arXiv subjects

Qiang Li

Publications and source records attributed to Qiang Li.

At least 451 records · Page 25Linked to original sources

arXiv2020

Depth Restoration: A fast low-rank matrix completion via dual-graph regularization

As a real scenes sensing approach, depth information obtains the widespread applications. However, resulting from the restriction of depth sensing technology, the depth map captured in practice usually suffers terrible noise and missing values at plenty of pixels. In this paper, we propose a fast low-rank matrix completion via dual-graph regularization for depth restoration. Specifically, the depth restoration can be transformed into a low-rank matrix completion problem. In order to complete the low-rank matrix and restore it to the depth map, the proposed dual-graph method containing the local and non-local graph regularizations exploits the local similarity of depth maps and the gradient consistency of depth-color counterparts respectively. In addition, the proposed approach achieves the high speed depth restoration due to closed-form solution. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods with respect to both objective and subjective quality evaluations, especially for serious depth degeneration.

cs.CV↗

arXiv2019

Deep Learning based Blind Symbol Packing Ratio Estimation for Faster-than-Nyquist Signaling

This letter proposes a blind symbol packing rartio estimation for faster-than-Nyquist (FTN) signaling based on state-of-the-art deep learning (DL) technology. The symbol packing rartio is a vital parameter to obtain the real symbol rate and recover the origin symbols from the received symbols by calculating the intersymbol interference (ISI). To the best of our knowledge, this is the first effective estimation approach for symbol packing rartio in FTN signaling and has shown its fast convergence and robustness to signal-to-noise ratio (SNR) by numerical simulations. Benefiting from the proposed blind estimation, the packing-ratio-based adaptive FTN transmission without dedicate channel or control frame becomes available. Also, the secure FTN communications based on secret symbol packing rartio can be easily cracked.

eess.SP↗

arXiv2019

Experimental observation of anomalous weak value without post-selection

Weak measurement has been shown to play important roles in the investigation of both fundamental and practical problems. Anomalous weak values are generally believed to be observed only when post-selection is performed, i.e, only a particular subset of the data is considered. Here, we experimentally demonstrated an anomalous weak value can be obtained without discarding any data by performing a sequential weak measurement on a single-qubit system. By controlling the blazing density of the hologram on a spatial light modulator, the measurement strength can be conveniently controlled. Such an anomalous phenomenon disappears when the measurement strength becomes strong. Moreover, we find that the anomalous weak value can not be observed without post-selection when the sequential measurement is performed on each of the components of a two-qubit system, which confirms that the observed anomalous weak value is based on sequential weak measurement of two noncommutative operators.

quant-ph↗

arXiv2019

Machine Learning-based Signal Detection for PMH Signals in Load-modulated MIMO System

Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the \textit{load-modulated} multiple-input multiple-output (MIMO) transmitters with central power amplifiers (CPA). However, it is difficult to obtain the precise channel state information (CSI), and the traditional optimal maximum likelihood (ML) detection scheme incurs high complexity which increases exponentially with the number of antennas and the number of bits carried per antenna in the PMH modulation. To detect the PMH signals without knowing the prior CSI, we first propose a signal detection scheme, termed as the hypersphere clustering scheme based on the expectation maximization (EM) algorithm with maximum likelihood detection (HEM-ML). By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector. To further reduce the computational complexity in the ML detection in HEM-ML, we also propose the second signal detection scheme, termed as the hypersphere clustering scheme based on the EM algorithm with KD-tree detection (HEM-KD). The CSI obtained from the EM algorithm is used to build a spatial KD-tree receiver codebook and the signal detection problem can be transformed into a nearest neighbor search (NNS) problem. The detection complexity of HEM-KD is significantly reduced without any detection performance loss as compared to HEM-ML. Extensive simulation results verify the effectiveness of our proposed detection schemes.

cs.IT↗

arXiv2019

Optimization of the power broadening in optically detected magnetic resonance of defect spins in silicon carbide

Defect spins in silicon carbide have become promising platforms with respect to quantum information processing and quantum sensing. Indeed, the optically detected magnetic resonance (ODMR) of defect spins is the cornerstone of the applications. In this work, we systematically investigate the contrast and linewidth of laser-and microwave power-dependent ODMR with respect to ensemble-divacancy spins in silicon carbide at room temperature. The results suggest that magnetic field sensing sensitivity can be improved by a factor of 10 for the optimized laser and microwave power range. The experiment will be useful for the applications of silicon carbide defects in quantum information processing and ODMR-dependent quantum sensing.

cond-mat.mes-hall↗

arXiv2019

The mass spectra and wave functions for the doubly heavy baryons with $J^P=1^+$ heavy diquark core

The mass spectra and wave functions for the doubly heavy baryons are computed under the picture that the two heavy quarks inside a doubly heavy baryon, such as two $c$-quarks in $Ξ_{cc}$, combine into a heavy `diquark core' in color anti-triplet firstly, then the diquark core turns into a color-less doubly heavy baryon via combining the light $q$-quark inside the baryon. Namely both of the combinations, the two heavy quarks inside the baryon into a diquark core in color anti-triplet and the heavy diquark core with the light quark into the baryon, are depicted by relativistic Bethe-Salpeter equations (BSEs) with an accordingly QCD inspired kernel respectively, although in the paper only the heavy diquark cores with the quantum numbers $J^P=1^+$ are considered. Since the `second combination' is of the heavy diquark core and the light quark, so the structure effect of the diquark core to the relevant kernel of the BSE is specially considered in terms of the diquark-core wave functions. The mass spectra and wave functions for the `low-laying' doubly heavy baryons in the flavors $(ccq)$, $(bcq)$ and $(bbq)$ and in the quantum numbers $J^P=\frac{1}{2}^+$, $J^P=\frac{3}{2}^+$, achieved by solving the equations under the so-called instantaneous approximation, are presented properly and some comparisons with the others' results under different approaches in the literature are made.

hep-ph↗

arXiv2019

Mixing angle and decay constants of $J^P=1^+$ heavy-light mesons

The mass spectra, mixing angle and decay constants of the $J^P=1^+$ heavy-light mesons are systematically studied within the framework of the Bethe-Salpeter equation (BSE). The full $1^+$ Salpeter wave function is given for the first time. The mixing between the $1^{+-}$ and $1^{++}$ in the $1^+$ heavy-light systems are automatically determined by the dynamics in the equation without any man-made mixing. The results indicate that in a rigorous study there exists the phenomenon of mixing angle inversion or mass inversion within $1^{+}$ heavy-light doublet, which is sensitive to the $s$-quark mass for the charmed mesons and $u$- or $d$-quark masses for the bottomed mesons. This inversion phenomenon can answer the question of why we have confused mixing angles in the literature and partly explain the lower mass of $D_{s1}(2460)$ compared to that of $D_{s1}(2536)$. The decay constants are also presented and can behave as a good quantity to distinguish the $1^+$ doublet in heavy-light mesons. This study indicates that the light-quark mass may play an important role in deciding the mass order, mixing angle, and decay constant relation between the $\ket{j_l=\frac{3}{2}}$ and $\ket{j_l=\frac{1}{2}}$ heavy-light mesons.

hep-ph↗

arXiv2019

Polarization fraction measurement in ZZ scattering using deep learning

Measuring longitudinally polarized vector boson scattering in the ZZ channel is a promising way to investigate unitarity restoration with the Higgs mechanism and to search for possible new physics. We investigated several deep neural network structures and compared their ability to improve the measurement of the longitudinal fraction Z_L Z_L. Using fast simulation with the Delphes framework, a clear improvement is found using a previously investigated 'particle-based' deep neural network on a preprocessed dataset and applying principle component analysis to the outputs.A significance of around 1.7 standard deviations can be achieved with the integrated luminosity of 3000 fb-1 that will be recorded at the High-Luminosity LHC.

hep-ph↗

arXiv2019

Measurements of differential and angle-integrated cross sections for the $^{10}$B($n, α$)$^{7}$Li reaction in the neutron energy range from 1.0 eV to 2.5 MeV

Differential and angle-integrated cross sections for the $^{10}$B($n, α$)$^{7}$Li, $^{10}$B($n, α$$_{0}$)$^{7}$Li and $^{10}$B($n, α$$_{1}$)$^{7}$Li$^{*}$ reactions have been measured at CSNS Back-n white neutron source. Two enriched (90%) $^{10}$B samples 5.0 cm in diameter and ~85.0 $μ$g/cm$^{2}$ in thickness each with an aluminum backing were prepared, and back-to-back mounted at the sample holder. The charged particles were detected using the silicon-detector array of the Light-charged Particle Detector Array (LPDA) system. The neutron energy E$_{n}$ was determined by TOF (time-of-flight) method, and the valid $α$ events were extracted from the E$_{n}$-Amplitude two-dimensional spectrum. With 15 silicon detectors, the differential cross sections of $α$-particles were measured from 19.2° to 160.8°. Fitted with the Legendre polynomial series, the ($n, α$) cross sections were obtained through integration. The absolute cross sections were normalized using the standard cross sections of the $^{10}$B($n, α$)$^{7}$Li reaction in the 0.3 - 0.5 MeV neutron energy region. The measurement neutron energy range for the $^{10}$B($n, α$)$^{7}$Li reaction is 1.0 eV $\le$ En < 2.5 MeV (67 energy points), and for the $^{10}$B($n, α$$_{0}$)$^{7}$Li and $^{10}$B($n, α$$_{1}$)$^{7}$Li$^{*}$ reactions is 1.0 eV $\le$ En < 1.0 MeV (59 energy points). The present results have been analyzed by the resonance reaction mechanism and the level structure of the $^{11}$B compound system, and compared with existing measurements and evaluations.

nucl-ex↗

arXiv2019

The New Purity and Capacity Models for the OAM-mmWave Communication Systems under Atmospheric Turbulence

The orbital angular momentum (OAM) wireless communication technology is widely studied in recent literatures. But the atmospheric turbulence is rarely considered in analyzing the capacity of OAM-based millimeter wave (OAM-mmWave) communication systems. The OAM-mmWave propagated in the atmosphere environments is usually interfered by the atmospheric turbulence, resulting in the crosstalk among OAM channels,capacity degradation, etc. By taking into account the atmospheric turbulence effect, this paper proposes a new purity model and a new capacity model for the OAM-mmWave communication systems. Simulation results indicate that the OAM-mmWave propagation in the atmosphere environments is evidently interfered by atmospheric turbulence, where the capacity of the OAMmmWave communication systems decreases with the increase of the transmission frequency.

eess.SP↗

arXiv2019

Joint Long-Term Admission Control and Beamforming in Green Downlink Networks: Offline and Online Approaches

Admission control is an effective solution to managing a wireless network with a large user set. It dynamically rejects users in bad conditions, and thereby guarantees high quality-of-service (QoS) for the others. Unfortunately, a frequently-varying admissible user set requests remarkable power to continually re-establish the transmission link. Hence, we explore the stability of admissible user set, and formulate a joint long-term admission control and beamforming problem for a network with one multi-antenna base station (BS) and multiple single-antenna users. We consider the downlink transmission in a time period containing multiple time slices. By jointly optimizing the admissible users and the BS transmit beamformers in different time slices, we aim to minimize the total power cost, including not only the power for QoS guarantee, but also the power for user status switching. Due to the NP-hardness of the problem, we develop two (offline and online) algorithms to find some efficient suboptimal solutions. The offline algorithm requires all channel state information (CSI), and solves the admissible users and beamformers in different time slices in one shot, based on successive upper-bound minimization (SUM). To support real-time data transmission, we further design an alternating direction method of multipliers (ADMM)-based online algorithm, which outputs the admissible users and beamformers in different time slices successively, utilizing the previous admissible user set, the actual value of current CSI, and the distribution of future CSI. Simulations validate the performance of the two algorithms, and show that the online algorithm is an efficient practical alternative to the offline algorithm.

cs.IT↗

arXiv2019

End-to-end Hand Mesh Recovery from a Monocular RGB Image

In this paper, we present a HAnd Mesh Recovery (HAMR) framework to tackle the problem of reconstructing the full 3D mesh of a human hand from a single RGB image. In contrast to existing research on 2D or 3D hand pose estimation from RGB or/and depth image data, HAMR can provide a more expressive and useful mesh representation for monocular hand image understanding. In particular, the mesh representation is achieved by parameterizing a generic 3D hand model with shape and relative 3D joint angles. By utilizing this mesh representation, we can easily compute the 3D joint locations via linear interpolations between the vertexes of the mesh, while obtain the 2D joint locations with a projection of the 3D joints.To this end, a differentiable re-projection loss can be defined in terms of the derived representations and the ground-truth labels, thus making our framework end-to-end trainable.Qualitative experiments show that our framework is capable of recovering appealing 3D hand mesh even in the presence of severe occlusions.Quantitatively, our approach also outperforms the state-of-the-art methods for both 2D and 3D hand pose estimation from a monocular RGB image on several benchmark datasets.

cs.CV↗

arXiv2019

Adversarial Training Methods for Network Embedding

Network Embedding is the task of learning continuous node representations for networks, which has been shown effective in a variety of tasks such as link prediction and node classification. Most of existing works aim to preserve different network structures and properties in low-dimensional embedding vectors, while neglecting the existence of noisy information in many real-world networks and the overfitting issue in the embedding learning process. Most recently, generative adversarial networks (GANs) based regularization methods are exploited to regularize embedding learning process, which can encourage a global smoothness of embedding vectors. These methods have very complicated architecture and suffer from the well-recognized non-convergence problem of GANs. In this paper, we aim to introduce a more succinct and effective local regularization method, namely adversarial training, to network embedding so as to achieve model robustness and better generalization performance. Firstly, the adversarial training method is applied by defining adversarial perturbations in the embedding space with an adaptive $L_2$ norm constraint that depends on the connectivity pattern of node pairs. Though effective as a regularizer, it suffers from the interpretability issue which may hinder its application in certain real-world scenarios. To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of the adversarial examples in the discrete graph domain. These two regularization methods can be applied to many existing embedding models, and we take DeepWalk as the base model for illustration in the paper. Empirical evaluations in both link prediction and node classification demonstrate the effectiveness of the proposed methods.

cs.LG↗

arXiv2019

A Framework for One-Bit and Constant-Envelope Precoding over Multiuser Massive MISO Channels

Consider the following problem: A multi-antenna base station (BS) sends multiple symbol streams to multiple single-antenna users via precoding. However, unlike conventional multiuser precoding, the transmitted signals are subjected to binary, unit-modulus, or even discrete unit-modulus constraints. Such constraints arise in the one-bit and constant-envelope (CE) massive MIMO scenarios, wherein high-resolution digital-to-analog converters (DACs) are replaced by one-bit DACs and phase shifters, respectively, for cutting down hardware cost and energy consumption. Multiuser precoding under one-bit and CE restrictions poses significant design difficulty. In this paper we establish a framework for designing multiuser precoding under the one-bit, continuous CE and discrete CE scenarios---all within one theme. We first formulate a precoding design that focuses on minimizations of symbol-error probabilities (SEPs), assuming quadrature amplitude modulation (QAM) constellations. We then devise an algorithm for our SEP-based design. The algorithm combines i) a novel penalty method for handling binary, unit-modulus and discrete unit-modulus constraints; and ii) a first-order non-convex optimization recipe custom-built for the design. Specifically, the latter is an inexact majorization-minimization method via accelerated projected gradient, which, as shown by simulations, runs very fast and can handle a large number of decision variables. Simulation results indicate that the proposed design offers significantly better bit-error rate performance than the existing designs.

cs.IT↗

arXiv2019

One-Bit Sigma-Delta MIMO Precoding

Coarsely quantized MIMO signalling methods have gained popularity in the recent developments of massive MIMO as they open up opportunities for massive MIMO implementation using cheap and power-efficient radio-frequency front-ends. This paper presents a new one-bit MIMO precoding approach using spatial Sigma-Delta ($ΣΔ$) modulation. In previous one-bit MIMO precoding research, one mainly focuses on using optimization to tackle the difficult binary signal optimization problem that arises from the precoding design. Our approach attempts a different route. Assuming angular MIMO channels, we apply $ΣΔ$ modulation---a classical concept in analog-to-digital conversion of temporal signals---in space. The resulting $ΣΔ$ precoding approach has two main advantages: First, we no longer need to deal with binary optimization in $ΣΔ$ precoding design. Particularly, the binary signal restriction is replaced by peak signal amplitude constraints. Second, the impact of the quantization error can be well controlled via modulator design and under appropriate operating conditions. Through symbol error probability analysis, we reveal that the very large number of antennas in massive MIMO provides favorable operating conditions for $ΣΔ$ precoding. In addition, we develop a new $ΣΔ$ modulation architecture that is capable of adapting the channel to achieve nearly zero quantization error for a targeted user. Furthermore, we consider multi-user $ΣΔ$ precoding using the zero-forcing and symbol-level precoding schemes. These two $ΣΔ$ precoding schemes perform considerably better than their direct one-bit quantized counterparts, as simulation results show.

cs.IT↗

arXiv2019

Effects of infall and outflow on massive star-forming regions

A total of 188 high-mass outflows have been identified from a sample of 694 clumps from the Millimetre Astronomy Legacy Team 90 GHz survey, representing a detection rate of approximately 27%. The detection rate of outflows increases from the protostellar stage to the H II stage, but decreases again at the photodissociation (PDR) stage suggesting that outflows are being switched off during the PDR stage. An intimate relationship is found between outflow action and the presence of masers, and water masers appear together with 6.7 GHz methanol masers. Comparing the infall detection rate of clumps with and without outflows, we find that outflow candidates have a lower infall detection rate. Finally, we find that outflow action has some influence on the local environment and the clump itself, and this influence decreases with increasing evolutionary time as the outflow action ceases.

astro-ph.GA↗

arXiv2019

Spatial Variation of the Chemical Properties of Massive Star-forming Clumps

We selected 90 massive star-forming clumps with strong N2H+, HCO+, HCN, and HNC emission from the Millimetre Astronomy Legacy Team 90 GHz survey. We obtained Herschel data for all 90 sources and NRAO VLA Sky Survey data for 51 of them. We convolved and regridded all images to the same resolution and pixel size and derived the temperature, H2 column density, molecules' abundances and abundance, and ratios of each pixel. Our analysis yields three main conclusions. First, the abundances of N2H+, HCO+, HCN, and HNC increase when the column density decreases and the temperature increases, with spatial variations in their abundances dominated by changes in the H2 column density. Second, the abundance ratios between N2H+, HCO+, HCN, and HNC also display systemic variations as a function of the column density due to the chemical properties of these molecules. Third, the sources associated with the 20 cm continuum emission can be classified into four types based on the behavior of the abundances of the four molecules considered here as a function of this emission. The variations of the first three types could also be attributed to the variation of the H2 column density.

astro-ph.GA↗

arXiv2019

Joint Fronthaul Multicast and Cooperative Beamforming for Cache-Enabled Cloud-Based Small Cell Networks: An MDS Codes-Aided Approach

The performance of cloud-based small cell networks (C-SCNs) relies highly on a capacity-limited fronthaul, which degrade quality of service when it is saturated. Coded caching is a promising approach to addressing these challenges, as it provides abundant opportunities for fronthaul multicast and cooperative transmissions. This paper investigates a cache-enabled C-SCNs, in which small-cell base stations (SBSs) are connected to the central processor via fronthaul, and can prefetch popular contents by applying maximum distance separable (MDS) codes. To fully capture the benefits of fronthaul multicast and cooperative transmissions, an MDS codes-aided transmission scheme is first proposed. We formulate the problem to minimize the content delivery latency by jointly optimizing fronthaul bandwidth allocation, SBS clustering, and beamforming. To efficiently solve the resulting nonlinear integer programming problem, we propose a penalty-based design by leveraging variational reformulations of binary constraints. To improve the solution of the penalty-based design, a greedy SBS clustering design is also developed. Furthermore, closed-form characterization of the optimal solution is obtained, through which the benefits of MDS codes can be quantified. Simulation results are given to demonstrate the significant benefits of the proposed MDS codes-aided transmission scheme.

cs.IT↗