arXiv subjects

Ming Ye

Publications and source records attributed to Ming Ye.

8 recordsLinked to original sources

arXiv2026

Selective Depthwise Separable Convolution for Lightweight Joint Source-Channel Coding in Wireless Image Transmission

Depthwise separable convolutional (DSConv) layers have been successfully applied to deep learning (DL)-based joint source-channel coding (JSCC) schemes to reduce computational complexity. However, a systematic investigation of the layerwise and ratio-wise replacement of standard convolutional (Conv) layers with DSConv layers in JSCC systems for wireless image transmission remains largely unexplored. In this letter, we propose a configurable lightweight JSCC framework that incorporates a selective replacement strategy, enabling flexible Conv-to-DSConv replacement at different replacement ratios and positions. By varying the replacement ratio, we obtain models with different computational complexities and analyze their impact on reconstruction performance. Furthermore, we investigate how replacements at different encoder and decoder depths influence reconstruction quality under a fixed replacement ratio. Our results show that Conv-to-DSConv replacement at the intermediate layers of the encoder and decoder achieves a favorable complexity-performance trade-off, revealing layer-wise redundancy in DL-based JSCC systems. Extensive experiments further demonstrate that the proposed framework achieves substantial parameter reduction with only slight performance degradation, enabling flexible complexity-performance trade-offs for resource-constrained edge devices.

eess.IV↗

arXiv2026

U-Net-Based Generative Joint Source-Channel Coding for Wireless Image Transmission

Deep learning (DL)-based joint source-channel coding (JSCC) methods have achieved remarkable success in wireless image transmission. However, these methods either focus on conventional distortion metrics that do not necessarily yield high perceptual quality or incur high computational complexity. In this paper, we propose two DL-based JSCC (DeepJSCC) methods that leverage deep generative architectures for wireless image transmission. Specifically, we propose G-UNet-JSCC, a scheme comprising an encoder and a U-Net-based generator serving as the decoder. Its skip connections enable multi-scale feature fusion to improve both pixel-level fidelity and perceptual quality of reconstructed images by integrating low- and high-level features. To further enhance pixel-level fidelity, the encoder and the U-Net-based decoder are jointly optimized using a weighted sum of structural similarity and mean-squared error (MSE) losses. Building upon G-UNet-JSCC, we further develop a DeepJSCC method called cGAN-JSCC, where the decoder is enhanced through adversarial training. In this scheme, we retain the encoder of G-UNet-JSCC and adversarially train the decoder's generator against a patch-based discriminator. cGAN-JSCC employs a two-stage training procedure. The outer stage trains the encoder and the decoder end-to-end using an MSE loss, while the inner stage adversarially trains the decoder's generator and the discriminator by minimizing a joint loss combining adversarial and distortion losses. Simulation results demonstrate that the proposed methods achieve superior pixel-level fidelity and perceptual quality on both high- and low-resolution images. For low-resolution images, cGAN-JSCC achieves better reconstruction performance and greater robustness to channel variations than G-UNet-JSCC.

eess.IV↗

arXiv2025

Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstruction

Data-centric artificial intelligence (AI) has remarkably advanced medical imaging, with emerging methods using synthetic data to address data scarcity while introducing synthetic-to-real gaps. Unsupervised domain adaptation (UDA) shows promise in ground truth-scarce tasks, but its application in reconstruction remains underexplored. Although multiple overlapping-echo detachment (MOLED) achieves ultra-fast multi-parametric reconstruction, extending its application to various clinical scenarios, the quality suffers from deficiency in mitigating the domain gap, difficulty in maintaining structural integrity, and inadequacy in ensuring mapping accuracy. To resolve these issues, we proposed frequency-aware perturbation and selection (FPS), comprising Wasserstein distance-modulated frequency-aware perturbation (WDFP) and hierarchical frequency-aware selection network (HFSNet), which integrates frequency-aware adaptive selection (FAS), compact FAS (cFAS) and feature-aware architecture integration (FAI). Specifically, perturbation activates domain-invariant feature learning within uncertainty, while selection refines optimal solutions within perturbation, establishing a robust and closed-loop learning pathway. Extensive experiments on synthetic data, along with diverse real clinical cases from 5 healthy volunteers, 94 ischemic stroke patients, and 46 meningioma patients, demonstrate the superiority and clinical applicability of FPS. Furthermore, FPS is applied to diffusion tensor imaging (DTI), underscoring its versatility and potential for broader medical applications. The code is available at https://github.com/flyannie/FPS.

eess.IV↗

arXiv2024

GAN Based Near-Field Channel Estimation for Extremely Large-Scale MIMO Systems

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technique to achieve ultra-high spectral efficiency for future 6G communications. The mixed line-of-sight (LoS) and non-line-of-sight (NLoS) XL-MIMO near-field channel model is adopted to describe the XL-MIMO near-field channel accurately. In this paper, a generative adversarial network (GAN) variant based channel estimation method is proposed for XL-MIMO systems. Specifically, the GAN variant is developed to simultaneously estimate the LoS and NLoS path components of the XL-MIMO channel. The initially estimated channels instead of the received signals are input into the GAN variant as the conditional input to generate the XL-MIMO channels more efficiently. The GAN variant not only learns the mapping from the initially estimated channels to the XL-MIMO channels but also learns an adversarial loss. Moreover, we combine the adversarial loss with a conventional loss function to ensure the correct direction of training the generator. To further enhance the estimation performance, we investigate the impact of the hyper-parameter of the loss function on the performance of our method. Simulation results show that the proposed method outperforms the existing channel estimation approaches in the adopted channel model. In addition, the proposed method surpasses the Cram$\acute{\mathbf{e}}$r-Rao lower bound (CRLB) under low pilot overhead.

eess.SP↗

arXiv2022

Recent Advances in Tunable Metasurfaces: Materials, Design and Applications

Metasurfaces, a two-dimensional (2D) form of metamaterials constituted by planar meta-atoms, exhibit exotic abilities to freely tailor electromagnetic (EM) waves. Over the past decade, tunable metasurfaces have come to the frontier in the field of nanophotonics, with tremendous effort focused on developing and integrating various active materials into metasurfaces. As a result, tunable/reconfigurable metasurfaces with multi-functionalities triggered by various external stimuli have been successfully demonstrated, openings a new avenue to dynamically manipulate and control EM waves for photonic applications in demand. In this review, we first brief the progress of tunable metasurfaces development in the last decade and highlight representative works from the perspectives of active materials development, design methodologies and application-driven exploration. Then, we elaborate on the active tuning mechanisms and relevant active materials. Next, we discuss recent achievements in theory as well as machine learning (ML) assisted design methodologies to sustain the development of this field. After that, we summarize and describe typical application areas of the tunable metasurfaces. We conclude this review by analyzing existing challenges and presenting our perspectives on future directions and opportunities in this vibrant and fast-developing field.

physics.optics↗

arXiv2019

Cascade Feature Aggregation for Human Pose Estimation

Human pose estimation plays an important role in many computer vision tasks and has been studied for many decades. However, due to complex appearance variations from poses, illuminations, occlusions and low resolutions, it still remains a challenging problem. Taking the advantage of high-level semantic information from deep convolutional neural networks is an effective way to improve the accuracy of human pose estimation. In this paper, we propose a novel Cascade Feature Aggregation (CFA) method, which cascades several hourglass networks for robust human pose estimation. Features from different stages are aggregated to obtain abundant contextual information, leading to robustness to poses, partial occlusions and low resolution. Moreover, results from different stages are fused to further improve the localization accuracy. The extensive experiments on MPII datasets and LIP datasets demonstrate that our proposed CFA outperforms the state-of-the-art and achieves the best performance on the state-of-the-art benchmark MPII.

cs.CV↗

arXiv2018

A minimal model for predicting ventilation rates of subterranean caves

The ventilation of carbon dioxide within subterranean caves regulates the growth of speleothems --- mineral deposits found in caves that provide important clues about past climate. While previous studies have used internal temperature measurements to predict ventilation rates, such data would not be available for the task of climate reconstruction. Here, we develop a parsimonious model to predict ventilation rates from knowledge of outside temperatures and the cave's physical dimensions only. In the model, ventilation arises from buoyancy-driven flows created in passageways that connect to the outside. A few key simplifications leads to a system amenable to perturbation analysis, resulting in explicit expressions for ventilation rates. We compare these predictions to time-resolved, in-situ measurements of transported cave gases (carbon dioxide and radon). The theory accurately accounts for the timing and magnitude of seasonal and synoptic variations of these gases, and is therefore diagnostic of seasonal bias in speleothem growth.

physics.geo-ph↗

arXiv2018

Generalized method of images and reflective color generation from ultra-thin multipole resonators

The multipole expansion has found limited applicability for optical dielectric resonators in inhomogeneous environment, such as on the surface of substrates. Here, we generalize the method of images to multipole analysis for light scattering by dielectric nanoparticles on conductive substrates. We present examples illustrating the physical insight provided by our method, including selection rules governing the excitation of the multipoles. We propose and experimentally demonstrate a new mechanism to generate high resolution surface color. The dielectric resonators employed are very thin (less than 50 nm), i.e. similar in thickness to the plasmonic resonators that are currently being investigated for structural color. The generalized method of images opens up new prospects for design and analysis of metasurfaces and optical dielectric resonators.

physics.app-ph↗