Tootfinder

A Fast Parallel Median Filtering Algorithm Using Hierarchical Tiling
Louis Sugy (NVIDIA)
https://arxiv.org/abs/2507.19926 https://arxiv.org/pdf/2507.19926

A Fast Parallel Median Filtering Algorithm Using Hierarchical Tiling
Median filtering is a non-linear smoothing technique widely used in digital image processing to remove noise while retaining sharp edges. It is particularly well suited to removing outliers (impulse noise) or granular artifacts (speckle noise). However, the high computational cost of median filtering can be prohibitive. Sorting-based algorithms excel with small kernels but scale poorly with increasing kernel diameter, in contrast to constant-time methods characterized by higher constant factors…

A Novel Two-Dimensional Smoothing Algorithm
Xufeng Chen, Liang Yan, Xiaoshan Gao
https://arxiv.org/abs/2507.15301 https://arxiv.org/p…

A Novel Two-Dimensional Smoothing Algorithm
Smoothing and filtering two-dimensional sequences are fundamental tasks in fields such as computer vision. Conventional filtering algorithms often rely on the selection of the filtering window, limiting their applicability in certain scenarios. To this end, we propose a novel Two-Dimensional Smoothing (TDS) algorithm for the smoothing and filtering problem of two-dimensional sequences. Typically, the TDS algorithm does not require assumptions about the type of noise distribution. It is simple a…

Robust Probability Hypothesis Density Filtering: Theory and Algorithms
Ming Lei, Shufan Wu
https://arxiv.org/abs/2507.13687 https://a…

Robust Probability Hypothesis Density Filtering: Theory and Algorithms
Multi-target tracking (MTT) serves as a cornerstone technology in information fusion, yet faces significant challenges in robustness and efficiency when dealing with model uncertainties, clutter interference, and target interactions. Conventional approaches like Gaussian Mixture PHD (GM-PHD) and Cardinalized PHD (CPHD) filters suffer from inherent limitations including combinatorial explosion, sensitivity to birth/death process parameters, and numerical instability. This study proposes an innov…

SONIC: Sound Optimization for Noise In Crowds
Pranav M N, Gandham Sai Santhosh, Tejas Joshi, S Sriniketh Desikan, Eswar Gupta
https://arxiv.org/abs/2506.13272

SONIC: Sound Optimization for Noise In Crowds
This paper presents SONIC, an embedded real-time noise suppression system implemented on the ARM Cortex-M7-based STM32H753ZI microcontroller. Using adaptive filtering (LMS), the system improves speech intelligibility in noisy environments. SONIC focuses on a novel approach to noise suppression in audio signals, specifically addressing the limitations of traditional Active Noise Cancellation (ANC) systems. The paper explores various signal processing algorithms in a micro-controller point of vie…

Quantum Search for Gravitational Wave of Massive Black Hole Binaries
Fangzhou Guo, Jibo He
https://arxiv.org/abs/2505.24459 https://a…

Quantum Search for Gravitational Wave of Massive Black Hole Binaries
Matched filtering is a common method for detecting gravitational waves. However, the computational costs of searching large template banks limit the efficiency of classical algorithms when searching for massive black hole binary (MBHB) systems. In this work, a quantum matched filtering algorithm based on Grover's algorithm is applied to the MBHB signals. It is demonstrated that the quantum approach can reduce the computational complexity from $O(N)$ to $O(\sqrt{N})$ theoretically, where $N$ is …

Performance Analysis of Linear Detection under Noise-Dependent Fast-Fading Channels
Almutasem Bellah Enad, Jihad Fahs, Hadi Sarieddeen, Hakim Jemaa, Tareq Y. Al-Naffouri
https://arxiv.org/abs/2507.05897

Performance Analysis of Linear Detection under Noise-Dependent Fast-Fading Channels
This paper presents a performance analysis framework for linear detection in fast-fading channels with possibly correlated channel and noise. The framework is both accurate and adaptable, making it well-suited for analyzing a wide range of channel and noise models. As such, it serves as a valuable tool for the design and evaluation of detection algorithms in next-generation wireless communication systems. By characterizing the distribution of the effective noise after zero-forcing filtering, we…

Yau-YauAL: A computer tool for solving nonlinear filtering problems
Yu Wang, Shuyuan Xu, Xueda Wei, Xinrui Luo, Stephen Shing-Toung Yau, Shing-Tung Yau, Rongling Wu
https://arxiv.org/abs/2506.08976

Yau-YauAL: A computer tool for solving nonlinear filtering problems
The Yau-Yau nonlinear filter has increasingly emerged as a powerful tool to study stochastic complex systems. To leverage it to a wider spectrum of application scenarios, we pack the Yau-Yau filtering ALgorithms (YauYauAL) into a package of computer software. Yau-YauAL was written in R, designed to simplify the implementation of the Yau-Yau filter for solving nonlinear filtering problems. Combining R's accessibility with C++ (via Rcpp) for computational efficiency, YauYauAL provides an intuitiv…

Skipper: Maximal Matching with a Single Pass over Edges
Mohsen Koohi Esfahani
https://arxiv.org/abs/2507.04420 https://arxiv.org/pdf/…

Skipper: Maximal Matching with a Single Pass over Edges
Maximal Matching (MM) is a fundamental graph problem with diverse applications. However, state-of-the-art parallel MM algorithms are limited by their need to process graph edges repeatedly over multiple iterations. Furthermore, optimized algorithms often require additional memory for graph contraction or edge filtering. In this paper, we introduce Skipper, an incremental asynchronous MM algorithm that (i) processes each edge deterministically and only once, (ii) skips a large fraction of edges …