The DSP block is a system built for reconstructing the signal. The device is distributed to three data paths  the pseudo inverse of the frame matrix, the multiplication with the samples (real time reconstruction) and the support change detector. This is a customized unit for the Sub Nyquist sampling system which starts with the Wideband Analog Converter".
Project tag: Nyquist


The project is part of the SubNyquist projects group. This project consists of two parts: a) A suggestion for an optimized implementation of the original system  compressing the system onto 2 FPGAs instead of 3 b) A VHDL & MATLAB simulation of the whole system

The projectâ€™s goal is to integrate a subsystem that would convert the incoming analog samples to digital signals. Then they will be processed and reconstructed in the subNyquist Sampling system.

The goal of this project is to design a digital architecture for the Sub Nyquist algorithm implementation according to given spec and also to implement debug environment for each of its components so they could be integrated to the total architecture system

Multiband Reconstruction Hardware on FPGA. Implementation in hardware of the CTF  Support Recovery module.

The first block of the digital part of the SubNyquist project. Takes 4 channels from the A2D which are sampled at high frequency (60[MHz]) and outputs 12 channels to the rest of the blocks at a lower sampling frequency (20[MHz]). The main purpose of this digital block is to use less analog hardware and to minimize costs.

The project is part of the SubNyquist sampling and reconstruction card. Our goal was to implement DSP unit on FlexRio FPGA card under NI LabView environment, it includes integration to the full system (NI Chassis with 3 FlexRio FPGA cards).

For decades, radar sampling was constrained to the Nyquist theorem. Recently, new research has provided techniques to sample shorttime pulses in subNyquist rates, and to reconstruct them in efficient robust ways. Our project studies the existing techniques and further improves them to achieve both noise robustness and estimation accuracy.

Finding optimal Mixing sequences for effective signal reconstruction. Finding the characteristics of those sequences

Expander implementation using filter banks

Implementation of the Cyclostationary feature detection Algorithm.