Effat Sahragard (20570917), Hassan Farsi (20570920), Sajad Mohamadzadeh (20570923) · Figshare · 2025
<div> This paper presents a novel method for improving semantic segmentation performance in computer vision tasks. Our approach utilizes an enhanced UNet architecture that leverages an improved ResNet50 backbone. We replace the last layer of ResNet50 with deformable convolution to enhance feature representation. Additionally, we incorporate an attention mechanism, specifically ECA-ASPP (Attention Spatial Pyramid Pooling), in the encoding path of UNet to capture multi-scale contextual information effectively. In the decoding path of UNet, we explore the use of attention mechanisms after concatenating low-level features with high-level features. Specifically, we investigate two types of attention mechanisms: ECA (Efficient Channel Attention) and LKA (Large Kernel Attention). Our experiments demonstrate that incorporating attention after concatenation improves segmentation accuracy. Furthermore, we compare the performance of ECA and LKA modules in the decoder path. The results indicate that the LKA module outperforms the ECA module. This finding highlights the importance of exploring different attention mechanisms and their impact on segmentation performance. To evaluate the effectiveness of the proposed method, we conduct experiments on benchmark datasets, including Stanford and Cityscapes, as well as the newly introduced WildPASS and DensPASS datasets. Based on our experiments, the proposed method achieved state-of-the-art results including mIoU 85.79 and 82.25 for the Stanford dataset, and the Cityscapes dataset, respectively. The results demonstrate that our proposed method performs well on these datasets, achieving state-of-the-art results with high segmentation accuracy. </div>
Cited by 0Open access
Santilli, Ciro · Zenodo (CERN European Organization for Nuclear Research) · 2019
The perfect emulation setup to study and develop the Linux kernel v5.1, kernel modules, QEMU, gem5 and x86_64, ARMv7 and ARMv8 userland and baremetal assembly, ANSI C, C++ and POSIX C. GDB step debug and KGDB just work. Powered by Buildroot and crosstool-NG. Highly automated. Thoroughly documented. Automated tests. "Tested" in an Ubuntu 18.04 host.
Cited by 0Open access
Ciro Santilli · Figshare · 2019
The perfect emulation setup to study and develop the Linux kernel v5.1, kernel modules, QEMU, gem5 and x86_64, ARMv7 and ARMv8 userland and baremetal assembly, ANSI C, C++ and POSIX C. GDB step debug and KGDB just work. Powered by Buildroot and crosstool-NG. Highly automated. Thoroughly documented. Automated tests. "Tested" in an Ubuntu 18.04 host.
Cited by 0Open access
Ori Pomerantz · 2000
From the Publisher:
So, you want to write a kernel module. You know C, you've written a number of normal programs to run as processes, and now you want to get to where the real action is, to where a single wild pointer can wipe out your file system and a core dump means a reboot.
Welcome to the club.
Linux Kernel Module Programming Guide is for people who want to write kernel modules. It takes a hands-on approach starting with writing a small hello, world program, and quickly moves from there. Far from a boring text on programming, Linux Kernel Module Programming Guide has a lively style that entertains while it educates.
Linux Kernel Module Programming Guide is one of the key documents of the Linux Documentation Project, an online compilation of freely distributed manuals and how-to guides on the popular Linux operating system. This book is a printed version of the online documentation, and is provided as a convenience for those who prefer printed manuals.
iUniverse.com Computer Books are composed of freely available online computer manuals, industry standard specifications, and electronically distributed computer-programming guides. They include open documents' collections of industry specifications and standards and a collection of the best open source documentation on the Web...the most complete open source library available anywhere.
Each book contains the complete, unabridged text of the original document. The on-demand publishing process gives you the actual text of the online document in a convenient, inexpensive, easy-to-use format.
And remember...iUniverse.com donates a portion of the profits from open document and open source books to support the mission of the free software community.
Cited by 80
Anonymous Anonymous · Zenodo (CERN European Organization for Nuclear Research) · 2026
Kernel module to protect against DNS injection attacks by limiting the amount of DNS replies that will be accepted with an incorrect transaction ID.
Cited by 0Open access
Nir Tzachar · 2003
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Anon · Zenodo (CERN European Organization for Nuclear Research) · 2025
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Anon · Zenodo (CERN European Organization for Nuclear Research) · 2025
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Anonymous Anonymous · Zenodo (CERN European Organization for Nuclear Research) · 2026
Kernel module to protect against DNS injection attacks by limiting the amount of DNS replies that will be accepted with an incorrect transaction ID.
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Joyce, Russell · Zenodo (CERN European Organization for Nuclear Research) · 2018
CharIO kernel module and related work
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Sujay Yadalam · Zenodo (CERN European Organization for Nuclear Research) · 2023
This repo and archive contains BypassD's kernel components: changes to the Linux kernel and a kernel module. BypassD was implemented on Linux v5.4. The entire kernel is included in this repo. This has been tested on a CloudLab instance.
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Denis Efremov, Ilya Shchepetkov · Lecture notes in computer science · 2020
Cited by 3Open access
B.L. Broadhead · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1990
QADS is a multidimensional point kernel computer code that utilizes the simplified free-form input of the SCALE system as well as compatibility with ORIGEN-S produced sources, SCALE cross section libraries, and standard composition data sets. QADS consists of a preprocessor that takes the free-form input and prepares input for the widely available QAD-CGGP code, which is then automatically executed by a driver module. This report describes the point kernel theory briefly, followed by numerous tips on successfully applying the theory to various types of shielding problems. The remainder of the document is devoted to input and output descriptions of the QADS code with several illustrative sample problems. 4 refs., 5 figs., 7 tabs.
Cited by 5Open access
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2017
Modern processors offer a wide range of control and measurement features. While those are traditionally accessed through libraries like PAPI, some newer features no longer follow the traditional model of counters that can be used to only read the state of the processor. For example, Precise Event Based Sampling (PEBS) can generate records that requires a kernel memory for storage. Additionally, new features like power capping and thermal control require similar new access methods. All of these features are ultimately controlled through Model Specific Registers (MSRs). We therefore need new mechanisms to make such features available to tools and ultimately to the user. libMSR provides a convenient interface to access MSRs and to allow tools to utilize their full functionality.
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Miaoqing Shen (383257), Corey D. Broeckling (206739), Elly Yiyi Chu (383258) et al. · Figshare · 2016
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Heiko Grimberg · Zenodo (CERN European Organization for Nuclear Research) · 2025
UCF v7.9.5 Universal Logic Kernel (Computational Module) 2. Resource type Software 3. Abstract / Description This software is the official Python implementation of the Unified Chronofractal Field (UCF) theory (v7.9.5). It serves as a computational audit protocol to verify the zero-parameter ($k=0$) predictive power of the theory across three distinct physical regimes: Quantum (Micro): Calculation of the Muon g-2 anomaly ($\Delta a_\mu$) via fractal vacuum damping, yielding a result of $\approx 2.64 \times 10^{-9}$ based on the geometric invariant $\nu$. Cosmological (Macro): Prediction of the Dark Energy Equation of State ($w_0$) for the 2026 Euclid mission, derived as $w_0 = -1 + \nu/3$. Galactic (Meso): Resolution of galactic rotation curves without Dark Matter, utilizing a dynamic topological shape factor ($\xi$). Included is a stress-test against the massive elliptical galaxy M87 and the spiral NGC 2683. The kernel is designed for transparency and reproducibility, providing an "Integrity Hash" (Target: 4.366) to ensure the logical consistency of the geometric invariants used.
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Ross Snider · 2022
Cited by 0
Heiko Grimberg · Zenodo (CERN European Organization for Nuclear Research) · 2025
UCF v7.9.5 Universal Logic Kernel (Computational Module) 2. Resource type Software 3. Abstract / Description This software is the official Python implementation of the Unified Chronofractal Field (UCF) theory (v7.9.5). It serves as a computational audit protocol to verify the zero-parameter ($k=0$) predictive power of the theory across three distinct physical regimes: Quantum (Micro): Calculation of the Muon g-2 anomaly ($\Delta a_\mu$) via fractal vacuum damping, yielding a result of $\approx 2.64 \times 10^{-9}$ based on the geometric invariant $\nu$. Cosmological (Macro): Prediction of the Dark Energy Equation of State ($w_0$) for the 2026 Euclid mission, derived as $w_0 = -1 + \nu/3$. Galactic (Meso): Resolution of galactic rotation curves without Dark Matter, utilizing a dynamic topological shape factor ($\xi$). Included is a stress-test against the massive elliptical galaxy M87 and the spiral NGC 2683. The kernel is designed for transparency and reproducibility, providing an "Integrity Hash" (Target: 4.366) to ensure the logical consistency of the geometric invariants used.
Cited by 0Open access
Andrew J. Hutton, Stephanie Donovan, Linux Symposium et al. · 2004
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Yuanyuan Wang (20181), Tongtong Yin (14758525), Xiuchuan Chen (22146211) et al. · Figshare · 2025
FFT and IFFT represent the Fast Fourier Transform and its inverse, respectively.
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