arXiv subjects

Changhao Liu

Publications and source records attributed to Changhao Liu.

16 recordsLinked to original sources

arXiv2026

RailSyn: Diagnosis-Guided Image Generation for Traceable Data Completion in Railway Foreign Object Detection

Railway foreign object detection (RFOD) is critical to safe railway operation, yet scarce real positive samples incompletely represent task-relevant variations in object scale, intrusion relation, railway scene, illumination, and adverse weather. Existing synthetic augmentation can improve RFOD detection, but its gains lack an explicit account of the task-relevant deficiencies complemented by the generated data. We therefore introduce RailSyn, a diagnosis-guided framework comprising a real-referenced Inspector and a requirement-aligned Generator. The Inspector constructs a variable-radius empirical cover from finite real observations to localize candidate completion regions and profile synthetic pools. The resulting audit identifies railway-context, intrusion-semantic, and visual-consistency requirements; the Generator addresses them through domain adaptation, agent-planned placement and physical contact relations, and plan-consistent conditional refinement. Using the Inspector, we further trace representation-space changes across generation variants; the complete system attains a local-shell occupation of $C_{gap}$ to 13.64%, which measures generated coverage of real-derived completion regions. Extensive experiments show AP50--95 gains of up to 4.9 points and consistent improvements across nine mainstream detectors, demonstrating broad cross-architecture utility.

cs.CV↗

arXiv2026

Unlocking the Potential of Movable Antennas: General and Practical Antenna Position Optimization

Recently, movable antenna (MA) has attracted wide attention in wireless communications due to its potential in enhancing wireless communication performance via local movement within a confined region. However, antenna position optimization (APO) has emerged as a major challenge for MAs, due to the lack of a tractable, analytical, and accurate channel model in terms of antenna positions. Although existing works have developed various algorithms for APO, most of them are based on simplified theoretical channel models, which limit their generality. To address this challenge, in this article, we present more general and effective APO algorithms for different purposes, categorized as continuous APO and discrete APO, respectively. Continuous APO is mainly applied for flexible array signal processing to boost large-scale communication performance, while discrete APO is applied for small-scale multi-path channel reshaping. Specifically, the discrete APO discretizes the antenna movement region into multiple sampling points and employs discrete algorithms to determine the optimal MA positions based on the point-wise channel state information (CSI), without the need for an analytical channel model. To reduce the overhead for CSI acquisition, we also present more efficient learning-based APO algorithms that operate without requiring full point-wise CSI. Finally, we compare the application scenarios of the proposed algorithms and validate their effectiveness with numerical results.

eess.SP↗

arXiv2026

Joint Communication and Trajectory Design for Movable Antenna Systems

Movable antennas (MAs) have attracted significant attention in wireless communications due to their ability to reconfigure channel conditions by flexibly adjusting the antenna positions within a confined region. However, MA movement generally incurs a non-negligible delay, which may significantly limit the data transmission time at optimized positions. To tackle this challenge, this paper investigates a new joint communication and trajectory optimization problem, where each MA transmits while moving along an optimized trajectory to prolong the effective data transmission time. Focusing on a single-MA system, our goal is to maximize the average data rate by optimizing the MA's positions over time, subject to its maximum velocity constraints. However, this continuous-time antenna position optimization problem is highly non-convex and challenging to solve. To tackle this challenge, we first consider a special case with two channel paths and derive the optimal MA trajectory in closed form. For other general cases, we ingeniously reformulate the average rate maximization problem into a fixed-hop shortest path problem in graph theory by sampling the antenna movement region into a multitude of discrete points, and solve it optimally. Simulation results demonstrate that our proposed algorithm can significantly improve the data rate compared to other baseline schemes.

eess.SP↗

arXiv2025

A General Optimization Framework for Movable Antenna Systems via Discrete Sampling

Movable antenna (MA) systems have attracted growing interest in wireless communications due to their ability to reshape wireless channels via local antenna movement within a confined region. However, optimizing antenna positions to enhance communication performance turns out to be challenging due to the highly nonlinear relationship between wireless channels and antenna positions. Existing approaches, such as gradient-based and heuristic algorithms, often suffer from high computational complexity or undesired local optima. To address the above challenge, this letter proposes a general and low-complexity optimization framework for MA position optimization. Specifically, we discretize the antenna movement region into a set of sampling points, thereby transforming the continuous optimization problem into a discrete point selection problem. Next, we sequentially update the optimal sampling point for each MA over multiple rounds. To avoid convergence to poor local optima, a Gibbs sampling (GS) phase is introduced between rounds to explore adjacent and randomly generated candidate solutions. As a case study, we investigate joint precoding and antenna position optimization for an MA-enhanced broadcast system by applying the proposed framework. Numerical results demonstrate that the proposed algorithm achieves near-optimal performance and significantly outperforms existing benchmarks.

eess.SP↗

arXiv2025

Prediction of Distant Metastasis in Head and Neck Cancer Patients Using Tumor and Peritumoral Multi-Modal Deep Learning

Although the combined treatment of surgery, radiotherapy, chemotherapy, and emerging target therapy has significantly improved the outcomes of patients with head and neck cancer, distant metastasis remains the leading cause of treatment failure. In this study, we propose a deep learning-based multimodal framework integrating CT imaging, radiomics, and clinical data to predict metastasis risk in HNSCC. A total of 1497 patients were retrospectively analyzed. Tumor and organ masks were generated from pretreatment CT scans, from which a 3D Swin Transformer extracted deep imaging features, while 1562 radiomics features were reduced to 36 via correlation filtering and random forest selection. Clinical data (age, sex, smoking, and alcohol status) were encoded and fused with imaging features, and the multimodal representation was fed into a fully connected network for prediction. Five-fold cross-validation was used to assess performance via AUC, accuracy, sensitivity, and specificity. The multimodal model outperformed all single-modality baselines. The deep learning module alone achieved an AUC of 0.715, whereas multimodal fusion significantly improved performance (AUC = 0.803, ACC = 0.752, SEN = 0.730, SPE = 0.758). Stratified analyses confirmed good generalizability across tumor subtypes. Ablation experiments demonstrated complementary contributions from each modality, and the 3D Swin Transformer provided more robust representations than conventional architectures. This multimodal deep learning model enables accurate, non-invasive metastasis prediction in HNSCC and shows strong potential for individualized treatment planning.

q-bio.QM↗

arXiv2025

Fine-tuning Language Models for Recipe Generation: A Comparative Analysis and Benchmark Study

This research presents an exploration and study of the recipe generation task by fine-tuning various very small language models, with a focus on developing robust evaluation metrics and comparing across different language models the open-ended task of recipe generation. This study presents extensive experiments with multiple model architectures, ranging from T5-small (Raffel et al., 2023) and SmolLM-135M(Allal et al., 2024) to Phi-2 (Research, 2023), implementing both traditional NLP metrics and custom domain-specific evaluation metrics. Our novel evaluation framework incorporates recipe-specific metrics for assessing content quality and introduces approaches to allergen substitution. The results indicate that, while larger models generally perform better on standard metrics, the relationship between model size and recipe quality is more nuanced when considering domain-specific metrics. SmolLM-360M and SmolLM-1.7B demonstrate comparable performance despite their size difference before and after fine-tuning, while fine-tuning Phi-2 shows notable limitations in recipe generation despite its larger parameter count. The comprehensive evaluation framework and allergen substitution systems provide valuable insights for future work in recipe generation and broader NLG tasks that require domain expertise and safety considerations.

cs.CL↗

arXiv2025

Calculus with combinatorial differential forms for fluid flow analysis in porous and fractured media

The fabric of porous and fractured media contains solid regions (grains) and voids. The space conducting fluids is a system of connected voids with variable geometries. Relative to the grain sizes, the voids can be voluminous with three comparable large extensions, narrow expansive with two comparable large extensions and one smaller extension, and thin long with one comparable large extension and two smaller extensions. The widely used representation of void spaces by systems of spheres connected by cylinders (pore network models) is an acceptable approximation for some special cases, but not for most porous and fractured media. We propose a flexible method for modelling such media by mapping their measured fabric's characteristics - void and grain volume distributions and shapes - onto polyhedral tessellations of space. The map assigns voluminous voids and grains to polyhedrons (3D), narrow expansive voids to some polyhedral faces (2D), and thin long voids to some polyhedral edges (1D), as dictated by experimental data. The analysis of transport through such discrete structures with components of different dimensions is performed by a novel mathematical method, which uses combinatorial differential forms to represent physical properties and their fluxes, as well as structure-preserving operators on such forms to formulate the conservation laws exactly and directly in matrix form, ready for computation. The method allows for individual material properties, such as conductivity, to be assigned to voids of all dimensions, so that the three types of voids are suitably represented. Publicly available XCT images of four different rocks are used to test the method.

math-ph↗

arXiv2024

UAV-Enabled Passive 6D Movable Antennas: Joint Deployment and Beamforming Optimization

Intelligent reflecting surface (IRS) is composed of numerous passive reflecting elements and can be mounted on unmanned aerial vehicles (UAVs) to achieve six-dimensional (6D) movement by adjusting the UAV's three-dimensional (3D) location and 3D orientation simultaneously. Hence, in this paper, we investigate a new UAV-enabled passive 6D movable antenna (6DMA) architecture by mounting an IRS on a UAV and address the associated joint deployment and beamforming optimization problem. In particular, we consider a passive 6DMA-aided multicast system with a multi-antenna base station (BS) and multiple remote users, aiming to jointly optimize the IRS's location and 3D orientation, as well as its passive beamforming to maximize the minimum received signal-to-noise ratio (SNR) among all users under the practical angle-dependent signal reflection model. However, this optimization problem is challenging to be optimally solved due to the intricate relationship between the users' SNRs and the IRS's location and orientation. To tackle this challenge, we first focus on a simplified case with a single user, showing that one-dimensional (1D) orientation suffices to achieve the optimal performance. Next, we show that for any given IRS's location, the optimal 1D orientation can be derived in closed form, based on which several useful insights are drawn. To solve the max-min SNR problem in the general multi-user case, we propose an alternating optimization (AO) algorithm by alternately optimizing the IRS's beamforming and location/orientation via successive convex approximation (SCA) and hybrid coarse- and fine-grained search, respectively. To avoid undesirable local sub-optimal solutions, a Gibbs sampling (GS) method is proposed to generate new IRS locations and orientations for exploration in each AO iteration. Numerical results validate our theoretical analyses.

eess.SP↗

arXiv2024

Transmission efficiency limit of single-switch and cascaded reconfigurable transmitarray elements

Reconfigurable transmitarray antennas (RTAs) are rapidly gaining popularity, but optimizing their performance requires systematic design theories. In particular, establishing a performance limit theory for RTA elements is valuable. This paper presents a transmission efficiency limit theory for single-switch RTA elements and their cascaded extensions. Employing microwave network analysis, we analytically investigate single-switch RTA elements, demonstrating that their transmission coefficients under two states must lie on or within a specific unit circle on the Smith chart. Therefore, the transmission phase difference is tightly constrained by the transmission amplitudes, indicating that the phase-shifting ability of a single-switch RTA element is limited. Subsequently, this analysis is extended to cascaded RTA elements. By cascading several single-switch layers, the phase variation range is extended, enabling the realization of 1-bit phase shifts with high transmission amplitudes. These findings have significant impact on the design and optimization of RTA elements.

physics.app-ph↗

arXiv2024

Centralized active reconfigurable intelligent surface: Architecture, path loss analysis and experimental verification

Reconfigurable intelligent surfaces (RISs) are promising candidate for the 6G communication. Recently, active RIS has been proposed to compensate the multiplicative fading effect inherent in passive RISs. However, conventional distributed active RISs, with at least one amplifier per element, are costly, complex, and power-intensive. To address these challenges, this paper proposes a novel architecture of active RIS: the centralized active RIS (CA-RIS), which amplifies the energy using a centralized amplifying reflector to reduce the number of amplifiers. Under this architecture, only as low as one amplifier is needed for power amplification of the entire array, which can eliminate the mutual-coupling effect among amplifiers, and significantly reduce the cost, noise level, and power consumption. We evaluate the performance of CA-RIS, specifically its path loss, and compare it with conventional passive RISs, revealing a moderate amplification gain. Furthermore, the proposed CA-RIS and the path loss model are experimentally verified, achieving a 9.6 dB net gain over passive RIS at 4 GHz. The CA-RIS offers a substantial simplification of active RIS architecture while preserving performance, striking an optimal balance between system complexity and the performance, which is competitive in various scenarios.

physics.app-ph↗

arXiv2023

A Radiation Viewpoint of Reconfigurable Reflectarray Elements: Performance Limit, Evaluation Criterion and Design Process

Reconfigurable reflectarray antennas (RRAs) have rapidly developed with various prototypes proposed in recent literatures. However, designing wideband, multiband, or high-frequency RRAs faces great challenges, especially the lengthy simulation time due to the lack of systematic design guidance. The current scattering viewpoint of the RRA element, which couples antenna structures and switches during the design process, fails to address these issues. Here, we propose a novel radiation viewpoint to model, evaluate, and design RRA elements. Using this viewpoint, the design goal is to match the element impedance to a characteristic impedance pre-calculated by switch parameters, allowing various impedance matching techniques developed in classical antennas to be applied in RRA element design. Furthermore, the theoretical performance limit can be pre-determined at given switch parameters before designing specific structures, and the constant loss curve is suggested as an intuitive tool to evaluate element performance in the Smith chart. The proposed method is validated by a practical 1-bit RRA element with degraded switch parameters. Then, a 1-bit RRA element with wideband performance is successfully designed using the proposed design process. The proposed method provides a novel perspective of RRA elements, and offers a systematic and effective guidance for designing wideband, multiband, and high-frequency RRAs.

physics.app-ph↗

arXiv2023

A Beam-Steering Reflectarray Antenna with Arbitrary Linear-Polarization Reconfiguration

This work presents a beam-steering reflectarray antenna that achieves arbitrary linear polarization (LP) reconfiguration. This antenna employs a dual-circular polarization (CP) reconfigurable reflectarray and an LP feed horn to generate an LP beam. The incident LP wave is decomposed into two CP components, whose reflection phases are independently adjusted. By re-combining these reflected CP beams with different phase constants, arbitrary LP states can be synthesized. Experimental validation is performed using a 16$\times$16 1-bit reconfigurable reflectarray operating at Ku band, demonstrating reconfigurable LP states of LP(0$^\circ$), LP(45$^\circ$), LP(90$^\circ$) and LP(135$^\circ$), as well as dynamic beam scanning functionality from 0$^\circ$ to 60$^\circ$. This polarization-reconfigurable, beam-steering reflectarray has potential for applications in satellite and mobile communications, where both beam patterns and polarization alignment are crucial.

physics.app-ph↗

arXiv2023

Active RIS vs. Passive RIS: Which Will Prevail in 6G?

As a revolutionary paradigm for controlling wireless channels, reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks. However, due to the "multiplicative fading" effect, the existing passive RISs only achieve limited capacity gains in many scenarios with strong direct links. In this paper, the concept of active RISs is proposed to overcome this fundamental limitation. Unlike passive RISs that reflect signals without amplification, active RISs can amplify the reflected signals via amplifiers integrated into their elements. To characterize the signal amplification and incorporate the noise introduced by the active components, we develop and verify the signal model of active RISs through the experimental measurements based on a fabricated active RIS element. Based on the verified signal model, we further analyze the asymptotic performance of active RISs to reveal the substantial capacity gain they provide for wireless communications. Finally, we formulate the sum-rate maximization problem for an active RIS aided multi-user multiple-input single-output (MU-MISO) system and a joint transmit beamforming and reflect precoding scheme is proposed to solve this problem. Simulation results show that, in a typical wireless system, passive RISs can realize only a limited sum-rate gain of 22%, while active RISs can achieve a significant sum-rate gain of 130%, thus overcoming the "multiplicative fading" effect.

cs.IT↗

arXiv2023

Reconfigurable Metasurface: A Systematic Categorization and Recent Advances

Considering the rapid progress of theory, design, fabrication and applications, metasurface (MTS) has become a new research frontier in microwave, terahertz and optical bands. Reconfigurable metasurface (R-MTS) can dynamically modulate electromagnetic (EM) wave with unparalleled flexibility, which leads to great research tide in recent years. Numerous R-MTSs with powerful capabilities and various functions are presented explosively. In light of the five dimensions of EM wave, this review proposes a unified model to describe the interactions among R-MTS, EM wave and EM information, and suggests information bit allocation strategy to categorize different types of R-MTSs systematically. As recent advances of R-MTS, 1-bit and 2-bit elements manipulating different wave dimensions are reviewed respectively in detail. Finally, this review discusses the future research trends of R-MTS. Hopefully, R-MTSs with diverse dimensions and functions can propel the next generation of communication, detection, sensing, imaging and computing applications.

physics.app-ph↗

arXiv2022

Active RISs: Signal Modeling, Asymptotic Analysis, and Beamforming Design

Reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks. However, due to the "multiplicative fading" effect, the existing passive RISs only achieve a negligible capacity gain in environments with strong direct links. In this paper, the concept of active RISs is studied to overcome this fundamental limitation. Unlike the existing passive RISs that reflect signals without amplification, active RISs can amplify the reflected signals via amplifiers integrated into their elements. To characterize the signal amplification and incorporate the noise introduced by the active components, we verify the signal model of active RISs through the experimental measurements on a fabricated active RIS element. Based on the verified signal model, we formulate the sum-rate maximization problem for an active RIS aided multi-user multiple-input single-output (MU-MISO) system and a joint transmit precoding and reflect beamforming algorithm is proposed to solve this problem. Simulation results show that, in a typical wireless system, the existing passive RISs can realize only a negligible sum-rate gain of 3%, while the active RISs can achieve a significant sum-rate gain of 62%, thus overcoming the "multiplicative fading" effect. Finally, we develop a 64-element active RIS aided wireless communication prototype, and the significant gain of active RISs is validated by field test.

cs.IT↗

arXiv2022

Deep-Learning-Empowered Inverse Design for Freeform Reconfigurable Metasurfaces

The past decade has witnessed the advances of artificial intelligence with various applications in engineering. Recently, artificial neural network empowered inverse design for metasurfaces has been developed that can design on-demand meta-atoms with diverse shapes and high performance, where the design process based on artificial intelligence is fast and automatic. However, once the inverse-designed static meta-atom is fabricated, the function of the metasurface is fixed. Reconfigurable metasurfaces can realize dynamic functions, while applying artificial intelligence to design practical reconfigurable meta-atoms inversely has not been reported yet. Here, we present a deep-learning-empowered inverse design method for freeform reconfigurable metasurfaces, which can generate on-demand reconfigurable coding meta-atoms at self-defined frequency bands. To reduce the scale of dataset, a decoupling method of the reconfigurable meta-atom based on microwave network theory is proposed at first, which can convert the inverse design process for reconfigurable coding meta-atoms to the inverse design for static structures. A convolutional neural network model is trained to predict the responses of free-shaped meta-atoms, and the genetic algorithm is applied to generate the optimal structure patterns rapidly. As a demonstration of concept, several inverse-designed examples are generated with different self-defined spectrum responses in microwave band, and an inverse-designed wideband reconfigurable metasurface prototype is fabricated and measured for beam scanning applications with broad bandwidth. Our work paves the way for the fast and automatic design process of high-performance reconfigurable metasurfaces.

cs.LG↗