arXiv subjects

Chen Pang

Publications and source records attributed to Chen Pang.

6 recordsLinked to original sources

arXiv2025

Low-Cost Architecture and Efficient Pattern Synthesis for Polarimetric Phased Array Based on Polarization Coding Reconfigurable Elements

Polarimetric phased arrays (PPAs) enhance radar target detection and anti-jamming capabilities, but their conventional dual transmit/receive (T/R) channel architecture leads to high cost and system complexity. To address these limitations, this paper proposes a polarization-coding reconfigurable phased array (PCRPA) and associated pattern synthesis techniques, which reduce the channel count while preserving key performance. In the PCRPA, each antenna element connects to a single T/R channel and is equipped with a two-level RF switch, enabling real-time control of its polarization state and subarray grouping. By optimizing both the element polarization codes and the excitation weights, the array can synthesize arbitrarily polarized and dual-polarized beams. Simulation results show that the proposed approach achieves suppressed cross-polarization and comparable sidelobe levels compared to conventional PPAs across a wide scan range, with performance improvements being more pronounced in larger arrays. The inherent channel reduction does, however, incur a trade-off in terms of radiated power and directivity. Experimental validation using an $8\times 8$ X-band array antenna confirms the feasibility and effectiveness of the proposed system. The PCRPA architecture and the accompanying synthesis methods offer a cost-effective solution for large-scale PPA systems, maintaining sidelobe and polarization control with significantly reduced hardware complexity.

eess.SY↗

arXiv2025

Learning Adaptive Node Selection with External Attention for Human Interaction Recognition

Most GCN-based methods model interacting individuals as independent graphs, neglecting their inherent inter-dependencies. Although recent approaches utilize predefined interaction adjacency matrices to integrate participants, these matrices fail to adaptively capture the dynamic and context-specific joint interactions across different actions. In this paper, we propose the Active Node Selection with External Attention Network (ASEA), an innovative approach that dynamically captures interaction relationships without predefined assumptions. Our method models each participant individually using a GCN to capture intra-personal relationships, facilitating a detailed representation of their actions. To identify the most relevant nodes for interaction modeling, we introduce the Adaptive Temporal Node Amplitude Calculation (AT-NAC) module, which estimates global node activity by combining spatial motion magnitude with adaptive temporal weighting, thereby highlighting salient motion patterns while reducing irrelevant or redundant information. A learnable threshold, regularized to prevent extreme variations, is defined to selectively identify the most informative nodes for interaction modeling. To capture interactions, we design the External Attention (EA) module to operate on active nodes, effectively modeling the interaction dynamics and semantic relationships between individuals. Extensive evaluations show that our method captures interaction relationships more effectively and flexibly, achieving state-of-the-art performance.

cs.CV↗

arXiv2025

Interplay of Zeeman field, Rashba spin-orbit interaction, and superconductivity: spin susceptibility

We present a self-consistent theory to calculate the static and uniform spin susceptibility in superconductors under simultaneous Zeeman magnetic fields and Rashba-type spin-orbit coupling (SOC). Employing a single-band Bogoliubov-de Gennes Hamiltonian, we solve the gap equation for both conventional $s$-wave spin-singlet and six representative $p$-wave spin-triplet pairing states, categorized into opposite-spin-pairing (OSP) and equal-spin-pairing (ESP) classes. The Kubo formula, decomposed into intra- and interband particle-hole and particle-particle channels, provides two key constraints: at zero temperature, only particle-particle terms contribute, while at the critical temperature $T_c$, only particle-hole terms remain, ensuring $\chi(T_c^{-}) = \chi_N$ for continuous phase transitions. For $s$-wave pairing, a Zeeman field reduces $T_c$, whereas Rashba SOC preserves $T_c$ but yields a residual zero temperature spin susceptibility $\chi(0)$ which approaches $2\chi_N/3$ in the strong SOC limit; combined fields create a Bogoliubov Fermi surface, resulting in a kink in $\chi(0)$. In contrast, $p$-wave states exhibit strong anisotropy: OSP states mimic spin-singlet pairing behavior for parallel Zeeman fields and ESP for transverse ones, while ESP states show the opposite, with Rashba SOC potentially changing the quasiparticle nodal structure, lowering $T_c$, or causing $\chi_{zz}(0)$ divergences. This framework offers quantitative benchmarks for Knight-shift experiments in non-centrosymmetric superconductors like A$_2$Cr$_3$As$_3$ (A = Na, K, Rb, and Cs), enabling diagnostics to disentangle pairing symmetry, SOC strength, and Zeeman effects.

cond-mat.supr-con↗

arXiv2024

Interplay of Zeeman field, Rashba spin-orbit interaction, and superconductivity: Transition temperature and quasiparticle excitation

This paper provides a theoretical analysis on the effects of an external Zeeman field and Rashba spin-orbit interactions on superconductivity. We have extensively studied their influence on the superconducting transition temperature $T_c$ and the quasiparticle excitation energy. Our investigation includes a detailed examination of both the $s$-wave and $p$-wave pairing states. Implications for the recently discovered family of superconductors, A$_2$Cr$_3$As$_3$ (A = Na, K, Rb and Cs), as well as the validation of our theory have been discussed.

cond-mat.supr-con↗

arXiv2024

Understanding the Role of Pathways in a Deep Neural Network

Deep neural networks have demonstrated superior performance in artificial intelligence applications, but the opaqueness of their inner working mechanism is one major drawback in their application. The prevailing unit-based interpretation is a statistical observation of stimulus-response data, which fails to show a detailed internal process of inherent mechanisms of neural networks. In this work, we analyze a convolutional neural network (CNN) trained in the classification task and present an algorithm to extract the diffusion pathways of individual pixels to identify the locations of pixels in an input image associated with object classes. The pathways allow us to test the causal components which are important for classification and the pathway-based representations are clearly distinguishable between categories. We find that the few largest pathways of an individual pixel from an image tend to cross the feature maps in each layer that is important for classification. And the large pathways of images of the same category are more consistent in their trends than those of different categories. We also apply the pathways to understanding adversarial attacks, object completion, and movement perception. Further, the total number of pathways on feature maps in all layers can clearly discriminate the original, deformed, and target samples.

cs.CV↗

arXiv2023

Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks

For pursuing accurate skeleton-based action recognition, most prior methods use the strategy of combining Graph Convolution Networks (GCNs) with attention-based methods in a serial way. However, they regard the human skeleton as a complete graph, resulting in less variations between different actions (e.g., the connection between the elbow and head in action ``clapping hands''). For this, we propose a novel Contrastive GCN-Transformer Network (ConGT) which fuses the spatial and temporal modules in a parallel way. The ConGT involves two parallel streams: Spatial-Temporal Graph Convolution stream (STG) and Spatial-Temporal Transformer stream (STT). The STG is designed to obtain action representations maintaining the natural topology structure of the human skeleton. The STT is devised to acquire action representations containing the global relationships among joints. Since the action representations produced from these two streams contain different characteristics, and each of them knows little information of the other, we introduce the contrastive learning paradigm to guide their output representations of the same sample to be as close as possible in a self-supervised manner. Through the contrastive learning, they can learn information from each other to enrich the action features by maximizing the mutual information between the two types of action representations. To further improve action recognition accuracy, we introduce the Cyclical Focal Loss (CFL) which can focus on confident training samples in early training epochs, with an increasing focus on hard samples during the middle epochs. We conduct experiments on three benchmark datasets, which demonstrate that our model achieves state-of-the-art performance in action recognition.

cs.CV↗