Exploring Material Interiors with Deep Learning

Previously Mentioned, now in more detail..

Machine learning & AI

Deep-learning system explores materials' interiors from the outside   by David L. Chandler , Massachusetts Institute of Technology 

Maybe you can't tell a book from its cover, but according to researchers at MIT you may now be able to do the equivalent for materials of all sorts, from an airplane part to a medical implant. Their new approach allows engineers to figure out what's going on inside simply by observing properties of the material's surface.

The team used a type of machine learning known as deep learning to compare a large set of simulated data about materials' external force fields and the corresponding internal structure, and used that to generate a system that could make reliable predictions of the interior from the surface data.

The results have been published in the journal Advanced Materials, in a paper by doctoral student Zhenze Yang and professor of civil and environmental engineering Markus Buehler.

"It's a very common problem in engineering," Buehler explains. "If you have a piece of material—maybe it's a door on a car or a piece of an airplane—and you want to know what's inside that material, you might measure the strains on the surface by taking images and computing how much deformation you have. But you can't really look inside the material. The only way you can do that is by cutting it and then looking inside and seeing if there's any kind of damage in there."

It's also possible to use X-rays and other techniques, but these tend to be expensive and require bulky equipment, he says. "So, what we have done is basically ask the question: Can we develop an AI algorithm that could look at what's going on at the surface, which we can easily see either using a microscope or taking a photo, or maybe just measuring things on the surface of the material, and then trying to figure out what's actually going on inside?" That inside information might include any damages, cracks, or stresses in the material, or details of its internal microstructure.

The same kind of questions can apply to biological tissues as well, he adds. "Is there disease in there, or some kind of growth or changes in the tissue?" The aim was to develop a system that could answer these kinds of questions in a completely noninvasive way.

Achieving that goal involved addressing complexities including the fact that "many such problems have multiple solutions," Buehler says. For example, many different internal configurations might exhibit the same surface properties. To deal with that ambiguity, "we have created methods that can give us all the possibilities, all the options, basically, that might result in this particular [surface] scenario."

The technique they developed involved training an AI model using vast amounts of data about surface measurements and the interior properties associated with them. This included not only uniform materials but also ones with different materials in combination. "Some new airplanes are made out of composites, so they have deliberate designs of having different phases," Buehler says. "And of course, in biology as well, any kind of biological material will be made out of multiple components and they have very different properties, like in bone, where you have very soft protein, and then you have very rigid mineral substances."  ... ' 

On Metamaterials: RIS

RIS: Reconfigurable Intelligent Surfaces.

Metamaterials Could Solve One Of 6G’S Big Problems 

There’s plenty of bandwidth available if we use reconfigurable intelligent surfaces

MARIOS POULAKIS   in IEEE Spectrum

FOR ALL THE TUMULTUOUS revolution in wireless technology over the past several decades, there have been a couple of constants. One is the overcrowding of radio bands, and the other is the move to escape that congestion by exploiting higher and higher frequencies. And today, as engineers roll out 5G and plan for 6G wireless, they find themselves at a crossroads: After years of designing superefficient transmitters and receivers, and of compensating for the signal losses at the end points of a radio channel, they’re beginning to realize that they are approaching the practical limits of transmitter and receiver efficiency. From now on, to get high performance as we go to higher frequencies, we will need to engineer the wireless channel itself. But how can we possibly engineer and control a wireless environment, which is determined by a host of factors, many of them random and therefore unpredictable?

Perhaps the most promising solution, right now, is to use reconfigurable intelligent surfaces. These are planar structures typically ranging in size from about 100 square centimeters to about 5 square meters or more, depending on the frequency and other factors. These surfaces use advanced substances called metamaterials to reflect and refract electromagnetic waves. Thin two-dimensional metamaterials, known as metasurfaces, can be designed to sense the local electromagnetic environment and tune the wave’s key properties, such as its amplitude, phase, and polarization, as the wave is reflected or refracted by the surface. So as the waves fall on such a surface, it can alter the incident waves’ direction so as to strengthen the channel. In fact, these metasurfaces can be programmed to make these changes dynamically, reconfiguring the signal in real time in response to changes in the wireless channel. Think of reconfigurable intelligent surfaces as the next evolution of the repeater concept.

Reconfigurable intelligent surfaces could play a big role in the coming integration of wireless and satetelite networks.

That’s important, because as we move to higher frequencies, the propagation characteristics become more “hostile” to the signal. The wireless channel varies constantly depending on surrounding objects. At 5G and 6G frequencies, the wavelength is vanishingly small compared to the size of buildings, vehicles, hills, trees, and rain. Lower-frequency waves diffract around or through such obstacles, but higher-frequency signals are absorbed, reflected, or scattered. Basically, at these frequencies, the line-of-sight signal is about all you can count on.

Such problems help explain why the topic of reconfigurable intelligent surfaces (RIS) is one of the hottest in wireless research. The hype is justified. A landslide of R&D activity and results has gathered momentum over the last several years, set in motion by the development of the first digitally controlled metamaterials almost 10 years ago.

This article was jointly produced by IEEE Spectrum and Proceedings of the IEEE with similar versions published in both publications.

RIS prototypes are showing great promise at scores of laboratories around the world. And yet one of the first major projects, the European-funded Visorsurf, began just five years ago and ran until 2020. The first public demonstrations of the technology occurred in late 2018, by NTT Docomo in Japan and Metawave, of Carlsbad, Calif.

Today, hundreds of researchers in Europe, Asia, and the United States are working on applying RIS to produce programmable and smart wireless environments. Vendors such as Huawei, Ericsson, NEC, Nokia, Samsung, and ZTE are working alone or in collaboration with universities. And major network operators, such as NTT Docomo, Orange, China Mobile, China Telecom, and BT are all carrying out substantial RIS trials or have plans to do so. This work has repeatedly demonstrated the ability of RIS to greatly strengthen signals in the most problematic bands of 5G and 6G.

How reconfigurable intelligent surfaces strengthen a wireless signal

To understand how RIS improves a signal, consider the electromagnetic environment. Traditional cellular networks consist of scattered base stations that are deployed on masts or towers, and on top of buildings and utility poles in urban areas. Objects in the path of a signal can block it, a problem that becomes especially bad at 5G’s higher frequencies, such as the millimeter-wave bands between 24.25 and 52.6 gigahertz. And it will only get worse if communication companies go ahead with plans to exploit subterahertz bands, between 90 and 300 GHz, in 6G networks. Here’s why. With 4G and similar lower-frequency bands, reflections from surfaces can actually strengthen the received signal, as reflected signals combine. However, as we move higher in frequencies, such multipath effects become much weaker or disappear entirely. The reason is that surfaces that appear smooth to a longer-wavelength signal are relatively rough to a shorter-wavelength signal. So rather than reflecting off such a surface, the signal simply scatters.

One solution is to use more powerful base stations or to install more of them throughout an area. But that strategy can double costs, or worse. Repeaters or relays can also improve coverage but here, too, the costs can be prohibitive. RIS, on the other hand, promises greatly improved coverage at just marginally higher cost ... ' 

Micro-Sized Camera will Turn Nanorobots into Photographers

 Quite an interesting thing here.  A new way to use sensors;

This Micro-Sized Camera will Turn Nanorobots into Photographers - News WWC  By Steven

Nanorobotics, like graphene, have been trending topics in research for years but are still far from full industrial production. Everyone is aware of its potential, but the technical hurdles remain. Fortunately, research is progressing steadily. The latest invention that would allow the world’s tiniest robots to take a giant leap forward is a camera barely the size of a grain of salt.

Imagine for a moment that, instead of using a bulky CAT scan or intrusive endoscopy, an almost invisible robot could inspect your arteries or the most inaccessible corners of your heart. Those could be some of the of applications enabled by a new camera designed by scientists at Princeton University in the U.S. It is the size of a grain of salt and works in a radically different way from traditional lenses.

An alliance between metasurfaces and neural networks

The inventors of this new device claim that the image quality is similar to a camera 500,000 times larger. To achieve this, they have had to reinvent the camera concept and resort to a combination of hardware and software based on neural networks. This is how they succeeded.

Firstly, their camera looks more like a microchip than a lens. In fact, the primary material is silicon nitride, used in the production of semiconductors. Thanks to it, instead of using a lens to bend the light rays, they have squeezed 1.6 million cylinders into a space of less than half a millimeter. Each of these cylinders has a different size to emulate the effect of a lens. Thus, they operate as optical antennas. In scientific jargon, this array is known as a “metasurface.”

What is a metasurface?

To understand what a metasurface is, we must first talk about metamaterials. These are structures designed at the micro- or nanoscale that interact with light and other types of energy in ways unknown in the natural world. One of these is negative refraction, a phenomenon that opens the door to such exotic results as invisibility or superlenses of unprecedented capacities, such as the Princeton University camera. Thus, metasurfaces are films that leverage the qualities of metamaterials.     

However, the physical part is only one element of the equation. The developers’ second achievement has been to create a design that processes signals through a neural network. This processing system achieves sharp images in natural light conditions.     .... '