Projects
Completed
Fall 2016
Communication
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:

PDR

Supervisor(s):

Student(s):

Idan Fried

Semester:

Fall 2016

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:

PDR

Supervisor(s):

Student(s):

Monin Sagi & Yona Cohen

Semester:

Fall 2016

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:

PDR

Supervisor(s):

Student(s):

Guy Asherov & דמיאן קלירוף

Semester:

Fall 2016

Biomedical
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:

PDR

Supervisor(s):

Student(s):

Inbal Fleischer | Brian Chmiel

Semester:

Fall 2016

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:

PDR

Supervisor(s):

Student(s):

Klein Roi

Semester:

Fall 2016

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:

PDR

Supervisor(s):

Student(s):

Semester:

Fall 2016

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:

PDR

Supervisor(s):

Student(s):

Cohen-Sidon Omer & Aviv Rabinovich

Semester:

Fall 2016

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:

PDR

Supervisor(s):

Student(s):

Jonathan Yarnitsky, reut azaria

Semester:

Fall 2016

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:

PDR

Supervisor(s):

Student(s):

Aviad Aberdam, Eliav Bar-Ilan

Semester:

Fall 2016

Project Title:

Super-Resolution in Computer Tomography (CT) – Part 2

Project ID:

3265

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.

Today, 3rd generation CTs can scan over 100 patients a day, but their radiation dose is relatively high. For instance, an abdomen CT scan radiation dose is equivalent to almost a 1000 chest X-ray scans.It is highly desirable to reduce the radiation dose, while preserving the resolution and details of the scans. This is where we come in.

The students are now after research, design and implementation for a novel reconstruction algorithm for CT scans, which could allow faster scan times and reduced radiation dosage. The basis for this algorithm will relies on principles from novel fields such as "Compressed Sensing" and "Super Resolution by Dictionary Learning".

During Part 2 the students will attempt novel ideas in order to improve the Dictionary Learning algorithm, and implement it on real data acquired from Rambam hospital. The main effort will focus on isolating artifacts related from foreign metal objects that penetrated the human body.

• A collaboration with Rambam Hospital will take place.

Description:

PDR

Supervisor(s):

Student(s):

Gal Sadeh & Dmitry Lavrov

Semester:

Fall 2016

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:

PDR

Supervisor(s):

Student(s):

Eran Ronen & Yana Grimovich

Semester:

Fall 2016

INTERIA Web Design & Development