arXiv subjects

Geonhee Lee

Publications and source records attributed to Geonhee Lee.

3 recordsLinked to original sources

arXiv2026

Efficient Solvers for Coupling-Aware Beamforming in Continuous Aperture Arrays

In continuous aperture arrays (CAPAs), careful consideration of the underlying physics is essential, among which electromagnetic (EM) mutual coupling plays a critical role in beamforming performance. Building on a physically consistent mutual coupling model, the beamforming design is formulated as a functional optimization whose optimality condition leads to a Fredholm integral equation. The incorporation of the coupling model, however, substantially increases computational complexity, necessitating efficient and accurate integral equation solvers. In this letter, we propose two efficient solvers: 1) a coordinate-transformation-based kernel approximation that preserves the operator structure while alleviating discretization demands, and 2) a direct lower-upper (LU)-based solver that stably handles the Nystr\"om-discretized system. Numerical results demonstrate improved accuracy and reduced computational overhead compared to conventional methods, with the LU-based solver emerging as an efficient and scalable solution for large-scale CAPA optimization via offline factorization.

cs.IT↗

arXiv2025

SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation

Accurate instrument pose estimation is a crucial step towards the future of robotic surgery, enabling applications such as autonomous surgical task execution. Vision-based methods for surgical instrument pose estimation provide a practical approach to tool tracking, but they often require markers to be attached to the instruments. Recently, more research has focused on the development of marker-less methods based on deep learning. However, acquiring realistic surgical data, with ground truth instrument poses, required for deep learning training, is challenging. To address the issues in surgical instrument pose estimation, we introduce the Surgical Robot Instrument Pose Estimation (SurgRIPE) challenge, hosted at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. The objectives of this challenge are: (1) to provide the surgical vision community with realistic surgical video data paired with ground truth instrument poses, and (2) to establish a benchmark for evaluating markerless pose estimation methods. The challenge led to the development of several novel algorithms that showcased improved accuracy and robustness over existing methods. The performance evaluation study on the SurgRIPE dataset highlights the potential of these advanced algorithms to be integrated into robotic surgery systems, paving the way for more precise and autonomous surgical procedures. The SurgRIPE challenge has successfully established a new benchmark for the field, encouraging further research and development in surgical robot instrument pose estimation.

cs.CV↗

arXiv2020

Normalized Convolutional Neural Network

We introduce a Normalized Convolutional Neural Layer, a novel approach to normalization in convolutional networks. Unlike conventional methods, this layer normalizes the rows of the im2col matrix during convolution, making it inherently adaptive to sliced inputs and better aligned with kernel structures. This distinctive approach differentiates it from standard normalization techniques and prevents direct integration into existing deep learning frameworks optimized for traditional convolution operations. Our method has a universal property, making it applicable to any deep learning task involving convolutional layers. By inherently normalizing within the convolution process, it serves as a convolutional adaptation of Self-Normalizing Networks, maintaining their core principles without requiring additional normalization layers. Notably, in micro-batch training scenarios, it consistently outperforms other batch-independent normalization methods. This performance boost arises from standardizing the rows of the im2col matrix, which theoretically leads to a smoother loss gradient and improved training stability.

cs.CV↗