Weekly recap (29/04/2018)

This week we have a lot of interesting stuff:

Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms

Adaptive Optics + Light Sheet Microscopy to see living cells inside the body of a Zebra fish (the favorite fish of biologists!). Really impressive images overcoming scattering caused by tissue. You can read more about the paper on Nature and/or Howard Hughes Medical Institute.

 


The Feynmann Lectures on Physics online

I just read on OpenCulture that The Feynmann Lectures on Physics have been made available online. Until now, only the first part was published, but now you can also find volumes 2 and 3. Time to reread the classics…


Imaging Without Lenses

An interesting text appeared this week in American Scientist covering some aspects of the coming symbiosis between optics, computation and electronics. We are already able to overcome optical resolution, obtain phase information, or even imaging without using traditional optical elements, such as lenses. What’s coming next?


All-Optical Machine Learning Using Diffractive Deep Neural Networks

A very nice paper appeared on arXiv this week.

Xing Lin, Yair Rivenson, Nezih T. Yardimci, Muhammed Veli, Mona Jarrahi, Aydogan Ozcan

We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively. We experimentally demonstrated the success of this framework by creating 3D-printed D2NNs that learned to implement handwritten digit classification and the function of an imaging lens at terahertz spectrum. With the existing plethora of 3D-printing and other lithographic fabrication methods as well as spatial-light-modulators, this all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs.

Imagine if Fourier Transforms were discovered before lenses, and then some day someone comes up with just a piece of glass and says “this can make the computations of FT at the speed of light”. Very cool read.


OPEN SPIN MICROSCOPY

I just stumbled upon this project while reading Lab on the Cheap. Seems like a very good resource if you plan to build a light-sheet microscope and do not wanna spend $$$$ on Thorlabs.


Artificial Inteligence kits from Google, updated edition

Last year, AIY Projects launched to give makers the power to build AI into their projects with two do-it-yourself kits. We’re seeing continued demand for the kits, especially from the STEM audience where parents and teachers alike have found the products to be great tools for the classroom. The changing nature of work in the future means students may have jobs that haven’t yet been imagined, and we know that computer science skills, like analytical thinking and creative problem solving, will be crucial.

We’re taking the first of many steps to help educators integrate AIY into STEM lesson plans and help prepare students for the challenges of the future by launching a new version of our AIY kits. The Voice Kit lets you build a voice controlled speaker, while the Vision Kit lets you build a camera that learns to recognize people and objects (check it out here). The new kits make getting started a little easier with clearer instructions, a new app and all the parts in one box.

To make setup easier, both kits have been redesigned to work with the new Raspberry Pi Zero WH, which comes included in the box, along with the USB connector cable and pre-provisioned SD card. Now users no longer need to download the software image and can get running faster. The updated AIY Vision Kit v1.1 also includes the Raspberry Pi Camera v2.

Looking forward to see the price tag and the date they become available.

Optical companding

Christmas came and gone, and I am still trying to keep up with some papers I’ve read in the last months.

The guys at UCLA keep doing impressive stuff. First time I saw something from them was their work on Nature about ultrafast optical imaging (woah!).

This time they have proposed a way to improve the digitization of an electrical signal. Living in the time of the ‘great convergence’, every time we are more aware than Optics, Electronics, and Computer Science are closely related. Nowadays, in order to acquire optical information, one has almost always to deal with electrical signals in the analog domain, which need to be digitized before working with them in a computer. To do so, the most used tools are analog-to-digital converters (ADC). These instruments receive an electrical signal (analog), and convert it to a digital signal (a number representing the voltage or the current you are working with). This quantification sometimes results problematic, given that the full dynamic range of the signal (from the maximum to the minimum value) has to be divided in a finite number of steps (bins). If the signal presents very low variations, the bins might be not small enough to see the full details. One can try to see those details by amplifying the signal, but then the bigger values of the signal might be larger than the maximum value measurable by the ADC, provoking saturation.

Jalali’s group proposes to use Optical Companding to overcome this issue. The fundamental idea is to use optical processes that are not linear to compress the high amplitude signal parts, while amplifying the small amplitude signal values at the same time. After that, a traditional ADC digitizes the signal, and the knowledge about the optical compressor makes it possible to restore the original signal with great accuracy.

Optical Companding,

Yunshan Jiang, Bahram Jalali, submitted on 29 Dec 2017, https://arxiv.org/abs/1801.00007

(featured image exctracted from Fig. 1 of the manuscript)

Abstract,
We introduce a new nonlinear analog optical computing concept that compresses the signal’s dynamic range and realizes non-uniform quantization that reshapes and improves the signal-to-noise ratio in the digital domain.