arXiv subjects

Tianliang Zhang

Publications and source records attributed to Tianliang Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Terahertz-based longitudinal phase space diagnostics of laser wakefield accelerated electron beams

Femtosecond relativistic electron beams are key probes of ultrafast dynamics, and their pulse duration directly limits the achievable temporal resolution. Laser wakefield acceleration (LWFA) provides a compact source of such beams, but the injection-induced energy spread makes bunch compression sensitive to nonlinear longitudinal transport, motivating direct longitudinal phase space (LPS) measurements. Here, a terahertz transverse-deflecting cavity (THz-TDC) combined with a dipole magnet is used to reconstruct the nonlinear LPS of LWFA electron bunches compressed in a double-bend achromat (DBA), resolving a characteristic C-shaped distribution associated with higher-order longitudinal transport. At an average energy of approximately 4.55 MeV, the diagnostic achieves a temporal resolving power of 1.8 fs and an energy resolution of 6.0 keV, corresponding to a relative energy resolution of 0.13%. For comparable energy spreads of approximately 2.9%, shifting the transmitted energy-window center from 4.574 MeV to 4.532 MeV moves the selected beam away from a low-slope region of the nonlinear LPS and increases the root-mean-square bunch length from 26 fs to 42 fs; with the window center held near 4.553 MeV, increasing the energy spread from 2.0% to 4.4% lengthens the bunch from 27 fs to 44 fs. These results show that the final bunch duration is governed by both the position and width of the transmitted energy window within the nonlinear LPS, establishing an LPS-guided strategy for optimizing DBA-compressed LWFA electron bunches and providing a basis for future higher-order phase-space correction.

physics.acc-ph↗

arXiv2026

Absolute charge calibration of DRZ phosphor screens for relativistic electron bunches

Laser-plasma accelerators have been the subject of extensive research in recent years. The electron beams they generate exhibit a broad energy spread. To conveniently characterize beams from laser wakefield acceleration (LWFA), electron spectrometers employing scintillating screens coupled with CCD cameras are typically used. In this work, we calibrate a series of DRZ phosphor screens and measure the spectra of the light they emit. The calibration was performed using the radio-frequency linear electron accelerator at Tsinghua University, which provided monoenergetic electron beams with peak energy of approximately 30 MeV.

physics.acc-ph↗

arXiv2025

Scaling Laws in Plasma Channels for Laser Wakefield Accelerators

Preformed plasma channels are essential for guiding high-power laser pulses over extended distances in laser wakefield accelerators, enabling the generation of multi-GeV electron beams for applications such as free-electron lasers and particle colliders. Above-threshold ionization heating provides a robust mechanism for creating laser-matched plasma channels across a wide parameter range, owing to its density- and geometry-independent heating effect. Establishing predictive scaling laws between channel parameters and formation conditions is critical for designing channels optimized for electron acceleration across energies spanning hundreds of MeV to tens of GeV. Through combined timescale analysis and numerical simulations, hydrodynamic expansion is identified as the dominant mechanism governing density profile evolution during ATI channel formation. Remarkably, this process maintains effective laser-guiding channel structures across a wide range of initial gas density, as evidenced by the persistent profile similarity observed despite these significant parameter variations. For parabolic channels matched to Gaussian laser drivers, rigorous scaling laws are established that, the on-axis density scales linearly with the initial gas density, while the matching radius has an exponential dependence on both the initial gas density and the ionization laser radius. These findings provide a systematic framework for the predictive design and optimization of plasma channels in high-efficiency and high-energy LWFA applications.

physics.plasm-ph↗

arXiv2025

Energy stability of supercontinuum via femtosecond filamentation in sapphire

The energy stability of supercontinuum (SC) significantly impacts its applications. To achieve the most stable SC, we systematically investigated how input pulse energy, numerical aperture (NA), and crystal thickness affect the energy stability of SC generated by femtosecond filamentation in sapphire. Our findings reveal that the SC energy does not always increase monotonically with input energy for different NA and thicknesses. This phenomenon occurs because, when the input pulse energy just exceeds the filamentation threshold, the pulse splitting structure and spectrum are still rapidly evolving. To generate a more stable SC, the numerical aperture and crystal thickness must be carefully coordinated to prevent this rapid evolution from occurring within the crystal.

physics.optics↗

arXiv2023

Non-fragile Finite-time Stabilization for Discrete Mean-field Stochastic Systems

In this paper, the problem of non-fragile finite-time stabilization for linear discrete mean-field stochastic systems is studied. The uncertain characteristics in control parameters are assumed to be random satisfying the Bernoulli distribution. A new approach called the ``state transition matrix method" is introduced and some necessary and sufficient conditions are derived to solve the underlying stabilization problem. The Lyapunov theorem based on the state transition matrix also makes a contribution to the discrete finite-time control theory. One practical example is provided to validate the effectiveness of the newly proposed control strategy.

math.OC↗

arXiv2022

HDNet: A Hierarchically Decoupled Network for Crowd Counting

Recently, density map regression-based methods have dominated in crowd counting owing to their excellent fitting ability on density distribution. However, further improvement tends to saturate mainly because of the confusing background noise and the large density variation. In this paper, we propose a Hierarchically Decoupled Network (HDNet) to solve the above two problems within a unified framework. Specifically, a background classification sub-task is decomposed from the density map prediction task, which is then assigned to a Density Decoupling Module (DDM) to exploit its highly discriminative ability. For the remaining foreground prediction sub-task, it is further hierarchically decomposed to several density-specific sub-tasks by the DDM, which are then solved by the regression-based experts in a Foreground Density Estimation Module (FDEM). Although the proposed strategy effectively reduces the hypothesis space so as to relieve the optimization for those task-specific experts, the high correlation of these sub-tasks are ignored. Therefore, we introduce three types of interaction strategies to unify the whole framework, which are Feature Interaction, Gradient Interaction, and Scale Interaction. Integrated with the above spirits, HDNet achieves state-of-the-art performance on several popular counting benchmarks.

cs.CV↗

arXiv2022

CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection

Pedestrian detection in the wild remains a challenging problem especially when the scene contains significant occlusion and/or low resolution of the pedestrians to be detected. Existing methods are unable to adapt to these difficult cases while maintaining acceptable performance. In this paper we propose a novel feature learning model, referred to as CircleNet, to achieve feature adaptation by mimicking the process humans looking at low resolution and occluded objects: focusing on it again, at a finer scale, if the object can not be identified clearly for the first time. CircleNet is implemented as a set of feature pyramids and uses weight sharing path augmentation for better feature fusion. It targets at reciprocating feature adaptation and iterative object detection using multiple top-down and bottom-up pathways. To take full advantage of the feature adaptation capability in CircleNet, we design an instance decomposition training strategy to focus on detecting pedestrian instances of various resolutions and different occlusion levels in each cycle. Specifically, CircleNet implements feature ensemble with the idea of hard negative boosting in an end-to-end manner. Experiments on two pedestrian detection datasets, Caltech and CityPersons, show that CircleNet improves the performance of occluded and low-resolution pedestrians with significant margins while maintaining good performance on normal instances.

cs.CV↗

arXiv2022

Feature Calibration Network for Occluded Pedestrian Detection

Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the deep learning framework, referred to as Feature Calibration Network (FC-Net), to adaptively detect pedestrians under various occlusions. FC-Net is based on the observation that the visible parts of pedestrians are selective and decisive for detection, and is implemented as a self-paced feature learning framework with a self-activation (SA) module and a feature calibration (FC) module. In a new self-activated manner, FC-Net learns features which highlight the visible parts and suppress the occluded parts of pedestrians. The SA module estimates pedestrian activation maps by reusing classifier weights, without any additional parameter involved, therefore resulting in an extremely parsimony model to reinforce the semantics of features, while the FC module calibrates the convolutional features for adaptive pedestrian representation in both pixel-wise and region-based ways. Experiments on CityPersons and Caltech datasets demonstrate that FC-Net improves detection performance on occluded pedestrians up to 10% while maintaining excellent performance on non-occluded instances.

cs.CV↗

arXiv2022

Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision

Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Although region-based methods are less likely to encounter issues with model generalizability than label-dependency methods, they often generate hundreds of meaningless or noisy proposals with non-discriminative information, and the contextual dependency among the localized regions is often ignored or over-simplified. This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning. Specifically, we propose category-aware weak supervision to concentrate on non-existent categories so as to provide deterministic information for local feature learning, restricting the local branch to focus on more high-quality regions of interest. Moreover, we develop a cross-granularity attention module to explore the complementary information between global and local features, which can build the high-order feature correlation containing not only global-to-local, but also local-to-local relations. Both advantages guarantee a boost in the performance of the whole network. Extensive experiments on two large-scale datasets (MS-COCO and VOC 2007) demonstrate that our framework achieves superior performance over state-of-the-art methods.

cs.CV↗

arXiv2022

Predefined-time Stabilization for Nonlinear Stochastic Systems

In this paper, a control scheme for stochastic predefined-time stabilization is proposed, which improves the control effect compared with stochastic finite-time or fixed-time stabilization. The stochastic predefined-time stabilization allows the upper bound of the mathematical expectation of the settling-time function below any given positive value. Some Lyapunov-type results for predefined-time stabilization of general stochastic Itô systems are presented. Moreover, a state feedback control scheme is designed for a class of stochastic nonlinear systems in strict-feedback form. Two simulation examples are supplied to show the usefulness of the proposed stochastic predefined-time stabilization.

math.OC↗

arXiv2022

Compact polarized X-ray source based on all-optical inverse Compton scattering

Polarized X-ray source is an important probe for many fields such as fluorescence imaging, magnetic microscopy, and nuclear physics research. All-optical inverse Compton scattering source (AOCS) based on laser wakefield accelerator (LWFA) has drawn great attention in recent years due to its compact scale and high performance, especially its potential to generate polarized X-rays. Here, polarization-tunable X-rays are generated by a plasma-mirror-based AOCS scheme. The linearly and circularly polarized AOCS pulses are achieved with the mean photon energy of 60($\pm$5)/64($\pm$3) keV and the single-shot photon yield of $\sim$1.1/1.3$\times10^7$. A Compton polarimeter is designed to diagnose the photon polarization states, demonstrating AOCS's polarization-tunable property, and indicating the average polarization degree of the linearly polarized AOCS is 75($\pm$3)%.

physics.app-ph↗

arXiv2021

A Comprehensive Study of Virtual Machine and Container Based Core Network Components Migration in OpenROADM SDN-Enabled Network

With the increasing demand for openness, flexibility, and monetization the Network Function Virtualization (NFV) of mobile network functions has become the embracing factor for most mobile network operators. Early reported field deployments of virtualized Evolved Packet Core (EPC) - the core network component of 4G LTE and 5G non-standalone mobile networks - reflect this growing trend. To best meet the requirements of power management, load balancing, and fault tolerance in the cloud environment, the need for live migration for these virtualized components cannot be shunned. Virtualization platforms of interest include both Virtual Machines (VMs) and Containers, with the latter option offering more lightweight characteristics. The first contribution of this paper is the implementation of a number of custom functions that enable migration of Containers supporting virtualized EPC components. The current CRIU-based migration of Docker Container does not fully support the mobile network protocol stack. CRIU extensions to support the mobile network protocol stack are therefore required and described in the paper. The second contribution is an experimental-based comprehensive analysis of live migration in two backhaul network settings and two virtualization technologies. The two backhaul network settings are the one provided by CloudLab and one based on a programmable optical network testbed that makes use of OpenROADM dense wavelength division multiplexing (DWDM) equipment. The paper compares the migration performance of the proposed implementation of OpenAirInterface (OAI) based containerized EPC components with the one utilizing VMs, running in OpenStack. The presented experimental comparison accounts for a number of system parameters and configurations, image size of the virtualized EPC components, network characteristics, and signal propagation time across the OpenROADM backhaul network.

cs.NI↗

arXiv2020

Measurements of the growth and saturation of electron Weibel instability in optical-field ionized plasmas

The temporal evolution of the magnetic field associated with electron thermal Weibel instability in optical-field ionized plasmas is measured using ultrashort (1.8 ps), relativistic (45 MeV) electron bunches from a linear accelerator. The self-generated magnetic fields are found to self-organize into a quasi-static structure consistent with a helicoid topology within a few ps and such a structure lasts for tens of ps in underdense plasmas. The measured growth rate agrees well with that predicted by the kinetic theory of plasmas taking into account collisions. Magnetic trapping is identified as the dominant saturation mechanism.

physics.plasm-ph↗

arXiv2020

Region-of-interest micro-focus CT based on an all-optical inverse Compton scattering source

Micro-focus computed tomography (CT), enabling the reconstruction of hyperfine structure within objects, is a powerful nondestructive testing tool in many fields. Current X-ray sources for micro-focus CT are typically limited by their relatively low photon energy and low flux. An all-optical inverse Compton scattering source (AOCS) based on laser wakefield accelerator (LWFA) can generate intense quasi-monoenergetic X/gamma-ray pulses in the keV-MeV range with micron-level source size, and its potential application for micro-focus CT has become very attractive in recent years due to the fast pace progress made in LWFA. Here we report the first experimental demonstration of high-fidelity micro-focus CT using AOCS (~70 keV) by imaging and reconstructing a test object with complex inner structures. A region-of-interest (ROI) CT method is adopted to utilize the relatively small field-of-view (FOV) of AOCS to obtain high-resolution reconstruction. This demonstration of the ROI micro-focus CT based on AOCS is a key step for its application in the field of hyperfine nondestructive testing.

physics.app-ph↗

arXiv2019

Multiple Anchor Learning for Visual Object Detection

Classification and localization are two pillars of visual object detectors. However, in CNN-based detectors, these two modules are usually optimized under a fixed set of candidate (or anchor) bounding boxes. This configuration significantly limits the possibility to jointly optimize classification and localization. In this paper, we propose a Multiple Instance Learning (MIL) approach that selects anchors and jointly optimizes the two modules of a CNN-based object detector. Our approach, referred to as Multiple Anchor Learning (MAL), constructs anchor bags and selects the most representative anchors from each bag. Such an iterative selection process is potentially NP-hard to optimize. To address this issue, we solve MAL by repetitively depressing the confidence of selected anchors by perturbing their corresponding features. In an adversarial selection-depression manner, MAL not only pursues optimal solutions but also fully leverages multiple anchors/features to learn a detection model. Experiments show that MAL improves the baseline RetinaNet with significant margins on the commonly used MS-COCO object detection benchmark and achieves new state-of-the-art detection performance compared with recent methods.

cs.CV↗

arXiv2019

GRID: a Student Project to Monitor the Transient Gamma-Ray Sky in the Multi-Messenger Astronomy Era

The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the transient gamma-ray sky in the energy range from 10 keV to 2 MeV using scintillation detectors onboard CubeSats in low Earth orbits. The primary targets of GRID are the gamma-ray bursts (GRBs) in the local universe. The scientific goal of GRID is, in synergy with ground-based gravitational wave (GW) detectors such as LIGO and VIRGO, to accumulate a sample of GRBs associated with the merger of two compact stars and study jets and related physics of those objects. It also involves observing and studying other gamma-ray transients such as long GRBs, soft gamma-ray repeaters, terrestrial gamma-ray flashes, and solar flares. With multiple CubeSats in various orbits, GRID is unaffected by the Earth occultation and serves as a full-time and all-sky monitor. Assuming a horizon of 200 Mpc for ground-based GW detectors, we expect to see a few associated GW-GRB events per year. With about 10 CubeSats in operation, GRID is capable of localizing a faint GRB like 170817A with a 90% error radius of about 10 degrees, through triangulation and flux modulation. GRID is proposed and developed by students, with considerable contribution from undergraduate students, and will remain operated as a student project in the future. The current GRID collaboration involves more than 20 institutes and keeps growing. On August 29th, the first GRID detector onboard a CubeSat was launched into a Sun-synchronous orbit and is currently under test.

astro-ph.IM↗

arXiv2018

Robust $H_\infty$ Filtering for Nonlinear Discrete-time Stochastic Systems

This paper mainly discusses the $H_{\infty}$ filtering of general nonlinear discrete time-varying stochastic systems. A nonlinear discrete-time stochastic bounded real lemma (SBRL) is firstly obtained by means of the smoothness of the conditional mathematical expectation, and then, based on the given SBRL and a stochastic LaSalle-type theorem, a sufficient condition for the existence of the $H_\infty$ filtering of general nonlinear discrete time-varying stochastic systems is presented via a new introduced Hamilton-Jacobi inequality (HJI), which is easily verified. When the worst-case disturbance $\{v^*_k\}_{k\in {\mathcal N}}$ is considered, the suboptimal $H_2/H_\infty$ filtering is studied. Two examples including a practical engineering example show the effectiveness of our main results.

math.OC↗

arXiv2018

Mixed $H_-/H_{\infty}$ Fault Detection Filtering for Itô-Type Affine Nonlinear Stochastic Systems

This paper studies the mixed $H_-/H_{\infty}$ fault detection filtering of Itô-type nonlinear stochastic systems. Mixed $H_-/H_{\infty}$ filtering combines the system robustness to the external disturbance and the sensitivity to the fault of the residual signal. Firstly, for Itô-type affine nonlinear stochastic systems, some sufficient criteria are obtained for the existence of $H_-/H_{\infty}$ filter in terms of Hamilton-Jacobi inequalities (HJIs). Secondly, for a class of quasi-linear Itô systems, a sufficient condition is given for the existence of $H_-/H_{\infty}$ filter by means of linear matrix inequalities (LMIs). Finally, a numerical example is presented to illustrate the effectiveness of the proposed results.

math.OC↗