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@carlesfernandez carlesfernandez released this 24 Aug 10:54
· 80 commits to master since this release

Geniux Thud v21.08


New release of the Geniux Thud manifest. It contains a list of Git repositories and their respective commit hashes that allow building images and SDKs defined by the meta-gnss-sdr OpenEmbedded layer

The name Geniux comes from GNSS-SDR for Embedded GNU/Linux.

Geniux Thud v21.08 is a customized embedded Linux distribution based on the Yocto Project version 2.6.4 Main features:

  • Development tools: Automake v1.16.1, CMake v3.12.2, GCC v8.2.0 (+ libgfortran), make v4.2.1, ninja v1.8.2, Python v2.7.16 and v3.5.6.
  • Goodies for signal processing:
    • SDR framework: GNU Radio v3.8.0+.
    • Number crunching libraries: Armadillo v10.5.0, FFTW v3.3.8, Lapack v3.7.0, VOLK v2.2.1.
    • C++ supporting libraries: Boost v1.64.0, gflags v2.2.2, glog v0.5.0, googletest v1.11.0, Matio v1.5.21, Protocol Buffers v3.6.1, Pugixml v1.11.4.
    • Graphical representation: gnss-sdr-monitor v1.0, Gnuplot v5.2.2.
  • Software drivers and tools for RF front-ends: UHD v3.13.0.1 (+ gr-uhd), libiio v2019_R2, libad9361-iio v2019_R2, iio-oscilloscope v2019_R2.
  • GNSS-SDR v0.0.15

More info at

Changes with respect to Geniux Thud 21.06

  • Updated meta-gnss-sdr layer.
  • Add meta-intel layer.
  • Point meta-adi layer to the 2019_R2 branch.
  • GNSS-SDR updated to v0.0.15
  • Update glog to v0.5.0.
  • Customized splash screen.
  • Added a script for easier maintenance of the manifest.

How to build images and the SDK

With Docker already installed on your system, build the SDK and images for your preferred machine:

$ git clone
$ cd yocto-geniux
$ ./ thud 21.08 zedboard-zynq7

The generated yocto-geniux image also provides an interactive mode that allows users to make changes, experiment, fine-tune, and generate their own custom images and SDKs according to their specific requirements in a virtualized environment. Please check the file of that repository for instructions.

Copyright and License

Copyright: © 2016-2021 Carles Fernández-Prades, CTTC. All rights reserved.

The content of this repository is released under the MIT license.


This work was partially supported by the Spanish Ministry of Science, Innovation, and Universities through the Statistical Learning and Inference for Large Dimensional Communication Systems (ARISTIDES, RTI2018-099722-B-I00) project.