Projects
Active
Deep Learning
Project Title:

A Deep Learning approach for image reconstruction from binary pixels

Project ID:

4039

Abstract:

Driven mostly by the booming mobile market, digital photography has advanced staggeringly. However, due to constraints in form factor, image quality in poorly lighten environments is often unsatisfactory.

Recently, Eric Fossum (inventor of CMOS image sensor) proposed a novel concept of an image sensor with dense

sub-diffraction limit one-bit pixels (jots). Newly developed computational photography techniques show great potential for this sort of camera to produce high quality HDR images.

In this project, we will make an attempt to improve upon state-of-the-art reconstruction methods by using advanced deep learning tools. The student will implement the special noise model of these sensors, which will be used to train deep convolutional neural network architectures. Finally, real data collected from these camera will be used for testing the quality of reconstruction.




Description:

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Supervisor(s):
Student(s):
Project Title:

Machine Learning methods For Pain Analysis from EEG Recordings

Project ID:

3903

Abstract:

Electroencephalogram (EEG) is a typically non-invasive test used to measure the electrical activity of the brain (i.e. neuronal activations), by placing electrodes along the scalp. Traditional analysis of EEG signals generally focus on measuring the: (i) Magnitudes of neuronal activations within a specific frequency bands. (ii)Time of the response to a stimulus (from the averaged signal called ERP).
The goal of this project is to perform automatic analysis of such signals recorded from patients suffering from neuropathic pain and a control group. In order to do so, we will: (i) Use signal processing tools in order to extract from the raw EEG recordings valuable information (called features). (ii) Apply a machine learning methodologies on the extracted features to separate between the conditions and extract valuable information regarding the origin of the problem.

Description:

File not available

Supervisor(s):
Student(s):
David Ben-Kalifa | Yuval Spiegel
Project Title:

Super-Resolution Fluorescence Microscopy using Deep Learning

Project ID:

3902

Abstract:

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.
However, these techniques are currently limited by low temporal resolution and long acquisition times. In this project, we will investigate an exciting new direction which will combine Deep Learning of both low and high resolution images, to improve both its
temporal and spatial resolution.
The students will get a hands on experience with a research project, combining disciplines in fluorescence microscopy, machine learning and
optimization techniques.

Description:

File not available

Supervisor(s):
Student(s):
Orr Avrech | Alexander Finkelshtein
Biomedical
Project Title:

Functional MRI via temporal mixing

Project ID:

3504

Abstract:

Functional Magnetic Resonance Imaging (fMRI) is the method of choice for evaluation and analysis of brain activity by detecting changes associated with blood flow. However, current acquisition of a routine brain fMRI suffers from low spatial and temporal resolution. As such, it causes many difficulties, such as low-quality maps of brain activity regions and brain networks.


Project description:

Functional Magnetic Resonance Imaging (fMRI) is the method of choice for evaluation and analysis of brain activity by detecting changes associated with blood flow. However, current acquisition of a routine brain fMRI suffers from low spatial and temporal resolution. As such, it causes many difficulties, such as low-quality maps of brain activity regions and brain networks.

Description:

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Supervisor(s):
Student(s):
Project Title:

Coded Excitation for diverging wave Imaging

Project ID:

3791

Abstract:

Medical ultrasound is used for tissue visualization by radiating it with acoustic energy transmitted by an array of elements. Novel imaging method based on insonification with diverging improves image quality and acquisition time, however, it is limited by data transfer rates and severe computational load. In addition, modern imaging systems use single-carrier short pulses for transducer excitation, while the usage of more sophisticated signals can be beneficial in terms of SNR, penetration depth and frame rate. Therefore, coded signals are extremely valuable in medical ultrasound imaging although their implementation is challenging due to imaging and not detection nature of ultrasound, its high dynamic range and frequency dependent attenuation. Recently, diverging waves imaging was implemented using Fourier domain beamforming developed in SAMPL, leading to significant reduction in both sampling and processing rates. In this project we aim to extend this work by developing Fourier-based coded ultrasound for diverging waves imaging. The combination of these novel techniques will pave a way to enhanced SNR, real-time ultrafast ultrasound system. Performance of the developed method will be tested on a Verasoincs (research-oriented) ultrasound system Matlab environment. Required background: Introduction to Digital Signal Processing (044198)



Description:

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Supervisor(s):

Regev Cohen, Tanya Chernyakova

Student(s):
Elad Hirsch | Gil Cherniak
Project Title:

Super Resolution of Ultrasound Doppler Imaging

Project ID:

3505

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.
In order to allow the medical doctor to navigate and choose the region in which the blood velocity is to be estimated, a B-mode image of the tissue is generated first. Only then, a velocity map of the blood is imaged on top ( See image above). Nowdays, 2 interleaved transmission sequences are used, one for Doopler and one for B-mode imaging, which results in degraded B-mode image and corrupted velocity estimation. In this project, we will perofrm the two imaging, B-mode and Doppler, simulatenously by using only Doppler transmission and applying super resolution on the received signal in order to yield a high resolution B-mode image. The project will be perfomred using Matlab and state of the art research oriented ultrasound system.


Description:

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Supervisor(s):
Student(s):
Deborah Levy | Yoval Belfair
Project Title:

Limited Angle Reconstruction for Computed Tomography

Project ID:

Abstract:

Medical CT imaging is probably the most widely used imaging tool in-use today. In this project, we consider the problem of scanning from only a limited angle of projections (less than 180o).

Successfully reconstructing from a limited projection angle would allow us to install small CT scanners in small clinics, and maybe even on-board ambulances. However, limited angle reconstruction is a tough mathematical problem, and we will attempt to solve it using recently developed, signal processing tools.

In this project, we will learn and use sophisticated tools from signal processing, such as sub-Nyquist sampling, algebraic reconstruction techniques and new optimization based solvers.

The students will first learn on tomographic imaging, and will get familiar with the required background in advanced signal processing. We will then design and build a simulator that can reconstruct images from limited scanning angles, using the structure of the tomographic scans.


Description:

File not available

Supervisor(s):
Student(s):
Rotem Hon | Dmitry Nabedrik
Project Title:

Reducing Metal Artifacts in CT

Project ID:

3819

Abstract:

Metal artifacts in CT scans are a major problem for radiologists today. Whenever a patient has a metallic object in his body (dental fills, platinum bolts or knife wounds for instance), the CT image gets completely ruined, as seen in the image below.

We suggest to use advanced signal processing tools, such as sparse representations, dictionary learning and others, in order to exploit the structure of the CT scans to our advantage. By smart modelling of the metal artifacts, we hope to reduce or even remove them altogether from the output scans.

Doctors from both Rambam and Ichilov hospitals have shown tremendous interest in an algorithm that can reduce these artifacts, and we will be working in collaboration with them. Our goal is to ultimately include our solution on a real CT machine, within the hospital. Real experiments can be conducted in General Electric, CT department.

In this project the students will learn state of the art methods in signal processing and sampling, and will get familiar with principles of tomography.



Description:

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Supervisor(s):
Student(s):
נגה איזנר | Dor Meron
Project Title:

CT Super-Resolution by Non-Uniform Detector Arrays

Project ID:

3818

Abstract:

Medical CT imaging is probably the most widely used imaging tool in-use today. The biomedical companies constantly work to improve and get better resolution for the CT images.

In this project, we will learn and use sophisticated tools from signal processing, such as sub-Nyquist sampling, array processing, and super-resolution algorithms. Using these tools, we will create enhanced CT images, with higher resolution than the native scanner’s resolution.

We will collaborate with General Electric Healthcare® to examine new ways to define the CT detector array in order to digitally create cleaner and sharper CT images. Doctors from Rambam hospital would also be happy to provide us with clinical feedback.

The students will first learn on tomographic imaging, and will get familiar with the required background in advanced signal processing. We will then design and build a simulator that can enhance the resolution in CT images.


Description:

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Supervisor(s):
Student(s):
Lioz Noy | Nir Bernard
Project Title:

Tensor Based Reconstruction for Computed Tomography

Project ID:

3817

Abstract:

Medical CT imaging is probably the most widely used imaging tool in-use today. CT images suffer from different artifacts and issues, and generally require radiating a patient with a big dose of X-Ray radiation in order to produce good images.

In this project, we will learn on new tensor based algebraic techniques, and use them for solving difficult CT reconstructions, made with low radiation dosages. Our goal would be to lower the radiation dosage, while improving the CT images, using tensor based methods that can exploit the inner structure of the human body.

The students will first learn on tomographic imaging, and will get familiar with the required background in advanced signal processing and tensor decompositions. We will then try and formulate a specific tensor based solution for the CT problem. We will work with state-of-the-art toolboxes, useful for many other problems, from the world of machine learning.


Description:

File not available

Supervisor(s):
Student(s):
Inbal Farbstein | אייל חנניה
Project Title:

Deep Learning MRI

Project ID:

3745

Abstract:

Deep leaning is a machine learning approach that has gained very much attention recently. It has been proven to be very successful in many real world problems, such as object detection and face recognition, and it is currently widely used in many search engines. Recentness, deep learning has been adopted also by the world of medical imaging. While Magnetic Resonance Imaging (MRI) is the method of choice for diagnosis, evaluation and follow-up of brain clinical pathologies, the bottleneck in many MRI applications is image reconstruction from undersampled data. The goal of the project is to use deep learning approaches for fast MRI reconstruction (unlike the common problems of deep learning, which are mostly classification problems). The project is expected to significantly improve many MRI applications, and speed-up the clinical workflow.



Description:

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Supervisor(s):
Student(s):
Gur Chemel | Raz Petel
Project Title:

שיטות MRF מתקדמות

Project ID:

3502

Abstract:

הדמיית תהודה מגנטית (MRI) היא השיטה המועדפת לאבחון, הערכה, מעקב וניתוח של פתולוגיות קליניות. יחד עם זאת, רכישה של הדמיית MRI מרובת רצפים היא תהליך איטי יחסית הדורש בממוצע כחצי שעה. MRF הינה שיטה חדשנית המספקות הערכה כמותית של הפרמטרים הפיזיקליים של הרקמות השונות. בפרוייקט זה נממש ונבחן שיטות מתקדמות לשערוך התמונות הכמותיות ונשווה את תוצאותיו לאלגוריתם מבוסס חישה דחוסה (CS). פרויקט כיפי ושאפתני עם אלמנט מחקרי שנועד להכיר לסטודנטים את עולם ה MRI ואת גישת "טביעת האצבע" ((MRF המספקת מדדים פיזיקליים כמותיים של הרקמות. בנוסף הסטודנטים ילמדו ויכירו שיטות עבוד אות המבוססות דגימה דחוסה.

קיימת אפשרות לפרוייקט המשך למעוניינים.

Description:

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Supervisor(s):
Student(s):
Inbal Fleischer | Brian Chmiel
Project Title:

multi-leads ECG compression

Project ID:

3503

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.

Project description:

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.


Description:

File not available

Supervisor(s):
Student(s):
Klein Roi
Project Title:

FDBF for Diverging Wave Imaging

Project ID:

3653

Abstract:

Medical ultrasound is used for tissue visualization by radiating it with acoustic energy transmitted by an array of elements. Novel imaging method based on insonification with diverging improves image quality and acquisition time.

This method, however, is limited by data transfer rates and severe computational load. Therefore, this imaging mode can significantly benefit from coupling with the low-rate frequency domain beamforming approach developed recently in SAMPL.

In this project we aim to adopt frequency domain beamforming framework to diverging wave compounding waves imaging mode. The combination of these two novel techniques will allow for significant reduction in both sampling and processing rates in paving a way to real-time ultrafast processing.

Performance of modified FDBF will be tested on data in Matlab environment.

Description:

File not available

Supervisor(s):
Student(s):
Cohen-Sidon Omer & Aviv Rabinovich
Project Title:

Limited Diffraction-based Imaging

Project ID:

3692

Abstract:

Ultrasound is used for tissue visualization by radiating it with acoustic energy transmitted by an array of elements. Image is derived by integrating the signals received by individual elements. A new novel approach based on limited diffraction beams allows acquiring data directly in k-space and recover the image using inverse Fourier transform leading to improved acquisition time and image quality.

In this project we aim to study and implement the above approach and introduce novel k-space re-sampling technique developed recently at SAMPL.


Description:

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Supervisor(s):
Student(s):
Aviad Aberdam, Eliav Bar-Ilan
Project Title:

Migraine classification via fMRI and EEG

Project ID:

3685

Abstract:

Migraine is a primary headache disorder characterized by recurrent headaches that are moderate to severe. Typically, the headaches affect one half of the head, are pulsating in nature, and last from two to 72 hours.

Functional MRI (fMRI) and EEG may be used to investigate the mechanisms that lead to migraine by measuring functional connectivity. This could provide fMRI and EEG based biomarkers that indicate early responses to preventive therapy. The goal of the project is to identify those biomarkers via acquisition of EEG and fMRI data of both healthy and Migraine diagnosed subjects.

Description:

File not available

Supervisor(s):
Student(s):
Jonathan Yarnitsky, reut azaria
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:

File not available

Supervisor(s):
Student(s):
Ido Cohen and Shai Yagil
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:

File not available

Supervisor(s):
Student(s):
Chechik Yonatan
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:

File not available

Supervisor(s):
Student(s):
Dan Cohen and Meged Shoham
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:

File not available

Supervisor(s):
Student(s):
Ben Finkelshtein & Tomer Zeagdone
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:
Supervisor(s):
Student(s):
Yotam Lubin & Noa Yehezkel
Project Title:

Fetal ECG

Project ID:

3279

Abstract:

The use of ECG signal, which demonstrate the heart's electric activity, is common in many applications. When sensing the signal of pregnant women, there is a significant importance to separate the maternal ECG from the fetal one. In this project, we will develop new method for this separation based on very popular field of dictionary learning in the world of signal processing. 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:

File not available

Supervisor(s):
Student(s):
Omri Berman & Yotam Nizri
Communication
Project Title:

Pilot Contamination Mitigation Using Graph Theory

Project ID:

3675

Abstract:

The first step in every communication scheme is to estimate the channel between the user and the base station that serves him. This can be done by letting the user send a known sequence to the base station, called "pilot sequence", and then let the base station estimate the channel from the received signal. Pilot contamination is a phenomenon in multi-user multi-cell wireless communication systems, where users from different cells interfere with each other channel estimation. The result is that the estimated channel is contaminated by interfering channels.

We wish to use tools from graph theory to solve the pilot contamination problem. The pilot contamination problem can be made similar to a clustering problem on graphs.

The goal of the project is to develop define the relevant graph for the problem and try different algorithms to solve it.

The project will include research next to matlab implementation

Description:

File not available

Supervisor(s):
Student(s):
עמית תאני | Gilad Feinberg
Project Title:

Smart Sensing for Off-Grid Error Reduction in Compressed Sensing Algorithms

Project ID:

3332

Abstract:

Compressed Sensing is a novel family of algorithms that make use of a sparse structure of the signal to perform an effective recovery. This kind of algorithms make use of a "sensing matrix" to relate the measurements to the original signal. In many cases this sensing matrix is a grid matrix that take a continuous parameter on which the signal depends, such as frequency or angle, and discretizes it. However, in this case, a problem arise when the true parameter from which the signal was generated is "off the grid".

The project goal is to design a smart sensing matrix that reduce the possible implications of the off grid problem.

The project will include research next to matlab and will contain algorithmic aspect.


Description:

File not available

Supervisor(s):
Student(s):
Idan Fried
Project Title:

Smart Sensing for Off-Grid Error Reduction in Compressed Sensing Algorithms

Project ID:

3332

Abstract:

Compressed Sensing is a novel family of algorithms that make use of a sparse structure of the signal to perform an effective recovery. This kind of algorithms make use of a "sensing matrix" to relate the measurements to the original signal. In many cases this sensing matrix is a grid matrix that take a continuous parameter on which the signal depends, such as frequency or angle, and discretizes it. However, in this case, a problem arise when the true parameter from which the signal was generated is "off the grid".

The project goal is to design a smart sensing matrix that reduce the possible implications of the off grid problem.
The project will include research next to matlab and will contain algorithmic aspect.

Description:

File not available

Supervisor(s):
Student(s):
Idan Fried
Project Title:

Cognitive Indoor Localization - Part 2

Project ID:

3562

Abstract:

It is often desired to locate objects or people inside buildings using some identification sensors attached to them such as smartphones. In outdoor environments, persons carrying smartphones can be easily located by GPS satellites. However, the GPS signal loses its strength inside buildings, garages and offices due to signal attenuation caused by construction materials and multipath fading. This challenge has led to active research on various localization techniques for indoor environments.

In this particular project, we plan to use cognitive transmission in IEEE 802.11ad 60 GHz link to enhance the SNR of the received signal and improve range detection. The signal setup comprises of a wireless access point installed indoors that communicates with the smartphones of the users to determine their locations.

Description:

File not available

Supervisor(s):
Student(s):
Monin Sagi & Yona Cohen
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:

File not available

Supervisor(s):
Student(s):
Rotem Turjeman & Inbal Fleischer
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:

File not available

Supervisor(s):
Student(s):
Irina Akhvlediany
Radar
Project Title:

Sub-Nyquist MIMO Doppler Processing and Clutter Removal

Project ID:

3458

Abstract:

Multiple input multiple output (MIMO) radar is a novel radar paradigm that uses an array of several transmit and receive antenna elements, with each transmitter radiating a different waveform. In a collocated MIMO radar, the antenna elements are placed close to each other so that the radar cross-section of a target appears identical to all the elements. The waveform diversity in a collocated MIMO is based on the mutual orthogonality - usually in time, frequency or code - of different transmitted signals. The receiver separates and coherently processes the target echoes corresponding to each transmitter. The angular resolution of MIMO is same as a virtual phased array with the same antenna aperture but many more antenna elements than MIMO.

Recently, we designed and developed a sub-Nyquist MIMO radar that requires less number of antenna elements and signal samples without degrading the angular and range resolutions of the radar. The objective of this project is to add Doppler processing and clutter removal algorithms to this prototype. We would use existing theoretical solutions to integrate these modules and then validate the results using the hardware prototype in real-time.

Description:

File not available

Supervisor(s):
Student(s):
Eran Ronen & Yana Grimovich
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:

File not available

Supervisor(s):
Student(s):
Mordov Shai & Rivka Emanuel
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:

File not available

Supervisor(s):
Student(s):
Itay Kahane & Aviad Kaufmann
Project Title:

Two-dimensional Sub-Nyquist SAR demo

Project ID:

3333

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 basic idea of a SAR system is to produce a two-dimensional mapping of the illuminated scene from received echoes by processing the reflected energy.
The goal of the project is to implement a full-cycle demo that allows to construct an image from low-rate samples at both fast time and slow time.

Description:

File not available

Supervisor(s):
Student(s):
Ran Ben-Izhak & Beary Fluss
Medical Imaging
Project Title:

Low resolution patch based denoising of high resolution images

Project ID:

3901

Abstract:

Denoising is a fundamental problem in image processing. Many algorithms have been suggested for image denoising, the vast majority are not application specific, but are rather generic.
Often, it is possible to acquire two datasets of images – one is a low resolution image with high SNR, and the other is a high resolution image.
In this project, we propose to sue the high SNR, low resolution images to denoise the
high resolution, low SNR images, and to achieve better performance over contemporary denoising algorithms.
The project can have immediate applications, such as denoisisng of biomedical images.



Description:

File not available

Supervisor(s):
Student(s):
Ron Ziv | Noa Englender
Optics
Project Title:

Super-Resolution Structured Illumination Microscopy

Project ID:

3501

Abstract:

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. However, these techniques are currently limited by low temporal resolution and long acquisition times. Super‐resolution optical fluctuation imaging (SOFI) is a fluorescence microscopy technique enabling sub‐diffraction limit imaging with high temporal resolution by calculating high order statistics of the fluctuating optical signal. Structured illumination microscopy (SIM) also enables sub‐diffraction resolution and rejection of out‐of‐focus light, for leaving specimen, with a fast imaging cycle.

Project description:

In this project, we will investigate an exciting new direction which will lead to a new type SIM, by taking inspiration from communication methods such as spear spectrum and the MWC developed in SAMPL, to enable increased spatial resolution, well below the diffraction limit. Such a microscope can lead to new advances in biological research. The students will get a hands on experience with a research project, combining disciplines in fluorescence microscopy, sparse representations and optimization techniques.

Description:

File not available

Supervisor(s):
Student(s):
Guy Asherov & דמיאן קלירוף
INTERIA Web Design & Development