859 research outputs found

    Hog 2023.1: a collaborative management tool to handle Git-based HDL repository

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    Hog (HDL on Git) is an open-source tool designed to manage Git-based HDL repositories. It aims to simplify HDL project development, maintenance, and versioning by using Git to guarantee synthesis and implementation reproducibility and binary file traceability. This is ensured by linking each produced binary file to a specific Git commit, embedding the Git commit hash (SHA) into the binary file via HDL generics stored in firmware registers. Hog is released twice a year, in January and in June. We present here the latest stable version 2023.1, which introduces major novel features, such as the support for Microchip Libero IDE, and the capability to run the Hog Continuous Integration (Hog-CI) workflow with GitHub Actions. A plan to integrate Hog with the OpenCores repository is also described, which is expected to be completed for Hog release 2023.2Comment: Presented at the 3rd Workshop on Open-Source Design Automation (OSDA), 2023 (arXiv:2303.18024

    Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

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    Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð„with constraintsð ð ð„ „ ðandðŽð„ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

    Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks