arXiv subjects

Chao Yu

Publications and source records attributed to Chao Yu.

180 records · Page 10Linked to original sources

arXiv2020

Deep Learning-based Modulation Detection for NOMA Systems

Since the signal with strong power should be demodulated first for successive interference cancellation (SIC) demodulation in non-orthogonal multiple access (NOMA) systems, the base station (BS) should inform the near user terminal (UT), which has allocated higher power, of modulation mode of the far user terminal. To avoid unnecessary signaling overhead in this process, a blind detection algorithm of NOMA signal modulation mode is designed in this paper. Taking the joint constellation density diagrams of NOMA signal as the detection features, deep residual network is built for classification, so as to detect the modulation mode of NOMA signal. In view of the fact that the joint constellation diagrams are easily polluted by high intensity noise and lose their real distribution pattern, the wavelet denoising method is adopted to improve the quality of constellations. The simulation results represent that the proposed algorithm can achieve satisfactory detection accuracy in NOMA systems. In addition, the factors affecting the recognition performance are also verified and analyzed.

eess.SP↗

arXiv2020

Electron-Backscattering-Assisted High Harmonic Generation from Bilayer Nanostructures

In the framework of time-dependent density functional theory, we obtain high-order harmonics of photon energies up to 10 Up from bilayer crystals with an interlayer spacing d = 70 Å. At grazing incidence, a clear double-plateau structure is observed in the harmonic spectrum. The photon energy of the second plateau far beyond atomic-like harmonics can be well explained by the inclusion of backscattering of ionized electrons. Ab initio simulations reveal that the cutoff of the second plateau is continuously extended with an increasing d. Our classical calculations predict that the maximum electronic kinetic energy is linearly dependent on d over a wide range. Moreover, the harmonic yield in the second plateau is significantly enhanced by increases in the wavelength of the driving laser. Owing to the confined spreading of the electronic wave packet, a beneficial wavelength scaling of λ2.85 is obtained. This study therefore establishes a novel and efficient way of producing high-energy light source based on layered nanostructures.

physics.optics↗

arXiv2020

Multi-IF : An Approach to Anomaly Detection in Self-Driving Systems

Autonomous driving vehicles (ADVs) are implemented with rich software functions and equipped with many sensors, which in turn brings broad attack surface. Moreover, the execution environment of ADVs is often open and complex. Hence, ADVs are always at risk of safety and security threats. This paper proposes a fast method called Multi-IF, using multiple invocation features of system calls to detect anomalies in self-driving systems. Since self-driving functions take most of the computation resources and upgrade frequently, Multi-IF is designed to work under such resource constraints and support frequent updates. Given the collected sequences of system calls, the combination of different syntax patterns is used to analyze and construct feature vectors of those sequences. By taking the feature vectors as inputs, one-class support vector machine is adopted to determine whether the current sequence of system calls is abnormal, which is trained with the feature vectors from the normal sequences. The evaluations on both simulated and real data prove that the proposed method is effective in identifying the abnormal behavior after minutes of feature extraction and training. Further comparisons with the existing methods on the ADFA-LD data set also validate that the proposed approach achieves a higher accuracy with less time overhead.

cs.CR↗

arXiv2020

Reinforcement Learning in Healthcare: A Survey

As a subfield of machine learning, reinforcement learning (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback. Unlike traditional supervised learning methods that usually rely on one-shot, exhaustive and supervised reward signals, RL tackles with sequential decision making problems with sampled, evaluative and delayed feedback simultaneously. Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure. This survey discusses the broad applications of RL techniques in healthcare domains, in order to provide the research community with systematic understanding of theoretical foundations, enabling methods and techniques, existing challenges, and new insights of this emerging paradigm. By first briefly examining theoretical foundations and key techniques in RL research from efficient and representational directions, we then provide an overview of RL applications in healthcare domains ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis from both unstructured and structured clinical data, as well as many other control or scheduling domains that have infiltrated many aspects of a healthcare system. Finally, we summarize the challenges and open issues in current research, and point out some potential solutions and directions for future research.

cs.LG↗

arXiv2020

How to obtain complex transition dipole moments satisfying crystal symmetry and periodicity from ab-initio calculations

Transition dipole moments (TDM) between energy bands of solids deserve special attention nowadays as intense lasers can easily drive non-adiabatic transitions of excited electron wave packets across the Brillouin zones. The TDM is required to be continuous, satisfying crystal symmetry, and periodic at zone boundaries. While present day ab-initio algorithms are powerful in calculating band structures of solids, they all introduced random phases into the eigenfunctions at each crystal momentum k. In this paper, we show how to choose a ``smooth-periodic'' gauge where TDMs can be smooth versus k, preserving crystal symmetry, as well as maintaining periodic at zone boundaries. Based on band structure and TDMs in the ``smooth-periodic'' gauge calculated from ab-initio algorithms, we revisit high-order harmonic generation from MgO which exhibits inversion symmetry and ZnO which has broken symmetry. The symmetry properties of TDMs with respect to k ensure the absence of even-order harmonics in system with inversion symmetry, while the TDM in the `smooth-periodic'' gauge for ZnO is shown to enhance even harmonics that were underestimated in previous simulations. These results reveal the importance of correctly treating the complex TDMs in nonlinear laser-solid interactions which has been elusive so far.

physics.optics↗

arXiv2020

Super-resolution single-photon imaging at 8.2 kilometers

Single-photon light detection and ranging (LiDAR), offering single-photon sensitivity and picosecond time resolution, has been widely adopted for active imaging applications. Long-range active imaging is a great challenge, because the spatial resolution degrades significantly with the imaging range due to the diffraction limit of the optics, and only weak echo signal photons can return but mixed with a strong background noise. Here we propose and demonstrate a photon-efficient LiDAR approach that can achieve sub-Rayleigh resolution imaging over long ranges. This approach exploits fine sub-pixel scanning and a deconvolution algorithm tailored to this long-range application. Using this approach, we experimentally demonstrated active three-dimensional (3D) single-photon imaging by recognizing different postures of a mannequin model at a stand-off distance of 8.2 km in both daylight and night. The observed spatial (transversal) resolution is about 5.5 cm at 8.2 km, which is about twice of the system's resolution. This also beats the optical system's Rayleigh criterion. The results are valuable for geosciences and target recognition over long ranges.

eess.IV↗

arXiv2019

Symmetrical Gaussian Error Linear Units (SGELUs)

In this paper, a novel neural network activation function, called Symmetrical Gaussian Error Linear Unit (SGELU), is proposed to obtain high performance. It is achieved by effectively integrating the property of the stochastic regularizer in the Gaussian Error Linear Unit (GELU) with the symmetrical characteristics. Combining with these two merits, the proposed unit introduces the capability of the bidirection convergence to successfully optimize the network without the gradient diminishing problem. The evaluations of SGELU against GELU and Linearly Scaled Hyperbolic Tangent (LiSHT) have been carried out on MNIST classification and MNIST auto-encoder, which provide great validations in terms of the performance, the convergence rate among these applications.

cs.LG↗

arXiv2019

Single-photon computational 3D imaging at 45 km

Long-range active imaging has a variety of applications in remote sensing and target recognition. Single-photon LiDAR (light detection and ranging) offers single-photon sensitivity and picosecond timing resolution, which is desirable for high-precision three-dimensional (3D) imaging over long distances. Despite important progress, further extending the imaging range presents enormous challenges because only weak echo photons return and are mixed with strong noise. Herein, we tackled these challenges by constructing a high-efficiency, low-noise confocal single-photon LiDAR system, and developing a long-range-tailored computational algorithm that provides high photon efficiency and super-resolution in the transverse domain. Using this technique, we experimentally demonstrated active single-photon 3D-imaging at a distance of up to 45 km in an urban environment, with a low return-signal level of $\sim$1 photon per pixel. Our system is feasible for imaging at a few hundreds of kilometers by refining the setup, and thus represents a significant milestone towards rapid, low-power, and high-resolution LiDAR over extra-long ranges.

eess.IV↗

arXiv2018

DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments

Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain circumstances. However, some problems are still not well solved, for example, how to tackle the moving objects in the dynamic environments, how to make the robots truly understand the surroundings and accomplish advanced tasks. In this paper, a robust semantic visual SLAM towards dynamic environments named DS-SLAM is proposed. Five threads run in parallel in DS-SLAM: tracking, semantic segmentation, local mapping, loop closing, and dense semantic map creation. DS-SLAM combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in the real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved by one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments. Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLAM

cs.RO↗

arXiv2018

Learning Shaping Strategies in Human-in-the-loop Interactive Reinforcement Learning

Providing reinforcement learning agents with informationally rich human knowledge can dramatically improve various aspects of learning. Prior work has developed different kinds of shaping methods that enable agents to learn efficiently in complex environments. All these methods, however, tailor human guidance to agents in specialized shaping procedures, thus embodying various characteristics and advantages in different domains. In this paper, we investigate the interplay between different shaping methods for more robust learning performance. We propose an adaptive shaping algorithm which is capable of learning the most suitable shaping method in an on-line manner. Results in two classic domains verify its effectiveness from both simulated and real human studies, shedding some light on the role and impact of human factors in human-robot collaborative learning.

cs.HC↗

arXiv2018

The Price of Governance: A Middle Ground Solution to Coordination in Organizational Control

Achieving coordination is crucial in organizational control. This paper investigates a middle ground solution between decentralized interactions and centralized administrations for coordinating agents beyond inefficient behavior. We first propose the price of governance (PoG) to evaluate how such a middle ground solution performs in terms of effectiveness and cost. We then propose a hierarchical supervision framework to explicitly model the PoG, and define step by step how to realize the core principle of the framework and compute the optimal PoG for a control problem. Two illustrative case studies are carried out to exemplify the applications of the proposed framework and its methodology. Results show that by properly formulating and implementing each step, the hierarchical supervision framework is capable of promoting coordination among agents while bounding administrative cost to a minimum in different kinds of organizational control problems.

cs.MA↗

arXiv2018

Compact and lightweight 1.5 μm lidar with a multi-mode fiber coupling free-running InGaAs/InP single-photon detector

We present a compact and lightweight 1.5 μm lidar using a free-running single-photon detector (SPD) based on a multi-mode fiber (MMF) coupling InGaAs/InP negative feedback avalanche diode. The ultimate light detection sensitivity of SPD highly reduces the power requirement of laser, whilst the enhanced collection efficiency due to MMF coupling significantly reduces the volume and weight of telescopes. We develop a specific algorithm for the corrections of errors caused by the SPD and erbium-doped fiber amplifier to extract accurate backscattering signals. We also perform a comparison between single-mode fiber (SMF) coupling and MMF coupling in the lidar receiver, and the results show that the collection efficiency with MMF coupling is five times higher than SMF coupling. In order to validate the functionality, we use the lidar system for the application of cloud detection. The lidar system exhibits the ability to detect both the cloud base height and the thickness of multi-layer clouds to an altitude of 12 km with a temporal resolution of 1 s and a spatial resolution of 15 m. Due to the advantages of compactness and lightweight, our lidar system can be installed on unmanned aerial vehicles for wide applications in practice.

physics.app-ph↗

arXiv2017

Note on acoustic black holes from black D3-brane

In this paper, we study the acoustic black hole emerged from the nonextremal black D3-brane, based on the holographic approaches in constructing the acoustic black hole in asymptotically Anti de-Sitter spacetime (AAdS) and the effective hydrodynamic description of the nonextremal black D3-brane. We show that the holographic dual description of the acoustic black hole appeared on the timelike cutoff surface in the nonextremal black D3-brane also exist. The duality includes the dynamical connection between the acoustic black hole and the bulk gravity, a universal equation relating the Hawking-like temperature and the Hawking temperature, and a phonon/scalar channel quasinormal mode correspondence.

hep-th↗

arXiv2017

Fully integrated free-running InGaAs/InP single-photon detector for accurate lidar applications

We present a fully integrated InGaAs/InP negative feedback avalanche diode (NFAD) based free-running single-photon detector (SPD) designed for accurate lidar applications. A free-piston Stirling cooler is used to cool down the NFAD with a large temperature range, and an active hold-off circuit implemented in a field programmable gate array is applied to further suppress the afterpulsing contribution. The key parameters of the free-running SPD including photon detection efficiency (PDE), dark count rate (DCR), afterpulse probability, and maximum count rate (MCR) are dedicatedly optimized for lidar application in practice. We then perform a field experiment using a Mie lidar system with 20 kHz pulse repetition frequency to compare the performance between the free-running InGaAs/InP SPD and a commercial superconducting nanowire single-photon detector (SNSPD). Our detector exhibits good performance with 1.6 Mcps MCR (0.6 μs hold-off time), 10% PDE, 950 cps DCR, and 18% afterpulse probability over 50 μs period. Such performance is worse than the SNSPD with 60% PDE and 300 cps DCR. However, after performing a specific algorithm that we have developed for afterpulse and count rate corrections, the lidar system performance in terms of range-corrected signal (Pr2) distribution using our SPD agrees very well with the result using the SNSPD, with only a relative error of ~2%. Due to the advantages of low-cost and small size of InGaAs/InP NFADs, such detector provides a practical solution for accurate lidar applications.

quant-ph↗

arXiv2017

Quantum-trajectory analysis for charge transfer in solid materials induced by strong laser fields

We investigate the dependence of charge transfer on the intensity of driving laser field when SiO2 crystal is irradiated by an 800 nm laser. It is surprising that the direction of charge transfer undergoes a sudden reversal when the driving laser intensity exceeds critical values with different carrier envelope phases. By applying quantum-trajectory analysis, we find that the Bloch oscillation plays an important role in charge transfer in solid. Also, we study the interaction of strong laser with gallium nitride (GaN) that is widely used in optoelectronics. A pump-probe scheme is applied to control the quantum trajectories of the electrons in the conduction band. The signal of charge transfer is controlled successfully by means of theoretically proposed approach.

physics.optics↗

arXiv2015

Population Redistribution among Multiple Electronic States of Molecular Nitrogen Ions in Strong Laser Fields

We carry out a combined theoretical and experimental investigation on the population distributions in the ground and excited states of tunnel ionized N2 molecules at various driver wavelengths in the near- and mid-infrared range. Our results reveal that efficient couplings (i.e., population exchanges) between the ground state and the excited states occur in strong laser fields. The couplings result in the population inversion between the ground and the excited states at the wavelengths near 800 nm, which is verified by our experiment by observing the amplification of a seed at ~391 nm. The result provides insight into the mechanism of free-space nitrogen ion lasers generated in remote air with strong femtosecond laser pulses.

physics.optics↗

arXiv2013

Spectra of geometric operators in three-dimensional LQG: From discrete to continuous

We study and compare the spectra of geometric operators (length and area) in the quantum kinematics of two formulations of three-dimensional Lorentzian loop quantum gravity. In the SU(2) Ashtekar-Barbero framework, the spectra are discrete and depend on the Barbero-Immirzi parameter $γ$ exactly like in the four-dimensional case. However, we show that when working with the self-dual variables and imposing the reality conditions the spectra become continuous and $γ$-independent.

gr-qc↗

arXiv2013

Testing the role of the Barbero-Immirzi parameter and the choice of connection in Loop Quantum Gravity

We study the role of the Barbero-Immirzi parameter $γ$ and the choice of connection in the construction of (a symmetry-reduced version of) loop quantum gravity. We start with the four-dimensional Lorentzian Holst action that we reduce to three dimensions in a way that preserves the presence of $γ$. In the time gauge, the phase space of the resulting three-dimensional theory mimics exactly that of the four-dimensional one. Its quantization can be performed, and on the kinematical Hilbert space spanned by SU(2) spin network states the spectra of geometric operators are discrete and $γ$-dependent. However, because of the three-dimensional nature of the theory, its SU(2) Ashtekar-Barbero Hamiltonian constraint can be traded for the flatness constraint of an sl(2,C) connection, and we show that this latter has to satisfy a linear simplicity-like condition analogous to the one used in the construction of spin foam models. The physically relevant solution to this constraint singles out the non-compact subgroup SU(1,1), which in turn leads to the disappearance of the Barbero-Immirzi parameter and to a continuous length spectrum, in agreement with what is expected from Lorentzian three-dimensional gravity.

gr-qc↗