Projects
Completed
Spring 2016
Communication
Project Title:

Parameter estimation of multiband signals from sub-Nyquist samples

Project ID:

3114

Abstract:

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.

Description:

PDR

Supervisor(s):

Student(s):

Rotem Turjeman & Inbal Fleischer

Semester:

Spring 2016

Project Title:

Image deconvolution In fluorescence microscopy - Part 2

Project ID:

3173

Abstract:

Project description:
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.

Description:

PDR

Supervisor(s):

Student(s):

Irina Akhvlediany

Semester:

Spring 2016

Project Title:

Application of ECG

Project ID:

3224

Abstract:

The use of ECG signal, which demonstrate the heart's electric activity, is common in many applications. In mobile applications such as pacemakers, there is a significant importance to save battery energy and hence to compress the signal and sub sampled it. In this project, we will continue the work that has been done in the lab for compressing the signal and sub sampling it, and we will realize different medical applications such as pain detector for a patient under the influence of anesthesia. The project is held with cooperation of Sheba hospital and with the assistance of the cardiologist Dr. Shai Tagmen Yarden. An ambitious and fun project, which focuses on introducing the ECG signal and the world of compressed sensing (CS), in the field of signal processing.
There is a possibility for a 2 semester project to those who will be interested.
For further details, please contact Gal Mazor: galmazor@campus.technion.ac.il

Description:

PDR

Supervisor(s):

Student(s):

Tomer Golany

Semester:

Spring 2016

Radar
Project Title:

Compressed Channel Estimation for Millimeter Wave MIMO system

Project ID:

3128

Abstract:

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)

Description:

PDR

Supervisor(s):

Student(s):

Mordov Shai & Rivka Emanuel

Semester:

Spring 2016

Project Title:

High Spatial Resolution Radar

Project ID:

3197

Abstract:

In Collaboration with: Mafat - המינהל למחקר, פיתוח אמצעי לחימה ותשתית טכנולוגית

Project description:
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 (mishra@ee.technion.ac.il) and David Cohen (davidcohenys@gmail.com)

Required background: Signal and systems, Mavlas , Random signals (optional)

Environment: Matlab

Description:

PDR

Supervisor(s):

Student(s):

Itay Kahane & Aviad Kaufmann

Semester:

Spring 2016

Biomedical
Project Title:

Ultrafast Sub-Nyquist Tissue Doppler Ultrasound System

Project ID:

3135

Abstract:

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.

Description:

PDR

Supervisor(s):

Student(s):

Ido Cohen and Shai Yagil

Semester:

Spring 2016

Project Title:

Parallel Imaging MRI

Project ID:

3211

Abstract:

Parallel Imaging MRI

Magnetic Resonance Imaging (MRI) is the method of choice for diagnosis, evaluation and follow-up of brain clinical pathologies. However, the acquisition of a routine brain MRI requires an average of 50 minutes per examination. To deal with this problem, there are in the modern scanners multiple acquisition coils. In each coil, some of the data is acquired partially or sparsely and then the entire image is reconstructed from all the coil based images. In this project we will learn, understand and realize this reconstruction method from data that has been taken from several coils in a real scanner. An ambitious and fun project which focuses on introducing the world of MRI and the popular parallel imaging field combined with compressed sensing (CS) methods, which leads to shorter scan time.
There is a possibility for a 2 semester project to those who will be interested.
For further details, please contact Gal Mazor: galmazor@campus.technion.ac.il

Description:

PDR

Supervisor(s):

Student(s):

Chechik Yonatan

Semester:

Spring 2016

Project Title:

Phase-Coherence for Medical Ultrasound

Project ID:

3221

Abstract:

Medical ultrasound is used for tissue visualization by radiating it with acoustic energy transmitted by an array of elements. The image quality is defined by the parameters of the array beampattern. The resolution is proportional to the main-lobe width and the contrast is defined by the side-lobes level.

In this project we aim to improve the above by applying phase coherence factor (PCF). This approach exploits the phase information at each transducer element to compute a weight factor per each pixel within an image. The factor is nearly one for the signal coming from the focal point and is close to zero for signals originating within the side and grating lobes.

The method will be first applied to real ultrasound data obtained in commercially used focused mode. Next it will be extended and applied to novel coherent phase compounding approach.

Description:

PDR

Supervisor(s):

Student(s):

Dan Cohen and Meged Shoham

Semester:

Spring 2016

Project Title:

Sub-Sampling of Functional CT

Project ID:

3243

Abstract:

Project description:
X-Ray Computer Tomography, known as CT, is one of the main tools Doctors use today to examine patients. In every main hospital, there are CT machines that work 24/7 to provide Doctors with in-depth views of the human body. CT Scans enable them to save lives on a daily basis.

In functional CT methodology the CT constantly acquires new data and updates the scan results. The output is a dynamic image of the human body. Functional CT allows us to view blood flow within the body, analyze dynamic organs, such as the heart, and observe other interesting temporal phenomena.

In this project the students will first study the basics of computer aided tomography, advanced mathematical imaging tools and cutting edge techniques in signal processing for sub-Nyquist sampling.

Today, functional CT isn't used at all in hospitals due to high radiation dosages involved. We will describe the model for functional CT, and design a clever way to sub-sample the scan. Our goal is to reduce radiation dosage and enable a widespread usage for functional CT applications.

• The project is performed in collaboration with researchers and radiologists from RAMBAM.

Description:

PDR

Supervisor(s):

Student(s):

Ben Finkelshtein & Tomer Zeagdone

Semester:

Spring 2016

Project Title:

Sub-Nyquist Ultrasound Doppler Imaging of Vascular Flow - Part 2

Project ID:

3225

Abstract:

Doppler ultrasound is a non-invasive and safe modality that is used for the estimation of blood velocities by transmitting high-frequency sound waves (ultrasound) and analyzing the signals reflected from circulating red blood cells. Doppler scans help diagnose many conditions, including: heart valve defects and congenital heart disease, artery occlusions and aneurysms. Classic Doppler processing methods do not make use of the underlying structure in the reflected signals in order to reduce the sampling rate or improve the estimation quality. Therefore, multitudes of ultrasound measurements are needed in order to produce reliable velocity estimation for each location and around each time point.
In the first part of this project, sparse representations of the ultrasound Doppler signal were investigated along with ways to estimate the velocity field. In the current project, these results will be used in order to define a sub-nyquist sampling and reconstruction framework for blood Doppler signals. Validation will be performed using numerical simulations, phantom scans and real Doppler ultrasound measurements.

Description:

PDR

Supervisor(s):

Student(s):

Yotam Lubin & Noa Yehezkel

Semester:

Spring 2016

Project Title:

The limit of phase retrieval - Project B

Project ID:

3133

Abstract:

The problem of recovering a signal from its Fourier transform magnitude arises in many areas in engineering and science, such as optics, X-ray crystallography, speech recognition and blind channel estimation. This problem is called phase retrieval and received considerable attention recently.
In this project we will investigate three widespread formulations of the phase retrieval problem:
1. Phase retrieval from short-time Fourier transform magnitude.
2. Phase retrieval under sparsity constraints.
3. Phase retrieval with Toeplitz matrices.
The aim of the project is to explore several algorithms for phase retrieval from both numerical and theoretical aspects.

Description:

PDR

Supervisor(s):

Student(s):

Moshe Lichtenstein | עמית אלפסי

Semester:

Spring 2016

Project Title:

Sub-Sampling of Spectral CT

Project ID:

3244

Abstract:

X-Ray Computer Tomography, known as CT, is one of the main tools Doctors use today to examine patients. In every main hospital, there are CT machines that work 24/7 to provide Doctors with in-depth views of the human body. CT Scans enable them to save lives on a daily basis.

Spectral CT is a new methodology, in which patients are irradiated by several (currently only two) X-Ray wavelengths. By combining the information obtained from the detectors for each wavelength, we can better characterize materials inside the human body, and create "colorful" CT images.

Applications for Spectral CT include better detection of cancerous cells, analysis of blood vessels and much more.

In this project the students will first study the basics of computer aided tomography, advanced mathematical imaging tools and cutting edge techniques in digital signal processing for sub-sampling. The students will then learn to model a spectral CT scan, and apply sub-sampling tools in order to hopefully reduce radiation and improve its performance.

• The project is performed in collaboration with researchers and radiologists from RAMBAM.

Description:

PDR

Supervisor(s):

Student(s):

Gil Ben-ari & Rotem Zamir

Semester:

Spring 2016

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