Nav: Home

Algorithm accurately predicts how electromagnetic waves and magnetic materials interact

September 10, 2018

UCLA Samueli engineers have developed a new tool to model how magnetic materials, which are used in smartphones and other communications devices, interact with incoming radio signals that carry data. It accurately predicts these interactions down to the nanometer scales required to build state-of-the-art communications technologies.

The tool allows engineers to design new classes of radio frequency-based components that are able to transport large amounts of data more rapidly, and with less noise interference. Future use cases include smartphones to implantable health monitoring devices.

Magnetic materials can attract or repel each other based on their polar orientation--positive and negative ends attract each other, while two positives or two negatives repel. When an electromagnetic signal like a radio wave passes through such materials, a magnetic material acts like a gatekeeper, letting in the signals that are desired, but keeping out others. They can also amplify the signal, or dampen the speed and strength of the signal.

Engineers have used these gatekeeper-like effects, called "wave-material interactions," to make devices used in communications technologies for decades. For example, these include circulators that send signals in specific directions or frequency-selective limiters that reduce noise by suppressing the strength of unwanted signals.

Current design tools are not comprehensive and precise enough to capture the complete picture of magnetism in dynamic systems, such as implantable devices. The tools also have limits in the design of consumer electronics.

"Our new computational tool addresses these problems by giving electronics designers a clear path toward figuring out how potential materials would be best used in communications devices," said Yuanxun "Ethan" Wang, a professor of electrical and computer engineering who led the research. "Plug in the characteristics of the wave and the magnetic material, and users can easily model nanoscale effects quickly and accurately. To our knowledge, this set of models is the first to incorporate all the critical physics necessary to predict dynamic behavior."

The study was published in the June 2018 print issue of IEEE Transactions on Microwave Theory and Techniques.

The computational tool is based on a method that jointly solves well-known Maxwell's equations, which describe how electricity and magnetism work and the Landau-Lifshitz-Gilbert equation, which describes how magnetization moves inside a solid object.

The study's lead author Zhi Yao is a postdoctoral scholar in Wang's laboratory. Co-authors are Rustu Umut Tok, a postdoctoral scholar in Wang's laboratory, and Tatsuo Itoh, a distinguished professor of electrical and computer engineering at UCLA and the Northrop Grumman Chair in Electrical Engineering. Itoh is also Yao's co-advisor.

The team is working to improve the tool to account for multiple types of magnetic and non-magnetic materials. These improvements could lead it to become a "universal solver," able to account for any type of electromagnetic wave interacting with any type of material.

Wang's research group recently received a $2.4 million grant from the Defense Advanced Research Project Agency to expand the tool's modeling capacity to include additional material properties.
-end-


UCLA Samueli School of Engineering

Related Data Articles:

Discrimination, lack of diversity, & societal risks of data mining highlighted in big data
A special issue of Big Data presents a series of insightful articles that focus on Big Data and Social and Technical Trade-Offs.
Journal AAS publishes first data description paper: Data collection and sharing
AAS published its first data description paper on June 8, 2017.
73 percent of academics say access to research data helps them in their work; 34 percent do not publish their data
Combining results from bibliometric analyses, a global sample of researcher opinions and case-study interviews, a new report reveals that although the benefits of open research data are well known, in practice, confusion remains within the researcher community around when and how to share research data.
Designing new materials from 'small' data
A Northwestern and Los Alamos team developed a novel workflow combining machine learning and density functional theory calculations to create design guidelines for new materials that exhibit useful electronic properties, such as ferroelectricity and piezoelectricity.
Big data for the universe
Astronomers at Lomonosov Moscow State University in cooperation with their French colleagues and with the help of citizen scientists have released 'The Reference Catalog of galaxy SEDs,' which contains value-added information about 800,000 galaxies.
What to do with the data?
Rapid advances in computing constantly translate into new technologies in our everyday lives.
Why keep the raw data?
The increasingly popular subject of raw diffraction data deposition is examined in a Topical Review in IUCrJ.
Infrastructure data for everyone
How much electricity flows through the grid? When and where?
Finding patterns in corrupted data
A new 'robust' statistical method from MIT enables efficient model fitting with corrupted, high-dimensional data.
Big data for little creatures
A multi-disciplinary team of researchers at UC Riverside has received $3 million from the National Science Foundation Research Traineeship program to prepare the next generation of scientists and engineers who will learn how to exploit the power of big data to understand insects.

Related Data Reading:

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann (Author)

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
by Seth Stephens-Davidowitz (Author)

Storytelling with Data: A Data Visualization Guide for Business Professionals
by Cole Nussbaumer Knaflic (Author)

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
by Foster Provost (Author), Tom Fawcett (Author)

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
by Wes McKinney (Author)

Dear Data
by Giorgia Lupi (Author), Stefanie Posavec (Author), Maria Popova (Foreword)

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
by Hadley Wickham (Author), Garrett Grolemund (Author)

Data Visualization: A Practical Introduction
by Kieran Healy (Author)

Introduction to Machine Learning with Python: A Guide for Data Scientists
by Andreas C. Müller (Author), Sarah Guido (Author)

Data Science from Scratch: First Principles with Python
by Joel Grus (Author)

Best Science Podcasts 2018

We have hand picked the best science podcasts for 2018. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Circular
We're told if the economy is growing, and if we keep producing, that's a good thing. But at what cost? This hour, TED speakers explore circular systems that regenerate and re-use what we already have. Guests include economist Kate Raworth, environmental activist Tristram Stuart, landscape architect Kate Orff, entrepreneur David Katz, and graphic designer Jessi Arrington.
Now Playing: Science for the People

#504 The Art of Logic
How can mathematics help us have better arguments? This week we spend the hour with "The Art of Logic in an Illogical World" author, mathematician Eugenia Cheng, as she makes her case that the logic of mathematics can combine with emotional resonance to allow us to have better debates and arguments. Along the way we learn a lot about rigorous logic using arguments you're probably having every day, while also learning a lot about our own underlying beliefs and assumptions.