arXiv subjects

Beibei Zhou

Publications and source records attributed to Beibei Zhou.

6 recordsLinked to original sources

arXiv2025

Learnt Microwave Image Reconstruction with A Conformal Antenna Array

A deep learning model is proposed for reconstructing 2D dielectric breast images from time-domain signals. Unlike existing learning models that employ a fixed antenna array, where input data consists solely of measurements, the proposed system integrates antenna positioning into the processing pipeline. This allows for a conformal antenna array that adapts to different breast sizes for optimal data collection across various patients, which eliminates undesired signal attenuation in coupling liquid when implemented for the fixed array. By leveraging antenna positions, the breast surface can be pre-estimated, enabling the neural network to focus on image reconstruction within the region of interest. Numerical results demonstrate that the proposed model may reconstruct breast images with good quality.

physics.med-ph↗

arXiv2025

Efficient Active Training for Deep LiDAR Odometry

Robust and efficient deep LiDAR odometry models are crucial for accurate localization and 3D reconstruction, but typically require extensive and diverse training data to adapt to diverse environments, leading to inefficiencies. To tackle this, we introduce an active training framework designed to selectively extract training data from diverse environments, thereby reducing the training load and enhancing model generalization. Our framework is based on two key strategies: Initial Training Set Selection (ITSS) and Active Incremental Selection (AIS). ITSS begins by breaking down motion sequences from general weather into nodes and edges for detailed trajectory analysis, prioritizing diverse sequences to form a rich initial training dataset for training the base model. For complex sequences that are difficult to analyze, especially under challenging snowy weather conditions, AIS uses scene reconstruction and prediction inconsistency to iteratively select training samples, refining the model to handle a wide range of real-world scenarios. Experiments across datasets and weather conditions validate our approach's effectiveness. Notably, our method matches the performance of full-dataset training with just 52\% of the sequence volume, demonstrating the training efficiency and robustness of our active training paradigm. By optimizing the training process, our approach sets the stage for more agile and reliable LiDAR odometry systems, capable of navigating diverse environmental conditions with greater precision.

cs.RO↗

arXiv2025

Generalizing Unsupervised Lidar Odometry Model from Normal to Snowy Weather Conditions

Deep learning-based LiDAR odometry is crucial for autonomous driving and robotic navigation, yet its performance under adverse weather, especially snowfall, remains challenging. Existing models struggle to generalize across conditions due to sensitivity to snow-induced noise, limiting real-world use. In this work, we present an unsupervised LiDAR odometry model to close the gap between clear and snowy weather conditions. Our approach focuses on effective denoising to mitigate the impact of snowflake noise and outlier points on pose estimation, while also maintaining computational efficiency for real-time applications. To achieve this, we introduce a Patch Spatial Measure (PSM) module that evaluates the dispersion of points within each patch, enabling effective detection of sparse and discrete noise. We further propose a Patch Point Weight Predictor (PPWP) to assign adaptive point-wise weights, enhancing their discriminative capacity within local regions. To support real-time performance, we first apply an intensity threshold mask to quickly suppress dense snowflake clusters near the LiDAR, and then perform multi-modal feature fusion to refine the point-wise weight prediction, improving overall robustness under adverse weather. Our model is trained in clear weather conditions and rigorously tested across various scenarios, including snowy and dynamic. Extensive experimental results confirm the effectiveness of our method, demonstrating robust performance in both clear and snowy weather. This advancement enhances the model's generalizability and paves the way for more reliable autonomous systems capable of operating across a wider range of environmental conditions.

cs.RO↗

arXiv2024

Impact of Imprecision of the Time Delay on Imaging Result in Confocal Algorithm

The confocal microwave imaging (CMI) algorithm, which is used in radar imaging, has been applied to microwave medical imaging (MMI) [1, 2]. In MMI, the diseased region to be imaged has different electromagnetic characteristics from that of the surroundings. Time -domain MMI system often radiates an ultra-wideband pulse and then collect the backscatter signals with an antenna array. The time delay is then compensated for every unit in the array according to the time flight to each focal point within the region of interest. The compensated signals are finally summed to calculate the pixel value of each focal point. This process is repeated to achieve the value for all focal points in a 3D space to reconstruct the image. Since the method is only constrained in the time domain, the complexity is minimal, and an image can be obtained usually in few seconds. This paper discusses the robustness of CMI in MMI. Specifically, we analyzed the variation of image contrast as errors occur in the time-delay calculation. A simplified model of breast cancer detection was implemented to study the relationship between time-shift errors and the impact on images, quantitatively.

physics.med-ph↗

arXiv2017

Fast Depth Imaging Denoising with the Temporal Correlation of Photons

This paper proposes a novel method to filter out the false alarm of LiDAR system by using the temporal correlation of target reflected photons. Because of the inevitable noise, which is due to background light and dark counts of the detector, the depth imaging of LiDAR system exists a large estimation error. Our method combines the Poisson statistical model with the different distribution feature of signal and noise in the time axis. Due to selecting a proper threshold, our method can effectively filter out the false alarm of system and use the ToFs of detected signal photons to rebuild the depth image of the scene. The experimental results reveal that by our method it can fast distinguish the distance between two close objects, which is confused due to the high background noise, and acquire the accurate depth image of the scene. Our method need not increase the complexity of the system and is useful in power-limited depth imaging.

physics.ins-det↗

arXiv2017

Adaptive Depth Imaging with Single-Photon Detectors

For active optical imaging, the use of single-photon detectors can greatly improve the detection sensitivity of the system. However, the traditional maximum-likelihood based imaging method needs a long acquisition time to capture clear three-dimensional (3D) image in low light-level. To tackle this problem, we present a novel imaging method for depth estimate, which can obtain the accurate 3D image in a short acquisition time. Our method combines the photon-count statistics with the temporal correlations of the reflected signal. According to the characteristics of the target surface, including the surface reflectivity, our method is capable of adaptively changing the dwell time in each pixel. The experimental results demonstrate that the proposed method can fast obtain the accurate depth image despite the existence of strong background noise.

physics.optics↗