Projects
Completed
Spring 2015
Communication
Project Title:

Collaborative Spectrum Sensing from Sub-Nyquist Samples for Cognitive Radios

Project ID:

2666

Abstract:

Project goal: examine, implement and test two (or even three) algorithms for efficient (centralized) support recovery from several cognitive radios' sampled data. The innovation of this project is using the collaborative properties of the data from each CR in order to maximize the efficiency (success rate).

Description:

PDR

Supervisor(s):

Student(s):

Alon Akiva, Barak Avraham

Semester:

Spring 2015

Project Title:

Sub Space Learning with Partial Information

Project ID:

2693

Abstract:

In this project the students will have the opportunity to be part of an active research in the fields of machine learning and compressed sensing. This project will focus on the sample complexity of subspace learning in a partial information setting, in which the learner can only observe r attributes from each instance vector. Under this setting we will learn and implement recently proposed state of the art algorithms work with real data and perform simulations.

Description:

PDR

Supervisor(s):

Student(s):

Semester:

Spring 2015

Project Title:

Increased sampling capacity in optical communication

Project ID:

2705

Abstract:

In optical communication, signals are transmitted at a very high rate (here, we consider 20G) but only one bit per interval. A simple sampler can then be used that only needs one bit per interval. The question is whether we can use MWC ideas in order to allow for more bits.
The goal of this project is to determine whether we can achieve gain in term of the information bits using the MWC.

Description:

PDR

Supervisor(s):

Student(s):

Omri Lev, Tal Wiener

Semester:

Spring 2015

Radar
Project Title:

Synthetic Aperture Radar simulator

Project ID:

2591

Abstract:

Synthetic Aperture Radar (SAR) is a well-proven radar imaging technology that is capable of producing high-resolution images of stationary surface targets and terrain. The main advantages of SAR are its ability to operate at night and in adverse weather conditions, hence overcoming limitations of both optical and infrared systems.
The goal of the project is to develop a Synthetic Aperture Radar simulator which enables to take radar process those signals into an image by advanced signal processing techniques.

Description:

PDR

Supervisor(s):

Student(s):

Yoav Charchamovitz

Semester:

Spring 2015

Project Title:

Sub-Nyquist MIMO Radar - Part I

Project ID:

2366

Abstract:

The goal of this project is to exploit the sparsity of the reflected signal using smart samples. In this project we will simulate the model for MIMO radar signal and try extracting the desired data by signal processing algorithm that has been developed at SAMPL Lab.
We hope to succeed reducing the sampling rate in the temporal and spatial domain and thus violating the sacred Nyquist-rate.

Description:

PDR

Supervisor(s):

Student(s):

Ilan Maniron, Yanai Yaffa

Semester:

Spring 2015

Project Title:

Sub-Nyquist MIMO Radar - Part II

Project ID:

2674

Abstract:

The goal of this project is to develop Sub-Nyquist MIMO radar algorithm that breaks the traditional link between sampling rate and resolution while reducing the number of elements as well as the sampling rate. Our algorithm takes advantage of the sparsity of the reflected signal using smart samples and thus significantly reduces the sampling rate in the temporal and spatial domain and violating the sacred Nyquist-rate. The proposed project expands ongoing research. In this project we will integrate Doppler estimation and thus provide a complete solution to the MIMO radar problem. The project aims at improving the current algorithm by promoting methods dealing with high dimensional data. We will also strive to cope with targets that do not fall on the grid as well as large dynamic range with robustness to noise.

Description:

PDR

Supervisor(s):

Student(s):

Ilan Menirom, Yanai Yafe

Semester:

Spring 2015

Project Title:

Staggered PRF in Pulse Doppler Radar Using Multi-Cosets – Part 2

Project ID:

2669

Abstract:

The goal of this project is to apply a communication framework to the problem of range ambiguity in pulse Doppler radar that would allow processing the measurements jointly, use an identical PRF and solving a known recovery problem.

Description:

PDR

Supervisor(s):

Student(s):

Lior Shani, Gal Winerich

Semester:

Spring 2015

Biomedical
Project Title:

Optimized Micro-Beamforming for Medical Ultrasound

Project ID:

2593

Abstract:

The goal of the project is to study and optimize the concept of micro-beamforming by adopting it to sub-Nyquist frequency domain beamforming approach developed recently in SAMPL. This will allow for reduction in number of required channels while retaining high image quality. Such a reduction is crucial for development of real-time 3D imaging the concept of wireless probes and remote processing. The performance of the developed approach will be verified on in-vivo data obtained by an actual imaging system.

Description:

PDR

Supervisor(s):

Student(s):

Daniel Jakubovitz, Atalya Alon

Semester:

Spring 2015

Project Title:

Sub-Nyquist Plane-Wave Imaging

Project ID:

Abstract:

In this project we aim to adopt algorithms for ultrasound signal reconstruction from the sub-Nyquist samples developed for standard imaging mode. The existing approach exploits the structure of the 1D image line. As a next step we aim to extend it and take into account the entire 2D image structure which is available in the plane-wave imaging mode.
The project is done in collaboration with Super Sonic Imaging (SSI), an innovative French company pioneered implementation of plane-wave imaging concept. The performance of sub-Nyquist processing will be tested on real data obtained by SSI imaging system in Matlab environment.

Description:

PDR

Supervisor(s):

Student(s):

Rotem Mulayof

Semester:

Spring 2015

Project Title:

Sparse Aperture for Plane-Wave Imaging

Project ID:

2594

Abstract:

The goal of the project is to develop an algorithm for the optimal choice of participating elements and verify its performance on real data. The data is provided by our collaborators from Super Sonic Imaging (SSI), an innovative French company pioneered implementation of plane-wave imaging concept.

Description:

PDR

Supervisor(s):

Student(s):

Kfir Goldberg, Dorin Drachsler

Semester:

Spring 2015

Project Title:

Minimal ECG data acquisition in cardiac patients

Project ID:

2664

Abstract:

In this project you will develop, simulate, trim and test a new compressed sensing method for ECG signals sampling and reconstruction, with application to future energy efficient pacemakers and defibrillators.

Description:

PDR

Supervisor(s):

Student(s):

Alon Ashkenasi, Tamar Loeub

Semester:

Spring 2015

Project Title:

Dictionary Reduction in Magnetic Resonance Fingerprinting

Project ID:

2598

Abstract:

In this project we will implement compressed sensing for MRF by significant reduction of the dictionary entries. This will bridge the gap between the theories of MRF and practical implementation and will result with MRI scanning speed-up, leading to less motion artifacts and less patient discomfort during acquisition. The project will be carried with the cooperation of Tel-Aviv medical center imaging unit.

Description:

PDR

Supervisor(s):

Student(s):

Aviya Maimon, Keren Cohen

Semester:

Spring 2015

Project Title:

Distributed Parallel Processing of Ultrasound Signals Sampled at Sub-Nyquist Rates - Part 2

Project ID:

2601

Abstract:

In this project the ultrasound system will be divided to three components. The first part simulates a hand-held ultrasonic transducer, capturing the signal and delivering low-rate, partial frequency domain data for processing. The second component is a server in a network cloud, applying sophisticated compressed sensing methods in order to reconstruct high quality image from the received low rate data. Since each line of the image is independent of the other, advanced parallel computing methods will be implemented in the server. The third component is an IPAD which will display the image.

In this project you will become familiar with Xampling and Compressed Sensing theory, Matlab simulations, C/C++ implementation, and parallel computing

Description:

PDR

Supervisor(s):

Student(s):

Amuel Londner

Semester:

Spring 2015

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