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Radio_Telescope_Signal_Processing

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By Alireza Poorhassan Astane
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Radio_Telescope_Signal_Processing

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Signal Processing for Radio Telescope Project

This project, completed under the supervision of Dr. Reza Rezaee at Sharif University of Technology, simulates a real-time signal processing pipeline for multiple radio telescopes. The system:

  • Accepts real-time data streams from telescopes.
  • Calculates phase differences between signals.
  • Tunes and overlaps the signals.
  • Computes the Fourier transform to generate a sky image.
  • Identifies optimal positions for multiple radio telescopes.

Repository Structure

  • C Sources:
    • process.c: Reads signal data, performs Hilbert transform (hilbertTransform).
    • client4.c & client5.c: Implement client routines for receiving data, performing FFT using AVX instructions (see fft_avx.c and fft_basic.c) and computing phase differences.
    • argmax.c & cumsum.c: Utility functions.
    • saver.c, server2.c, server2b.c: Additional signal processing and networking routines.
  • Notebooks:
    • hilbert.ipynb & methods.ipynb: Demonstrate signal processing methods (Hilbert transform, FFT, cross-correlation). These notebooks also illustrate the use of Torch for GPU-accelerated tensor computations and Numba to optimize numerical Python code.
  • Build System:
    • The build/ directory contains CMake configuration files.
    • The provided Makefile (see below) compiles the core C executables.
  • Libraries & Figures:
    • libtfhe/ and tfhe/: Contain supporting libraries.
    • figs/: Contains generated plots and images.

Tools & Technologies

  • AVX:
    Many computationally intensive routines (like in fft_avx.c and client4.c) use AVX instructions for SIMD parallelism to accelerate vectorized math operations.

  • Torch:
    Used in Python notebooks (methods.ipynb) for fast, GPU-accelerated mathematical computing and simulations (e.g., computing sine functions on large tensors).

  • Numba:
    Applied to speed up Python numerical functions with just-in-time compilation, reducing runtime in signal processing algorithms.

How to Use This Repository

  1. Building the C Executables:

    Open a terminal in the project root and run:

    ```sh make

GitHub, Repositories
github radio_telescope_signal_processing
This post is licensed under CC BY 4.0 by the author.