arXiv subjects

Shuai Sun

Publications and source records attributed to Shuai Sun.

At least 19 recordsLinked to original sources

arXiv2025

Shell effects in nuclear charge radii based on Skyrme density functionals

A unified description of the charge radii throughout the entire nuclide chart plays an essential role for our understanding of nuclear structure and fundamental nuclear interactions. In this work, the influence of new term, which catches the spirit of neutron and proton pairs condensation around Fermi surface, on the charge radii has been investigated based on the Skyrme density functionals with the effective forces SLy5 and SkM$^{*}$. The differential charge radii of even-even Ca, Ni, Sn, and Pb isotopes are employed to evaluate the validity of this theoretical model. Meanwhile, the results obtained by the relativistic density functional with the effective Lagrangian NL3 are also shown for the quantitative comparison. The calculated results suggest that the modified model can improve the trend of changes of the differential charge radii along Ca, Ni, Sn, and Pb isotopic chains, especially the shell closure effect at the neutron numbers $N=28$, 82 and 126. The shell quenching phenomena of charge radii can also be predicted at the neutron number $N=50$ along the corresponding Ni and Sn isotopes, respectively. The inverted parabolic-like shapes between the two fully filled shells can also be observed, but the amplitude is gradually weakened from Ca to Pb isotopic chains. Combining the existing literatures, it suggests that the discontinuous behavior in nuclear charge radii can be described well by considering the influence of neutron Cooper pairs condensation around Fermi surface.

nucl-th↗

arXiv2025

Non-contact Vital Signs Detection in Dynamic Environments

Accurate phase demodulation is critical for vital sign detection using millimeter-wave radar. However, in complex environments, time-varying DC offsets and phase imbalances can severely degrade demodulation performance. To address this, we propose a novel DC offset calibration method alongside a Hilbert and Differential Cross-Multiply (HADCM) demodulation algorithm. The approach estimates time-varying DC offsets from neighboring signal peaks and valleys, then employs both differential forms and Hilbert transforms of the I/Q channel signals to extract vital sign information. Simulation and experimental results demonstrate that the proposed method maintains robust performance under low signal-to-noise ratios. Compared to existing demodulation techniques, it offers more accurate signal recovery in challenging scenarios and effectively suppresses noise interference.

eess.SP↗

arXiv2025

Finite Sample Analysis of System Poles for Ho-Kalman Algorithm

The Ho-Kalman algorithm has been widely employed for the identification of discrete-time linear time-invariant (LTI) systems. In this paper, we investigate the pole estimation error for the Ho-Kalman algorithm based on finite input/output sample data. Building upon prior works, we derive finite sample error bounds for system pole estimation in both single-trajectory and multiple-trajectory scenarios. Specifically, we prove that, with high probability, the estimation error for an $n$-dimensional system decreases at a rate of at least $\mathcal{O}(T^{-1/2n})$ in the single-trajectory setting with trajectory length $T$, and at a rate of at least $\mathcal{O}(N^{-1/2n})$ in the multiple-trajectory setting with $N$ independent trajectories. Furthermore, we reveal that in both settings, achieving a constant estimation error requires a super-polynomial sample size in $ \max\{n/m, n/p\} $, where $n/m$ and $n/p$ denote the state-to-output and state-to-input dimension ratios, respectively. Finally, numerical experiments are conducted to validate the non-asymptotic results of system pole estimation.

eess.SY↗

arXiv2025

Finite Sample Analysis of Subspace Identification for Stochastic Systems

The subspace identification method (SIM) has become a widely adopted approach for the identification of discrete-time linear time-invariant (LTI) systems. In this paper, we derive finite sample high-probability error bounds for the system matrices $A,C$, the Kalman filter gain $K$ and the estimation of system poles. Specifically, we demonstrate that, ignoring the logarithmic factors, for an $n$-dimensional LTI system with no external inputs, the estimation error of these matrices decreases at a rate of at least $ \mathcal{O}(\sqrt{1/N}) $, while the estimation error of the system poles decays at a rate of at least $ \mathcal{O}(N^{-1/2n}) $, where $ N $ represents the number of sample trajectories. Furthermore, we reveal that achieving a constant estimation error requires a super-polynomial sample size in $n/m $, where $n/m$ denotes the state-to-output dimension ratio. Finally, numerical experiments are conducted to validate the non-asymptotic results.

eess.SY↗

arXiv2024

Non-asymptotic Error Analysis of Subspace Identification for Deterministic Systems

The subspace identification method (SIM) has been extensively employed in the identification of discrete-time multiple-input multiple-output (MIMO) linear time-invariant (LTI) systems. This paper focuses on the analysis of perturbation errors for the system matrices in state-space models and the corresponding system poles, under two unified SIMs, based on a single finite-length input/output sample trajectory. Specifically, we derive non-asymptotic upper bounds on these errors, providing a unified perspective across various SIM variants. Furthermore, we prove that SIMs become ill-conditioned for MIMO systems when the state-to-output dimensionality ratio $n/m$ is large, regardless of system parameters. Finally, numerical experiments are conducted to validate the non-asymptotic results and the ill-conditionedness of SIMs.

eess.SY↗

arXiv2024

Correlation between the charge radii difference in mirror partner nuclei and the symmetry energy slope

A correlation between the charge radii difference of mirror partner nuclei $\Delta{R_{\mathrm{ch}}}$ and the slope parameter $L$ of symmetry energy has been built to ascertain the equation of state of isospin asymmetric nuclear matter. In this work, the influences of pairing correlations and isoscalar compression modulus on the $\Delta{R_{\mathrm{ch}}}$ are systematically investigated based on the Skyrme energy density functional theory. The calculated results suggest that the linear correlation between $\Delta{R_{\mathrm{ch}}}$ and $L$ is decreased by the surface pairing correlations. The slope parameter deduced from the difference of charge radii of mirror-pair nuclei $^{32}$Ar-$^{32}$Si, $^{36}$Ca-$^{36}$S, $^{38}$Ca-$^{38}$Ar, and $^{54}$Ni-$^{54}$Fe falls into the range of $L$=42.57-50.64 MeV, that is, the rather soft equation of state of asymmetric nuclear matter. Besides, the range of the slope parameter can also be influenced by the effective forces classified by various isoscalar incompressibility coefficients.

nucl-th↗

arXiv2023

New quantification of symmetry energy from neutron skin thicknesses of $^{48}$Ca and $^{208}$Pb

Precise knowledge of the nuclear symmetry energy can be tentatively calibrated through multimessenger constraints. The neutron skin thickness of a heavy nucleus is one of the most sensitive indicators for probing the isovector components of effective interactions in asymmetric nuclear matter. Recent studies have suggested that the experimental data from the CREX and PREX2 Collaborations are not mutually compatible within existing nuclear models. In this study, we review the quantification of the slope parameter of symmetry energy $L$ from the neutron skin thicknesses of $^{48}$Ca and $^{208}$Pb. Skyrme energy density functionals classified by various isoscalar incompressibility coefficients $K$ are employed to evaluate the bulk properties of finite nuclei. The calculated results suggest that the slope parameter $L$ deduced from $^{208}$Pb is sensitive to the compression modulus of symmetric nuclear matter, but not that from $^{48}$Ca. The effective parameter sets classified by $K=220$ MeV can provide an almost overlaping range of $L$ from $^{48}$Ca and $^{208}$Pb.

nucl-th↗

arXiv2023

Microscopic study of $M$1 resonances in Sn isotopes

The magnetic dipole ($M$1) resonances of even-even $^{112-120, 124}$Sn isotopes are investigated in the framework of the self-consistent Skyrme Hartree-Fock (HF) + BCS and Quasiparticle Random Phase Approximation (QRPA). The Skyrme energy density functionals SLy5 and T11 with and without tensor terms are adopted in our calculations. The mixed type pairing interaction is used to take care of the pairing effect for open-shell nuclei both in the ground and excited states calculations. The calculated magnetic dipole strengths are compared with available experimental data. The QRPA results calculated by SLy5 and T11 with tensor force show a better agreement with the experimental data than those without the tensor force. By analyzing the HF and QRPA strength distributions of $^{112}$Sn and $^{124}$Sn, we discuss the effect of tensor force on the $M$1 resonances in detail. It is found that the $M$1 resonance is sensitive to the tensor interaction, and favors especially a negative triplet-odd tensor one. Depending on the nucleus, a quenching factor of the $M$1 operator of about 0.71-0.95 is needed to reproduce the total observed transition strength. In our calculations, we also find some low-lying, pygmy-type magnetic dipole states distributed below 6.0 MeV, and they are formed mainly from the neutron configuration $\nu$2$d_{5/2}$$\rightarrow$$\nu$2$d_{3/2}$.

nucl-th↗

arXiv2023

Finite Sample Performance Analysis of MIMO Systems Identification

This paper is concerned with the finite sample identification performance of an n dimensional discrete-time Multiple-Input Multiple-Output (MIMO) Linear Time-Invariant system, with p inputs and m outputs. We prove that the widely-used Ho-Kalman algorithm and Multivariable Output Error State Space (MOESP) algorithm are ill-conditioned for MIMO systems when n/m or n/p is large. Moreover, by analyzing the Cra\'mer-Rao bound, we derive a fundamental limit for identifying the real and stable (or marginally stable) poles of MIMO system and prove that the sample complexity for any unbiased pole estimation algorithm to reach a certain level of accuracy explodes superpolynomially with respect to n/(pm). Numerical results are provided to illustrate the ill-conditionedness of Ho-Kalman algorithm and MOESP algorithm as well as the fundamental limit on identification.

eess.SY↗

arXiv2023

Constraining nuclear symmetry energy with the charge radii of mirror-pair nuclei

The nuclear charge radius plays a vital role in determining the equation of state of isospin asymmetric nuclear matter. Based on the correlation between the differences in charge radii of mirror-partner nuclei and the slope parameter ($L$) of symmetry energy at the nuclear saturation density, an analysis of the calibrated slope parameter $L$ was performed in finite nuclei. In this study, relativistic and non-relativistic energy density functionals were employed to constrain the nuclear symmetry energy through the available databases of the mirror-pair nuclei $^{36}$Ca-$^{36}$S, $^{38}$Ca-$^{38}$Ar, and $^{54}$Ni-$^{54}$Fe. The deduced nuclear symmetry energy was located in the range 29.89-31.85 MeV, and $L$ of the symmetry energy essentially covered the range 22.50-51.55 MeV at the saturation density. Moreover, the extracted $L_s$ at the sensitivity density $\rho_{s}=0.10~\mathrm{fm}^{-3}$ was located in the interval range 30.52-39.76 MeV.

nucl-th↗

arXiv2022

Mapping Extended Landmarks for Radar SLAM

Simultaneous localization and mapping (SLAM) using automotive radar sensors can provide enhanced sensing capabilities for autonomous systems. In SLAM applications, with a greater requirement for the environment map, information on the extent of landmarks is vital for precise navigation and path planning. Although object extent estimation has been successfully applied in target tracking, its adaption to SLAM remains unaddressed due to the additional uncertainty of the sensor platform, bias in the odometer reading, as well as the measurement non-linearity. In this paper, we propose to incorporate the Bayesian random matrix approach to estimate the extent of landmarks in radar SLAM. We describe the details for implementation of landmark extent initialization, prediction and update. To validate the performance of our proposed approach we compare with the model-free ellipse fitting algorithm with results showing more consistent extent estimation. We also demonstrate that exploiting the landmark extent in the state update can improve localization accuracy.

cs.RO↗

arXiv2022

Landmark Management in the Application of Radar SLAM

This paper focuses on efficient landmark management in radar based simultaneous localization and mapping (SLAM). Landmark management is necessary in order to maintain a consistent map of the estimated landmarks relative to the estimate of the platform's pose. This task is particularly important when faced with multiple detections from the same landmark and/or dynamic environments where the location of a landmark can change. A further challenge with radar data is the presence of false detections. Accordingly, we propose a simple yet efficient rule based solution for radar SLAM landmark management. Assuming a low-dynamic environment, there are several steps in our solution: new landmarks need to be detected and included, false landmarks need to be identified and removed, and the consistency of the landmarks registered in the map needs to be maintained. To illustrate our solution, we run an extended Kalman filter SLAM algorithm in an environment containing both stationary and temporally stationary landmarks. Our simulation results demonstrate that the proposed solution is capable of reliably managing landmarks even when faced with false detections and multiple detections from the same landmark.

cs.RO↗

arXiv2022

Ghost Aperture Synthesis Imaging with Computational Aberration Cancellation

Although optical aperture synthesis has been generally regarded as the only access to very large imager for over a century, the problem of phasing all the giant sub-apertures on the scale of wavelength is still prohibitive. Besides, the accompanied adaptive optics combatting the atmospheric turbulence is also bulky and complicated. We here propose a new paradigm aperture synthesis imager through turbulence, based on computational ghost imaging method. The complex aberrations on the signal path are computationally cancelled by introducing an optimum compensation phase on the reference path. With the advanced aberration cancellation, our imager is free from phasing and aberrations problem. The image degradation due to turbulence is also suppressed and even eliminated without any guide star or wavefront shaping device. Experimentally, diffraction-limited imaging is achieved under turbulence featuring transverse coherence length far less than the optical aperture of the system.

physics.optics↗

arXiv2021

Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations

User interest exploration is an important and challenging topic in recommender systems, which alleviates the closed-loop effects between recommendation models and user-item interactions. Contextual bandit (CB) algorithms strive to make a good trade-off between exploration and exploitation so that users' potential interests have chances to expose. However, classical CB algorithms can only be applied to a small, sampled item set (usually hundreds), which forces the typical applications in recommender systems limited to candidate post-ranking, homepage top item ranking, ad creative selection, or online model selection (A/B test). In this paper, we introduce two simple but effective hierarchical CB algorithms to make a classical CB model (such as LinUCB and Thompson Sampling) capable to explore users' interest in the entire item space without limiting it to a small item set. We first construct a hierarchy item tree via a bottom-up clustering algorithm to organize items in a coarse-to-fine manner. Then we propose a hierarchical CB (HCB) algorithm to explore users' interest in the hierarchy tree. HCB takes the exploration problem as a series of decision-making processes, where the goal is to find a path from the root to a leaf node, and the feedback will be back-propagated to all the nodes in the path. We further propose a progressive hierarchical CB (pHCB) algorithm, which progressively extends visible nodes which reach a confidence level for exploration, to avoid misleading actions on upper-level nodes in the sequential decision-making process. Extensive experiments on two public recommendation datasets demonstrate the effectiveness and flexibility of our methods.

cs.IR↗

arXiv2021

Security Protection in Cooperative Control of Multi-agent Systems

Due to the wide application of average consensus algorithm, its security and privacy problems have attracted great attention. In this paper, we consider the system threatened by a set of unknown agents that are both "malicious" and "curious", who add additional input signals to the system in order to perturb the final consensus value or prevent consensus, and try to infer the initial state of other agents. At the same time, we design a privacy-preserving average consensus algorithm equipped with an attack detector with a time-varying exponentially decreasing threshold for every benign agent, which can guarantee the initial state privacy of every benign agent, under mild conditions. The attack detector will trigger an alarm if it detects the presence of malicious attackers. An upper bound of false alarm rate in the absence of malicious attackers and the necessary and sufficient condition for there is no undetectable input by the attack detector in the system are given. Specifically, we show that under this condition, the system can achieve asymptotic consensus almost surely when no alarm is triggered from beginning to end, and an upper bound of convergence rate and some quantitative estimates about the error of final consensus value are given. Finally, numerical case is used to illustrate the effectiveness of some theoretical results.

eess.SY↗

arXiv2021

An Adaptive All-Pass Filter for Time-Varying Delay Estimation

The focus of this paper is the estimation of a delay between two signals. Such a problem is common in signal processing and particularly challenging when the delay is non-stationary in nature. Our proposed solution is based on an all-pass filter framework comprising of two elements: a time delay is equivalent to all-pass filtering and an all-pass filter can be represented in terms of a ratio of a finite impulse response (FIR) filter and its time reversal. Using these elements, we propose an adaptive filtering algorithm with an LMS style update that estimates the FIR filter coefficients and the time delay. Specifically, at each time step, the algorithm updates the filter coefficients based on a gradient descent update and then extracts an estimate of the time delay from the filter. We validate our algorithm on synthetic data demonstrating that it is both accurate and capable of tracking time-varying delays.

eess.SP↗

arXiv2020

Single-pixel Tracking and Imaging under Weak Illumination

Under weak illumination, tracking and imaging moving object turns out to be hard. By spatially collecting the signal, single pixel imaging schemes promise the capability of image reconstruction from low photon flux. However, due to the requirement on large number of samplings, how to clearly image moving objects is an essential problem for such schemes. Here we present a principle of single pixel tracking and imaging method. Velocity vector of the object is obtained from temporal correlation of the bucket signals in a typical computational ghost imaging system. Then the illumination beam is steered accordingly. Taking the velocity into account, both trajectory and clear image of the object are achieved during its evolution. Since tracking is achieved with bucket signals independently, this scheme is valid for capturing moving object as fast as its displacement within the interval of every sampling keeps larger than the resolution of the optical system. Experimentally, our method works well with the average number of detected photons down to 1.88 photons/speckle.

physics.optics↗

arXiv2020

Analog Computing with Metatronic Circuits

Analog photonic solutions offer unique opportunities to address complex computational tasks with unprecedented performance in terms of energy dissipation and speeds, overcoming current limitations of modern computing architectures based on electron flows and digital approaches. The lack of modularization and lumped element reconfigurability in photonics has prevented the transition to an all-optical analog computing platform. Here, we explore a nanophotonic platform based on epsilon-near-zero materials capable of solving in the analog domain partial differential equations (PDE). Wavelength stretching in zero-index media enables highly nonlocal interactions within the board based on the conduction of electric displacement, which can be monitored to extract the solution of a broad class of PDE problems. By exploiting control of deposition technique through process parameters, we demonstrate the possibility of implementing the proposed nano-optic processor using CMOS-compatible indium-tin-oxide, whose optical properties can be tuned by carrier injection to obtain programmability at high speeds and low energy requirements. Our nano-optical analog processor can be integrated at chip-scale, processing arbitrary inputs at the speed of light.

physics.optics↗