arXiv subjects

Chuang Yang

Publications and source records attributed to Chuang Yang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Channel Modeling of Satellite-to-Underwater Laser Communication Links: An Analytical-Monte Carlo Hybrid Approach

Channel modeling for satellite-to-underwater laser communication (StULC) links remains challenging due to long distances and the diversity of the channel constituents. The StULC channel is typically segmented into three isolated channels: the atmospheric channel, the air-water interface channel, and the underwater channel. Previous studies involving StULC channel modeling either focused on separated channels or neglected the combined effects of particles and turbulence on laser propagation. In this paper, we established a comprehensive StULC channel model by an analytical-Monte Carlo hybrid approach, taking into account the effects of both particles and turbulence. We first obtained the intensity distribution of the transmitted laser beam after passing through the turbulent atmosphere based on the extended Huygens-Fresnel principle. Then we derived a closed-form probability density function of the photon propagating direction after passing through the air-water interface, which greatly simplified the modeling of StULC links. At last, we employed a Monte Carlo method to model the underwater links and obtained the power distribution at the receiving plane. Based on the proposed StULC channel model, we analyzed the bit error rate and the outage probability under different environmental conditions. Numerical results demonstrated that, the influence of underwater particle concentration on the communication performance is much pronounced than those of both the atmospheric turbulence and the underwater turbulence. Notably, increasing the wind speed at the air-water interface does not significantly worsen the communication performance of the StULC links.

eess.SP↗

arXiv2025

Lyapunov exponents, phase transition and chaos bound in Kerr-Newman AdS spacetime

In this paper, we investigate Lyapunov exponents associated with chaotic motions of both massless and massive particles in the vicinity of a Kerr-Newman AdS black hole. Our exploration focuses on their correlations with the black hole phase transition and the chaos bound. The results demonstrate that these exponents serve as effective probes of the phase transition, with the chaotic Lyapunov exponent of the massless particle offering a more precise characterization. Further calculations indicate that critical exponents linked to these Lyapunov exponents are uniformly 1/2. Notably, the violation of the chaos bound occurs irrespective of whether a phase transition is taking place. Through comparative analysis, we identify a critical radius, and the violation consistently arises when the black hole's radius is less than this critical radius. Moreover, this violation is observed in the spacetime of the stable small black hole during the phase transition.

hep-th↗

arXiv2025

Diagnostic performance of echocardiography in detecting and differentiating cardiac amyloidosis: a meta-analysis

Aims: This meta-analysis aimed to evaluate the diagnostic performance of echocardiographic parameters for cardiac amyloidosis (CA), with a focus on subtype stratification and comparisons with healthy controls. Methods and Results: A comprehensive search identified 26 studies published before February 2025, encompassing 3,802 patients. Compared to healthy individuals, CA patients demonstrated significant echocardiographic abnormalities, including reduced left ventricular ejection fraction (LVEF; WMD = -10.65, 95% CI: [-11.84, -9.46]), increased left atrial volume index (WMD = +15.87, 95% CI: [14.35, 17.38]), and thickened posterior wall (WMD = +5.14, 95% CI: [4.85, 5.42]). Subtype analyses revealed that transthyretin cardiac amyloidosis (ATTR-CA) was associated with more pronounced systolic dysfunction than light-chain cardiac amyloidosis (AL-CA), evidenced by lower global longitudinal strain (WMD = -2.02, 95% CI: [-2.66, -1.37]), reduced LVEF (WMD = -5.31, 95% CI: [-6.63, -3.99]), and diminished tricuspid annular plane systolic excursion (WMD = -1.59, 95% CI: [-2.23, -0.95]). Additionally, ATTR-CA patients exhibited greater ventricular wall thickening in both posterior wall (WMD = +1.87, 95% CI: [1.51, 2.23]) and interventricular septum (WMD = +2.24, 95% CI: [1.85, 2.63]). Conclusion: Echocardiography plays a pivotal role in diagnosing CA and distinguishing between AL-CA and ATTR-CA. Key indices such as LVEF and global longitudinal strain are especially valuable for early detection, while subtype-specific patterns highlight distinct underlying pathophysiologies, offering guidance for tailored diagnostic and therapeutic strategies.

physics.med-ph↗

arXiv2025

Road Similarity-Based BEV-Satellite Image Matching for UGV Localization

To address the challenge of autonomous UGV localization in GNSS-denied off-road environments,this study proposes a matching-based localization method that leverages BEV perception image and satellite map within a road similarity space to achieve high-precision positioning.We first implement a robust LiDAR-inertial odometry system, followed by the fusion of LiDAR and image data to generate a local BEV perception image of the UGV. This approach mitigates the significant viewpoint discrepancy between ground-view images and satellite map. The BEV image and satellite map are then projected into the road similarity space, where normalized cross correlation (NCC) is computed to assess the matching score.Finally, a particle filter is employed to estimate the probability distribution of the vehicle's pose.By comparing with GNSS ground truth, our localization system demonstrated stability without divergence over a long-distance test of 10 km, achieving an average lateral error of only 0.89 meters and an average planar Euclidean error of 3.41 meters. Furthermore, it maintained accurate and stable global localization even under nighttime conditions, further validating its robustness and adaptability.

cs.RO↗

arXiv2025

Lyapunov exponents as probes for a phase transition of a Kerr-AdS black hole

In this letter, we study proper time Lyapunov exponents and coordinate time Lyapunov exponents of chaos for both massless and massive particles orbiting a four-dimensional Kerr-AdS black hole, and explore their relationships with the phase transition of this black hole. The result reveals that both types of Lyapunov exponents can reflect the occurrence of the phase transition. Specifically, when compared to the proper time and coordinate time Lyapunov exponents of massive particles in chaotic states, the exponents corresponding to massless particles demonstrate a more robust capability in describing the phase transition. Furthermore, we conduct a study on critical exponents associated with these Lyapunov exponents in this black hole, identifying a critical exponent value of 1/2.

hep-th↗

arXiv2025

Edge Approximation Text Detector

Pursuing efficient text shape representations helps scene text detection models focus on compact foreground regions and optimize the contour reconstruction steps to simplify the whole detection pipeline. Current approaches either represent irregular shapes via box-to-polygon strategy or decomposing a contour into pieces for fitting gradually, the deficiency of coarse contours or complex pipelines always exists in these models. Considering the above issues, we introduce EdgeText to fit text contours compactly while alleviating excessive contour rebuilding processes. Concretely, it is observed that the two long edges of texts can be regarded as smooth curves. It allows us to build contours via continuous and smooth edges that cover text regions tightly instead of fitting piecewise, which helps avoid the two limitations in current models. Inspired by this observation, EdgeText formulates the text representation as the edge approximation problem via parameterized curve fitting functions. In the inference stage, our model starts with locating text centers, and then creating curve functions for approximating text edges relying on the points. Meanwhile, truncation points are determined based on the location features. In the end, extracting curve segments from curve functions by using the pixel coordinate information brought by truncation points to reconstruct text contours. Furthermore, considering the deep dependency of EdgeText on text edges, a bilateral enhanced perception (BEP) module is designed. It encourages our model to pay attention to the recognition of edge features. Additionally, to accelerate the learning of the curve function parameters, we introduce a proportional integral loss (PI-loss) to force the proposed model to focus on the curve distribution and avoid being disturbed by text scales.

cs.CV↗

arXiv2025

ANNEXE: Unified Analyzing, Answering, and Pixel Grounding for Egocentric Interaction

Egocentric interaction perception is one of the essential branches in investigating human-environment interaction, which lays the basis for developing next-generation intelligent systems. However, existing egocentric interaction understanding methods cannot yield coherent textual and pixel-level responses simultaneously according to user queries, which lacks flexibility for varying downstream application requirements. To comprehend egocentric interactions exhaustively, this paper presents a novel task named Egocentric Interaction Reasoning and pixel Grounding (Ego-IRG). Taking an egocentric image with the query as input, Ego-IRG is the first task that aims to resolve the interactions through three crucial steps: analyzing, answering, and pixel grounding, which results in fluent textual and fine-grained pixel-level responses. Another challenge is that existing datasets cannot meet the conditions for the Ego-IRG task. To address this limitation, this paper creates the Ego-IRGBench dataset based on extensive manual efforts, which includes over 20k egocentric images with 1.6 million queries and corresponding multimodal responses about interactions. Moreover, we design a unified ANNEXE model to generate text- and pixel-level outputs utilizing multimodal large language models, which enables a comprehensive interpretation of egocentric interactions. The experiments on the Ego-IRGBench exhibit the effectiveness of our ANNEXE model compared with other works.

cs.CV↗

arXiv2025

MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing

The rapid advancement of deep generative models (DGMs) has significantly advanced research in computer vision, providing a cost-effective alternative to acquiring vast quantities of expensive imagery. However, existing methods predominantly focus on synthesizing remote sensing (RS) images aligned with real images in a global layout view, which limits their applicability in RS image object detection (RSIOD) research. To address these challenges, we propose a multi-class and multi-scale object image generator based on DGMs, termed MMO-IG, designed to generate RS images with supervised object labels from global and local aspects simultaneously. Specifically, from the local view, MMO-IG encodes various RS instances using an iso-spacing instance map (ISIM). During the generation process, it decodes each instance region with iso-spacing value in ISIM-corresponding to both background and foreground instances-to produce RS images through the denoising process of diffusion models. Considering the complex interdependencies among MMOs, we construct a spatial-cross dependency knowledge graph (SCDKG). This ensures a realistic and reliable multidirectional distribution among MMOs for region embedding, thereby reducing the discrepancy between source and target domains. Besides, we propose a structured object distribution instruction (SODI) to guide the generation of synthesized RS image content from a global aspect with SCDKG-based ISIM together. Extensive experimental results demonstrate that our MMO-IG exhibits superior generation capabilities for RS images with dense MMO-supervised labels, and RS detectors pre-trained with MMO-IG show excellent performance on real-world datasets.

cs.CV↗

arXiv2025

Joint Communication and Radar Sensing for Terahertz Space-Air-Ground Integrated Networks (SAGIN)

The transition from isolated systems to integrated solutions has driven the development of space-air-ground integrated networks (SAGIN) as well as the integration of communication and radar sensing functionalities. By leveraging the unique properties of the Terahertz (THz) band, THz joint communication and radar sensing (JCRS) supports high-speed communication and precise sensing, addressing the growing demands of SAGIN for connectivity and environmental awareness. However, most existing THz studies focus on terrestrial and static scenarios, with limited consideration for the dynamic and non-terrestrial environments of SAGIN. In this paper, the THz JCRS techniques for SAGIN are comprehensively investigated. Specifically, propagation characteristics and channel models of THz waves in non-terrestrial environments are analyzed. A link capacity comparison with millimeter-wave, THz, and free-space optical frequency bands is conducted to highlight the advantages of THz frequencies. Moreover, novel JCRS waveform design strategies are presented to achieve mutual merit of communication and radar sensing, while networking strategies are developed to overcome challenges in SAGIN such as high mobility. Furthermore, advancements in THz device technologies, including antennas and amplifiers, are reviewed to assess their roles in enabling practical JCRS implementations.

eess.SP↗

arXiv2025

Taming the Long Tail in Human Mobility Prediction

With the popularity of location-based services, human mobility prediction plays a key role in enhancing personalized navigation, optimizing recommendation systems, and facilitating urban mobility and planning. This involves predicting a user's next POI (point-of-interest) visit using their past visit history. However, the uneven distribution of visitations over time and space, namely the long-tail problem in spatial distribution, makes it difficult for AI models to predict those POIs that are less visited by humans. In light of this issue, we propose the Long-Tail Adjusted Next POI Prediction (LoTNext) framework for mobility prediction, combining a Long-Tailed Graph Adjustment module to reduce the impact of the long-tailed nodes in the user-POI interaction graph and a novel Long-Tailed Loss Adjustment module to adjust loss by logit score and sample weight adjustment strategy. Also, we employ the auxiliary prediction task to enhance generalization and accuracy. Our experiments with two real-world trajectory datasets demonstrate that LoTNext significantly surpasses existing state-of-the-art works.

cs.SI↗

arXiv2024

SignEye: Traffic Sign Interpretation from Vehicle First-Person View

Traffic signs play a key role in assisting autonomous driving systems (ADS) by enabling the assessment of vehicle behavior in compliance with traffic regulations and providing navigation instructions. However, current works are limited to basic sign understanding without considering the egocentric vehicle's spatial position, which fails to support further regulation assessment and direction navigation. Following the above issues, we introduce a new task: traffic sign interpretation from the vehicle's first-person view, referred to as TSI-FPV. Meanwhile, we develop a traffic guidance assistant (TGA) scenario application to re-explore the role of traffic signs in ADS as a complement to popular autonomous technologies (such as obstacle perception). Notably, TGA is not a replacement for electronic map navigation; rather, TGA can be an automatic tool for updating it and complementing it in situations such as offline conditions or temporary sign adjustments. Lastly, a spatial and semantic logic-aware stepwise reasoning pipeline (SignEye) is constructed to achieve the TSI-FPV and TGA, and an application-specific dataset (Traffic-CN) is built. Experiments show that TSI-FPV and TGA are achievable via our SignEye trained on Traffic-CN. The results also demonstrate that the TGA can provide complementary information to ADS beyond existing popular autonomous technologies.

cs.CV↗

arXiv2024

Sensing-Assisted Beam Tracking with Real-Time Beamwidth Adaptation for THz Communications

Terahertz (THz) communications, with their substantial bandwidth, are essential for meeting the ultra-high data rate demands of emerging high-mobility scenarios such as vehicular-to-everything (V2X) networks. In these contexts, beamwidth adaptation has been explored to address the problem that high-mobility targets frequently move out of the narrow THz beam range. However, existing approaches cannot effectively track targets due to a lack of real-time motion awareness. Consequently, we propose a sensing-assisted beam tracking scheme with real-time beamwidth adaptation. Specifically, the base station (BS) periodically collects prior sensing information to predict the target's motion path by applying a particular motion model. Then, we build a pre-calculated codebook by optimising precoders to align the beamwidth with various predicted target paths, thereby maximising the average achievable data rates within each sensing period. Finally, the BS selects the optimal precoder from the codebook to maintain stable and continuous connectivity. Simulation results show that the proposed scheme significantly improves the rate performance and reduces outage probability compared to existing approaches under various target mobility.

eess.SP↗

arXiv2024

Real-Time Text Detection with Similar Mask in Traffic, Industrial, and Natural Scenes

Texts on the intelligent transportation scene include mass information. Fully harnessing this information is one of the critical drivers for advancing intelligent transportation. Unlike the general scene, detecting text in transportation has extra demand, such as a fast inference speed, except for high accuracy. Most existing real-time text detection methods are based on the shrink mask, which loses some geometry semantic information and needs complex post-processing. In addition, the previous method usually focuses on correct output, which ignores feature correction and lacks guidance during the intermediate process. To this end, we propose an efficient multi-scene text detector that contains an effective text representation similar mask (SM) and a feature correction module (FCM). Unlike previous methods, the former aims to preserve the geometric information of the instances as much as possible. Its post-progressing saves 50$\%$ of the time, accurately and efficiently reconstructing text contours. The latter encourages false positive features to move away from the positive feature center, optimizing the predictions from the feature level. Some ablation studies demonstrate the efficiency of the SM and the effectiveness of the FCM. Moreover, the deficiency of existing traffic datasets (such as the low-quality annotation or closed source data unavailability) motivated us to collect and annotate a traffic text dataset, which introduces motion blur. In addition, to validate the scene robustness of the SM-Net, we conduct experiments on traffic, industrial, and natural scene datasets. Extensive experiments verify it achieves (SOTA) performance on several benchmarks. The code and dataset are available at: \url{https://github.com/fengmulin/SMNet}.

cs.CV↗

arXiv2024

Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks. Our approach addresses three research questions: aligning LLMs with real-world urban mobility data, developing reliable activity generation strategies, and exploring LLM applications in urban mobility. The key technical contribution is a novel LLM agent framework that accounts for individual activity patterns and motivations, including a self-consistency approach to align LLMs with real-world activity data and a retrieval-augmented strategy for interpretable activity generation. We evaluate our LLM agent framework and compare it with state-of-the-art personal mobility generation approaches, demonstrating the effectiveness of our approach and its potential applications in urban mobility. Overall, this study marks the pioneering work of designing an LLM agent framework for activity generation based on real-world human activity data, offering a promising tool for urban mobility analysis.

cs.AI↗

arXiv2024

SIMformer: Single-Layer Vanilla Transformer Can Learn Free-Space Trajectory Similarity

Free-space trajectory similarity calculation, e.g., DTW, Hausdorff, and Frechet, often incur quadratic time complexity, thus learning-based methods have been proposed to accelerate the computation. The core idea is to train an encoder to transform trajectories into representation vectors and then compute vector similarity to approximate the ground truth. However, existing methods face dual challenges of effectiveness and efficiency: 1) they all utilize Euclidean distance to compute representation similarity, which leads to the severe curse of dimensionality issue -- reducing the distinguishability among representations and significantly affecting the accuracy of subsequent similarity search tasks; 2) most of them are trained in triplets manner and often necessitate additional information which downgrades the efficiency; 3) previous studies, while emphasizing the scalability in terms of efficiency, overlooked the deterioration of effectiveness when the dataset size grows. To cope with these issues, we propose a simple, yet accurate, fast, scalable model that only uses a single-layer vanilla transformer encoder as the feature extractor and employs tailored representation similarity functions to approximate various ground truth similarity measures. Extensive experiments demonstrate our model significantly mitigates the curse of dimensionality issue and outperforms the state-of-the-arts in effectiveness, efficiency, and scalability.

cs.LG↗

arXiv2024

Spotlight Text Detector: Spotlight on Candidate Regions Like a Camera

The irregular contour representation is one of the tough challenges in scene text detection. Although segmentation-based methods have achieved significant progress with the help of flexible pixel prediction, the overlap of geographically close texts hinders detecting them separately. To alleviate this problem, some shrink-based methods predict text kernels and expand them to restructure texts. However, the text kernel is an artificial object with incomplete semantic features that are prone to incorrect or missing detection. In addition, different from the general objects, the geometry features (aspect ratio, scale, and shape) of scene texts vary significantly, which makes it difficult to detect them accurately. To consider the above problems, we propose an effective spotlight text detector (STD), which consists of a spotlight calibration module (SCM) and a multivariate information extraction module (MIEM). The former concentrates efforts on the candidate kernel, like a camera focus on the target. It obtains candidate features through a mapping filter and calibrates them precisely to eliminate some false positive samples. The latter designs different shape schemes to explore multiple geometric features for scene texts. It helps extract various spatial relationships to improve the model's ability to recognize kernel regions. Ablation studies prove the effectiveness of the designed SCM and MIEM. Extensive experiments verify that our STD is superior to existing state-of-the-art methods on various datasets, including ICDAR2015, CTW1500, MSRA-TD500, and Total-Text.

cs.CV↗

arXiv2024

Focus Entirety and Perceive Environment for Arbitrary-Shaped Text Detection

Due to the diversity of scene text in aspects such as font, color, shape, and size, accurately and efficiently detecting text is still a formidable challenge. Among the various detection approaches, segmentation-based approaches have emerged as prominent contenders owing to their flexible pixel-level predictions. However, these methods typically model text instances in a bottom-up manner, which is highly susceptible to noise. In addition, the prediction of pixels is isolated without introducing pixel-feature interaction, which also influences the detection performance. To alleviate these problems, we propose a multi-information level arbitrary-shaped text detector consisting of a focus entirety module (FEM) and a perceive environment module (PEM). The former extracts instance-level features and adopts a top-down scheme to model texts to reduce the influence of noises. Specifically, it assigns consistent entirety information to pixels within the same instance to improve their cohesion. In addition, it emphasizes the scale information, enabling the model to distinguish varying scale texts effectively. The latter extracts region-level information and encourages the model to focus on the distribution of positive samples in the vicinity of a pixel, which perceives environment information. It treats the kernel pixels as positive samples and helps the model differentiate text and kernel features. Extensive experiments demonstrate the FEM's ability to efficiently support the model in handling different scale texts and confirm the PEM can assist in perceiving pixels more accurately by focusing on pixel vicinities. Comparisons show the proposed model outperforms existing state-of-the-art approaches on four public datasets.

cs.CV↗

arXiv2024

Hybrid Precoding With Low-Resolution PSs for Wideband Terahertz Communication Systems in The Face of Beam Squint

Terahertz (THz) communication is considered one of the most critical technologies for 6G because of its abundant bandwidth. To compensate the high propagation of THz, analog/digital hybrid precoding for THz massive multiple input multiple output (MIMO) is proposed to focus signals and extend communication range. Notably, considering hardware cost and power consumption, infinite and high-resolution phase shifters (PSs) are difficult to implement in THz massive MIMO and low-resolution PSs are typically adopted in practice. However, low-resolution PSs cause severe performance degradation. Moreover, the beam squint in wideband THz massive MIMO increases the performance degradation because of the frequency independence of the analog PSs. Motivated by the above factors, in this paper, we firstly propose a heuristic algorithm under fully connected (FC) structure, which optimize the digital precoder and the analog precoder alternately. Then we migrate the proposed heuristic algorithm to the partially-connected (PC) architecture. To further improve the performance, we extend our design to dynamic subarrays in which each RF chain is connected to any antenna that does not duplicate. The numerical results demonstrate that our proposed wideband hybrid precoding with low-resolution PSs achieves better performance to the comparisons for both FC structure and PC structure.

eess.SP↗