Parameter estimation of multiband signals from sub-Nyquist samples
The MWC efficiently samples and reconstructs sparse wideband, or multiband, signals below the Nyquist rate,without a priori knowledge of their support. However, in some applications, the transmissions composing the wideband signal are known to belong to certain function families, such as CW, pulses with known shapes,chirps… The unknown parameters are the carrier frequencies, delays, amplitudes, scaling factors, symbol rates…
In this project, our goal is to incorporate this a priori knowledge and derive a sampling scheme that allows recovering the unknown parameters while reducing the sampling rate and sensing time.
Rotem Turjeman & Inbal Fleischer
Image deconvolution In fluorescence microscopy - Part 2
Super resolution fluorescence microscopy techniques are an ensemble of light-microscopy techniques which achieve spatial resolution beyond the limitations imposed by the diffraction of light. On the other hand, since its introduction in 1983, deconvolution microscopy has become a key processing tool for the visualization of cellular structures of fixed and living specimens in three dimensions and at sub-resolution scale.
Deconvolution is also referred to as an inverse problem, since given the output of the system we aim at recovering the input to the system.
In the proposed project we will extend deconvolution techniques to image de-blurring in microscopy. We will exploit the sparsity prior in acquired images to improve the results.
Compressed Channel Estimation for Millimeter Wave MIMO system
In the race toward increasing data rates at cellular networks, mmWave MIMO (Multiple Input Multiple Output) systems are considered a leading candidate for 5G standard, the next generation of cellular technology.
Using mmWave offers multiple advantages, such as channel bandwidths far greater than previously available and larger antenna arrays, but also arouse new difficulties such as expensive RF chains, and massive amount of data need to be processed digitally.
This difficulties raise the need to come up with a new solution that will consider both the analog and digital domains.
To reduce the necessary amount of expensive RF chains we aim to exploit the sparse mmWave channel model.
The goal of the project is to develop a new sensing scheme for the mmWave model along with an algorithm for the channel estimation.
The project will include research next to matlab implementation
Required background: Introduction to Digital Signal Processing (044198)
Mordov Shai & Rivka Emanuel
High Spatial Resolution Radar
In Collaboration with: Mafat - המינהל למחקר, פיתוח אמצעי לחימה ותשתית טכנולוגית
A radar transmits electromagnetic waves in very short pulses and measures the returned power, time lag and frequency of em waves backscattered from targets as the pulse travels away from the radar. From the properties of the backscattered signal, one may obtain information such as location, velocity and size of the radar target. One of the advanced radar antennas in use today is phased array, which consists of several radiating elements and phase shifters. The radiating beam pattern of a phased array is highly directional and allows for agile scanning of target scene. The spatial resolution is determined by the aperture of the array which determines the number of elements needed to prevent spatial aliasing. The more the number of elements, better would be spatial resolution.
The goal of this project is to break the traditional link between the spatial resolution and the number of elements. We will use time division approach in order to radiate on the targets using a sparse sub-array for each transmission. By fusing all the received data and integrating a new processing algorithm, we would try to achieve a better effective spatial resolution with reduced number of elements.
Supervisor: Kumar Vijay Mishra (firstname.lastname@example.org) and David Cohen (email@example.com)
Required background: Signal and systems, Mavlas , Random signals (optional)
Itay Kahane & Aviad Kaufmann
Ultrafast Sub-Nyquist Tissue Doppler Ultrasound System
Tissue Doppler ultrasound imaging (TDI) enables the estimation of cardiac function by transmitting streams of pulses in a certain direction and estimating the velocity of the tissue from the phase shifts of the returning echoes. In order to estimate the velocity of the tissue precisely and separate slow tissue movement from dominant clutter, a large number of pulses has to be transmitted in the same direction. TDI has two main limitations: First, the number of transmitted pulses per unit of time is limited by the speed of sound in tissue and the desired imaging depth, therefore there is an inherent tradeoff between spectral and spatial resolution. This limitation impedes TDI usage to a few measurements through the LV wall. Second, in current systems the echoes detected by transducer elements are sampled at high rates of 3-4 times beyond their Nyquist rate, and processed to create a focused reception along a beam. A recent trend in ultrasound imaging is the transmission of unfocused beams (diverging waves) that enable the simultaneous scan of entire sectors. This shift in paradigm increases the frame rate significantly.Xampling and Compressed Sensing methods developed at SAMPL, allows the reconstruction of signals sampled at a sub-Nyquist rate with reduced number of pulses per velocity estimation, using priors on the sparsity of the signal. This reduced sapling is performed without compromising the same time temporal and spatial resolutions.In this project the implementation of the sub-Nyquist TDI demo system will be extended to include transmission of diverging waves. The system developed in this project will pave the way for quantitative cardiac imaging. In this project you will become familiar with Xampling and Compressed Sensing theory and Matlab simulations. In addition, you will gain experience in implementing signal processing algorithms on real ultrasound systems.
Ido Cohen and Shai Yagil