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Embedded 3D interconnects in glass substrates by a combined laser trenching and printing process
By:
Yuval Berg
Ph.D. student under supervision of Prof. Yosi Shacham-Diamand

Abstract
Control of grooved structured profiles can be achieved by a femtosecond laser ablation process in different materials – dielectrics, semi-conductors and metals. In addition, high accuracy additive manufacturing techniques, e.g. laser induced forward transfer (LIFT), provide flexibility in 3D printed structures deposited on a variety of substrates.
The combination of those two laser technologies allows the integration of embedded circuitry and other components, such as microfluidic and micromechanical systems, paving the way to a wide range of applications where conventional subtractive patterning is a problem.
Embedding is advantageous in terms of mechanical stability and adherence of the printed metal allowing a favorable aspect ratio and thereby providing improved electrical properties of the conducting lines as well as planar and debris-free surfaces.
In this work I report on a combination of laser grooving and laser printing processes and demonstrate the manufacturing of buried copper structures in a grooved borosilicate glass substrate.
On Monday, March 11, 2019 at 9:30
Room 011, Kitot Building

Speaker: Yehya Massalha

M.Sc. student under the supervision of Prof. Joseph Appelbaum

Sunday, March.3^rd, 2019 at 15:30

Room 011, Kitot Bldg., Faculty of Engineering

The location of PV modules on collector rows affects the modules output power - experimental verification

Abstract

In multiple collector rows of photovoltaic fields, the collectors may be installed with several modules placed one above the other along the collector width. The PV modules experience uneven incident diffuse radiation caused by differences in the modules' sky view factor. The present experimental study verifies the sky view factor model, and shows the differences in output power of the PV modules placed in different locations along the width of the second collector row. This work is a complementary experimental study to the theoretical previous work, and emphasizes the importance of the incident diffuse radiation, associated with the sky view factor, on the energy loss of the PV field. Two collector rows deployed with PV modules were tested on the laboratory roof for several days for different inclination angles and distances between collector rows. The results show that a top module may generate 8 percent more power than a bottom module at noon time. The findings of this experimental study have technical significance in designing PV systems.

Speaker: Anna Darzi

M.Sc. student under the supervision of Prof. Shai Avidan

Wednesday, March 6^th, 2019 at 15:30

Room 011, Kitot Bldg., Faculty of Engineering

Co-occurrence Based Texture Synthesis

Abstract

We model local texture patterns using the co-occurrence statistics of pixel values. We then train a conditional generative adversarial network (cGAN) to synthesize new textures from the co-occurrence statistics and a random seed noise. Co-occurrences have long been used to measure similarity between textures. That is, two textures are considered similar if their corresponding co-occurrence matrices are similar. By the same token, we show that multiple textures generated from the same co-occurrence matrix are similar to each other. This gives rise to a new texture synthesis algorithm.

We use co-occurrence based texture synthesis in various settings. For example, we generate variations on the input texture by using the same co-occurrence statistics with different seed noise, or we merge two co-occurrence matrices to smoothly
interpolate between different textures.

In another case, we synthesize a dynamic texture sequence by interpolating between two co-occurrence matrices. Yet another option is to create a sequence that
summarizes the various local texture patterns in a given texture image. And because co-occurrence statistics have clear and intuitive meaning we develop a tool that lets users modify them directly and hence influence the local characteristics of the synthesized texture image.

Speaker: Elad Tzoreff

Ph.D. student under the supervision of Prof. Anthony J. Weiss

Monday, March 4^th, 2019 at 15:00

Room 011, Kitot Bldg., Faculty of Engineering

Single Sensor Trajectory Optimization for Best Emitter Localization

Abstract

Passive emitter localization has many civilian, commercial and military applications. The rapidly increasing utilization of smartphones and, therefore mobile applications, has created a high demand for location based services, both in commercial applications and social networking, for multiple and varied uses. Location based services are also critical to many businesses and government organizations to derive real insight from data tied to specific locations where activities take place. The spatial patterns that location-related data and services can provide is one of the most powerful and useful aspects when location is a common denominator in all of these activities and can be leveraged to better understand patterns and relationships. Accordingly, precise, and personalized localization solutions become a fundamental requirement of any commercial/social service.

In this presentation I will address the problem of a single platform trajectory optimization, aims to provide a targeted localization solution for a given emitter based on TOA measurements (i.e., minimizing the localization error of the emitter). The problem of trajectory optimization is a constraint non-convex optimization problem. Constraints arise due to physical limitation of the platforms, and geographical constrains such as restricted areas and safety zones in which the receiver is not allowed to travel. I will discuss two use-cases, a pre-mission design in which the entire trajectory is optimized based on prior knowledge on the emitter location. The second use-case is a real-time path design, in which the receiver begins with a coarse estimation of the emitter location, and searches for the next best way-point to travel to. In this case, the uncertainty in the estimation is incorporated into the optimization problem, in order to avoid over-optimistic steps in preliminary stages of the process. For both use-cases, we propose convex relaxation solutions based on Semi-definite relaxation methods and demonstrate their impressive results in terms of performance and robustness. Next, I will discuss the trajectory optimization of a pair of sensors which cooperate to localize an emitter based on TDOA observations. The presence of more than a single sensor imposes additional constraints on the pairwise distances between the sensors. We derive a solution based on the alternating direction of multipliers (ADMM) with intermediate steps carried out using the majorization minimization (MM) and SDR methods. The algorithm is demonstrated to outperform global optimizers such as genetic and the basin-hoping algorithms, both in terms of performance (better localization error) and speed of convergence.

As a final step, in order to provide an algorithmic solution that is capable of operating in real time environments, we introduce a differential dynamic programming (DDP) solution that is demonstrated to converge quadratically to good local optima, exploiting the desired properties of Newton method.