arXiv subjects

Shuhang Zhang

Publications and source records attributed to Shuhang Zhang.

At least 19 recordsLinked to original sources

arXiv2026

ControlRadio: Prompt-Driven Controllable Diffusion for Cross-Modal Radio Map Generation

Radio maps describe how wireless signals propagate across space and are essential for wireless communication, sensing, and network planning. However, constructing accurate radio maps traditionally requires either dense measurements or computationally expensive physical simulations, which limits scalability and real-time deployment. Recent advances in generative artificial intelligence offer a promising alternative, but existing approaches lack fine-grained control and physical consistency when applied to real-world wireless environments. Here we present \textbf{ControlRadio}, a controllable generative framework that produces radio maps from natural-language descriptions and environmental layouts, including building structures and transmitter locations. Joint semantic and spatial conditioning enables interpretable, propagation-plausible generation, while a controlled latent prior and layout-aware conditioning improve stability and structural consistency. Extensive experiments demonstrate that ControlRadio achieves state-of-the-art accuracy and strong generalization across diverse urban scenarios, while reducing computation time by more than four orders of magnitude compared with conventional simulation-based methods. Such results suggest a new paradigm for scalable and controllable wireless environment modeling, with broad implications for next-generation communication systems and data-driven radio sensing.

cs.CV↗

arXiv2026

Theoretical Analysis of Diffusion Models for Radio Map Estimation with Ultra-low Sampling Rates

Radio maps, which characterize the spatial distribution of radio frequency metrics such as received signal strength, are essential for a wide range of wireless applications. The problem of radio map estimation involves constructing a radio map from sparse sensor measurements at multiple locations. This problem is particularly challenging due to ultra-low sampling rates, where available sensor measurements are far fewer than the high resolution requirement of radio maps to be estimated. Recently, diffusion models have been increasingly adopted for this problem, yet its theoretical performance remains unexamined. This paper bridges this gap by formulating radio map estimation as a non-linear matrix completion problem. Based on this formulation, we first derive a theoretical lower bound on the minimum estimation error achievable by diffusion models, which is fundamentally governed by the discrepancy between the deployment distribution and the true underlying radio propagation law. We then extend this bound to incorporate the effect of sampling sparsity, capturing the additional error introduced by ultra-low sampling rates. Furthermore, we establish a critical sampling rate threshold necessary for diffusion models to achieve performance convergence. Finally, considering that the derived error bounds depend on certain information that is difficult to obtain in practice, we propose empirical approximations that are readily computable from observable data. Extensive simulations based on real-world traces demonstrate that these empirical formulas tightly approximate the theoretical error bounds, validating their effectiveness for practical deployment.

eess.SP↗

arXiv2026

Three-dimensional density and air-rock interface reconstruction with muography: Application to the TianQin tunnel

Muography is a non-invasive imaging technique that uses cosmic-ray muons, commonly divided into transmission (absorption) and scattering muography. For transmission muography, the inversion algorithm critically determines reconstruction quality. However, widely used schemes may produce smearing artifacts when measurement locations are limited and data are sparse. We develop an optimized Metropolis--Hastings (M--H) algorithm that mitigates smearing and retrieves sharper, more accurate density distributions without auxiliary data. Additionally, we implement an inverse distance weighting (IDW) approach to reconstruct the air--rock interface from muon measurements. The optimized M--H algorithm is applied in Monte Carlo simulations and applied to field data from the TianQin Tunnel experiment using the MuGrid-v2 detector. The IDW-reconstructed air--rock interface is validated against Light Detection and Ranging (LiDAR) measurements. In simulations, the optimized M--H algorithm improves high-density anomaly detection precision from $42\%$ to $100\%$ at threshold $5.1\,\mathrm{g/cm^3}$, with gains of $6\%$ to $42\%$ across other threshold and low-density scenarios, together with the TianQin Tunnel reconstructions, these results demonstrate the effectiveness of the proposed approach.

physics.ins-det↗

arXiv2026

Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigm

Learning-based radio map estimation (RME) plays a critical role in UAV-assisted wireless sensing, enabling tasks such as coverage prediction and network optimization. Most current methods assume an independently and identically distributed (i.i.d.) training and testing setting based on random sampling. However, practical UAV measurements are collected sequentially along feasible trajectories, resulting in highly structured and spatially correlated patterns. This mismatch introduces a sampling distribution shift that increases the intrinsic difficulty of spatial field recovery and compromises the generalization of models trained under i.i.d. assumptions. To mitigate this issue, we propose a trajectory-aware training paradigm based on Stochastic-Triggered Trajectory-Based Sampling (ST-TBS), which preserves trajectory continuity while introducing sampling variability. Moreover, from a statistical perspective, we show that trajectory-based sampling reduces spatial diversity and increases information redundancy compared to random sampling. Extensive experiments on the RadioMapSeer and SpectrumNet datasets demonstrate that models trained with random sampling suffer significant performance degradation under trajectory-based observations, with RMSE increasing from 0.0391 to 0.2632 on SpectrumNet. Conversely, our proposed ST-TBS method effectively reduces the RMSE to 0.0571. These results highlight the necessity of aligning training and deployment sampling distributions for reliable RME.

cs.CV↗

arXiv2026

A Fine-Grained 3D Radio Map Construction Paradigm with Ultra-Low Sampling Rates by Large Generative Models

A radio map captures the spatial distribution of wireless channel parameters, such as the strength of the signal received, across a geographic area. The problem of fine-grained three-dimensional (3D) radio map construction involves inferring a high-resolution radio map for the two-dimensional (2D) area at an arbitrary target height within a 3D region of interest, using radio samples collected by sensors sparsely distributed in that 3D region. Solutions to the problem are crucial for efficient spectrum management in 3D spaces, particularly for drones in the rapidly developing low-altitude economy. However, this problem is challenging due to ultra-sparse sampling, where the number of collected radio samples is far fewer than the desired resolution of the radio map to be estimated. In this paper, we design RadioLAM, a fine-grained 3D radio map construction paradigm built on generative Large Artificial Intelligence Models (LAMs). RadioLAM employs the creative power and the strong generalization capability of LAM to address the ultra-sparse sampling challenge. It consists of three key blocks: 1) an augmentation block, using the radio propagation model to project the radio samples collected at different heights to the 2D area at the target height; 2) a generation block, leveraging a diffusion-based LAM under an Mixture of Experts (MoE) architecture to generate a candidate set of fine-grained radio maps for the target 2D area; and 3) an election block, utilizing the radio propagation model as a guide to find the best map from the candidate set. Extensive simulations show that RadioLAM is able to solve the fine-grained 3D radio map construction problem efficiently from an ultra-low sampling rate of 0.1%, and significantly outperforms state-of-the-art (SOTA). Furthermore, real-world experiments confirm that RadioLAM achieves superior performance compared to SOTA.

eess.SP↗

arXiv2025

RadioPiT: Radio Map Generation with Pixel Transformer Driven by Ultra-Sparse Real-World Data

As wireless communication networks rapidly evolve, spectrum resources are increasingly scarce, making effective spectrum management critically important. Radio map is a spatial representation of signal characteristics across different locations in a given area, which serves as a key tool for enabling precise spectrum management. To generate accurate radio maps, extensive research efforts have been made. However, most existing studies are conducted on simulation data, which differs significantly from real-world data and cannot accurately reflect the spectrum characteristics of practical environments. To tackle this problem, we construct a dataset of real-world radio map with a self-developed measurement system. Due to the limited volume of real-world data and the distributional discrepancies between simulation and real-world data, we propose a Pixel Transformer (PiT)- based model enhanced with the test-time adaptation (TTA) strategy, named RadioPiT, for real-world radio map generation. Experimental results demonstrate that our proposed RadioPiT significantly outperforms baseline methods in real-world scenarios, yielding a 21.9% decrement in the root mean square error (RMSE) compared to RadioUNet.

eess.SP↗

arXiv2025

Fully Automated Verification Framework for Configurable IPs: From Requirements to Results

The increasing competition in the semiconductor industry has created significant pressure to reduce chip prices while maintaining quality and reliability. Functional verification, particularly for configurable IPs, is a major contributor to development costs due to its complexity and resource-intensive nature. To address this, we propose a fully automated framework for requirements driven functional verification. The framework automates key processes, including vPlan generation, testbench creation, regression execution, and reporting in a requirements management tool, drastically reducing verification effort. This approach accelerates development cycles, minimizes human error, and enhances coverage, offering a scalable and efficient solution to the challenges of verifying configurable IPs.

cs.AR↗

arXiv2025

PARROT: An Open Multilingual Radiology Reports Dataset

Rationale and Objectives: To develop and validate PARROT (Polyglottal Annotated Radiology Reports for Open Testing), a large, multicentric, open-access dataset of fictional radiology reports spanning multiple languages for testing natural language processing applications in radiology. Materials and Methods: From May to September 2024, radiologists were invited to contribute fictional radiology reports following their standard reporting practices. Contributors provided at least 20 reports with associated metadata including anatomical region, imaging modality, clinical context, and for non-English reports, English translations. All reports were assigned ICD-10 codes. A human vs. AI report differentiation study was conducted with 154 participants (radiologists, healthcare professionals, and non-healthcare professionals) assessing whether reports were human-authored or AI-generated. Results: The dataset comprises 2,658 radiology reports from 76 authors across 21 countries and 13 languages. Reports cover multiple imaging modalities (CT: 36.1%, MRI: 22.8%, radiography: 19.0%, ultrasound: 16.8%) and anatomical regions, with chest (19.9%), abdomen (18.6%), head (17.3%), and pelvis (14.1%) being most prevalent. In the differentiation study, participants achieved 53.9% accuracy (95% CI: 50.7%-57.1%) in distinguishing between human and AI-generated reports, with radiologists performing significantly better (56.9%, 95% CI: 53.3%-60.6%, p<0.05) than other groups. Conclusion: PARROT represents the largest open multilingual radiology report dataset, enabling development and validation of natural language processing applications across linguistic, geographic, and clinical boundaries without privacy constraints.

cs.CL↗

arXiv2025

End-Edge Model Collaboration: Bandwidth Allocation for Data Upload and Model Transmission

The widespread adoption of large artificial intelligence (AI) models has enabled numerous applications of the Internet of Things (IoT). However, large AI models require substantial computational and memory resources, which exceed the capabilities of resource-constrained IoT devices. End-edge collaboration paradigm is developed to address this issue, where a small model on the end device performs inference tasks, while a large model on the edge server assists with model updates. To improve the accuracy of the inference tasks, the data generated on the end devices will be periodically uploaded to edge server to update model, and a distilled model of the updated one will be transmitted back to the end device. Subjected to the limited bandwidth for the communication link between the end device and the edge server, it is important to investigate whether the system should allocate more bandwidth to data upload or to model transmission. In this paper, we characterize the impact of data upload and model transmission on inference accuracy. Subsequently, we formulate a bandwidth allocation problem. By solving this problem, we derive an efficient optimization framework for the end-edge collaboration system. The simulation results demonstrate our framework significantly enhances mean average precision (mAP) under various bandwidths and datasizes.

cs.ET↗

arXiv2025

WiFi-Diffusion: Achieving Fine-Grained WiFi Radio Map Estimation With Ultra-Low Sampling Rate by Diffusion Models

Fine-grained radio map presents communication parameters of interest, e.g., received signal strength, at every point across a large geographical region. It can be leveraged to improve the efficiency of spectrum utilization for a large area, particularly critical for the unlicensed WiFi spectrum. The problem of fine-grained radio map estimation is to utilize radio samples collected by sparsely distributed sensors to infer the map. This problem is challenging due to the ultra-low sampling rate, where the number of available samples is far less than the fine-grained resolution required for radio map estimation. We propose WiFi-Diffusion -- a novel generative framework for achieving fine-grained WiFi radio map estimation using diffusion models. WiFi-Diffusion employs the creative power of generative AI to address the ultra-low sampling rate challenge and consists of three blocks: 1) a boost block, using prior information such as the layout of obstacles to optimize the diffusion model; 2) a generation block, leveraging the diffusion model to generate a candidate set of radio maps; and 3) an election block, utilizing the radio propagation model as a guide to find the best radio map from the candidate set. Extensive simulations demonstrate that 1) the fine-grained radio map generated by WiFi-Diffusion is ten times better than those produced by state-of-the-art (SOTA) when they use the same ultra-low sampling rate; and 2) WiFi-Diffusion achieves comparable fine-grained radio map quality with only one-fifth of the sampling rate required by SOTA.

eess.SP↗

arXiv2025

Automated Formal Verification of Area-Optimized Safety Registers in Automotive SoCs

Registers are primary storage elements in System-on-chip~(SoC) designs and play an important role in maintaining state information and processing data in digital systems. With respect to the ISO26262 standard, these registers require high levels of reliability and fault tolerance. For this reason, safety-critical applications require that normal registers are equipped with additional safety components to construct safety registers, which ensure system stability and fault tolerance. However, the process of integrating these safety registers is complex and error-prone, because of highly-configurable features provided by a safety library such as parameterized modules and flexible safety structures. In addition, to address the overhead caused by the safety registers, we have applied area optimization techniques to their implementation. However, this optimization can make the integration process more susceptible to errors. To avoid any integration mistakes, rigorous verification is always required, but it is time-consuming and error-prone if the verification is implemented manually when dealing with numerous verification requests. To address these challenges, we propose an automated flow for the verification of safety registers with the formal approach. The results indicate that this automated verification approach has the potential to reduce the verification effort by more than 80\%. Additionally, it ensures a comprehensive examination of every requirement of this safety library, which is reflected in faster detection of bugs. The proposed framework can be replicated for the verification of other safety components enabling an early detection of potential issues and saving valuable time and resources.

cs.FL↗

arXiv2024

Automated Formal Verification of a Highly-Configurable Register Generator

Registers in IP blocks of an SoC perform a variety of functions, most of which are essential to the SoC operation. The complexity of register implementation is relatively low when compared with other design blocks. However, the extensive number of registers, combined with the various potential functions they can perform, necessitates considerable effort during implementation, especially when using a manual approach. Therefore, an in-house register generator was proposed by the design team to reduce the manual effort in the register implementation. This in-house register generator supports not only the generation of register blocks but also bus-related blocks. Meanwhile, to support various requirements, 41 generation options are used for this generator, which is highly-configurable. From the verification perspective, it is infeasible to achieve complete verification results with a manual approach for all options combinations. Besides the complexity caused by configurability, the register verification is still time-consuming due to two widely recognized issues: the unreliability of specifications and the complexity arising from diverse access policies. To deal with the highly-configurable feature and both register verification issues, we propose an automated register verification framework using formal methods following the Model Driven Architecture (MDA). Based on our results, the human effort in the register verification can be reduced significantly, from 20Person-Day (20PD) to 3PD for each configuration, and 100\% code coverage can be achieved. During the project execution, eleven new design bugs were found with the proposed verification framework.

cs.AR↗

arXiv2024

RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D Ultrasound Imaging Shape Reconstruction

The reconstruction of high-quality shape geometry is crucial for developing freehand 3D ultrasound imaging. However, the shape reconstruction of multi-view ultrasound data remains challenging due to the elevation distortion caused by thick transducer probes. In this paper, we present a novel learning-based framework RoCoSDF, which can effectively generate an implicit surface through continuous shape representations derived from row-column scanned datasets. In RoCoSDF, we encode the datasets from different views into the corresponding neural signed distance function (SDF) and then operate all SDFs in a normalized 3D space to restore the actual surface contour. Without requiring pre-training on large-scale ground truth shapes, our approach can synthesize a smooth and continuous signed distance field from multi-view SDFs to implicitly represent the actual geometry. Furthermore, two regularizers are introduced to facilitate shape refinement by constraining the SDF near the surface. The experiments on twelve shapes data acquired by two ultrasound transducer probes validate that RoCoSDF can effectively reconstruct accurate geometric shapes from multi-view ultrasound data, which outperforms current reconstruction methods. Code is available at https://github.com/chenhbo/RoCoSDF.

eess.IV↗

arXiv2024

Large Models for Aerial Edges: An Edge-Cloud Model Evolution and Communication Paradigm

The future sixth-generation (6G) of wireless networks is expected to surpass its predecessors by offering ubiquitous coverage through integrated air-ground facility deployments in both communication and computing domains. In this network, aerial facilities, such as unmanned aerial vehicles (UAVs), conduct artificial intelligence (AI) computations based on multi-modal data to support diverse applications including surveillance and environment construction. However, these multi-domain inference and content generation tasks require large AI models, demanding powerful computing capabilities, thus posing significant challenges for UAVs. To tackle this problem, we propose an integrated edge-cloud model evolution framework, where UAVs serve as edge nodes for data collection and edge model computation. Through wireless channels, UAVs collaborate with ground cloud servers, providing cloud model computation and model updating for edge UAVs. With limited wireless communication bandwidth, the proposed framework faces the challenge of information exchange scheduling between the edge UAVs and the cloud server. To tackle this, we present joint task allocation, transmission resource allocation, transmission data quantization design, and edge model update design to enhance the inference accuracy of the integrated air-ground edge-cloud model evolution framework by mean average precision (mAP) maximization. A closed-form lower bound on the mAP of the proposed framework is derived, and the solution to the mAP maximization problem is optimized accordingly. Simulations, based on results from vision-based classification experiments, consistently demonstrate that the mAP of the proposed framework outperforms both a centralized cloud model framework and a distributed edge model framework across various communication bandwidths and data sizes.

cs.NI↗

arXiv2024

Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps

Radio map is an efficient demonstration for visually displaying the wireless signal coverage within a certain region. It has been considered to be increasingly helpful for the future sixth generation (6G) of wireless networks, as wireless nodes are becoming more crowded and complicated. However, the construction of high resolution radio map is very challenging due to the sparse sampling in practical systems. Generative artificial intelligence (AI), which is capable to create synthetic data to fill in gaps in real-world measurements, is an effective technique to construct high precision radio maps. Currently, generative models for radio map construction are trained with two-dimension (2D) single band radio maps in urban scenario, which has poor generalization in diverse terrain scenarios, spectrum bands, and heights. To tackle this problem, we provide a multiband three-dimension (3D) radio map dataset with consideration of terrain and climate information, named SpectrumNet. It is the largest radio map dataset in terms of dimensions and scale, which contains the radio map of 3 spacial dimensions, 5 frequency bands, 11 terrain scenarios, and 3 climate scenarios. We introduce the parameters and settings for the SpectrumNet dataset generation, and evaluate three baseline methods for radio map construction based on the SpectrumNet dataset. Experiments show the necessity of the SpectrumNet dataset for training models with strong generalization in spacial, frequency, and scenario domains. Future works on the SpectrumNet dataset are also discussed, including the dataset expansion and calibration, as well as the extended studies on generative models for radio map construction based on the SpectrumNet dataset.

eess.SP↗

arXiv2024

Neural Implicit Surface Reconstruction of Freehand 3D Ultrasound Volume with Geometric Constraints

Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging modality that allows non-invasive imaging of medical anatomy without radiation exposure. Surface reconstruction of US volume is vital to acquire the accurate anatomical structures needed for modeling, registration, and visualization. However, traditional methods cannot produce a high-quality surface due to image noise. Despite improvements in smoothness, continuity, and resolution from deep learning approaches, research on surface reconstruction in freehand 3D US is still limited. This study introduces FUNSR, a self-supervised neural implicit surface reconstruction method to learn signed distance functions (SDFs) from US volumes. In particular, FUNSR iteratively learns the SDFs by moving the 3D queries sampled around volumetric point clouds to approximate the surface, guided by two novel geometric constraints: sign consistency constraint and onsurface constraint with adversarial learning. Our approach has been thoroughly evaluated across four datasets to demonstrate its adaptability to various anatomical structures, including a hip phantom dataset, two vascular datasets and one publicly available prostate dataset. We also show that smooth and continuous representations greatly enhance the visual appearance of US data. Furthermore, we highlight the potential of our method to improve segmentation performance, and its robustness to noise distribution and motion perturbation.

eess.IV↗

arXiv2023

Thermodynamic phase transition of Euler-Heisenberg-AdS black hole on free energy landscape

We study the first order phase transition of Euler-Heisenberg-AdS black hole based on free energy landscape. By solving the Fokker-Planck equation, we research the probability distribution of the system states. The small (large) black hole can have the chance to switch to the large (small) black hole due to the change of the temperature $T$ or Euler-Heisenberg parameter $a$. A higher (lower) $T$ corresponds to a larger probability for a large (small) black hole. The coexistent small and large black hole states can be acquired for some conditions. For $0< a\leq \frac{32}{7} Q^2 $, the small-large black hole phase transition can be acquired with a small $a$. The probability of small (large) black holes will decrease to zero for a large $a$. For a small $a$, a higher peak of the first passage time can be acquired for higher (lower) $T$ or smaller (larger) $a$ with the initial small (large) black hole state. For $a<0$, a smaller (larger) $a$ corresponds to a larger probability for a large (small) black hole. A higher peak of the first passage time can also be obtained for higher (lower) $T$ or smaller (larger) $a$ with initial small (large) black hole state.

gr-qc↗

arXiv2023

Design and manufacture of edible microfluidic logic gates

Edible robotics is an emerging research field with potential use in environmental, food, and medical scenarios. In this context, the design of edible control circuits could increase the behavioral complexity of edible robots and reduce their dependence on inedible components. Here we describe a method to design and manufacture edible control circuits based on microfluidic logic gates. We focus on the choice of materials and fabrication procedure to produce edible logic gates based on recently available soft microfluidic logic. We validate the proposed design with the production of a functional NOT gate and suggest further research avenues for scaling up the method to more complex circuits.

cs.RO↗