Chengshuo Zhang | Searcharxiv

arXiv2026

CTPX1: A Highly Integrated and High-Throughput Data-Driven Camera Based on Timepix4

The upgrade of the China Spallation Neutron Source (CSNS-II) will raise the proton beam power to 500 kW. Consequently, the existing Timepix3-based detector systems, limited to a count rate of 80 Mhits/s, will encounter severe saturation challenges. To address the demand of the Energy-Resolved Neutron Imaging instrument (ERNI) for next-generation higher count-rate electronics, this paper presents CTPX1, a high-performance data-driven camera system based on the Timepix4 ASIC. The system adopts a compact modular architecture, integrating readout electronics, a precision high-voltage bias unit, and a TEC temperature control subsystem. To fully exploit the readout potential of the Timepix4 ASIC's 16 high-speed serial links, this paper proposes a two-stage parallel processing architecture. This architecture achieves real-time data aggregation with a total bandwidth of up to 81.92 Gbps. Over a continuous 12-hour operation period, temperature fluctuations were kept within 0.1 {\deg}C while the high-voltage output noise remained below 1 mV. High-flux X-ray testing indicates that the system achieves a peak event readout rate of 1.17 Ghits/s, approaching the limit of the configured link speed. In-beam neutron verification at CSNS confirms that the camera successfully resolves fine spatial structures, achieving an imaging performance consistent with the 55 {\mu}m pixel pitch of the sensor. Furthermore, the clear observation of spectral features in the Time of flight (TOF) spectrum of a {\gamma}-Fe sample validates the system's good time resolution. This camera effectively addresses the data readout saturation challenges, validates the feasibility of Timepix4 technology for neutron imaging, and provides a viable solution for next-generation high-performance neutron imaging instruments.

physics.ins-det↗

arXiv2025

Personalized Federated Dictionary Learning for Modeling Heterogeneity in Multi-site fMRI Data

Data privacy constraints pose significant challenges for large-scale neuroimaging analysis, especially in multi-site functional magnetic resonance imaging (fMRI) studies, where site-specific heterogeneity leads to non-independent and identically distributed (non-IID) data. These factors hinder the development of generalizable models. To address these challenges, we propose Personalized Federated Dictionary Learning (PFedDL), a novel federated learning framework that enables collaborative modeling across sites without sharing raw data. PFedDL performs independent dictionary learning at each site, decomposing each site-specific dictionary into a shared global component and a personalized local component. The global atoms are updated via federated aggregation to promote cross-site consistency, while the local atoms are refined independently to capture site-specific variability, thereby enhancing downstream analysis. Experiments on the ABIDE dataset demonstrate that PFedDL outperforms existing methods in accuracy and robustness across non-IID datasets.

cs.LG↗