arXiv subjects

Qingfeng Zhang

Publications and source records attributed to Qingfeng Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Background Intensity Estimation for Cassini-ISS Image Using Deep Learning-Based Diffusion Model

Accurate background intensity estimation is crucial for precise astrometric measurements in astronomical imaging, particularly in complex scenarios such as those encountered in Cassini Imaging Science Subsystem (ISS) observations of Saturn's ring system. Traditional methods, like polynomial fitting and statistical method, often fail in non-uniform conditions, such as those caused by Saturn's rings or scattered light, due to mismatched assumptions and reliance on prior knowledge. This results in biased estimates and poor generalizability. We propose a deep learning framework based on Denoising Diffusion Probabilistic Model (DDPM) to address these challenges. By learning noise patterns and iteratively reconstructing backgrounds, DDPM improve background intensity estimation in ring-gap regions of ISS images by up to 62% relative to polynomial fitting. Additionally, When applied to centroiding of unresolved satellites in ring-gap, DDPM-based background estimation enhances positional precision by about 14% in the line direction and 15% in the sample direction. The framework autonomously captures spatial correlations, requires no manual parameter tuning, and generalizes across diverse backgrounds. These characteristics make it a promising, assumption-light solution for background estimation tasks in astrometry, photometry, and source detection, with potential applications to exoplanet transit imaging, deep-field surveys, and future missions.

astro-ph.IM↗

arXiv2026

Automated Shape-Model-Based Astrometry of Phobos from Mars Express SRC Images

High-resolution spacecraft images provide important astrometric constraints for orbit refinement, but measurements of resolved bodies are often limited by labor-intensive control-point selection and the difficulty of achieving consistent reductions over large image archives. We present an automated shape-model-based astrometric pipeline for Phobos and apply it to Mars Express Super Resolution Channel (SRC) images. For each exposure, a synthetic image is rendered from a high-resolution 3D shape model under the nominal spacecraft-target-Sun geometry. Feature correspondences between the observed and synthetic images are established using SuperPoint and SuperGlue, followed by RANSAC filtering. The matched synthetic-image keypoints are then associated with surface points through ray-shape intersection. The geometric adjustment fixes the adopted body orientation, spacecraft state, and corrected camera pointing and estimates only two effective plane-of-sky position offsets using the exact perspective-projection model. These offsets are used to derive the center-of-figure position of Phobos. We first test the method on an image set previously analysed with a control-point approach and obtain comparable astrometric performance. We then extend the analysis to a larger SRC dataset spanning 2007-2025 and obtain 1113 successful measurements. Relative to the JPL MAR099 ephemeris, the resulting observed-minus-computed residuals have mean values of 0.186 km in $\alpha \times cos(\delta)$ and 0.053 km in $\delta$, with corresponding standard deviations of 0.609 km and 0.583 km. These results demonstrate that the proposed pipeline provides a practical approach to large-scale, homogeneous astrometric reduction of archival spacecraft images of Phobos, with potential application to other resolved bodies.

astro-ph.IM↗

arXiv2026

Locally Deployable Small Language Models for Emergency Department Decision Support: A Systematic Benchmark of Fine-Tuning Strategies

Deploying large language models (LLMs) for decision support in emergency departments (EDs) faces two major challenges: privacy risks of transmitting patient data to closed-source commercial LLMs and the lack of systematic evaluation of fine-tuning strategies for locally deployable open-source small language models (SLMs). We benchmarked eight open-source SLMs using zero-shot prompting, prefix tuning, Low-Rank Adaptation (LoRA), and full fine-tuning on three ED tasks: triage level prediction, specialist referral recommendation, and diagnosis prediction. Using 2,083 MIMIC-IV-ED cases and Claude Haiku 4.5 and Claude Sonnet 4.5 as baselines, we found that LoRA fine-tuned open-source SLMs outperform commercial baselines on triage level prediction and specialist referral recommendation, while diagnosis prediction remains challenging for open-source SLMs. Confusion matrix analysis further shows that fine-tuned open-source SLMs can detect highest-severity patients missed by the commercial baselines. These results demonstrate that locally deployable SLMs can achieve clinically competitive performance for ED decision support.

cs.CL↗

arXiv2026

Deanonymizing Monero Transactions in Tor Network

Monero is a privacy-focused cryptocurrency that deploys the Dandelion++ protocol and incorporates anonymity networks (such as Tor and I2P) to prevent malicious attackers from linking transactions with their source IPs. In this paper, we demonstrate that Monero's integration of the Tor network introduces a fundamental vulnerability: a Monero Tor node's originated transactions are exclusively forwarded to two outgoing Tor hidden service nodes (proxy nodes) prior to clearnet propagation, enabling an adversary to capture originated transactions by occupying the target node's outgoing connections. Based on this observation, we propose \textit{ProxyMark}, a three-stage deanonymization framework for the Monero Tor network, comprising node role identification, originated transaction identification, and node location deanonymization. Through experiments on the live Tor network, Monero mainnet, and testnet, we empirically demonstrate the effectiveness of \textit{ProxyMark} in successfully deanonymizing transactions originating from Monero nodes over Tor.

cs.CR↗

arXiv2026

Unifying Acoustic Features and Text with Multimodal LLMs for Neurodegenerative Screening

Voice-based screening offers a scalable and non-invasive way to assess neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), but their staging remains challenging due to the difficulty of integrating heterogeneous data. This paper presents NeurMLLM, an efficient multimodal generative framework for neurodegenerative disease staging. NeurMLLM first encodes the spectrograms and Mel-frequency cepstral coefficients of audio data with vision transformers and projects their representations into the embedding space of a large language model (LLM), where they are concatenated with transcript and demographic instruction tokens as a single unified sequence. The LLM is then instruction-tuned via Low-Rank Adaptation using task prompts to autoregressively predict a constrained label token, enabling a generative classification. By evaluating on the Bridge2AI-Voice dataset for fine-grained staging of AD and PD, we observe that NeurMLLM achieves strong performance, consistently outperforming classical machine learning methods and existing LLM-based approaches. The results show the high potential of multimodal LLMs in neurodegenerative disease staging, improving staging accuracy and supporting accessible deployment.

cs.SD↗

arXiv2025

WiRainbow: Single-Antenna Direction-Aware Wi-Fi Sensing via Dispersion Effect

Recently, Wi-Fi signals have emerged as a powerful tool for contactless sensing. During the sensing process, obtaining target direction information can provide valuable contextual insights for various applications. Existing direction estimation methods typically rely on antenna arrays, which are costly and complex to deploy in real-world scenarios. In this paper, we present WiRainbow, a novel approach that enables single-antenna-based direction awareness for Wi-Fi sensing by leveraging the dispersion effect of frequency-scanning antennas (FSAs), which can naturally steer Wi-Fi subcarriers toward distinct angles during signal transmission. To address key challenges in antenna design and signal processing, we propose a coupled-resonator-based antenna architecture that significantly expands the narrow Field-of-View inherent in conventional FSAs, improving sensing coverage. Additionally, we develop a sensing signal-to-noise-ratio-based signal processing framework that reliably estimates target direction in multipath-rich environments. We prototype WiRainbow and evaluate its performance through benchmark experiments and real-world case studies, demonstrating its ability to achieve accurate, robust, and cost-effective direction awareness for diverse Wi-Fi sensing applications.

eess.SP↗

arXiv2025

A Centroiding Algorithm for Point-source Trails

Astrometric measurements are significantly challenged by the relative motion between the point source and the telescope, primarily due to the difficulty in accurately determining the position of the point source at the mid-exposure moment. Especially when the trail is irregular in shape or results from nonuniform relative motion, determining the centroid of such a trail becomes significantly more challenging. To address this issue, a new centroiding algorithm for point-source trails has been developed. This algorithm employs a piecewise linear model to approximate the irregular trajectory of a point source. An estimated intensity distribution of the trail is constructed by integrating the point-spread function with the approximated trajectory. The cost function is defined as the difference between the estimated and observed trail intensity distributions, with an added smoothness constraint term. Optimizing this cost function yields a refined trajectory fit. A coarse-to-fine iterative approach is used to progressively converge on the true trajectory of the point source, ultimately determining both the trail's centroid and the trajectory of the point source. The efficacy of the algorithm is validated using synthetic images. Furthermore, this technique is applied to Cassini Imaging Science Subsystem images of several inner Saturnian satellites, successfully processing 267 astrometric observations. The results demonstrate the effectiveness of the algorithm in real astronomical applications.

astro-ph.IM↗

arXiv2020

Resonance locking in giant planets indicated by the rapid orbital expansion of Titan

Tidal effects in planetary systems are the main driver in the orbital migration of natural satellites. They result from physical processes occurring deep inside celestial bodies, whose effects are rarely observable from surface imaging. For giant planet systems, the tidal migration rate is determined by poorly understood dissipative processes in the planet, and standard theories suggest an orbital expansion rate inversely proportional to the power 11/2 in distance, implying little migration for outer moons such as Saturn's largest moon, Titan. Here, we use two independent measurements obtained with the Cassini spacecraft to measure Titan's orbital expansion rate. We find Titan migrates away from Saturn at 11.3 $\pm$ 2.0 cm/year, corresponding to a tidal quality factor of Saturn of Q $\simeq$ 100, and a migration timescale of roughly 10 Gyr. This rapid orbital expansion suggests Titan formed significantly closer to Saturn and has migrated outward to its current position. Our results for Titan and five other moons agree with the predictions of a resonance locking tidal theory, sustained by excitation of inertial waves inside the planet. The associated tidal expansion is only weakly sensitive to orbital distance, motivating a revision of the evolutionary history of Saturn's moon system. The resonance locking mechanism could operate in other systems such as stellar binaries and exoplanet systems, and it may allow for tidal dissipation to occur at larger orbital separations than previously believed.

astro-ph.EP↗

arXiv2020

Automatic removal of false image stars in disk-resolved images of the Cassini Imaging Science Subsystem

Taking a large amount of images, the Cassini Imaging Science Subsystem (ISS) has been routinely used in astrometry. In ISS images, disk-resolved objects often lead to false detection of stars that disturb the camera pointing correction. The aim of this study was to develop an automated processing method to remove the false image stars in disk-resolved objects in ISS images. The method included the following steps: extracting edges, segmenting boundary arcs, fitting circles and excluding false image stars. The proposed method was tested using 200 ISS images. Preliminary experimental results show that it can remove the false image stars in more than 95% of ISS images with disk-resolved objects in a fully automatic manner, i.e. outperforming the traditional circle detection based on Circular Hough Transform (CHT) by 17%. In addition, its speed is more than twice as fast as that of the CHT method. It is also more robust (no manual parameter tuning is needed) when compared with CHT. The proposed method was also applied to a set of ISS images of Rhea to eliminate the mismatch in pointing correction in automatic procedure. Experiment results showed that the precision of final astrometry results can be improve by roughly 2 times than that of automatic procedure without the method. It proved that the proposed method is helpful in the astrometry of ISS images in fully automatic manner.

astro-ph.IM↗

arXiv2019

Contour Detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field

In Cassini ISS (Imaging Science Subsystem) images, contour detection is often performed on disk-resolved object to accurately locate their center. Thus, the contour detection is a key problem. Traditional edge detection methods, such as Canny and Roberts, often extract the contour with too much interior details and noise. Although the deep convolutional neural network has been applied successfully in many image tasks, such as classification and object detection, it needs more time and computer resources. In the paper, a contour detection algorithm based on H-ELM (Hierarchical Extreme Learning Machine) and DenseCRF (Dense Conditional Random Field) is proposed for Cassini ISS images. The experimental results show that this algorithm's performance is better than both traditional machine learning methods such as SVM, ELM and even deep convolutional neural network. And the extracted contour is closer to the actual contour. Moreover, it can be trained and tested quickly on the general configuration of PC, so can be applied to contour detection for Cassini ISS images.

astro-ph.IM↗

arXiv2017

Synthesis of Negative Group Delay Using Lossy Coupling Matrix

In this paper, a systematic synthesis approach is proposed for achieving negative group delay responses using lossy coupling matrix. It is mathematically proved that, for a passive and reciprocal network, loss is the necessary condition to realize a negative group delay. Also, the optimum strategy is to place zeros and poles of the transfer function both on the left complex plane. A closed-form relation between the group delay and magnitude is then derived based on this strategy, and followed by a complete synthesis approach using coupling matrix. Two numerical and one experimental examples are finally given to illustrate the proposed synthesis method.

physics.app-ph↗

arXiv2017

Low-Profile Spoof Surface Plasmon Polaritons Traveling-Wave Antenna for Endfire Radiation

This paper proposes a low-profile and highly efficient endfire radiating travelling-wave antenna based on spoof surface plasmon polaritons (SSPPs) transmission line. The aperture is approximately $0.32\lambda_0\times0.01\lambda_0$ where $\lambda_0$ is the space wavelength at the operational frequency 8 GHz. This antenna provides an endfire radiation beam within 7.5-8.5 GHz. The maximum gain and total efficiency reaches 9.2 dBi and $96\%$, respectively. In addition to the endfire operation, it also provides a beam scanning functionality within 9-12 GHz. Measurement results are finally given to validate the proposed SSPPs antenna.

physics.app-ph↗

arXiv2017

Spoof Surface Plasmon Polariton Leaky-Wave Antennas using Periodically Loaded Patches above PEC and AMC Ground Planes

This paper proposes two spoof surface plasmon polariton (SSPP) leaky-wave antennas using periodically loaded patches above perfect electric conductor (PEC) and artificial magnetic conductor (AMC) ground planes, respectively. The SSPP leaky-wave antenna is based on a SSPP transmission line, along which circular patches are periodically loaded on both sides to provide an additional momentum for phase matching with the radiated waves in the air. The PEC and AMC ground planes underneath the antenna reflect the radiated waves into the upward space, leading to an enhanced radiation gain. Both PEC- and AMC-grounded antenna prototypes are fabricated and measured in comparison with the one without any ground plane. The experimental results show that the PEC and AMC ground planes increase the radiation gain by approximately 3 dB within the operational frequency range 4.5-6.5 GHz. It also demonstrates that the AMC-grounded leaky-wave antenna, with a thickness of 0.08\lambda at 6 GHz, features more compact profile than the PEC-grounded one (with a thickness of 0.3\lambda at 6 GHz).

physics.app-ph↗

arXiv2017

Capacitor-Loaded Spoof Surface Plasmon (SSP) for Flexible Dispersion Control and High-Selectivity Filtering

This letter proposes a new spoof surface plasmon transmission line (SSP-TL) using capacitor loading techniques. This new SSP-TL features flexible and reconfigurable dispersion control and highly selective filtering performance without resorting to configuration change. Moreover, it requires much smaller line width than the conventional SSP-TLs for achieving a extremely slow wave (or a highly confined field), which is quite useful for a compact system. To illustrate the design principle, several examples are designed within the frequency range of 2-8 GHz. Both numerical and experimental results are given in comparison with the conventional SSP-TL. It is demonstrated that the proposed technique provides a better performance in size reduction and dispersion reconfigurability.

physics.class-ph↗

arXiv2016

Enhanced Bandwidth and Diversity in Real-Time Analog Signal Processing (R-ASP) using Nonuniform C-section Phasers

We show that a continuously nonuniform coupled line C-section phaser, as the limiting case of the step discontinuous coupled-line multisection commensurate and non-commensurate phasers, provides enhanced bandwidth and diversity in real-time analog signal processing (R-ASP). The phenomenology of the component is explained in comparison with the step-discontinuous using multiple-reflection theory and a simple synthesis procedure is provided. The bandwidth enhancement results from the suppression of spurious group delay harmonics or quasi-harmonics, while the diversity enhancement results from the greater level of freedom provided by the continuous nature of the nonuniform profile of the phaser. These statements are supported by theoretical and experimental results.

physics.ins-det↗

arXiv2015

Single-Step Tunable Group Delay Phaser for Real-Time Spectrum Sniffing

This paper presents a single-step tunable group delay phaser for spectrum sniffing. This device may be seen as a "time filter", where frequencies are suppressed by time separation rather than by spectral attenuation. Compared to its multiple-step counterpart, this phaser features higher processing resolution, greater simplicity, lower loss and better channel equalization, due to the smaller and channel-independent group delay swing. A three-channel example is provided for illustration.

physics.optics↗

arXiv2014

Compact Reflection-Type Phaser Using Quarter-Wavelength Transmission Line Resonators

A compact reflection-type phaser composed of quarter-wavelength transmission line resonators interconnected by alternating K- and J-inverters is proposed. A design method is also presented. To validate this method, a 4th-order example is designed and fabricated. The proposed phaser is shown to exhibit the benefits of smaller size, easier fabrication and suppressed even-order harmonics compared with previously reported half-wavelength phasers.

physics.class-ph↗

arXiv2014

Coupling Matrix Synthesis of Nonreciprocal Lossless Two-Port Networks using Gyrators and Inverters

A coupling matrix technique is presented for the synthesis of nonreciprocal lossless two-port networks. This technique introduces complex inverters to build the nonreciprocal transversal network corresponding to the nonreciprocal coupling matrix. It subsequently transforms this matrix into canonical topologies through complex similarity transformations. The complex inverters in the final topology are transformed into real inverters and gyrators for implementation simplicity. A second-order example is then given to illustrate the proposed technique. Possible implementations of the gyrators involved in the final design are also discussed.

physics.optics↗