arXiv subjects

Fei Gao

Publications and source records attributed to Fei Gao.

At least 325 records · Page 18Linked to original sources

arXiv2022

Adaptive De-noising of Photoacoustic Signal and Image based on Modified Kalman Filter

As a burgeoning medical imaging method based on hybrid fusion of light and ultrasound, photoacoustic imaging (PAI) has demonstrated high potential in various biomedical applications recently, especially in revealing the functional and molecular information to improve diagnostic accuracy. However, stemming from weak amplitude and unavoidable random noise, caused by limited laser power and severe attenuation in deep tissue imaging, PA signals are usually of low signal-to-noise ratio (SNR), and reconstructed PA images are of low quality. Despite that conventional Kalman Filter (KF) can remove Gaussian noise in time domain, it lacks adaptability in real-time estimating condition due to its fixed model. Moreover, KF-based de-noising algorithm has not been applied in PAI before. In this paper, we propose an adaptive Modified Kalman Filter (MKF) targeted at PAI de-noising by tuning system noise matrix Q and measurement noise matrix R in the conventional KF model. Additionally, in order to compensate the signal skewing caused by KF, we cascade the backward part of Rauch-Tung-Striebel smoother (BRTS), which also utilizes the newly determined Q. Finally, as a supplement, we add a commonly used differential filter to remove in-band reflection artifacts. Experimental results using phantom and ex vivo colorectal tissue are provided to prove the validity of the algorithm.

eess.SP↗

arXiv2022

Positive effects of multiplicative noise on the explosion of nonlinear fractional stochastic differential equations

For the nonlinear stochastic partial differential equation which is driven by multiplicative noise of the form \[D_t^βu = \left[ { - {{\left( { - Δ} \right)}^s}u + ζ\left( u \right)} \right]dt + A\sum\limits_{m \in Z_0^d} {\sum\limits_{j = 1}^{d - 1} {{θ_m}{σ_{m,j}}\left( x \right)} } \circ dW_t^{m,j},\;\; s \ge 1,\;\;\frac{1}{2} < β< 1,\] where $D_{t}^β$ denotes the Caputo derivative, $A>0$ is a constant depending on the noise intensity, $\circ$ represent the Stratonovich-type stochastic differential, we consider the blow-up time of its solutions. We find that the introduction of noise can effectively delay the blow-up time of the solution to the deterministic differential equation when $ζ$ in the above equation satisfies some assumptions. A key element in our construction is using the Galerkin approximation and a priori estimates methods to prove the existence and uniqueness of the solutions to the above stochastic equations, which can be regarded as the fractional order extension of the conclusions in \cite{flandoli2021delayed}. We also verify the validation of hypotheses in the time fractional Keller-Segel and time fractional Fisher-KPP equations in 3D case.

math.PR↗

arXiv2022

Boundedness and exponential stabilization for time-space fractional parabolic-elliptic Keller-Segel model in higher dimensions

For the time-space fractional degenerate Keller-Segel equation \begin{equation*} \begin{cases} \partial _{t}^{β}u=-(-Δ)^{\fracα{2}}(ρ(v)u),& t>0\\ (-Δ)^{\fracα{2}} v+v=u,& t>0 \end{cases} \end{equation*} $x\inΩ, Ω\subset \mathbb{R}^{n}, β\in (0,1),α\in (1,2)$, we consider for $n\geq 3$ the problem of finding a time-independent upper bound of the classical solution such that as $θ>0,C>0$ \begin{equation*} \left \| u(\cdot ,t)-\overline{u_{0}} \right \|_{L^{\infty }(Ω)}+\left \| v(\cdot ,t)-\overline{u_{0}} \right \|_{W^{1,\infty }(Ω)}\leq Ce^{(-θ)^{1/β}t}, \end{equation*} where $\overline{u_{0}}=\frac{1}{\left | Ω\right |}\int _{Ω}u_{0}dx$. We find such solution in the special cases of time-independent upper bound of the concentration with Alikakos-Moser iteration and fractional differential inequality. In those cases the problem is reduced to a time-space fractional parabolic-elliptic equation which is treated with Lyapunov functional methods. A key element in our construction is a proof of the exponential stabilization toward the constant steady states by using fractional Duhamel type integral equation.

math.AP↗

arXiv2022

Revealing the Signal of QCD Phase Transition in Heavy-Ion Collisions

We propose a novel method to construct the Landau thermodynamic potential directly from the fluctuations measured in heavy-ion collisions. The potential is capable of revealing the signal of the critical end-point (CEP) and the first order phase transition (FOPT) of QCD in the system even away from the phase transition region. With the available experimental data, we show that the criterion of the FOPT is negative for most of the collision energies which indicates no signal of FOPT. The data at $\sqrt{s_\mathrm{NN}}=7.7$ GeV with 0-5% centrality shows a different behavior and the mean value of the data satisfies the criterion. However, the uncertainty is still too large to make a certain conclusion. The higher order fluctuations are also required for confirming the signal. We emphasize therefore that new measurements with higher precision for the $C_{1,...,6}$ within 0-5% centrality in the vicinity of $\sqrt{s_\mathrm{NN}}=7.7$ GeV are in demand which may finally reveal the signal of QCD phase transition.

hep-ph↗

arXiv2022

Tunable spin and transport in porphyrin-graphene nanoribbon hybrids

Recently, porphyrin units have been attached to graphene nanoribbons (Por-GNR) enabling a multitude of possible structures. Here we report first principles calculations of two prototypical, experimentally feasible, Por-GNR hybrids, one of which displays a small band gap relevant for its use as electrode in a device. Embedding a Fe atom in the porphyrin causes spin polarization with a spin ground state $S=1$. We employ density functional theory and nonequilibrium Green's function transport calculations to examine a 2-terminal setup involving one Fe-Por-GNR between two metal-free, small band gap, Por-GNR electrodes. The coupling between the Fe-$d$ and GNR band states results in a Fano anti-resonance feature in the spin transport close to the Fermi energy. This feature makes transport highly sensitive to the Fe spin state. We demonstrate how mechanical strain or chemical adsorption on the Fe give rise to a spin-crossover to $S=1$ and $S=0$, respectively, directly reflected in a change in transport. Our theoretical results provide a clue for the on-surface synthesis of Por-GNRs hybrids, which can open a new avenue for carbon-based spintronics and chemical sensing.

cond-mat.mes-hall↗

arXiv2022

Enhanced Decentralized Autonomous Aerial Robot Teams with Group Planning

Designing autonomous aerial robot team systems remains a grand challenge in robotics. Existing works in this field can be categorized as centralized and decentralized. Centralized methods suffer from scale dilemmas, while decentralized ones often lead to poor planning quality. In this paper, we propose an enhanced decentralized autonomous aerial robot team system with group planning. According to the spatial distribution of agents, the system dynamically divides the team into several groups and isolated agents. For conflicts within each group, we propose a novel coordination mechanism named group planning. The group planning consists of efficient multi-agent pathfinding (MAPF) and trajectory joint optimization, which can significantly improve planning quality and success rate. We demonstrate through simulations and real-world experiments that our method not only has applicability for a large-scale team but also has top-level planning quality

cs.RO↗

arXiv2022

Bearing-based Relative Localization for Robotic Swarm with Partially Mutual Observations

Mutual localization provides a consensus of reference frame as an essential basis for cooperation in multirobot systems. Previous works have developed certifiable and robust solvers for relative transformation estimation between each pair of robots. However, recovering relative poses for robotic swarm with partially mutual observations is still an unexploited problem. In this paper, we present a complete algorithm for it with optimality, scalability and robustness. Firstly, we fuse all odometry and bearing measurements in a unified minimization problem among the Stiefel manifold. Furthermore, we relax the original non-convex problem into a semi-definite programming (SDP) problem with a strict tightness guarantee. Then, to hold the exactness in noised cases, we add a convex (linear) rank cost and apply a convex iteration algorithm. We compare our approach with local optimization methods on extensive simulations with different robot amounts under various noise levels to show our global optimality and scalability advantage. Finally, we conduct real-world experiments to show the practicality and robustness.

cs.RO↗

arXiv2022

Expectation-Maximizing Network Reconstruction and MostApplicable Network Types Based on Binary Time Series Data

Based on the binary time series data of social infection dynamics, we propose a general framework to reconstruct 2-simplex complexes with two-body and three-body interactions by combining the maximum likelihood estimation in statistical inference and introducing the expectation maximization. In order to improve the code running efficiency, the whole algorithm adopts vectorization expression. Through the inference of maximum likelihood estimation, the vectorization expression of the edge existence probability can be obtained, and through the probability matrix, the adjacency matrix of the network can be estimated. We apply a two-step scheme to improve the effectiveness of network reconstruction while reducing the amount of computation significantly. The framework has been tested on different types of complex networks. Among them, four kinds of networks can obtain high reconstruction effectiveness. Besides, we study the influence of noise data or random interference and prove the robustness of the framework, then the effects of two kinds of hyper-parameters on the experimental results are tested. Finally, we analyze which type of network is more suitable for this framework, and propose methods to improve the effectiveness of the experimental results.

physics.soc-ph↗

arXiv2022

A Topological Directional Coupler Fed by Microstrip Line with Configurable Coupling Coefficient

Topological waveguides have been extensively studied for their robust transmission properties immune to defects and their application potentials for microwave and terahertz integrated circuits. In this work, by using grounded planar valley-Hall photonic topological insulators, a high-efficiency topological directional coupler fed directly by microstrip line with configurable coupling coefficient is theoretically proposed and experimentally demonstrated. The topological directional coupler consists of two coupled topological waveguides, which can be directly integrated with microstrip circuits. Different coupling coefficients were achieved by configuring the coupling between the two topological waveguides. Both simulation and measurement demonstrated the proposed designs. In addition, the proposed design is applicable in both microwave and terahertz bands.

physics.app-ph↗

arXiv2022

Efficient View Path Planning for Autonomous Implicit Reconstruction

Implicit neural representations have shown promising potential for the 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain for view path planning. Effective as it is, the computation of the information gain is expensive, and compared with that using volumetric representations, collision checking using the implicit representation for a 3D point is much slower. In the paper, we propose to 1) leverage a neural network as an implicit function approximator for the information gain field and 2) combine the implicit fine-grained representation with coarse volumetric representations to improve efficiency. Further with the improved efficiency, we propose a novel informative path planning based on a graph-based planner. Our method demonstrates significant improvements in the reconstruction quality and planning efficiency compared with autonomous reconstructions with implicit and explicit representations. We deploy the method on a real UAV and the results show that our method can plan informative views and reconstruct a scene with high quality.

cs.RO↗

arXiv2022

Echo chamber effects based on a novel three-dimensional Deffuant-Weisbuch model

In order to solve the problem of opinion polarization and distortion caused by echo chamber effect in the evolution process of online public opinion,a three-dimensional Deffuant-Weisbuch model is proposed to study the formation and elimination of echo chamber effect in this paper. Firstly, the original pairwise interaction model is generalized to three-point interaction model. Secondly, we consider individual psychological mechanism and introduce individual emotional factor into the trust threshold of original model. Finally, the natural evolution coefficient of opinion is introduced to modify the model. The improved model is used to conduct simulation experiments on social networks with different structures, and opinion leaders and active agents are introduced into the network, so as to study the corresponding generation and breaking mechanism of echo chamber. The experimental results show that the change of network structure cannot eliminate the echo chamber effect, and the increase of network stability and connectivity can only slow down the echo chamber effect. Opinion leaders can aggregate opinions within their scope of influence and have a guiding effect on opinions. Therefore, if opinion leaders can change their opinions over time, they can well guide opinions to converge to neutral opinions, thus achieving the purpose of breaking the echo chamber. Active agents can lead the opinions in the network to converge to the neutral, and active agents with high stubbornness can lead the free views to converge to the neutral, thus achieving the purpose of breaking the echo chamber effect.

physics.soc-ph↗

arXiv2022

Quantum Algorithm for Anomaly Detection of Sequences

Anomaly detection of sequences is a hot topic in data mining. Anomaly Detection using Piecewise Aggregate approximation in the Amplitude Domain (called ADPAAD) is one of the widely used methods in anomaly detection of sequences. The core step in the classical algorithm for performing ADPAAD is to construct an approximate representation of the subsequence, where the elements of each subsequence are divided into several subsections according to the amplitude domain and then the average of the subsections is computed. It is computationally expensive when processing large-scale sequences. In this paper, we propose a quantum algorithm for ADPAAD, which can divide the subsequence elements and compute the average in parallel. Our quantum algorithm can achieve polynomial speedups on the number of subsequences and the length of subsequences over its classical counterpart.

quant-ph↗

arXiv2022

A brief review of topological photonics in one, two, and three dimensions

Topological photonics has attracted increasing attention in recent years due to the unique opportunities it provides to manipulate light in a robust way immune to disorder and defects. Up to now, diverse photonic platforms, rich physical mechanisms and fruitful device applications have been proposed for topological photonics, including one-way waveguide, topological lasing, topological nanocavity, Dirac and Weyl points, Fermi arcs, nodal lines, etc. In this review, we provide an introduction to the field of topological photonics through the lens of topological invariants and bulk-boundary correspondence in one, two, and three dimensions, which may not only offer a unified understanding about the underlying robustness of diverse and distinct topological phenomena of light, but could also inspire further developments by introducing new topological invariants and unconventional bulk-boundary correspondence to the research of topological photonics.

physics.optics↗

arXiv2022

Bubble Planner: Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors

Quadrotors are agile platforms. With human experts, they can perform extremely high-speed flights in cluttered environments. However, fully autonomous flight at high speed remains a significant challenge. In this work, we propose a motion planning algorithm based on the corridor-constrained minimum control effort trajectory optimization (MINCO) framework. Specifically, we use a series of overlapping spheres to represent the free space of the environment and propose two novel designs that enable the algorithm to plan high-speed quadrotor trajectories in real-time. One is a sampling-based corridor generation method that generates spheres with large overlapped areas (hence overall corridor size) between two neighboring spheres. The second is a Receding Horizon Corridors (RHC) strategy, where part of the previously generated corridor is reused in each replan. Together, these two designs enlarge the corridor spaces in accordance with the quadrotor's current state and hence allow the quadrotor to maneuver at high speeds. We benchmark our algorithm against other state-of-the-art planning methods to show its superiority in simulation. Comprehensive ablation studies are also conducted to show the necessity of the two designs. The proposed method is finally evaluated on an autonomous LiDAR-navigated quadrotor UAV in woods environments, achieving flight speeds over 13.7 m/s without any prior map of the environment or external localization facility.

cs.RO↗

arXiv2022

Quantum mean centering for block-encoding-based quantum algorithm

Mean Centering (MC) is an important data preprocessing technique, which has a wide range of applications in data mining, machine learning, and multivariate statistical analysis. When the data set is large, this process will be time-consuming. In this paper, we propose an efficient quantum MC algorithm based on the block-encoding technique, which enables the existing quantum algorithms can get rid of the assumption that the original data set has been classically mean-centered. Specifically, we first adopt the strategy that MC can be achieved by multiplying by the centering matrix $C$, i.e., removing the row means, column means and row-column means of the original data matrix $X$ can be expressed as $XC$, $CX$ and $CXC$, respectively. This allows many classical problems involving MC, such as Principal Component Analysis (PCA), to directly solve the matrix algebra problems related to $XC$, $CX$ or $CXC$. Next, we can employ the block-encoding technique to realize MC. To achieve it, we first show how to construct the block-encoding of the centering matrix $C$, and then further obtain the block-encodings of $XC$, $CX$ and $CXC$. Finally, we describe one by one how to apply our MC algorithm to PCA and other algorithms.

quant-ph↗

arXiv2022

Meeting-Merging-Mission: A Multi-robot Coordinate Framework for Large-Scale Communication-Limited Exploration

This letter presents a complete framework Meeting-Merging-Mission for multi-robot exploration under communication restriction. Considering communication is limited in both bandwidth and range in the real world, we propose a lightweight environment presentation method and an efficient cooperative exploration strategy. For lower bandwidth, each robot utilizes specific polytopes to maintains free space and super frontier information (SFI) as the source for exploration decision-making. To reduce repeated exploration, we develop a mission-based protocol that drives robots to share collected information in stable rendezvous. We also design a complete path planning scheme for both centralized and decentralized cases. To validate that our framework is practical and generic, we present an extensive benchmark and deploy our system into multi-UGV and multi-UAV platforms.

cs.RO↗

arXiv2022

Real-Time Trajectory Planning for Aerial Perching

This paper presents a novel trajectory planning method for aerial perching. Compared with the existing work, the terminal states and the trajectory durations can be adjusted adaptively, instead of being determined in advance. Furthermore, our planner is able to minimize the tangential relative speed on the premise of safety and dynamic feasibility. This feature is especially notable on micro aerial robots with low maneuverability or scenarios where the space is not enough. Moreover, we design a flexible transformation strategy to eliminate terminal constraints along with reducing optimization variables. Besides, we take precise SE(3) motion planning into account to ensure that the drone would not touch the landing platform until the last moment. The proposed method is validated onboard by a palm-sized micro aerial robot with quite limited thrust and moment (thrust-to-weight ratio 1.7) perching on a mobile inclined surface. Sufficient experimental results show that our planner generates an optimal trajectory within 20ms, and replans with warm start in 2ms.

cs.RO↗

arXiv2022

LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane

Visual-inertial-odometry has attracted extensive attention in the field of autonomous driving and robotics. The size of Field of View (FoV) plays an important role in Visual-Odometry (VO) and Visual-Inertial-Odometry (VIO), as a large FoV enables to perceive a wide range of surrounding scene elements and features. However, when the field of the camera reaches the negative half plane, one cannot simply use [u,v,1]^T to represent the image feature points anymore. To tackle this issue, we propose LF-VIO, a real-time VIO framework for cameras with extremely large FoV. We leverage a three-dimensional vector with unit length to represent feature points, and design a series of algorithms to overcome this challenge. To address the scarcity of panoramic visual odometry datasets with ground-truth location and pose, we present the PALVIO dataset, collected with a Panoramic Annular Lens (PAL) system with an entire FoV of 360°x(40°-120°) and an IMU sensor. With a comprehensive variety of experiments, the proposed LF-VIO is verified on both the established PALVIO benchmark and a public fisheye camera dataset with a FoV of 360°x(0°-93.5°). LF-VIO outperforms state-of-the-art visual-inertial-odometry methods. Our dataset and code are made publicly available at https://github.com/flysoaryun/LF-VIO

cs.CV↗