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Category: engineering

Your phone’s GPS, the Wi-Fi in your house and communications on aircraft are all powered by radio-frequency, or RF, waves, which carry information from a transmitter at one point to a sensor at another. The sensors interpret this information in different ways. For example, a GPS sensor uses the angle at which it receives an RF wave to determine its own relative location. The more precisely it can measure the angle, the more accurately it can determine location.

In a new paper published in Physical Review Letters, University of Arizona engineering and optical sciences researchers, in collaboration with engineers from General Dynamics Mission Systems, demonstrate how a combination of two techniques—radio frequency photonics sensing and quantum metrology—can give sensor networks a previously unheard-of level of precision. The work involves transferring information from electrons to photons, then using to increase the photons’ sensing capabilities.

“This quantum sensing paradigm could create opportunities to improve GPS systems, astronomy laboratories and biomedical imaging capabilities,” said Zheshen Zhang, assistant professor of materials science and engineering and , and principal investigator of the university’s Quantum Information and Materials Group. “It could be used to improve the performance of any application that requires a of sensors.”

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Only 10 years ago, scientists working on what they hoped would open a new frontier of neuromorphic computing could only dream of a device using miniature tools called memristors that would function/operate like real brain synapses.

But now a team at the University of Massachusetts Amherst has discovered, while on their way to better understanding protein , how to use these biological, electricity conducting filaments to make a neuromorphic memristor, or “memory transistor,” device. It runs extremely efficiently on very low power, as brains do, to carry signals between neurons. Details are in Nature Communications.

As first author Tianda Fu, a Ph.D. candidate in electrical and , explains, one of the biggest hurdles to neuromorphic computing, and one that made it seem unreachable, is that most conventional computers operate at over 1 volt, while the brain sends signals called action potentials between neurons at around 80 millivolts—many times lower. Today, a decade after early experiments, memristor voltage has been achieved in the range similar to conventional computer, but getting below that seemed improbable, he adds.

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O,.o circa 2007.

Theoretical physicists at the University of St. Andrews have created ‘incredible levitation effects’ by engineering the force of nature which normally causes objects to stick together by quantum force. By reversing this phenomenon, known as ‘Casimir force’, the scientists hope to solve the problem of tiny objects sticking together in existing novel nanomachines.

Professor Ulf Leonhardt and Dr Thomas Philbin of the University’s School of Physics & Astronomy believe that they can engineer the Casimir force of quantum physics to cause an object to repel rather than attract another in a vacuum.

Casimir force (discovered in 1948 and first measured in 1997) can be demonstrated in a gecko’s ability to stick to a surface with just one toe. However, it can cause practical problems in nanotechnology, and ways of preventing tiny objects from sticking to each other is the source of much interest.

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Kilopower is a small, light-weight fission nuclear power system capable of providing up to 10 kilowatts of electrical power — enough to run several average households continuously for at least 10 years.

Four Kilopower units would provide enough power to establish an outpost on the Moon or Mars.

#engineering

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Here’s another neat thing drones can do—beam power across the sky to recharge sensors in hard-to-reach places.

Remote sensors play a valuable role in collecting data—but recharging these devices while they are scattered over vast and isolated areas can be tedious. A new system is designed to make the charging process easier by using unmanned aerial vehicles (UAVs) to deliver power using radio waves during a flyby. A specialized antenna on the sensor harvests the signals and converts them into electricity. The design is described in a study published 23 March in IEEE Sensors Letters.

Joseph Costantine and his colleagues at the American University of Beirut, in partnership with researchers at the Institute of Electronics, Computer, and Telecommunications Engineering in Italy, were exploring ways to remotely charge sensors using radio frequency waves (the same form of energy used to transmit Wi-Fi). However, a major challenge was that the source of the radio waves must be fairly close to the sensor in order to sufficiently charge it.

This prompted the researchers to consider the use of UAVs, which could soar over each sensor. “In addition, a UAV can follow an optimized trajectory that maximizes energy transfer to the sensors in question,” Costantine explains. He says his team developed this system to control and recharge sensors used in agriculture, but that it could be extended to any situation where sensors are deployed in hard-to-reach areas.

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Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

Let us know if you have suggestions for next week, and enjoy today’s videos.

In 2017, a team of USC Viterbi researchers created ADAMMS (Agile Dexterous Autonomous Mobile Manipulation System), a robot designed to support repetitive human tasks, like transporting equipment or tending a 3D printer at 3 a.m. ADAMMS can perform specific actions like opening doors or picking up objects autonomously. These researchers, including postdoctoral researcher in the USC Viterbi Department of Aerospace and Mechanical Engineering Pradeep Rajendran, AME Ph.D student Shantanu Thakar, Department of Computer Science master’s student Hyojeong Kim and M.S. AME’18 Vivek Annem, envisioned a tool that could support humans remotely.

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Inspired by how human bone and colorful coral reefs adjust mineral deposits in response to their surrounding environments, Johns Hopkins researchers have created a self-adapting material that can change its stiffness in response to the applied force. This advancement can someday open the doors for materials that can self-reinforce to prepare for increased force or stop further damage. A report of the findings was published today in Advanced Materials.

“Imagine a bone implant or a bridge that can self-reinforce where a high force is applied without inspection and maintenance. It will allow safer implants and bridges with minimal complication, cost and downtime,” says Sung Hoon Kang, an assistant professor in the Department of Mechanical Engineering, Hopkins Extreme Materials Institute, and Institute for NanoBioTechnology at The Johns Hopkins University and the study’s senior author.

While other researchers have attempted to create similar synthetic materials before, doing so has been challenging because such materials are difficult and expensive to create, or require active maintenance when they are created and are limited in how much stress they can bear. Having materials with adaptable properties, like those of wood and bone, can provide safer structures, save money and resources, and reduce harmful environmental impact.

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MYSTERY WIRE — Are psychic abilities real, and if so, can they be measured? Yes to both questions, says Dr. Dean Radin.

For decades, the Department of Defense sponsored secret studies of psychic phenomena in hopes of training an elite team of psychic soldiers. Officially, the program was canceled, but research into psychic abilities continues in the private sector, and one of the scientists on the cutting edge is featured this week on mysterywire.com.

Radin earned advanced degrees in both electrical engineering and psychology, worked for prestigious companies and labs, and has spent decades trying to measure psychic abilities.

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The Universidad Carlos III de Madrid (UC3M), together with the Universidad Pontificia de Comillas and the University of Porto, has patented a magnetic cork that could remove polluting particles from water, among other uses.

The magnetic has been created through a process of co-precipitation of iron oxide through which magnetite is obtained. This mineral is absorbed as soon as it comes into contact with the surface of the cork. “The patent arises from the need to make graded adhesive joints. It occurred to me, when reading about the various techniques that are used for graded joints and about cork, that we could make the cork magnetic using the process that is currently used to obtain magnetite,” notes Juana Abenojar, researcher in the Department of Materials Science and Engineering and Chemical Engineering at the UC3M.

Thanks to the magnetisation of cork, the ease of capturing particles with the help of a magnet allowing them to be positioned in a —for example, to modify rigid polymers when an area needs to be more ductile than the rest as it is going to be subjected to impact—is added to the inherent advantages of the material, such as its low weight an impact resistance. Using the magnet, a greater number of magnetic cork particles are put in a certain place to achieve greater flexibility.

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The synthesis of plastic precursors, such as polymers, involves specialized catalysts. However, the traditional batch-based method of finding and screening the right ones for a given result consumes liters of solvent, generates large quantities of chemical waste, and is an expensive, time-consuming process involving multiple trials.

Ryan Hartman, professor of chemical and at the NYU Tandon School of Engineering, and his laboratory developed a lab-based “intelligent microsystem” employing , for modeling that shows promise for eliminating this costly process and minimizing environmental harm.

In their research, “Combining automated microfluidic experimentation with machine learning for efficient polymerization design,” published in Nature Machine Intelligence, the collaborators, including doctoral student Benjamin Rizkin, employed a custom-designed, rapidly prototyped microreactor in conjunction with automation and in situ infrared thermography to study exothermic (heat generating) polymerization—reactions that are notoriously difficult to control when limited experimental kinetic data are available. By pairing efficient microfluidic technology with machine learning algorithms to obtain high-fidelity datasets based on minimal iterations, they were able to reduce chemical waste by two orders of magnitude and catalytic discovery from weeks to hours.

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