool of intraoperative diagnosis. In order to make full use of the spectral and spatial information of hyperspectral images (HSIs) to achieve accurate GBM tumor identification, a method based on the fusion of multiple deep models is proposed for in vivo human brain HSI classification. The proposed method includes the following major steps: 1) spectral phasor analysis and data oversampling; 2) 1-D deep neural network (1D-DNN)-based spectral HSI feature extraction and classification; 3) 2-D convolution neural network (2D-CNN)-based spectral–spatial HSI feature extraction and classification; 4) edge-preserving filtering-based classification result fusion and optimization; and 5) fully convolutional network (FCN)-based background segmentation. To verify the capabilities of the proposed method, experiments are performed on two real human brain hyperspectral datasets, including 36 in vivo HSIs captured from 16 different patients. The proposed method can achieve an overall accuracy of 96.69% for four-class classification and overall accuracy of 96.34% for GBM tumor identification. Experimental results demonstrate that the proposed method exhibits competitive classification performance and can generate satisfactory thematic maps of the location of the GBM tumor, which can provide the surgeon with guidance on successful and precise tumor resection. [ABSTRACT FROM AUTHOR]

Proxy re-encryption (PRE) allows a proxy to transform one ciphertext to another under different encryption keys while keeping the underlying plaintext secret. Because of the ciphertext transformability of PRE, there are many potential private communicating applications of this feature. However, existing PRE schemes are not as full-fledged as expected. The lack of necessary features makes them hard to apply in real-world scenarios. So far, there does not exist a unidirectional multihop PRE scheme with constant decryption efficiency and constant ciphertext size without extensions. Impractical performance and weak scalability also hinder PRE from most real-world applications. In this work, we present a new PRE scheme with secure hardware enclave named hPRESS (hardware-enhanced PRE scheme using secure enclave). To the best of our knowledge, hPRESS is the first unidirectional multihop PRE scheme which achieves both constant decryption efficiency and constant ciphertext size without extensions. A detailed security analysis demonstrates that our proposal is CCA secure based on the security of the underlying encryption schemes and the secure enclave. We also implement a prototype based on Intel SGX, one of the most popular secure enclave techniques in recent years, and evaluate its performance. The experimental results show that, compared with previous PRE schemes, our hPRESS is almost one order of magnitude faster in terms of the decryption and transformation. [ABSTRACT FROM AUTHOR]

Natural type selection was originally proposed by Zamir and Rose for universal single description coding. In this paper, we generalize this principle to universal multiple description coding (MDC). Two schemes based on random codebooks are proposed: one is of fixed distortion and the other is of fixed weight. Their operational sum-rate-distortion functions are derived, which coincide with the EGC (El Gamal-Cover) sum-rate bound if the parameters of the schemes are optimized. It is also shown that in both schemes the joint type of reconstruction codewords can be used to improve the rate-distortion (R-D) performance. Based on our theoretical results, a practical universal scheme is proposed by leveraging the MDC methods based on low-density generator matrix (LDGM) codes. The performance of this scheme is compared experimentally with the EGC bound, which shows its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we are to solve the drift problem of multiple description video coding on packet loss channels by using state-of-the-art distributed techniques. We first present an asymptotically optimal code design of multiple descriptions in the Wyner–Ziv (MDWZ) setting. Then we propose a distributed multiple description video coding (DMDVC) scheme, which performs MDWZ coding on each nonintra coded frame. Instead of the prediction loops used in traditional multiple description video coding, Slepian–Wolf based coding is used to exploit interframe correlations. A bitplane extraction scheme is proposed to improve the balance between two descriptions, so that side informations can be interchanged between the side decoders of DMDVC with negligible quality degradation, which is crucial to robust transmission over packet loss channels. Experiment results demonstrate the robustness of our scheme, especially at high packet loss rates. [ABSTRACT FROM AUTHOR]

Developmental dysplasia of the hip (DDH) is one of the most common orthopedic disorders in infants and young children. Accurately detecting and identifying the misshapen anatomical landmarks plays a crucial role in the diagnosis of DDH. However, the diversity during the calcification and the deformity due to the dislocation lead it a difficult task to detect the misshapen pelvis landmarks for both human expert and computer. Generally, the anatomical landmarks exhibit stable morphological features in part regions and rigid structural features in long ranges, which can be strong identification for the landmarks. In this paper, we investigate the local morphological features and global structural features for the misshapen landmark detection with a novel Pyramid Non-local UNet (PN-UNet). Firstly, we mine the local morphological features with a series of convolutional neural network (CNN) stacks, and convert the detection of a landmark to the segmentation of the landmark’s local neighborhood by UNet. Secondly, a non-local module is employed to capture the global structural features with high-level structural knowledge. With the end-to-end and accurate detection of pelvis landmarks, we realize a fully automatic and highly reliable diagnosis of DDH. In addition, a dataset with 10,000 pelvis X-ray images is constructed in our work. It is the first public dataset for diagnosing DDH and has been already released for open research. To the best of our knowledge, this is the first attempt to apply deep learning method in the diagnosis of DDH. Experimental results show that our approach achieves an excellent precision in landmark detection (average point to point error of 0.9286mm) and illness diagnosis over human experts. Project is available at http://imcc.ustc.edu.cn/project/ddh/. [ABSTRACT FROM AUTHOR]

In this article, a position estimation method is proposed to realize sensorless control of planar switched reluctance motor (PSRM) with core loss. First, a square-wave voltage generated through bipolar pulsewidth modulation (PWM) is injected into the idle phase of a PSRM, thereby generating core loss. The corresponding core-loss average power (CLAP), which contains position information, is calculated in real time. Then, depending on the six-phase CLAP, the initial position can be estimated for the PSRM startup. To stabilize the position estimation in different regions of pole pitches, position data measured in a long stroke are utilized to determine a function mapping from the CLAP with respect to position. Furthermore, a sensorless control system and commutation strategy are designed. Finally, the capability of different methods for position estimation and sensorless control of a PSRM system are experimentally investigated. The feasibility and effectiveness of the proposed method are verified through the experimental results. [ABSTRACT FROM AUTHOR]

An accurate mover position is important for a planar switched reluctance motor (PSRM) to achieve precision positioning; however, position sensors are susceptible to the influence from harsh conditions and are relatively expensive. In this article, an assistant-mover-based position estimation method is proposed to increase the reliability and reduce the cost of a PSRM system. The method is based on a mapping relationship between the flux density and position of the assistant mover. First, the configuration of the assistant movers is presented. Then, an analytical model of the flux density of the assistant mover is developed by using an equivalent magnetic circuit method. Based on the analytical model, the parameters of the assistant mover are determined. According to the flux density characteristics, a position estimation algorithm with the advantages of not requiring previous experimental data, simple computation, and small memory consumption is developed. Moreover, considering the deviation in the characteristics of each assistant mover, a compensation strategy is proposed to obtain better position estimation accuracy. Finally, the proposed method is implemented with the PSRM system. The effectiveness of the proposed position estimation method is verified experimentally. [ABSTRACT FROM AUTHOR]

Hyperspectral image super-resolution addresses the problem of fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to produce a high-resolution hyperspectral image (HR-HSI). In this paper, we propose a novel fusion approach for hyperspectral image super-resolution by exploiting the specific properties of matrix decomposition, which consists of four main steps. First, an endmember extraction algorithm is used to extract an initial spectral matrix from LR-HSI. Then, with the initial spectral matrix, we estimate the spatial matrix, i.e., the spatial-contextual information, from the degraded observations of HR-HSI. Third, the spatial matrix is further utilized to estimate the spectral matrix from LR-HSI by solving a least squares (LS)-based problem. Finally, the target HR-HSI is constructed by combing the estimated spectral and spatial matrixes. In particular, two models are proposed to estimate the spatial matrix. One is a simple case that involves a LS-based problem, and the other is an elaborate case that consists of two fidelity terms and a spatial regularizer, where the spatial regularizer aiming to restrain the range of solutions is achieved by exploiting the superpixel-level low-rank characteristics of HR-HSI. Experiment results conducted on both synthetic and real data sets demonstrate the effectiveness of the proposed approach as compared to other hyperspectral image super-resolution methods. [ABSTRACT FROM AUTHOR]

Screen content image (SCI) prevails because of the explosive growth of screen oriented applications. This leads to extensive studies on SCI quality assessment and modeling for application optimization. In this paper, we propose a full reference multiscale edge attention (MSEA) similarity index to efficiently measure the perceptual quality of a screen image. This model considers the perceptual impacts of fixation attention, edge structure and edge contrast jointly, to accurately capture the masking phenomena (e.g., frequency selectivity, luminance, contrast, etc.) of our human visual system (HVS) when viewing a screen image. Specifically, we decompose the images using Gaussian and Laplacian pyramids which are then used to derive the edge structure, and edge contrast feature maps. Together with the fixation attention map generated by weighted luminance difference between the reference and distorted SCIs, we could eventually offer a MSEA similarity map for final index score. We have evaluated this model using a publicly accessible screen image database. Simulation results have shown that the MSEA similarity index correlates with the collected subjective mean opinion score (MOS) very well. In fact, it is ranked at the first place for both Pearson linear correlation coefficient (PLCC) and Root mean squared error (RMSE), and ranked at the second place for Spearman rank-order correlation coefficient (SROCC) measurements, among existing quality metrics. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel tensorial approach, namely, generalized tensor regression, for hyperspectral image classification. First, a simple and effective classifier, i.e., the ridge regression for multivariate labels, is extended to its tensorial version by taking advantages of tensorial representation. Then, the discrimination information of different modes is exploited to further strengthen the capacity of the model. Moreover, the model can be simplified and solved easily. Different from traditional tensorial methods, the proposed model can be utilized to capture not only the intrinsic structure of data in a physical sense but also the generalized relationship of data in a logical sense. Our proposed approach is shown to be effective for different classification purposes on a series of instantiations. Specifically, our experiment results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer, the reflective optics spectrographic imaging system and the ITRES CASI-1500 demonstrate the effectiveness of the proposed approach as compared to other tensor-based classifiers and multiple kernel learning methods. [ABSTRACT FROM AUTHOR]

For the 360-degree virtual reality (VR) videos, to achieve the same user-perceived playout quality with traditional video, the overall video bitrate has to be much higher, which significantly increases the bandwidth requirement. To overcome this issue, most of rate adaptation schemes are proposed based on the users’ Field of View (FoV), since users only view part of the entire videos when watching 360-degree videos. However, the FoV prediction is only valid in short terms which incurs low robustness against network dynamics and results in frozen screen events. Since the playback buffer can effectively avoid re-buffering by storing videos in advance, we propose a two-layered hierarchical buffer-based rate adaptation scheme for 360-degree VR videos in this paper. The tiles will be updated to enhance the video quality in the short buffer layer based on FoV prediction, meanwhile the long buffer layer takes charge of downloading new chunks to guarantee the buffer occupancy. To better improve the quality of experience (QoE), we jointly consider the users’ FoV, channel conditions and buffer status. Our method achieves a good trade-off between video quality and buffer occupancy. Besides, the maximum buffer level selection is optimized to further improve the performance. The simulation results show that our proposed scheme and algorithms outperform other methods by about 5%-30% of QoE improvement. [ABSTRACT FROM AUTHOR]

Recently, a new structure of low density parity check (LDPC) code named raptor-like LDPC code has attracted much attention. It has better performance at low code rate. In this paper, a novel decoding scheme for raptor-like LDPC code is proposed. First, the Gaussian approximation density evolution (GADE) algorithm is used to track and analyze the message transmission during the decoding process. It is found that certain log likelihood ratio (LLR) messages can be approximated by “zero” setting in the early iteration of raptor-like LDPC decoding. In other words, some column and row operations could be eliminated without compromise the performance. Next, we propose a new decoding scheme, which can skip unnecessary column and row operations. In comparison with the traditional belief propagation (BP)-based LDPC decoding scheme, the proposed scheme can reduce the decoding complexity. Additionally, a new algorithm is developed to facilitate the selection of the early iteration number. With this novel design, the proposed decoding scheme performs almost the same as the traditional BP-based scheme. To proof the concept, the raptor-like LDPC codes in the ATSC3.0 digital TV system are used to evaluate the proposed scheme, in comparison with the traditional BP-based schemes, i.e., sum-product algorithm (SPA), offset min-sum algorithm (OMSA), and normalized min-sum algorithm (NMSA). The simulation results confirm that the proposed scheme can reduce the decoding complexity without sacrifice in performance for all SPA, OMSA, and NMSA methods. About 10% complexity reduction can be achieved. This will reduce the buttery consumption for Internet of Things (IoT) and handheld devices. [ABSTRACT FROM AUTHOR]