arXiv subjects

Chang Liu

Publications and source records attributed to Chang Liu.

At least 973 records · Page 54Linked to original sources

arXiv2021

Non-equilibrium Flux Rope Formation by Confined Flares Preceding a Solar Coronal Mass Ejection

We present evidence that a magnetic flux rope was formed before a coronal mass ejection (CME) and its associated long-duration flare during a pair of preceding confined eruptions and associated impulsive flares in a compound event in NOAA Active Region 12371. Extreme-ultraviolet images and the extrapolated nonlinear force-free field show that the first two, impulsive flares, SOL2015-06-21T01:42, result from the confined eruption of highly sheared low-lying flux, presumably a seed flux rope. The eruption spawns a vertical current sheet, where magnetic reconnection creates flare ribbons and loops, a nonthermal microwave source, and a sigmoidal hot channel which can only be interpreted as a magnetic flux rope. Until the subsequent long-duration flare, SOL2015-06-21T02:36, the sigmoid's elbows expand, while its center remains stationary, suggesting non-equilibrium but not yet instability. The "flare reconnection" during the confined eruptions acts like "tether-cutting reconnection" whose flux feeding of the rope leads to instability. The subsequent full eruption is seen as an accelerated rise of the entire hot channel, seamlessly evolving into the fast halo CME. Both the confined and ejective eruptions are consistent with the onset of the torus instability in the dipped decay index profile which results from the region's two-scale magnetic structure. We suggest that the formation or enhancement of a non-equilibrium but stable flux rope by confined eruptions is a generic process occurring prior to many CMEs.

astro-ph.SR↗

arXiv2021

Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References

It is suggested that low-light image enhancement realizes one-to-many mapping since we have different definitions of NORMAL-light given application scenarios or users' aesthetic. However, most existing methods ignore subjectivity of the task, and simply produce one result with fixed brightness. This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference. Inspired by style transfer, our method decomposes an image into two low-coupling feature components in the latent space, which allows the concatenation feasibility of the content components from low-light images and the luminance components from reference images. In such a way, the network learns to extract scene-invariant and brightness-specific information from a set of image pairs instead of learning brightness differences. Moreover, information except for the brightness is preserved to the greatest extent to alleviate color distortion. Extensive results show strong capacity and superiority of our network against existing methods.

cs.CV↗

arXiv2020

Observation of spin-momentum-layer locking in centrosymmetric BiOI

Spin polarization effects in nonmagnetic materials are generally believed as an outcome of spin-orbit coupling provided that the global inversion symmetry is lacking, also known as 'spin-momentum locking'. The recently discovered hidden spin polarization indicates that specific atomic site asymmetry could also induce measurable spin polarization, leading to a paradigm shift to centrosymmetric crystals for potential spintronic applications. Here, combining spin- and angle-resolved photoemission spectroscopy and theoretical calculations, we report distinct spin-layer locking phenomena surrounding different high-symmetry momenta in a centrosymmetric, layered material BiOI. The measured spin is highly polarized along the Brillouin zone boundary, while is almost vanishing around the zone center due to its nonsymmorphic crystal structure. Our work not only demonstrates the existence of hidden spin polarization, but also uncovers the microscopic mechanism of the way spin, momentum and layer locking to each other, shedding lights on the design metrics for future spintronic devices.

cond-mat.mtrl-sci↗

arXiv2020

Constraining gravitational-wave polarizations with Taiji

Space-based gravitational-wave detectors consist of a triangle of three spacecraft, which makes it possible to detect polarization modes of gravitational waves due to the motion of the detectors in space. In this paper we explore the ability of Taiji to detect the polarization modes in the parametrized post-Einsteinian framework. Assuming massive black hole binaries with the total mass of $M=4\times10^5\,M_{\odot}$ at redshift of $z=1$, we find that Taiji can measure the dipole and quadruple emission ($Δα_D/α_D$ and $Δα_Q/α_Q$) with the accuracy of up to $\sim 0.04\%$, with the fiducial value of $α_D=0.001$, the scalar transverse and longitudinal modes ($Δα_B$ and $Δα_L$) up to $\sim 0.01$, and the vector modes ($Δα_V$) up to $\sim 0.0005$.

gr-qc↗

arXiv2020

Analytical solution for the surface states of antiferromagnetic topological insulator MnBi$_2$Te$_4$

Recently, the intrinsic magnetic topological insulator MnBi$_2$Te$_4$ has attracted great attention. It has an out-of-plane antiferromagnetic order, which is believed to open a sizable energy gap in the surface states. This gap, however, was not always observable in the latest angle-resolved photoemission spectroscopy (ARPES) experiments. To address this issue, we analytically derive an effective model for the two-dimensional (2D) surface states by starting from a three-dimensional (3D) Hamiltonian for bulk MnBi$_2$Te$_4$ and taking into account the spatial profile of the bulk magnetization. Our calculations suggest that the diminished surface gap may be caused by a much smaller and more localized intralayer ferromagnetic order. In addition, we calculate the spatial distribution and penetration depth of the surface states, which indicates that the surface states are mainly embedded in the first two septuple layers from the terminating surface. From our analytical results, the influence of the bulk parameters on the surface states can be found explicitly. Furthermore, we derive a $\bf{k}\cdot \bf{p}$ model for MnBi$_2$Te$_4$ thin films and show the oscillation of the Chern number between odd and even septuple layers. Our results will be helpful for the ongoing explorations of the MnBi$_x$Te$_y$ family.

cond-mat.mes-hall↗

arXiv2020

Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization

In computed tomography (CT), automatic exposure control (AEC) is frequently used to reduce radiation dose exposure to patients. For organ-specific AEC, a preliminary CT reconstruction is necessary to estimate organ shapes for dose optimization, where only a few projections are allowed for real-time reconstruction. In this work, we investigate the performance of automated transform by manifold approximation (AUTOMAP) in such applications. For proof of concept, we investigate its performance on the MNIST dataset first, where the dataset containing all the 10 digits are randomly split into a training set and a test set. We train the AUTOMAP model for image reconstruction from 2 projections or 4 projections directly. The test results demonstrate that AUTOMAP is able to reconstruct most digits well with a false rate of 1.6% and 6.8% respectively. In our subsequent experiment, the MNIST dataset is split in a way that the training set contains 9 digits only while the test set contains the excluded digit only, for instance "2". In the test results, the digit "2"s are falsely predicted as "3" or "5" when using 2 projections for reconstruction, reaching a false rate of 94.4%. For the application in medical images, AUTOMAP is also trained on patients' CT images. The test images reach an average root-mean-square error of 290 HU. Although the coarse body outlines are well reconstructed, some organs are misshaped.

eess.IV↗

arXiv2020

Strain-Rate-Dependent Deformation Behavior of Ti29Zr24Nb23Hf24 High Entropy Alloys at Elevated and Room Temperature

We investigate the strain-rate-dependent mechanical behavior and deformation mechanisms of a refractory high entropy alloy, Ti29Zr24Nb23Hf24 (at.%), with a single-phase body-centered cubic (BCC) structure. High-temperature compression tests were conducted at temperatures from 700 to 1100°C at strain rates ranging from 10-3 to 10 s-1. A sudden stress drop after yield point was observed at higher temperatures and lower strain rates with the Zener-Holloman parameter, lnZ, in the range of 17.2-20.7. Such a softening behavior can be related to the interaction of dislocations with short-range clustering. However, at higher strain rates or lower temperatures (lnZ>25.0), kink bands were activated being responsible for the continuous strengthening of the alloy in competition with the softening mechanism. Systematic TEM investigations reveal that dislocation walls formed along {110} planes and dislocation loops were observed at a low strain of 6% at a high strain rate of 1 s-1 and 800°C. Kink band induced dynamic recrystallization is evident upon further straining. On the other hand, at low strain rate of 10-3 s-1 and 800°C, discontinuous recrystallization mechanisms become dominant with arrays of dislocations forming in front of the bulged boundaries of parent grains. These sub-grain boundaries eventually turn into high-angle grain boundaries. We also investigate the deformation mechanism of the alloy under extremely high strain rate (103 s-1) at room temperature. The specimen exhibits extensive kink bands with arrays of dislocation walls. As further strained, multiple slip systems can be activated and the interaction of dislocation walls plays a vital role in the strain hardening of the alloy.

cond-mat.mtrl-sci↗

arXiv2020

Broadband holography-assisted coherent imaging -- towards attosecond imaging at the nanoscale

In recent years nanoscale coherent imaging has emerged as an indispensable imaging modality allowing to surpass the resolution limit given by classical imaging optics. At the same time, attosecond science has experienced enormous progress and has revealed the ultrafast dynamics in atoms, molecules, and complex materials. Combining attosecond temporal resolution of pump-probe experiments with nanometer spatial resolution would allow studying ultrafast dynamics on the smallest spatio-temporal scales but has not been demonstrated yet. Unfortunately, the large bandwidth of attosecond pulses usually hinders high-resolution coherent imaging. Here we present a robust holography-enhanced coherent imaging method, which allows combining high quality and high spatial resolution coherent imaging with a large spectral bandwidth. By implementing our method at a high harmonic source we demonstrate, for the first time, a spatial resolution of 34 nm (2.5 λ) in combination with a spectral bandwidth supporting a Fourier limited pulse duration of only 380 as. The method is single-shot capable, additionally retrieves the spectrum from the measured diffraction pattern, and is thus immune against shot-to-shot fluctuations. This paves the way for an ultrafast view on nanoscale dynamics e.g. ultrafast charge transfer or ultrafast spin currents being relevant for Petahertz electronics and future data storage.

physics.optics↗

arXiv2020

Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems

Channel estimation is of great importance in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems. However, different from traditional communication systems, an IRS-MC system generally involves a cascaded channel with a sophisticated statistical distribution, which hinders the implementations of the Bayesian estimators. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a data-driven approach to realize the channel estimation. Specifically, we propose a convolutional neural network (CNN)-based deep residual network (CDRN) to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. In the proposed CDRN, a CNN denoising block equipped with an element-wise subtraction structure is designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously, which further improves the estimation accuracy. Simulation results demonstrate that the proposed method can almost achieve the same estimation accuracy as that of the optimal minimum mean square error (MMSE) estimator requiring the knowledge of the channel distribution.

eess.SP↗

arXiv2020

Reconstruction Condition of Quantized Signals in Unlimited Sampling Framework

The latest theoretical advances in the field of unlimited sampling framework (USF) show the potential to avoid clipping problems of analog-to-digital converters (ADC). To date, most of the related works have focused on real-valued modulo samples, but little has been reported about the impact of quantization. In this paper, we study more practical USF system where modulo samples are quantized to a finite number of bits. In particular, we present a new requirement about the lower bound of sampling rate to ensure exact recovery of original signals from quantized modulo samples. The minimum sampling rate is jointly determined by signal bandwidth and quantization bits. Numerical results show that in the presence of quantization noise, signals with different waveforms and bandwidths are recovered perfectly at the new minimum sampling rate while the recovery fails at minimum sampling rate before modification, which also verifies the correctness of the theory. The trade-offs of sampling rates, quantization bits and computational complexity of recovery algorithm are also given for practitioners to weigh.

eess.SP↗

arXiv2020

Cost-Effective Quasi-Parallel Sensing Instrumentation for Industrial Chemical Species Tomography

Chemical Species Tomography (CST) has been widely applied for imaging of critical gas-phase parameters in industrial processes. To acquire high-fidelity images, CST is typically implemented by line-of-sight Wavelength Modulation Spectroscopy (WMS) measurements from multiple laser beams. The modulated transmission signal on each laser beam needs to be a) digitised by a high-speed analogue-to-digital converter (ADC); b) demodulated by a digital lock-in (DLI) module; and c) transferred to high-level processor for image reconstruction. Although a fully parallel data acquisition (DAQ) and signal processing system can achieve these functionalities with maximised temporal response, it leads to a highly complex, expensive and power-consuming instrumentation system with high potential for inconsistency between the sampled beams due to the electronics alone. In addition, the huge amount of spectral data sampled in parallel significantly burdens the communication process in industrial applications where in situ signal digitisation is distanced from the high-level data processing. To address these issues, a quasi-parallel sensing technique and electronic circuits were developed for industrial CST, in which the digitisation and demodulation of the multi-beam transmission signals are multiplexed over the high-frequency modulation within a wavelength scan. Our development not only maintains the temporal response of the fully parallel sensing scheme, but also facilitates the cost-effective implementation of industrial CST with very low complexity and reduced load on data transfer. The proposed technique is analytically proven, numerically examined by noise-contaminated CST simulations, and experimentally validated using a lab-scale CST system with 32 laser beams.

eess.SY↗

arXiv2020

Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering

With only bounding-box annotations in the spatial domain, existing video scene text detection (VSTD) benchmarks lack temporal relation of text instances among video frames, which hinders the development of video text-related applications. In this paper, we systematically introduce a new large-scale benchmark, named as STVText4, a well-designed spatial-temporal detection metric (STDM), and a novel clustering-based baseline method, referred to as Temporal Clustering (TC). STVText4 opens a challenging yet promising direction of VSTD, termed as ST-VSTD, which targets at simultaneously detecting video scene texts in both spatial and temporal domains. STVText4 contains more than 1.4 million text instances from 161,347 video frames of 106 videos, where each instance is annotated with not only spatial bounding box and temporal range but also four intrinsic attributes, including legibility, density, scale, and lifecycle, to facilitate the community. With continuous propagation of identical texts in the video sequence, TC can accurately output the spatial quadrilateral and temporal range of the texts, which sets a strong baseline for ST-VSTD. Experiments demonstrate the efficacy of our method and the great academic and practical value of the STVText4. The dataset and code will be available soon.

cs.CV↗

arXiv2020

An input-output inspired method for permissible perturbation amplitude of transitional wall-bounded shear flows

The precise set of parameters governing the transition to turbulence in wall-bounded shear flows remains an open question; many theoretical bounds have been obtained, but there is not yet a consensus between these bounds and experimental/simulation results. In this work, we focus on a method to provide a provable Reynolds number dependent bound on the amplitude of perturbations a flow can sustain while maintaining the laminar state. Our analysis relies on an input--output approach that partitions the dynamics into a feedback interconnection of the linear and nonlinear dynamics (i.e., a Luré system that represents the nonlinearity as static feedback). We then construct quadratic constraints of the nonlinear term that is restricted by system physics to be energy-conserving (lossless) and to have bounded input--output energy. Computing the region of attraction of the laminar state (set of safe perturbations) and permissible perturbation amplitude are then reformulated as Linear Matrix Inequalities (LMI), which provides a more computationally efficient solution than prevailing nonlinear approaches based on the sum of squares programming. The proposed framework can also be used for energy method computations and linear stability analysis. We apply our approach to low dimensional nonlinear shear flow models for a range of Reynolds numbers. The results from our analytically derived bounds are consistent with the bounds identified through exhaustive simulations. However, they have the added benefit of being achieved at a much lower computational cost and providing a provable guarantee that a certain level of perturbation is permissible.

physics.flu-dyn↗

arXiv2020

Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols. To this end, we develop a DTL detection framework which consists of offline learning, transfer learning, and online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is derived based on the minimum error probability (MEP) criterion. As a realization of the developed framework, we then apply convolutional neural networks (CNN) to intelligently explore the features of the sample covariance matrix, which facilitates the design of a CNN-based algorithm for tag signal detection. Exploiting the powerful capability of CNN in extracting features of data in the matrix formation, the proposed method is able to further improve the system performance. In addition, an asymptotic explicit expression is also derived to characterize the properties of the proposed CNN-based method when the number of samples is sufficiently large. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.

eess.SP↗

arXiv2020

Computational Design and Fabrication of Corrugated Mechanisms from Behavioral Specifications

Orthogonally assembled double-layered corrugated (OADLC) mechanisms are a class of foldable structures that harness origami-inspired methods to enhance the structural stiffness of resulting devices; these mechanisms have extensive applications due to their lightweight, compact nature as well as their high strength-to-weight ratio. However, the design of these mechanisms remains challenging. Here, we propose an efficient method to rapidly design OADLC mechanisms from desired behavioral specifications, i.e. in-plane stiffness and out-of-plane stiffness. Based on an equivalent plate model, we develop and validate analytical formulas for the behavioral specifications of OADLC mechanisms; the analytical formulas can be described as expressions of design parameters. On the basis of the analytical expressions, we formulate the design of OADLC mechanisms from behavioral specifications into an optimization problem that minimizes the weight with given design constraints. The 2D folding patterns of the optimized OADLC mechanisms can be generated automatically and directly delivered for fabrication. Our rapid design method is demonstrated by developing stiffness-enhanced mechanisms with a desired out-of-plane stiffness for a foldable gripper that enables a blimp to perch steadily under air disturbance and weight limit.

cs.RO↗

arXiv2020

Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of communication channel and directly recover tag symbols. Inspired by the powerful capability of convolutional neural networks (CNN) in exploring the features of data in a matrix form, we design a novel covariance matrix aware neural network (CMNet)-based detection scheme to facilitate DTL for tag signal detection, which consists of offline learning, transfer learning, and online detection. Specifically, a CMNet-based likelihood ratio test (CMNet-LRT) is derived based on the minimum error probability (MEP) criterion. Taking advantage of the outstanding performance of DTL in transferring knowledge with only a few training data, the proposed scheme can adaptively fine-tune the detector for different channel environments to further improve the detection performance. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.

eess.SP↗

arXiv2020

Adaptive Linear Span Network for Object Skeleton Detection

Conventional networks for object skeleton detection are usually hand-crafted. Although effective, they require intensive priori knowledge to configure representative features for objects in different scale granularity.In this paper, we propose adaptive linear span network (AdaLSN), driven by neural architecture search (NAS), to automatically configure and integrate scale-aware features for object skeleton detection. AdaLSN is formulated with the theory of linear span, which provides one of the earliest explanations for multi-scale deep feature fusion. AdaLSN is materialized by defining a mixed unit-pyramid search space, which goes beyond many existing search spaces using unit-level or pyramid-level features.Within the mixed space, we apply genetic architecture search to jointly optimize unit-level operations and pyramid-level connections for adaptive feature space expansion. AdaLSN substantiates its versatility by achieving significantly higher accuracy and latency trade-off compared with state-of-the-arts. It also demonstrates general applicability to image-to-mask tasks such as edge detection and road extraction. Code is available at \href{https://github.com/sunsmarterjie/SDL-Skeleton}{\color{magenta}github.com/sunsmarterjie/SDL-Skeleton}.

cs.CV↗

arXiv2020

Unified gas-kinetic wave-particle methods V: diatomic molecular flow

In this paper, the unified gas-kinetic wave-particle (UGKWP) method is further developed for diatomic gas with the energy exchange between translational and rotational modes for flow study in all regimes. The multiscale transport mechanism in UGKWP is coming from the direct modeling in a discretized space, where the cell's Knudsen number, defined by the ratio of particle mean free path over the numerical cell size, determines the flow physics simulated by the wave particle formulation. The non-equilibrium distribution function in UGKWP is tracked by the discrete particle and analytical wave. The weights of distributed particle and wave in different regimes are controlled by the accumulating evolution solution of particle transport and collision within a time step, where distinguishable macroscopic flow variables of particle and wave are updated inside each control volume. With the variation of local cell's Knudsen number, the UGKWP becomes a particle method in the highly rarefied flow regime and converges to the gas-kinetic scheme (GKS) for the Navier-Stokes solution in the continuum flow regime without particles. Even targeting on the same solution as the discrete velocity method (DVM)-based unified gas-kinetic scheme (UGKS), the computational cost and memory requirement in UGKWP could be reduced by several orders of magnitude for the high speed and high temperature flow simulation, where the translational and rotational non-equilibrium becomes important in the transition and rarefied regime. As a result, 3D hypersonic computations around a flying vehicle in all regimes can be conducted using a personal computer. The UGKWP method for diatomic gas will be validated in various cases from one dimensional shock structure to three dimensional flow over a sphere, and the numerical solutions will be compared with the reference DSMC results and experimental measurements.

physics.comp-ph↗