arXiv subjects

Jintao Wu

Publications and source records attributed to Jintao Wu.

5 recordsLinked to original sources

arXiv2026

A Unified Explanation of Gamma-Ray and Neutrino Spectra from Astrophysical Sources Based on the Gluon Condensation Model

The advent of multi-messenger astronomy has provided abundant information for understanding the acceleration and particle-production mechanisms of cosmic rays. In this work, we present a unified study of cosmic gamma-ray and neutrino spectra within the Gluon Condensation (GC) model. Derived from Quantum Chromodynamics (QCD), the GC model predicts that, in high-energy hadronic processes, gluons may condense near a critical momentum, leading to a dramatic enhancement in secondary-pion production and imprinting a characteristic broken power-law feature on the gamma-ray spectrum. Within this framework, we first derive the neutrino spectrum corresponding to the GC scenario and then investigate three astrophysical sources with both gamma-ray observations and neutrino candidate signals: the active galactic nuclei TXS 0506+056 and NGC 1068, and the supernova remnant G54.1+0.3. Using the GC model, we fit the observed gamma-ray spectra of these sources and predict their corresponding neutrino spectra. Our results show that the gamma-ray spectra of TXS 0506+056 and NGC 1068 are well described by the GC model, and that the predicted neutrino spectra are consistent with IceCube observations within uncertainties; in particular, clear relations are found between their relative magnitudes. For SNR G54.1+0.3, however, the GC-predicted neutrino spectrum exhibits continuous hardening after the break, deviating from the typical power-law behavior expected for cosmic-ray secondaries and thus disfavoring a common GC origin. This study represents the first systematic attempt to correlate gamma-ray and neutrino spectra within the GC framework, offering a new perspective on multi-messenger emission from high-energy astrophysical sources.

astro-ph.HE↗

arXiv2026

DOA Estimation for Low-Altitude Networks: HAD Architectures, Methods, and Challenges

With the rapid expansion of low-altitude economy (LAE) services and the growing demand for integrated sensing and communication (ISAC) in air-ground networks, reliable direction-of-arrival (DOA) estimation has become essential for both directional communication and sensing functions. DOA underpins beam alignment, spatial-reuse scheduling, and ISAC-critical tasks such as airspace situational awareness and multi-target monitoring. Hybrid analog-digital (HAD) architectures have emerged as a practical solution for large-aperture directional operation under stringent radio frequency (RF), analog-to-digital converter (ADC), and size, weight, and power (SWaP) constraints. However, HAD compresses antenna-domain observations through analog combining, fundamentally reshaping the measurement model and introducing new algorithmic and system-level challenges for DOA estimation. This article first reviews the principles and representative architectures of HAD, highlighting their advantages for scalable beam-centric and ISAC-oriented operation in LAE scenarios. We then provide a structured overview of HAD-enabled DOA estimation methodologies, including spatial covariance matrix (SCM) reconstruction, multi-combiner scan-based acquisition, and pilot-aided estimation, along with key design tradeoffs. Finally, we discuss open challenges and outline reliability-driven research directions toward robust, deployable HAD-enabled DOA solutions for practical ISAC-enabled low-altitude environments.

eess.SP↗

arXiv2025

ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network

Electrocardiogram (ECG) signals are frequently corrupted by noise, such as baseline wander (BW), muscle artifacts (MA), and electrode motion (EM), which significantly degrade their diagnostic utility. To address this issue, we propose ECGDeDRDNet, a deep learning-based ECG Denoising framework leveraging a Double Recurrent Dense Network architecture. In contrast to traditional approaches, we introduce a double recurrent scheme to enhance information reuse from both ECG waveforms and the estimated clean image. For ECG waveform processing, our basic model employs LSTM layers cascaded with DenseNet blocks. The estimated clean ECG image, obtained by subtracting predicted noise components from the noisy input, is iteratively fed back into the model. This dual recurrent architecture enables comprehensive utilization of both temporal waveform features and spatial image details, leading to more effective noise suppression. Experimental results on the MIT-BIH dataset demonstrate that our method achieves superior performance compared to conventional image denoising methods in terms of PSNR and SSIM while also surpassing classical ECG denoising techniques in both SNR and RMSE.

eess.SP↗

arXiv2025

EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks

While multimodal large language models (MLLMs) have made groundbreaking progress in embodied intelligence, they still face significant challenges in spatial reasoning for complex long-horizon tasks. To address this gap, we propose EmbodiedVSR (Embodied Visual Spatial Reasoning), a novel framework that integrates dynamic scene graph-guided Chain-of-Thought (CoT) reasoning to enhance spatial understanding for embodied agents. By explicitly constructing structured knowledge representations through dynamic scene graphs, our method enables zero-shot spatial reasoning without task-specific fine-tuning. This approach not only disentangles intricate spatial relationships but also aligns reasoning steps with actionable environmental dynamics. To rigorously evaluate performance, we introduce the eSpatial-Benchmark, a comprehensive dataset including real-world embodied scenarios with fine-grained spatial annotations and adaptive task difficulty levels. Experiments demonstrate that our framework significantly outperforms existing MLLM-based methods in accuracy and reasoning coherence, particularly in long-horizon tasks requiring iterative environment interaction. The results reveal the untapped potential of MLLMs for embodied intelligence when equipped with structured, explainable reasoning mechanisms, paving the way for more reliable deployment in real-world spatial applications. The codes and datasets will be released soon.

cs.RO↗

arXiv2024

A Collaborative Explanation of Cosmic Ray Spectrum Based on the Gluon Condensation Model

Based on the Gluon Condensation (GC) model, the relationship between the spectra of electrons, $γ$ rays, and neutrinos in cosmic rays can be deduced. It has been found that these particles share the same parameter, $β_p$, and have an identical GC threshold values. This paper explores the connection between the second excess spectra of electron and the spectra of gamma rays and neutrinos. According to the observed gamma-ray data, it is suggested that the source LHAASO J2108+5157 might contribute to the second excess of electron.

astro-ph.HE↗