arXiv subjects

Haojie Ren

Publications and source records attributed to Haojie Ren.

33 records · Page 2Linked to original sources

arXiv2025

Ghost Points Matter: Far-Range Vehicle Detection with a Single mmWave Radar in Tunnel

Vehicle detection in tunnels is crucial for traffic monitoring and accident response, yet remains underexplored. In this paper, we develop mmTunnel, a millimeter-wave radar system that achieves far-range vehicle detection in tunnels. The main challenge here is coping with ghost points caused by multi-path reflections, which lead to severe localization errors and false alarms. Instead of merely removing ghost points, we propose correcting them to true vehicle positions by recovering their signal reflection paths, thus reserving more data points and improving detection performance, even in occlusion scenarios. However, recovering complex 3D reflection paths from limited 2D radar points is highly challenging. To address this problem, we develop a multi-path ray tracing algorithm that leverages the ground plane constraint and identifies the most probable reflection path based on signal path loss and spatial distance. We also introduce a curve-to-plane segmentation method to simplify tunnel surface modeling such that we can significantly reduce the computational delay and achieve real-time processing. We have evaluated mmTunnel with comprehensive experiments. In two test tunnels, we conducted controlled experiments in various scenarios with cars and trucks. Our system achieves an average F1 score of 93.7% for vehicle detection while maintaining real-time processing. Even in the challenging occlusion scenarios, the F1 score remains above 91%. Moreover, we collected extensive data from a public tunnel with heavy traffic at times and show our method could achieve an F1 score of 91.5% in real-world traffic conditions.

cs.NI↗

arXiv2025

e-GAI: e-value-based Generalized $α$-Investing for Online False Discovery Rate Control

Online multiple hypothesis testing has attracted a lot of attention in many applications, e.g., anomaly status detection and stock market price monitoring. The state-of-the-art generalized $α$-investing (GAI) algorithms can control online false discovery rate (FDR) on p-values only under specific dependence structures, a situation that rarely occurs in practice. The e-LOND algorithm (Xu & Ramdas, 2024) utilizes e-values to achieve online FDR control under arbitrary dependence but suffers from a significant loss in power as testing levels are derived from pre-specified descent sequences. To address these limitations, we propose a novel framework on valid e-values named e-GAI. The proposed e-GAI can ensure provable online FDR control under more general dependency conditions while improving the power by dynamically allocating the testing levels. These testing levels are updated not only by relying on both the number of previous rejections and the prior costs, but also, differing from the GAI framework, by assigning less $α$-wealth for each rejection from a risk aversion perspective. Within the e-GAI framework, we introduce two new online FDR procedures, e-LORD and e-SAFFRON, and provide strategies for the long-term performance to address the issue of $α$-death, a common phenomenon within the GAI framework. Furthermore, we demonstrate that e-GAI can be generalized to conditionally super-uniform p-values. Both simulated and real data experiments demonstrate the advantages of both e-LORD and e-SAFFRON in FDR control and power.

stat.ME↗

arXiv2025

ElectricSight: 3D Hazard Monitoring for Power Lines Using Low-Cost Sensors

Protecting power transmission lines from potential hazards involves critical tasks, one of which is the accurate measurement of distances between power lines and potential threats, such as large cranes. The challenge with this task is that the current sensor-based methods face challenges in balancing accuracy and cost in distance measurement. A common practice is to install cameras on transmission towers, which, however, struggle to measure true 3D distances due to the lack of depth information. Although 3D lasers can provide accurate depth data, their high cost makes large-scale deployment impractical. To address this challenge, we present ElectricSight, a system designed for 3D distance measurement and monitoring of potential hazards to power transmission lines. This work's key innovations lie in both the overall system framework and a monocular depth estimation method. Specifically, the system framework combines real-time images with environmental point cloud priors, enabling cost-effective and precise 3D distance measurements. As a core component of the system, the monocular depth estimation method enhances the performance by integrating 3D point cloud data into image-based estimates, improving both the accuracy and reliability of the system. To assess ElectricSight's performance, we conducted tests with data from a real-world power transmission scenario. The experimental results demonstrate that ElectricSight achieves an average accuracy of 1.08 m for distance measurements and an early warning accuracy of 92%.

cs.CV↗

arXiv2025

Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach

Conformal prediction is a powerful tool for constructing prediction intervals for black-box models, providing a finite sample coverage guarantee for exchangeable data. However, this exchangeability is compromised when some entries of the test feature are contaminated, such as in the case of cellwise outliers. To address this issue, this paper introduces a novel framework called detect-then-impute conformal prediction. This framework first employs an outlier detection procedure on the test feature and then utilizes an imputation method to fill in those cells identified as outliers. To quantify the uncertainty in the processed test feature, we adaptively apply the detection and imputation procedures to the calibration set, thereby constructing exchangeable features for the conformal prediction interval of the test label. We develop two practical algorithms, PDI-CP and JDI-CP, and provide a distribution-free coverage analysis under some commonly used detection and imputation procedures. Notably, JDI-CP achieves a finite sample $1-2α$ coverage guarantee. Numerical experiments on both synthetic and real datasets demonstrate that our proposed algorithms exhibit robust coverage properties and comparable efficiency to the oracle baseline.

stat.ML↗

arXiv2024

Dimension of Bernoulli Convolutions in $\mathbb{R}^{d}$

For $(λ_{1},...,λ_{d})=λ\in(0,1)^{d}$ with $λ_{1}>...>λ_{d}$, denote by $μ_λ$ the Bernoulli convolution associated to $λ$. That is, $μ_λ$ is the distribution of the random vector $\sum_{n\ge0}\pm\left(λ_{1}^{n},...,λ_{d}^{n}\right)$, where the $\pm$ signs are chosen independently and with equal weight. Assuming for each $1\le j\le d$ that $λ_{j}$ is not a root of a polynomial with coefficients $\pm1,0$, we prove that the dimension of $μ_λ$ equals $\min\left\{ \dim_{L}μ_λ,d\right\} $, where $\dim_{L}μ_λ$ is the Lyapunov dimension. More generally, we obtain this result in the context of homogeneous diagonal self-affine systems on $\mathbb{R}^{d}$ with rational translations. The proof extends to higher dimensions the works of Breuillard and Varjú and Varjú regarding Bernoulli convolutions on the real line. The main novelty and contribution of the present work lies in an extension of an entropy increase result, due to Varjú, in which the amount of increase in entropy is given explicitly. The extension of this result to the higher-dimensional non-conformal case requires significant new ideas.

math.DS↗

arXiv2024

The high-dimensional Weierstrass functions

For a real analytic periodic function $ϕ:\mathbb{R}\to\mathbb{R}^d$, an integer $b \ge 2$ and $λ\in(1/b,1)$, we prove that the box dimension and the Hausdorff dimension of the graph of the Weierstrass function $W(x)=\sum_{n=0}^{\infty}{λ^nϕ(b^nx)}$ are both equal to $$\min\left\{\log_{λ^{-1}}b,\,1+\left(\,d-q\,\right)\left(1+\log_bλ\right)\right\},$$ where $q = q(ϕ, b, λ)$ denotes the maximum dimension of all linear spaces $V < \mathbb{R}^d$ such that the projection $π_V W$ is Lipschitz.

math.CA↗

arXiv2024

Selective conformal inference with false coverage-statement rate control

Conformal inference is a popular tool for constructing prediction intervals (PI). We consider here the scenario of post-selection/selective conformal inference, that is PIs are reported only for individuals selected from an unlabeled test data. To account for multiplicity, we develop a general split conformal framework to construct selective PIs with the false coverage-statement rate (FCR) control. We first investigate the Benjamini and Yekutieli (2005)'s FCR-adjusted method in the present setting, and show that it is able to achieve FCR control but yields uniformly inflated PIs. We then propose a novel solution to the problem, named as Selective COnditional conformal Predictions (SCOP), which entails performing selection procedures on both calibration set and test set and construct marginal conformal PIs on the selected sets by the aid of conditional empirical distribution obtained by the calibration set. Under a unified framework and exchangeable assumptions, we show that the SCOP can exactly control the FCR. More importantly, we provide non-asymptotic miscoverage bounds for a general class of selection procedures beyond exchangeablity and discuss the conditions under which the SCOP is able to control the FCR. As special cases, the SCOP with quantile-based selection or conformal p-values-based multiple testing procedures enjoys valid coverage guarantee under mild conditions. Numerical results confirm the effectiveness and robustness of SCOP in FCR control and show that it achieves more narrowed PIs over existing methods in many settings.

stat.ME↗

arXiv2023

Vortex-induced vibration of a flexible pipe under oscillatory sheared flow

Vortex-induced vibration (VIV) test of a tensioned flexible pipe in oscillatory sheared flow was performed in an ocean basin. The model was 28.41 mm in diameter and 3.88 m in length. The test was performed on a rotating test rig to simulate oscillatory sheared flow conditions. One end of the test pipe is fixed, and one end is forced to harmonically oscillate to simulate oscillatory sheared flows with various combinations of amplitudes and periods, Keulegan-Carpenter ($KC$) numbers from $25$ to $160$ and five kinds of reduced velocities $Vr$ from $6$ to $14$. Fiber Bragg Grating (FBG) strain sensors were arranged along the test pipe to measure bending strains, and the modal analysis approach was used to determine the VIV response. The VIV response in the cross flow (CF) direction is investigated. The results show that VIV under oscillatory sheared flow exhibit amplitude modulation and hysteresis phenomena. Compared with oscillatory uniform flow-induced VIV, the Strouhal number is smaller in oscillatory sheared flow-induced VIVs. The VIV developing process in oscillatory sheared flow is analyzed, and critical $KC$ is proposed to describe the occurrence of modulated VIV under oscillatory sheared flow.

physics.flu-dyn↗

arXiv2023

EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration

In multimodal perception systems, achieving precise extrinsic calibration between LiDAR and camera is of critical importance. Previous calibration methods often required specific targets or manual adjustments, making them both labor-intensive and costly. Online calibration methods based on features have been proposed, but these methods encounter challenges such as imprecise feature extraction, unreliable cross-modality associations, and high scene-specific requirements. To address this, we introduce an edge-based approach for automatic online calibration of LiDAR and cameras in real-world scenarios. The edge features, which are prevalent in various environments, are aligned in both images and point clouds to determine the extrinsic parameters. Specifically, stable and robust image edge features are extracted using a SAM-based method and the edge features extracted from the point cloud are weighted through a multi-frame weighting strategy for feature filtering. Finally, accurate extrinsic parameters are optimized based on edge correspondence constraints. We conducted evaluations on both the KITTI dataset and our dataset. The results show a state-of-the-art rotation accuracy of 0.086° and a translation accuracy of 0.977 cm, outperforming existing edge-based calibration methods in both precision and robustness.

cs.CV↗

arXiv2023

TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching

Recently, it has become popular to deploy sensors such as LiDARs on the roadside to monitor the passing traffic and assist autonomous vehicle perception. Unlike autonomous vehicle systems, roadside sensors are usually affiliated with different subsystems and lack synchronization both in time and space. Calibration is a key technology which allows the central server to fuse the data generated by different location infrastructures, which can deliver improve the sensing range and detection robustness. Unfortunately, existing calibration algorithms often assume that the LiDARs are significantly overlapped or that the temporal calibration is already achieved. Since these assumptions do not always hold in the real world, the calibration results from the existing algorithms are often unsatisfactory and always need human involvement, which brings high labor costs. In this paper, we propose TrajMatch -- the first system that can automatically calibrate for roadside LiDARs in both time and space. The main idea is to automatically calibrate the sensors based on the result of the detection/tracking task instead of extracting special features. More deeply, we propose a mechanism for evaluating calibration parameters that is consistent with our algorithm, and we demonstrate the effectiveness of this scheme experimentally, which can also be used to guide parameter iterations for multiple calibration. Finally, to evaluate the performance of TrajMatch , we collect two dataset, one simulated dataset LiDARnet-sim 1.0 and a real-world dataset. Experiment results show that TrajMatch can achieve a spatial calibration error of less than 10cm and a temporal calibration error of less than 1.5ms.

cs.RO↗

arXiv2022

A Dichotomy for the dimension of solenoidal attractors on high dimensional space

We study dynamical systems generated by skew products: $$T: [0,1)\times\mathbb{C}\to [0,1)\times\mathbb{C} \quad\quad T(x,y)=(bx\mod1,γy+ϕ(x))$$ where integer $b\ge2$, $γ\in\mathbb{C}$ such that $0<|γ|<1$, and $ϕ$ is a real analytic $\mathbb{Z}$-periodic function. Let $Δ\in[0,1) $ such that $γ=|γ|e^{2πiΔ}$. For the case $Δ\notin\mathbb{Q}$ we prove the following dichotomy for the solenoidal attractor $K^ϕ_{b,\,γ}$ for $T$: Either $K^ϕ_{b,\,γ}$ is a graph of real analytic function, or the Hausdorff dimension of $K^ϕ_{b,\,γ}$ is equal to $\min\{3,1+\frac{\log b}{\log1/|γ|}\}$. Furthermore, given $b$ and $ϕ$, the former alternative only happens for countable many $γ$ unless $ϕ$ is constant.

math.DS↗

arXiv2022

Box dimension of the graphs of the generalized Weierstrass-type functions

For a Lipschitz $\mathbb{Z}-$periodic function $ϕ:\mathbb{R}\to \mathbb{R}^2$ satisfied that $\mathbb{R}^2\setminus\{ϕ(x):x\in\mathbb{R}\}$ is not connected, an integer $b\ge 2$ and $λ\in (c/{b^{\frac12}},1)$, we prove the following for the generalized Weierstrass-type function $W(x)=\sum\limits_{n=0}^{\infty}{λ^nϕ(b^nx)}$: the box dimension of its graph is equal to $3+2\log_bλ$, where $c$ is a constant depending on $ϕ$.

math.CA↗

arXiv2022

A Dichotomy for the dimension of SRB measure

We study dynamical systems generated by skew products: $$T: [0,1)\times\mathbb{R}\to [0,1)\times\mathbb{R} \quad\quad T(x,y)=(bx\mod1,γy+ϕ(x))$$ where integer $b\ge2$, $0<γ<1$ and $ϕ$ is a real analytic $\mathbb{Z}$-periodic function. We prove the following dichotomy for the SRB measure $ω$ for $T$: Either the support of $ω$ is a graph of real analytic function, or the dimension of $ω$ is equal to $\min\{2,1+\frac{\log b}{\log1/γ}\}$. Furthermore, given $b$ and $ϕ$, the former alternative only happens for finitely many $γ$ unless $ϕ$ is constant.

math.DS↗

arXiv2020

A Dichotomy for the Weierstrass-type functions

For a real analytic periodic function $ϕ:\mathbb{R}\to \mathbb{R}$, an integer $b\ge 2$ and $λ\in (1/b,1)$, we prove the following dichotomy for the Weierstrass-type function $W(x)=\sum\limits_{n\ge 0}{λ^nϕ(b^nx)}$: Either $W(x)$ is real analytic, or the Hausdorff dimension of its graph is equal to $2+\log_bλ$. Furthermore, given $b$ and $ϕ$, the former alternative only happens for finitely many $λ$ unless $ϕ$ is constant.

math.DS↗

arXiv2020

A New Procedure for Controlling False Discovery Rate in Large-Scale t-tests

This paper is concerned with false discovery rate (FDR) control in large-scale multiple testing problems. We first propose a new data-driven testing procedure for controlling the FDR in large-scale t-tests for one-sample mean problem. The proposed procedure achieves exact FDR control in finite sample settings when the populations are symmetric no matter the number of tests or sample sizes. Comparing with the existing bootstrap method for FDR control, the proposed procedure is computationally efficient. We show that the proposed method can control the FDR asymptotically for asymmetric populations even when the test statistics are not independent. We further show that the proposed procedure with a simple correction is as accurate as the bootstrap method to the second-order degree, and could be much more effective than the existing normal calibration. We extend the proposed procedure to two-sample mean problem. Empirical results show that the proposed procedures have better FDR control than existing ones when the proportion of true alternative hypotheses is not too low, while maintaining reasonably good detection ability.

math.ST↗