arXiv subjects

Jing Dong

Publications and source records attributed to Jing Dong.

154 records · Page 9Linked to original sources

arXiv2017

Sparse Gaussian Processes for Continuous-Time Trajectory Estimation on Matrix Lie Groups

Continuous-time trajectory representations are a powerful tool that can be used to address several issues in many practical simultaneous localization and mapping (SLAM) scenarios, like continuously collected measurements distorted by robot motion, or during with asynchronous sensor measurements. Sparse Gaussian processes (GP) allow for a probabilistic non-parametric trajectory representation that enables fast trajectory estimation by sparse GP regression. However, previous approaches are limited to dealing with vector space representations of state only. In this technical report we extend the work by Barfoot et al. [1] to general matrix Lie groups, by applying constant-velocity prior, and defining locally linear GP. This enables using sparse GP approach in a large space of practical SLAM settings. In this report we give the theory and leave the experimental evaluation in future publications.

cs.RO↗

arXiv2016

What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?

Previous work has shown that feature maps of deep convolutional neural networks (CNNs) can be interpreted as feature representation of a particular image region. Features aggregated from these feature maps have been exploited for image retrieval tasks and achieved state-of-the-art performances in recent years. The key to the success of such methods is the feature representation. However, the different factors that impact the effectiveness of features are still not explored thoroughly. There are much less discussion about the best combination of them. The main contribution of our paper is the thorough evaluations of the various factors that affect the discriminative ability of the features extracted from CNNs. Based on the evaluation results, we also identify the best choices for different factors and propose a new multi-scale image feature representation method to encode the image effectively. Finally, we show that the proposed method generalises well and outperforms the state-of-the-art methods on four typical datasets used for visual instance retrieval.

cs.CV↗

arXiv2016

4D Crop Monitoring: Spatio-Temporal Reconstruction for Agriculture

Autonomous crop monitoring at high spatial and temporal resolution is a critical problem in precision agriculture. While Structure from Motion and Multi-View Stereo algorithms can finely reconstruct the 3D structure of a field with low-cost image sensors, these algorithms fail to capture the dynamic nature of continuously growing crops. In this paper we propose a 4D reconstruction approach to crop monitoring, which employs a spatio-temporal model of dynamic scenes that is useful for precision agriculture applications. Additionally, we provide a robust data association algorithm to address the problem of large appearance changes due to scenes being viewed from different angles at different points in time, which is critical to achieving 4D reconstruction. Finally, we collected a high quality dataset with ground truth statistics to evaluate the performance of our method. We demonstrate that our 4D reconstruction approach provides models that are qualitatively correct with respect to visual appearance and quantitatively accurate when measured against the ground truth geometric properties of the monitored crops.

cs.RO↗

arXiv2016

Exact Sampling of the Infinite Horizon Maximum of a Random Walk Over a Non-linear Boundary

We present the first algorithm that samples $\max_{n\geq0}\{S_{n}-n^α\},$ where $S_n$ is a mean zero random walk, and $n^α$ with $α\in(1/2,1)$ defines a nonliner boundary. We show that our algorithm has finite expected running time. We also apply the algorithm to construct the first exact simulation method for the steady-state departure process of a $GI/GI/\infty$ queue where the service time distribution has infinite mean.

math.PR↗

arXiv2016

Development of a two-dimensional imaging GEM detector using the resistive anode readout method with $6\times6$ cells

We report the application of the resistive anode readout method on a two dimensional imaging GEM detector. The resistive anode consists of $6\times6$ cells with the cell size $6~\mathrm{mm}\times6~\mathrm{mm}$. New electronics and DAQ system are used to process the signals from the 49 readout channels. The detector has been tested by using the X-ray tube (8~keV). The spatial resolution of the detector is about $103.46~\mathrm{μm}$ with the detector response part about $66.41~\mathrm{μm}$. The nonlinearity of the detector is less than $1.5\%$. A quite good two dimensional imaging capability is achieved as well. The performances of the detector show the prospect of the resistive anode readout method for the large readout area imaging detectors.

physics.ins-det↗

arXiv2016

$ε$-Strong Simulation for Multidimensional Stochastic Differential Equations via Rough Path Analysis

Consider a multidimensional diffusion process $X=\{X\left(t\right) :t\in\lbrack0,1]\}$. Let $\varepsilon>0$ be a \textit{deterministic}, user defined, tolerance error parameter. Under standard regularity conditions on the drift and diffusion coefficients of $X$, we construct a probability space, supporting both $X$ and an explicit, piecewise constant, fully simulatable process $X_{\varepsilon}$ such that \[ \sup_{0\leq t\leq1}\left\Vert X_{\varepsilon}\left(t\right) -X\left(t\right) \right\Vert_{\infty}<\varepsilon \] with probability one. Moreover, the user can adaptively choose $\varepsilon^{\prime}\in\left(0,\varepsilon\right) $ so that $X_{\varepsilon^{\prime}}$ (also piecewise constant and fully simulatable) can be constructed conditional on $X_{\varepsilon}$ to ensure an error smaller than $\varepsilon^{\prime}>0$ with probability one. Our construction requires a detailed study of continuity estimates of the Ito map using Lyon's theory of rough paths. We approximate the underlying Brownian motion, jointly with the Lévy areas with a deterministic $\varepsilon$ error in the underlying rough path metric.

math.PR↗

arXiv2015

Observation of Fundamental Thermal Noise in Optical Fibers down to Infrasonic Frequencies

The intrinsic thermal noise in optical fibers is the ultimate limit of fiber-based systems. However, at infrasonic frequencies, the spectral behavior of the intrinsic thermal noise remains unclear so far. We present the measurements of the fundamental thermal noise in optical fibers obtained using a balanced fiber Michelson interferometer. When an ultra-stable laser is used as the laser source and other noise sources are carefully controlled, the 1/f spectral density of thermal noise is observed down to infrasonic frequencies and the measured magnitude is consistent with the theoretical predictions at the frequencies from 0.2 Hz to 20 kHz. Moreover, as observed in the experiment, the level of 1/f thermal noise is reduced by changing the coating of optical fibers. Therefore, a possible way to reduce the thermal noise in optical fibers at low Fourier frequencies is indicated. Finally, the inconsistency between the experimental data on thermomechanical noise and existing theory is discussed.

physics.optics↗

arXiv2015

Perfect Sampling of GI/GI/c Queues

We introduce the first class of perfect sampling algorithms for the steady-state distribution of multi-server queues with general interarrival time and service time distributions. Our algorithm is built on the classical dominated coupling from the past protocol. In particular, we use a coupled multi-server vacation system as the upper bound process and develop an algorithm to simulate the vacation system backwards in time from stationarity at time zero. The algorithm has finite expected termination time with mild moment assumptions on the interarrival time and service time distributions.

math.PR↗

arXiv2013

Perfect sampling for infinite server and loss systems

We present the first class of perfect sampling (also known as exact simulation) algorithms for the steady-state distribution of non-Markovian loss networks. We use a variation of Dominated Coupling From The Past for which we simulate a stationary infinite server queue backwards in time and analyze the running time in heavy traffic. In particular, we are able to simulate stationary renewal marked point processes in unbounded regions. We use the infinite server queue as an upper bound process to simulate loss systems. The running time analysis of our perfect sampling algorithm for loss systems is performed in the Quality-Driven (QD) and the Quality-and-Efficiency-Driven regimes. In both cases, we show that our algorithm achieves sub-exponential complexity as both the number of servers and the arrival rate increase. Moreover, in the QD regime, our algorithm achieves a nearly optimal rate of convergence.

math.PR↗

arXiv2012

Sampling Point Processes on Stable Unbounded Regions and Exam Simulation of Queues

Given a marked renewal point process (assuming that the marks are i.i.d.) we say that an unbounded region is stable if it contains finitely many points of the point process with probability one. In this paper we provide algorithms that allow to sample these finitely many points efficiently. We explain how exact simulation of the steady-state measure valued state descriptor of the infinite server queue follows as a simple corollary of our algorithms. We provide numerical evidence supporting that our algorithms are not only theoretically sound but also practical. Finally, having simulation optimization in mind, we also apply our results to gradient estimation of steady-state performance measures.

math.PR↗