הפקולטה להנדסה ע"ש איבי ואלדר פליישמן

School of Mechanical Engineering Yoav Green

07 בינואר 2019, 14:00 - 15:00

בניין וולפסון חדר 206

School of Mechanical Engineering Yoav Green

School of Mechanical Engineering Seminar
Monday, March 12, 2018 at 14:00
Wolfson Building of Mechanical Engineering, Room 206

The Fluid Dynamics of Nanochannels and Biological Systems

Yoav Green

Post doc guest of Touvia Miloh

Nanochannels and nanopores are ubiquitous to nature and technology. They can be found in macroscopically large permselective membranes such as those used for desalination in electrodialysis systems, or small system such as cell membranes. The sub-micron scale in such systems allows them not only to desalinate water, harvest energy, and serve as highly sensitive bio-molecular detectors, but it also allows these nanochannels to behave as diode-like current rectifiers.

In nanofluidics systems, a nanochannel is typically connected to much larger microchannels/reservoirs. Until recently, in the nanofluidics community it was assumed that the effects of the microchannels is negligible. In this talk, I will present contradicting evidence to this assumption. I will then present a new modified paradigm which emphasizes the importance of the microchannels themselves as well as the microchannel-nanochannel interfaces. These new insights are extremely useful for designing new nanofluidic based systems.

To conclude, I will present my recent research in biomechanics that focuses on relating the kinematics of an epithelial monolayer of cells to its kinetics. As the epithelial monolayer migrates collectively, each constituent cell exerts intercellular stresses on neighboring cells and exerts traction forces on its substrate. The relationship between the velocities, stresses and tractions is fundamental to collective cell migration but it remains unknown. It will be shown that the observed dynamics does not conform to the simple and commonly assumed laws of a linear Hookean solid or Newtonian fluid. Rather the mechanics are much more complicated and likely because of the active nature of the cells. These findings are crucial for developing a deeper understanding of collective cellular behavior.

Yoav Green is currently a post-doctoral researcher in the Harvard T. H. Chan School of Public Health where he is working in the field of biomechanics. Before that Yoav received his PhD in mechanical engineering from the Technion - Israel Institute of Technology where his research fields were nanofluidics and electrokinetics. Yoav also holds a MSc in physics (astrophysics and astronomy) from the Weizmann Institute of Science, and BSc in aerospace engineering from the Technion.

School of Mechanical Engineering Roman Golkov

17 בדצמבר 2018, 14:00 - 15:00

בניין וולפסון חדר 206

School of Mechanical Engineering Roman Golkov

SCHOOL OF MECHANICAL ENGINEERING SEMINAR Monday, December 17, 2018 at 14.00 Wolfson Building of Mechanical Engineering, Room 206

Active Elastic Interactions between Living Cells

Roman Golkov

Ph.D. student of Prof. Yair Shokef

Live cells apply mechanical forces on their environment and sense and respond to the forces created by neighboring cells. The elasticity of the extra-cellular matrix can alter these forces and subsequently the cell’s biological behavior. For example, experiments with pairs of cells plated on synthetic substrates show that cells behavior changes from attractive to repulsive due to changes in the rigidity of the substrate. We theoretically analyze the mechanical interaction between distant cells, with the goal to deduce the biological regulatory mechanisms of cells from their mechanical and geometrical behavior.

We first consider a model of spherical force dipoles, i.e. spherical cells surrounded by a three-dimensional infinite elastic medium that apply radial forces on their environment. We distinguish between ‘dead’ behavior, in which the force dipoles apply fixed external forces and self-displacements and ‘live’ behavior, in which the forces and self-displacements applied change in response to changes that the force dipoles sense in their environment. We compare four different regulatory behaviors, in which the force dipoles preserve their spherical shape and in addition volume, position, both or neither. We identify an interaction energy, which does not exist in the absence of this regulation, and identify which regulatory ingredients govern the sign and which the magnitude of this interaction.

Subsequently, we model cells as disc force dipoles adhered to the top of a semi-infinite elastic medium, and study the effect of the anisotropy of their active contractile forces. We find the quantitative decay of interaction energy with cell-cell distance, and demonstrate how the relative phase angles of their contractility anisotropy can invert the sign of their interaction from repulsive to attractive.

סמינר מחלקתי אלקטרוניקה פיזיקאלית : Mihael Fugenfirov

18 בדצמבר 2018, 13:00

פקולטה להנדסה, ביניין כיתות, חדר 011

סמינר מחלקתי אלקטרוניקה פיזיקאלית : Mihael Fugenfirov

סמינר מיכאל

You are invited to attend a department seminar on

Incremental Solidification (3D-Printing) of Magnetically-Confined Metal-Powder by Localized Microwave Heating

:By

Mihael Fugenfirov

MSc student under the supervision of Prof. Eli Jerby

Abstract

This seminar presents an experimental and theoretical study oriented to investigate the potential utilization of the localized microwave-heating (LMH) effect in 3D-printing and additive-manufacturing (AM) processes. The phenomenon of intentional LMH is made possible by the thermal runaway instability. It enables an intentional rapid heating within a localized zone (namely a hotspot). Following our previous LMH-AM study, a magnetic confinement technique is developed here as a non-contact support for the incremental solidification of small metal-powder batches by LMH. Various experimental schemes were investigated in this work. Among them, one scheme has been selected for a more in-depth research. The process and the products of the experimental setup are presented, as well as future possibilities

On Tuesday, December 18, 2018, 13:00

Room 011, EE-Class Building

הרצאת אורח עם מנכ"ל ומייסד משותף של vayyar: רביב מלמד

החיישן שרואה מבעד לעצמים וקירות

12 בדצמבר 2018, 16:00 - 19:00

הפקולטה להנדסה אוניברסיטת תל-אביב

סמינר מחלקתי אלקטרוניקה פיזיקאלית : Itamar Levi

11 בדצמבר 2018, 15:00

פקולטה להנדסה, ביניין כיתות, חדר 011

סמינר מחלקתי אלקטרוניקה פיזיקאלית : Itamar Levi

סמינר איתמר

Physical Security - on the vulnerability of crypto. algorithms, architectures and platforms

:By

Itamar Levi

The Crypto-group & the Electronics Systems and Circuits (ECS) group.

Université Catholique de Louvain (UCLouvain), Belgium.

Abstract

The integration of billions (soon trillions) of wireless sensing, computing and communicating nodes in a so-called “Internet of Things” is a reality which considerably affect our lives. Whereas it brings breakthrough opportunities for a wide range of applications (e.g. automotive, smart grids/cities, medical implants and industrial cyber-physical systems), it serves as a concrete challenge for security, integrity and privacy. Perhaps the most challenging aspect relates to the physical-security of these devices due to their physical exposure and accessibility. Moreover, the cost of securing these devices, to-date, is simply too high for their specifications (i.e. low energy, small area, large range of activity-factor). This talk will start with discussing limitations of state-of-the-art “consensus” approaches to protect against physical attacks by adversaries which utilize side-channels (e.g. masking by secret-sharing). For example, it will be demonstrated how an adversary which is aware to the physical aspects of the devices (electronics, architecture etc.) can easily crumble the theoretical security promises of such constructions. Then, it will be shown how a close interaction between crypto. algorithms, architectures and platforms (uCs, FPGAs and ASICs) can foster considerable security- and performance-improvement of countermeasures and novelty. Finally, we discuss the most alarming class of threats, i.e. devices tempering, EM attacks and faults injection. We briefly demonstrate a unique ASIC device which was designed in the architectural level with a clear target: to resist such attacks and limit the amount of information an adversary can extract from our devices.

On Tuesday, Dec 11, 2018, 15:00

Room 011, Kitot building

סמינר מחלקתי אלקטרוניקה פיזיקאלית : Dr. Richard Al Hadi

10 בדצמבר 2018, 13:00

סמינר ריצרד

Sub-mm-Wave (300-600GHz) Silicon Based Source Array

:By

Dr. Richard Al Hadi

University of California, Los Angeles, CA 90095

This talk presents the design and implementation of a sub-mm-Wave fully integrated signal generator array. It is based on a harmonic oscillator designed in a standard 65-nm digital CMOS technology. The talk will cover the design methodology, the electromagnetic modeling and simulations. The harmonic oscillator concept is further elaborated to build a unique beamforming technique called Y-vector network. This approach has been recently developed by the High-Speed Electronics Laboratory (HSEL) at UCLA. It will be also presented. This beamforming technique features minimized chip area and DC power consumption. It requires no traditional adjustable phase shifter at multi-channel front end which usually suffer from high insertion loss at these frequencies. A compact 1x4 Beam Steering Phased Array (BSPA) is validated at 0.55THz with about ±30° steering angle range.

Bio

Dr. Richard Al Hadi received the engineering diploma from Caen's National Graduate School of Engineering in Electronics and Applied Physics and the master’s degree from the University of Caen Basse-Normandie, France, in 2009. He received the Ph.D. degree, suma cum lauda, from the University of Wuppertal, Germany, in 2014. In 2011 he worked as a research fellow at Korea University, Seoul, South-Korea. Dr. Al Hadi has joined University of California, Los Angeles (UCLA) in 2015 as a postdoctoral fellow. His research interests are terahertz integrated circuits in silicon technologies. Dr. Al Hadi is senior IEEE member, he is a co-recipient of the 2012 Jan Van Vessem Award for the Outstanding European Paper at the IEEE International Solid-State Circuit Conference and the 2014 EuCAP best paper award.

On Monday, Dec 10th, 2018, 13:00

Room 011, Kitot building

EE Seminar: On Expressiveness and Optimization in Deep Learning

24 בדצמבר 2018, 15:00

חדר 011, בניין כיתות-חשמל

(The talk will be given in English)

Speaker: Dr. Nadav Cohen
School of Mathematics, Institute for Advanced Study, Princeton NJ

Monday, December 24^th, 2018
15:00 - 16:00

Room 011, Kitot Bldg., Faculty of Engineering

On Expressiveness and Optimization in Deep Learning

Abstract

Understanding deep learning calls for addressing three fundamental questions: expressiveness, optimization and generalization. Expressiveness refers to the ability of compactly sized deep neural networks to represent functions capable of solving real-world problems. Optimization concerns the effectiveness of simple gradient-based algorithms in solving non-convex neural network training programs. Generalization treats the phenomenon of deep learning models not overfitting despite having much more parameters than examples to learn from. This talk will describe a series of works aimed at unraveling some of the mysteries behind expressiveness and optimization. I will begin by establishing an equivalence between convolutional and recurrent networks --- the most successful deep learning architectures to date --- and hierarchical tensor decompositions. The equivalence will be used to answer various questions concerning expressiveness, resulting in new theoretically-backed tools for deep network design. I will then turn to discuss a recent line of work analyzing optimization of deep linear neural networks. By studying the trajectories of gradient descent, we will derive the most general guarantee to date for efficient convergence to global minimum of a gradient-based algorithm training a deep network. Moreover, in stark contrast with conventional wisdom, we will see that sometimes, gradient descent can train a deep linear network faster than a classic linear model. In other words, depth can accelerate optimization, even without any gain in expressiveness, and despite introducing non-convexity to a formerly convex problem.

Works covered in this talk were in collaboration with Amnon Shashua, Sanjeev Arora, Elad Hazan, Or Sharir, Yoav Levine, Noah Golowich, Wei Hu, Ronen Tamari and David Yakira.

Short Bio

Nadav Cohen is a postdoctoral member at the School of Mathematics in the Institute for Advanced Study. His research focuses on the theoretical and algorithmic foundations of deep learning. In particular, he is interested in mathematically analyzing aspects of expressiveness, optimization and generalization, with the goal of deriving theoretically founded procedures and algorithms that will improve practical performance. Nadav earned his PhD at the School of Computer Science and Engineering in the Hebrew University of Jerusalem, under the supervision of Prof. Amnon Shashua. Prior to that, he obtained a BSc in electrical engineering and a BSc in mathematics (both summa cum laude) at the Technion Excellence Program for distinguished undergraduates. For his contributions to the theoretical understanding of deep learning, Nadav received a number of awards, including the Google Doctoral Fellowship in Machine Learning, the Rothschild Postdoctoral Fellowship, and the Zuckerman Postdoctoral Fellowship.

EE Seminar: Re-rendering Reality

17 בדצמבר 2018, 15:00

חדר 011, בניין כיתות-חשמל

(The talk will be given in English)

Speaker: Dr. Tali Dekel
Google, Cambridge

Monday, December 17^th, 2018
15:00 - 16:00

Room 011, Kitot Bldg., Faculty of Engineering

Re-rendering Reality

Abstract

We all capture the world around us through digital data such as images, videos and sound. However, in many cases, we are interested in certain properties of the data that are either not available or difficult to perceive directly from the input signal. My goal is to “Re-render Reality”, i.e., develop algorithms that analyze digital signals and then create a new version of it that allows us to see and hear better. In this talk, I’ll present a variety of methodologies aimed at enhancing the way we perceive our world through modified, re-rendered output. These works combine ideas from signal processing, optimization, computer graphics, and machine learning, and address a wide range of applications. More specifically, I’ll demonstrate how we can automatically reveal subtle geometric imperfection in images, visualize human motion in 3D, and use visual signals to help us separate and mute interference sound in a video. Finally, I'll discuss some of my future directions and work in progress.

Short Bio

Tali is a Senior Research Scientist at Google, Cambridge, developing algorithms at the intersection of computer vision and computer graphics. Before Google, she was a Postdoctoral Associate at the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, working with Prof. William T. Freeman. Tali completed her Ph.D studies at the school of electrical engineering, Tel-Aviv University, Israel, under the supervision of Prof. Shai Avidan, and Prof. Yael Moses. Her research interests include computational photography, image synthesize, geometry and 3D reconstruction.

EE Seminar: Enhancing Transfer Learning for Pulmonary Nodule Detection using Preprocessing Techniques

19 בדצמבר 2018, 15:30

חדר 011, בניין כיתות-חשמל

Speaker: Max Fomin

M.Sc. student under the supervision of Prof. Hayit Greenspan

Wednesday, December 19^th, 2018 at 15:30

Room 011, Kitot Bldg., Faculty of Engineering

Enhancing Transfer Learning for Pulmonary Nodule Detection using Preprocessing Techniques

One of the biggest challenges today in implementing deep learning systems in the medical world is obtaining sufficiently large datasets that enable training neural networks without overfitting. One notices that the state-of-the-art systems in everyday object detection tasks are trained on huge datasets, in order to achieve their accuracy. In contrast, the medical world, which inherently requires the highest accuracy because of the high cost of errors, suffers from smaller datasets by far, dictating the creation of much simpler neural networks that thus achieve worse results.
Exisiting CAD (Computer Aided Diagnosis) systems for pulmonary nodule detection have two main stages. The first stage is the nodule candidate generation, which aims to produce as many high quality candidates as possible using 2D slices for fast runtimes. The second stage is a false-positive filtering stage, which aims to pass only the candidates that are true nodules. This stage, which operates only on the candidates, and not on the entire slice, can thus work in higher dimensions, i.e. 2/2.5/3 dimensional object classification.

Our work is the introduction of the MiMax Technique, a unique pre-processing method for medical images that improves the transfer learning process from public datasets of everyday images. In order to give a theoretical introduction to this method, we also present the SPCLAHE technique.

The SPCLAHE method is a proven pre-processing method for boosting the analytics of medical images (specifically, malignancy detection in mammographies) using CNNs. A significant advantage of SPCLAHE is that its product is a color image. Eventually, we introduced the MiMax Technique, our novel contribution in this work, presenting the best results. The MiMax Technique essentially fuses together the CLAHE algorithm from the SPCLAHE method with the jet colormap, including the best of all worlds. Running the MiMax Technique resulted in up to 14% improvement in object detection performance, a very impressive result given that we have not changed any parameters in the neural network itself or in its training procedure.

Our work is based on 2D object detection and therefore the aim of its product is to improve the candidate generation stage in these systems. The idea is plugging this method into any existing CAD system, in order to boost its performance, without the need of changing the system itself.

עמודים