arXiv subjects

Muchen Dong

Publications and source records attributed to Muchen Dong.

5 recordsLinked to original sources

arXiv2026

Novel Transition Mechanisms of Vector Localized Waves Induced by the Fourth-Order Effect

We investigate novel vector localized wave solutions in the coupled Lakshmanan-Porsezian-Daniel equa?tions, which describe the dynamics of the Heisenberg ferromagnetic spin chain. We present Tajiri-Watanabe breathers, rogue waves and resonant modes in both the degenerate and non-degenerate regions, together with the degenerate beating solitons. The fourth-order effect induces state transitions in both regions. In particular, the degenerate breathers can be transformed into solitons, whereas such transitions are absent in the coupled Hirota equations. Moreover, beating solitons can be converted into stable solitons only in the degenerate region, the phenomena not found in the Manakov system. We further uncover the state transitions of the resonant modes and derive the corresponding transition conditions for each branch. We derive the physical spectra and subsequently identify the state transition conditions in the spectral domain for both the degenerate and non-degenerate cases. These spectra provide an additional characterization of the transition dynamics. Finally, direct numerical simulations are performed to verify the validity of the exact solutions.

nlin.PS↗

arXiv2026

Mechanism of State Transitions for the Vector Kuznetsov-Ma Breathers

We study two types of mechanisms of state transitions for vector Kuznetsov-Ma breathers (KMBs) in the coupled Fokas-Lenells framework on unequal backgrounds. The amplitude imbalance breaks spectral reflection symmetry and generates richer breather morphologies. In the degenerate sector, KMBs approaching a non-self-conjugate degeneration curve from opposite sides yield different limiting solitons, revealing a discontinuous KMB-to-soliton transition. In the nondegenerate sector, the background plane waves become frequency matched at a special wavenumber, converting the KMB into a single- or two-soliton state. We determine the exact transition threshold and characterize how nearby solutions vary with the parameter. We further investigate special limiting mechanisms arising on the self-conjugate degeneration curve and on the real spectrum at the state-transition wavenumber. Numerical excitation provides further evidence for these results.

nlin.PS↗

arXiv2026

Real-Spectrum Darboux Limits at Multiple Spatial Roots of the Coupled Fokas-Lenells System

We investigate Darboux constructions generated by multiple spatial roots of the coupled Fokas-Lenells system on a plane-wave background. Nonreal double roots give regular one-fold transformations, whereas real double and triple roots require directional real-spectrum limits. We classify the admissible projective null sets, derive the leading spectral corrections, and establish global regularity. A critical background relation additionally produces a persistent zero branch, leading to distinct nonlocal plateaus along parabolic corridors, cubic level sets, and a characteristic line.

nlin.SI↗

arXiv2025

Towards Loss-Resilient Image Coding for Unstable Satellite Networks

Geostationary Earth Orbit (GEO) satellite communication demonstrates significant advantages in emergency short burst data services. However, unstable satellite networks, particularly those with frequent packet loss, present a severe challenge to accurate image transmission. To address it, we propose a loss-resilient image coding approach that leverages end-to-end optimization in learned image compression (LIC). Our method builds on the channel-wise progressive coding framework, incorporating Spatial-Channel Rearrangement (SCR) on the encoder side and Mask Conditional Aggregation (MCA) on the decoder side to improve reconstruction quality with unpredictable errors. By integrating the Gilbert-Elliot model into the training process, we enhance the model's ability to generalize in real-world network conditions. Extensive evaluations show that our approach outperforms traditional and deep learning-based methods in terms of compression performance and stability under diverse packet loss, offering robust and efficient progressive transmission even in challenging environments. Code is available at https://github.com/NJUVISION/LossResilientLIC.

cs.CV↗

arXiv2024

Accelerating block-level rate control for learned image compression

Despite the unprecedented compression efficiency achieved by deep learned image compression (LIC), existing methods usually approximate the desired bitrate by adjusting a single quality factor for a given input image, which may compromise the rate control results. Considering the Rate-Distortion (R - D) characteristics of different spatial content, this work introduces the block-level rate control based on a novel D - λ model specific for LIC. Furthermore, we try to exploit the inter-block correlations and propose a block-wise R - D prediction algorithm which greatly speeds up block-level rate control while still guaranteeing high accuracy. Experimental results show that the proposed rate control achieves up to 100 times, speed-up with more than 98% accuracy. Our approach provides an optimal bit allocation for each block and therefore improves the overall compression performance, which offers great potential for block-level LIC.

eess.IV↗