Sub-Nyquist Space-Time Adaptive Processing (STAP)
A radar mounted on an airborne platform transmits short pulses and
collects the returned echo from the targets on the ground. The received
signal contains the returns from the targets-of-interest as well as the
illuminated ground surface. The latter is undesirable interference
commonly termed as clutter. For a ground-based radar, the clutter is
stationary and occupies the low-frequency part of the signal spectrum.
It can, therefore, be easily filtered using a notch filter at low
frequencies. However, this filtering method cannot be extended to
airborne radars as the relative motion of the aircraft spread the
clutter in all directions and Doppler frequencies.
Space-Time Adaptive Processing (STAP) techniques have been proposed to filter the radar signal in both the angular and Doppler domains. The goal of this project is to formulate and devise STAP algorithms for sub-Nyquist radars. We will make use of the existing theoretical advances to integrate STAP in the sub-Nyquist processing framework.
Cognitive Foliage Penetration Radar (FPR)
The objective of this project is to improve detection of low Doppler
targets in the ground-based Foliage Penetration Radar. The native radar
system transmits a train of pulses and suffers from interference in the
UHF band. We propose cognitive transmission to mitigate the
radio-frequency interference and sub-Nyquist sampling to enhance
the Doppler resolution to detect slowly moving targets. We focus
on the transmission of sparse stepped-frequency continuous waveform
(s-SFW) to enable the cognition and then develop sub-Nyquist processing
for such a waveform.
A foliage penetration (FOPEN) radar detects, tracks and images targets such as human intruders in a dense forest environment. The radar transmission should be able to penetrate the foliage without significant signal attenuation and the received signal should have sufficient backscatter from targets (vehicles and humans) to achieve good detection performance. In order to meet these criteria, often VHF and UHF frequencies are considered suitable candidates.
Spectrum Sharing Radar Prototype
With the advent of mobile communications, the microwave spectrum is becoming increasingly dense. Several surveillance radar systems have traditionally operated in L-, S-, and C-bands for decades. However, their operation now faces challenges due to spectrum scarcity and RF interference from communication networks. Recently, there has been considerable interest in designing radar systems that have the ability to share spectrum with communication networks. Programs such as DARPA SSPARC, NSF EARS, CommRad, and RC3S are also actively working on equipping existing radar systems with spectrum-sharing transmission and reception. This design methodology has been termed as ‘coexistence’ spectrum sharing.
The goal of this project is to develop a hardware prototype of a coexistence spectrum-sharing radar. We would leverage on existing hardware prototypes and designs of cognitive radio and cognitive radar in the design process. The cognitive transmission allows the system to only transmit in those frequency bands that are sampled by the receiver.
Joint spectrum sensing and direction-of-arrival estimation
Spectrum blind reconstruction and direction-of-arrival (DOA) estimation of several narrowband signals spread over a wide spectrum from sub-Nyquist samples has been thoroughly investigated separately. In many communication applications, both the carrier frequencies and DOAs of the narrowband transmissions are unknown and their joint reconstruction is of great interest. Although many methods have been proposed for their joint estimation from Nyquist rate samples, very little has been done in the sub-Nyquist rate regime.
Reduced RF Chains for Massive MIMO - Analog Combiner Demo
Reducing the number of RF chains in a multi-antenna system is a central effort in next generation communication systems, as the RF components grow more expensive and area consuming. This issue had been the subject of many of the latest researches in the communication area and is also of great interest in the industry side. One common method to allow for RF chains reduction is to shift some of the data processing to the analog side, using an analog combiner. The purpose of the analog combiner is to project the high number of analog samples to the low number of RF chains while preserving as much relevant information as possible.
Compressed DOA 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 DOA estimation.
The project will include research next to matlab implementation
Pilot Contamination Mitigation for Multi Antenna Users
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. To mitigate this problem, one can design the pilot sequences of the different users to minimize the MMSE of the channel estimation. Prior work solved this problem for a single antenna users. It is now of interest to generalize this method for multi antenna users.
The goal of the project is to develop the theoretical solution for the above problem in the multi antenna case, and back the results with numerical simulations.
The project will include research next to Matlab implementation
The student will acquire advanced tools in signal processing and be exposed to new application in communication wireless networks.
Sub-Nyquist MWC Miniaturization
In light of the ever-increasing demand for new spectral bands and the underutilization of those already allocated, the new concept of Cognitive Radio (CR) has emerged. Opportunistic users could exploit temporarily vacant bands after detecting the absence of activity of their owners. A CR deals with wideband signals, with high Nyquist rate. To overcome the sampling rate bottleneck, a sub-Nyquist sampling and reconstruction prototype has been developed in the SAMPL lab, the modulated wideband converter (MWC).
One of the major challenges today is the adaptation of the MWC research platform to real world devices, such as laptops, cellular phones and other personal communication devices. The goal of this project is to use cutting edge technologies in order to implement the MWC system on a small scale factor, effectively miniaturizing the system by first designing and then implementing unique engineering solutions.
The students will learn and use advanced signal processing tools from the world of sub-sampling, and will gain a lot of hands-on experience on both software and hardware development.
We are confident the tools acquired will be of great use to the students in the future, in whichever field they would like to specialize.
High Resolution Beamforming using Deep Learning
Standard beamforming is a generic process, performed without exploiting the knowledge of the medium being scanned. Often, it results with poor images due to low resolution or technician's movement. This makes it difficult for the doctor to give a reliable diagnosis.
In this project, we aim to develop an organ-based beamformer using deep learning nets. Selecting an area and applying the corresponding learned beamformer will allow the doctor to view the region of interest with high resolution. The project will include the study of the basics ultrasound imaging and designing CNNs.
Correlation Beamformer for Ultrasound Diverging-Waves
Recently, a new array geometry have been introduced, which provides a novel way to perform array processing with much fewer physical sensors when the second-order statistics of the received data is used.
In this project, we will derive a model for diverging waves imaging, based on second-order statistics. Incorporating the new array geometry into our model will allow to create a way of imaging which requires much less transducer elements, paving the way to 3D imaging. This work will include the basics of ultrasound imaging as well as advanced tools for array processing and MATLAB simulations.
Ultrasound Tomography for Breast Cancer
Ultrasound tomography (UST) is an imaging technique that combines sonography with computed tomography (CT) methods to solve an inverse problem. It is well suited for inferring biomechanical properties of a volume of tissue from measurements made along a surface surrounding the tissue. One clinically relevant application is the detection of breast cancer.
UST has been under development for more than 30 years, motivated by many potential advantages over x-ray CT in the area of medical imaging. At diagnostic levels, sound waves do not appreciably heat tissue and, unlike x-rays, do not damage tissue through the process of ionization. With mounting concerns over radiation exposure, UST offers a nonsignificant risk alternative for medical imaging. In the area of breast imaging, UST offers the possibility of a comfortable alternative to mammography, which requires substantial compression that many women find uncomfortable and some even painful. Furthermore, UST is poised to address limitations associated with current clinical breast imaging.
In this research project, we will develop a new recovery method for ultrasound tomography based on sparse regularizer. The work will involve studying the basics of ultrasound imaging and tomography, deriving new theory and implementing it in MATLAB simulations.
Velocity Estimation in Spatial-Temporal Frequency Domain
Compressed Sensing for Ultrasound Elastography
Elastography is a medical imaging modality that maps the elastic properties of soft tissue. The main idea is that whether the tissue is hard or soft will give diagnostic information about the presence or status of disease. For example, elastography is used for detection and diagnosis of breast, thyroid and prostate cancers. Certain types of elastography are also used to investigate disease in the liver.
Compressed sensing (CS) is a signal processing technique for efficiently acquiring and reconstructing a signal. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem
Super-resolution ultrasound scans of moving microvasculature
Super Resolution of Ultrasound Doppler Imaging
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.
דופלר אולטרסאונד היא שיטת הדמיה לא פולשנית ובטוחה שמשמשת למדידה של מהירות הדם באמצעות משלוח של גלי קול בתדרים גבוהים וניתוח של ההחזר המתקבל מתאי הדם הנעים בזרם הדם. סריקות דופלר מאפשרות אבחון של מגוון מחלות כגון פגמים בשסתומי הלב, מחלות לב מולדות חסימות של כלי דם ומפרצות.
במטרה לאפשר לרופא לבחור את האזור בו יתבצע שערוך המהירות, יש לבצע ראשית שידור להדמיה בגווני אפור של הרקמה ורק לאחר מכן מתבצע שידור לשכבה נוספת של הדמיית המהירות (כפי שמופיע בתמונה למטה). כיום שני השידורים השונים מתבצעים לסירוגין, דבר הפוגע הן באיכות התמונה והן באיכות שערוך המהירות.
בפרויקט זה נשלב בין ההדמיות השונות בו-זמנית על די שידור להדמיית מהירות בלבד וביצוע סופר-רזולוציה על האות המתקבל לקבלת תמונת גווני אפור באיכות גבוהה. הפרויקט יתבצע ב-Matlabוכן ישתמש בסורק אולטרסאונד מחקרי חדיש.
Sub-Nyquist Vector Tissue Doppler Ultrasound System - Part 2
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. Recently, Xampling and Compressed Sensing methods developed at SAMPL were applied to TDI, allowing 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 sampling was performed without compromising the temporal and spatial resolutions.
Currently, TDI imaging enables the estimation of the component of cardiac velocity parallel to the transmitted ultrasonic beam. Therefore, TDI measurements depends on the scan angle and thus suffer from high variability. Several methods have been developed in the last few years for the estimation of the full flow field including the transverse component, collectively known as vector Doppler imaging methods.
In the first part of this project, estimators for the full 2d velocity field were invastigated and implemented. In this project, sub-Nyquist TDI system incorporating vector Doppler imaging will be implemented using new programmable research ultrasound platform. The system developed in this project will pave the way for future high-end portable ultrasound systems enabling reliable quantification of cardiac function.
In this project you will become familiar with Xampling and Compressed Sensing theory and novel ultrasonic signal processing methods. In addition, you will gain experience in implementing signal processing algorithms on real ultrasound systems.
Super-Resolution without contrast agents in US
Ultrasound super-localization microscopy techniques presented in the last few years enable non-invasive imaging of vascular structures at the capillary level by tracking the flow of ultrasound contrast agents (gas microbubbles). 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.
In this project we will attempt to achieve super-resolution without the use of contrast agents.
We will explore different methods for separating the blood vessels from the tissue and apply super-resolution methods to the separated blood vessels.
Super-Resolution Photoacoustic imaging
Photoacoustic imaging is a new method for visualizing blood vessels in-vivo. The key idea is to excite the red blood cells with short laser pulses and record the acoustic vibrations which are emitted afterwards. Recently, we have been able to achieve sub-diffraction resolution in contrast enhanced ultrasonic imaging and in fluorescent microscopy imaging using compressed sensing techniques.
In this project we will apply similar super-resolution concepts to photoacoustic data to improve the spatial resolution of this imaging modality.
Super-Resolution Light-sheet Fluorescence Microscopy using dictionary learning
Faster selective plane illumination microscopy (SPIM)
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. Light-sheet microscopy enables fast 3D volumetric imaging of living specimens, but with degraded spatial resolution. Selective plane illumination microscopy allows for faster acquisition rates by changing the measurement process from Cartesian scanning to cylindrical scanning.
Super-Resolution Light-sheet Fluorescence Microscopy
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. On the other hand, Light‐sheet microscopy enables fast 3D volumetric imaging of living specimens, but with degraded spatial resolution.
In this project, we will investigate an exciting new direction which will combine the SOFI technique for sub‐diffraction imaging and light‐sheet microscopy, which potentially may lead to a new type of fast super‐resolution microscope for living specimen. The students will get a hands on experience with a research project, combining disciplines in fluorescence microscopy, sparse representations and optimization techniques.