סמינר המחלקה להנדסה ביו-רפואית

09 במאי 2021, 14:00 
זום  
ללא עלות
סמינר המחלקה להנדסה ביו-רפואית

Modeling and engineering gene expression based on a computational approach: methodology and applications 

Gene expression is a process intrinsic to all life, whereby information encoded in the genetic material is used to build proteins that drive the functioning of living organisms. Gene expression is a multi-step process mediated by millions of intracellular biological machines (e.g. RNA polymerases, transcription factors, ribosomes, tRNAs, miRNAs), with complex interactions among them. Deciphering, modelling, and engineering the process of gene expression is therefore an extremely challenging undertaking, but one with enormous potential to impact any biomedical field.

In our lab, we develop various computational models and algorithms that are used in conjunction with state-of-the-art approaches for genome editing to understand and engineer this process. Our pipeline is based among others on biophysical simulations, machine learning, tools from electrical engineering, computer science, and computational molecular evolution.

In recent years these models have been used in various applications such as generating vaccines and oncolytic viruses, producing therapeutic proteins and green energy, generating cheap food ingredients, designing efficient biosensors, and developing novel diagnosis tools based on cancer genomes.

The aim of this seminar is to provide an introduction to the field and to the approaches developed in our lab for mastering gene expression, and to demonstrate the applications of our tools. The talk will be self-contained and will not require prior knowledge.

סמינר מקוון עם רועי אלטרמן

Roi Alterman

M.Sc. student under the supervision of Prof. Shlomo Ruschin

 

 

11 במאי 2021, 15:00 
זום  
Efficiency enhancement of high energy, actively Q-switched Nd:YAG Laser

 

 

אבסטרקט

 

 

 

 

Diode-pumped solid-state (DPSS) lasers are widely used on account of their compactness and high efficiency. The Q-switched 1.06µm Nd:YAG laser studied in this thesis is presumably the most prominent DPSS. In recent years, interest in high-efficiency lasers has increased, especially for harsh environment applications where power resources and volume are limited, such as military space and metrology applications.

High-efficiency lasers are essential for several reasons: the higher the efficiency of the laser, the less the unwanted thermal effects, therefore improving birefringence, polarization-dependent loss, cavity stability, and beam quality while reducing the dependency on working conditions.

Several methods and techniques for achieving high efficiency using direct pumping at 885 nm were carefully examined.

Different laser rod dopings were tested, analyzing the effects of Nd concentration levels on laser performance. Various aspects of this performance, such as beam quality efficiency and maximum internal intensity, were studied. Throughout all the study stages, starting with the design considerations and including the experiments, great emphasis was placed on developing analytical and numerical predictive models.

The various results and insights achieved were incorporated into constructing a compact efficiency enhanced high energy laser. The Q-switched laser operated at a 5 Hz pulse repetition rate (PRF) with a 128 mJ/pulse output and light-to-light conversion efficiency of 33.6%; to our knowledge, it is the most efficient DPSS Nd:YAG laser of its type and class. 
To compensate for the pulse width’s reduction caused by the short cavity, a pulse-stretching technique based on an electro-optical modulator was demonstrated. This technique allowed QS pulse temporal shape adjustment.

 

 

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 ד"ר ערן טוך 
בריאיון לגלי צה"ל על האסון במירון.

 ד"ר ערן טוך 
אין בינה מלאכותית שיכולה למנוע את האסון הבא.

 ד"ר ערן טוך 

הידע קיים פשוט לא השתמשו בו.

פרופ' נעם אליעז

"חומר חכם" שינה צורה בננו-לוויין ישראלי בחלל.

סמינר מקוון עם אוהד זילביגר

 Ohad Silbiger

MSc student under the supervision of Dr. Yakir Hadad

 

 

20 במאי 2021, 13:00 
זום  
 One-way Acoustic Guiding in a Fluid with Mean Flow

 

 

אבסטרקט

 

 

 

In a moving acoustic medium, sound waves travel differently with and against the fluid flow. This well-established acoustic effect is backed by the intuition that the fluid velocity-bias imparts momentum on the propagating acoustic waves, thus violating reciprocity. Based on this conception, fluid flow that is transverse to the wave direction of propagation will not break reciprocity. In this work we contrast this common wisdom and theoretically show that the interplay between transverse mean flow and transverse structural gliding-asymmetry can yield strong nonreciprocity and even, surprisingly, one-way acoustic waveguiding. To demonstrate that, we analyze a waveguide that comprises of a few adjacent acoustic sub-diffraction chains, each consists of acoustic scatterers with monopolar or dipolar response. The structure is embedded in a fluid with mean flow velocity transverse to the waveguide axis. We find the symmetry breaking conditions under which nonreciprocity is obtained, and we show how under transverse mean flow, with Mach numbers as low as 0.02, one-way propagation of the acoustic wave is obtained on a sub-wavelength-thick acoustic waveguide.

As opposed to the case when the flow is transverse to the waveguide axis, nonreciprocity in a waveguide in longitudinal fluid flow is of no surprise. However, one-way guiding in this regime is not trivial. In the second part of this work we demonstrate this effect in a single sub-diffraction chain of dipoles, embedded in a medium with longitudinal mean flow with Mach number of 0.1.

Our results may open another venue for the design of nonreciprocal acoustic wave devices for various applications.

 

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סמינר מקוון עם גל שטדנל

 Gal Shtendel

M.Sc. student under the supervision of Prof. Amir Averbuch and Prof. Menachem Nathan

 

 

18 במאי 2021, 14:00 
זום  
Improving the generalization of a snapshot spectral imaging system with system characterization,  and Deep-Learning based solutions

 

 

אבסטרקט

 

 

As part of the research field of Computational Imaging, Snapshot Spectral Imaging (SSI) systems aim to capture the 3-Dimensional (3D) spatial and spectral scene information using a co-design of hardware and software. So far, many efforts had been invested in developing optical systems that can capture such an encoded version of the scene information, and corresponding reconstruction algorithms for recovering a 3D hyperspectral “cube” (hyperspectral refers to spectral information of some approximately continuous wavelengths range) from the encoded measurements.

In line with this, our research group had previously demonstrated a cost-effective, portable, and simple SSI system that utilizes a single diffractive element (“diffuser”) and produces Dispersed and Diffused (DD) monochromatic images, which are encoded measurements of the scene.  

Recently, our group also presented a Machine-Learning based algorithms called “DD-Net”, that achieved state-of-the-art results for the reconstruction of HS cubes from the DD images. However, experiments showed that the recovery algorithms tend to be sensitive and unsatisfactory for data

that is significantly different than the data they were optimized on, in what being called the “Generalization problem” of data-driven solutions. In the case of supervised learning, this problem is present both in the data’s input samples domain and in the output labels domain, independently.

This thesis work focuses on decreasing the generalization error the system obtains, which is the error obtained from DD-Net evaluation on test datasets, without additional physical data acquisition process. Based on how the generalization problem manifests differently in the input and output algorithm’s domains, a slightly different approach was taken. 

The results of the two parallel research directions led to a significant contribution to the system's HS reconstruction quality, as well as important and beneficial insights regarding the system capabilities. As part of this work, we developed the “SHS-GAN”, which is a novel general-purpose, end-to-end framework for enlarging HS datasets based on RGB samples that can easily be adapted and contribute to a variety of HS-related tasks.

 

* A scientific paper describing the “SHS-GAN” implement and utilization will be soon published in: “IEEE Transactions In Computational Imaging” journal.

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