הפקולטה להנדסה ע"ש איבי ואלדר פליישמן

13/6/16

You are invited to attend a lecture

Snapshot Spectral Imaging Using a Digital Camera with an Optical Diffuser and Compressed Sensing Algorithms

Jonathan Hauser

M.Sc. student of

Professor Michael Golub and Professor Menachem Nathan

Physical Electronics Department, Tel Aviv University

Spectral Imaging (SI) systems are designed to capture the spatial and spectral information of an object or a scene, which is referred to generally as "data cube" or "spectral cube". There is a strong link between the spectrum, physical state and components of matter. Accordingly, SI systems are practical in various fields such as medicine, agriculture, food inspection, military and homeland security, as well as in various academic research fields related to physics, chemistry, biology, geography, engineering and more.

Many of the SI systems that exist in the industry are based on scanning techniques, are quite complicated and require long spectral cube acquisition time. Snapshot Spectral Imaging (SSI) systems aim to capture the spectral cube with a single snapshot, thus eliminating the need of scanning the spatial and/or spectral space of the scene. Known SSI systems are generally complex and expensive in terms of their electrical, optical and mechanical implementation.

The aim of this study is to realize a working prototype of a compact SSI system based on a standard monochromatic digital camera, a single thin optical diffuser and a computational unit. The diffuser is a transparent phase element, which is designed to fulfill Compressed Sensing (CS) mathematical conditions that enable the spectral cube reconstruction from a single "shot".

The achieved results show, for the first time, feasibility proof for successful restoration of a full spectral cube, from a snapshot taken with a monochromatic image sensor in a relatively simple CS-SSI system.

Monday, June 13, 2016, at 09:00

Room 011, Kitot Building

13 ביוני 2016, 9:00

011 Kitot

6/9/16

Control of thermal radiation by near-field effects

Dr. Nir Dahan

SCD – Semiconductor Devices Ltd., Israel

Abstract

In this talk, I discuss how excitation of surface waves can be utilized to modify the optical properties of materials. In particular, it is the mechanism to produce polarized and coherent thermal emission. Although the correlation length is defined by the intrinsic character of the bulk, it is enhanced by coherent coupling between resonant cavities, as in a phase antenna array. Furthermore, the interaction of surface waves with rotational symmetry structure shown to be a manifestation of geometric phase in thermal radiation. In solar applications, enhanced absorption is proposed by means of light scattering from particles close to an interface. Therein, the absorption mechanism is coupling of scattered light to surface modes. A detailed analysis is reviewed where reciprocity relation and energy conservation are satisfied on the interface. In addition, a semi-coherent model was developed to handle scattering and absorption in thin photovoltaic cells. Finally, the field of radiative energy transport in the nano-scale will be briefly presented.

Thursday, June 9, 2016, at 15:00

Room 011, Kitot Building

09 ביוני 2016, 15:00

011 Kitot

EE SEminar: Inference in Perturbation Models for Enhanced Matching

~~
Speaker: Chen Harel
M.Sc. student under the supervision of Prof. Daniel Cohen-Or and Prof. Shai Avidan

Wednesday, June 15th, 2016 at 15:30
Room 011, Kitot Bldg., Faculty of Engineering

Inference in Perturbation Models for Enhanced Matching

Matching meaningful correspondences is a challenging and important problem in computer vision. In particular, bipartite graph matching is a useful tool in many visual applications. In this work, we introduce the notion of perturbations for enhanced matching, and present the power of perturbation matching in the context of shape matching of image contours.
Perturbation models are high dimensional probability models that measure the stability of max-prediction to random shifts of the computed scores. The probability of a given matching is the volume of scores for which this matching is the highest scoring matching.
We demonstrate the effectiveness of our approach in pair of images with background clutter, showing that inference with perturbation models is more robust than finding the highest scoring matching. We also show that for perturbation matching it is favorable to use logistic perturbation models rather than Gumbel perturbation models, proving that inference using logistic perturbation models is more efficient.

15 ביוני 2016, 15:30

Oz Oshri ביה"ס להנדסה מכאנית

06 ביוני 2016, 15:00

וולפסון 206

School of Mechanical Engineering Seminar
Monday, June 6, 2016 at 15:00
Wolfson Building of Mechanical Engineering, Room 206

Strain tensor selection and the elastic theory of incompatible thin sheets

Oz Oshri

School of Physics & Astronomy, Tel Aviv University

We re-examine the present theory of incompatible elastic sheets [Efrati et al., J. Mech. Phys. Solids {\bf 57}, 762 (2009)]. When applied to compatible sheets, deformed uniaxially by pure bending moments, it generates spurious in-plane stresses. We present an alternative formulation for a class of simple axisymmetric problems. Our theory yields linear, exactly solvable, equations of equilibrium in the case of planar deformations, replacing the non-linear ones derived earlier for these problems. In addition, we obtain a simple criterion determining whether an isometric immersion of such sheets satisfies mechanical equilibrium. When reduced to unidirectional (one-dimensional) deformations the formulation correctly converges to the extensible elastica.

bio:
BSc. Mechanical Engineering, Tel Aviv University (2004).
MSc. Mechanical Engineering, Tel Aviv University (2005), advisor: Prof. Isaac Goldhirsch.
2012-Toward a PhD in Physics, advisor: Prof. Haim Diamant. Subject: Pattern formation in thin elastic sheets.

EE Seminar: Distributed Estimation of Regression Coefficients in Bandwidth Constrained Sensor Networks

~~Speaker: Liran Gispan,
M.Sc. student under the supervision of Prof. Amir Leshem and Prof. Yair Be’ery

Wednesday, June 15, 2016 at 15:00
Room 011, Kitot Bldg., Faculty of Engineering

Distributed Estimation of Regression Coefficients in Bandwidth Constrained Sensor Networks
Abstract

Consider a wireless sensor network with a fusion center deployed to estimate a common non-random parameter vector. Each sensor obtains a noisy observation vector of the non-random parameter vector according to a linear regression model. The observation noise is correlated across the sensors. Due to power, bandwidth and complexity limitations, each sensor linearly compresses its data. The compressed data from the sensors are transmitted to the fusion center, which linearly estimates the non-random parameter vector. The goal is to design the compression matrices at the sensors and the linear unbiased estimator at the fusion center such that the total variance of the estimation error is minimized.
In this work, we provide necessary and sufficient conditions for achieving the performance of the centralized best linear unbiased estimator. We also provide the optimal compression matrices and the optimal linear unbiased estimator when these conditions are satisfied. When these conditions are not satisfied, we propose a sub-optimal algorithm to determine the compression matrices and the linear unbiased estimator. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.

15 ביוני 2016, 15:00

חדר 011, בניין כיתות-חשמל