Tiny wireless implant detects oxygen deep within the body

CS Prof. and Chan Zuckerberg Biohub investigator Michel Maharbiz is the senior author of a paper in Nature Biotechnology titled "Monitoring deep-tissue oxygenation with a millimeter-scale ultrasonic implant," which describes a tiny wireless implant that can provide real-time measurements of tissue oxygen levels deep underneath the skin. The device, which is smaller than the average ladybug and powered by ultrasound waves, could help doctors monitor the health of transplanted organs or tissue and provide an early warning of potential transplant failure.  “It’s very difficult to measure things deep inside the body,” said Maharbiz. “The device demonstrates how, using ultrasound technology coupled with very clever integrated circuit design, you can create sophisticated implants that go very deep into tissue to take data from organs.”

New method of harnessing light waves radically increases amount of data transmitted

EECS Associate Prof. Boubacar Kanté and his research team have found a new way to harness properties of light waves that can radically increase the amount of data they carry.  They demonstrated the emission of discrete twisting laser beams from antennas made up of concentric rings roughly equal to the diameter of a human hair, small enough to be placed on computer chips.  Described in a paper published in Nature Physics, this new technology overcomes current data capacity limits through a characteristic of light called orbital angular momentum (OAM). Potential applications include biological imaging, quantum cryptography, high-capacity communications and sensors.   “Having a larger quantum number is like having more letters to use in the alphabet,” said Kanté. “We’re allowing light to expand its vocabulary. In our study, we demonstrated this capability at telecommunication wavelengths, but in principle, it can be adapted to other frequency bands. Even though we created three lasers, multiplying the data rate by three, there is no limit to the possible number of beams and data capacity.”

New wearable device detects intended hand gestures before they're made

A team of researchers, including EECS graduate students Ali Moin, Andy Zhou, Alisha Menon, George Alexandrov, Jonathan Ting and Yasser Khan, Profs. Ana Arias and Jan Rabaey, postdocs Abbas Rahimi and Natasha Yamamoto, visiting scholar Simone Benatti, and BWRC research engineer Fred Burghardt, have created a new flexible armband that combines wearable biosensors with artificial intelligence software to help recognize what hand gesture a person intends to make based on electrical signal patterns in the forearm.  The device, which was described in a paper published in Nature Electronics in December, can read the electrical signals at 64 different points on the forearm.  These signals are then fed into an electrical chip, which is programmed with an AI algorithm capable of associating these signal patterns in the forearm with 21 specific hand gestures, including a thumbs-up, a fist, a flat hand, holding up individual fingers and counting numbers. The device paves the way for better prosthetic control and seamless interaction with electronic devices.

Deep learning helps robots grasp and move objects with ease

CS Prof. Ken Goldberg is the co-author of a study published in Science Robotics which describes the creation of a new artificial intelligence software that gives robots the speed and skill to grasp and smoothly move objects, making it feasible for them to soon assist humans in warehouse environments.  He and postdoc Jeffrey Ichnowski had previously created a Grasp-Optimized Motion Planner that could compute both how a robot should pick up an object and how it should move to transfer the object from one location to another, but the motions it generated were jerky.  Then they, along with EECS graduate student Yahav Avigal and undergraduate (3rd year MS) student Vishal Satish, integrated a deep learning neural network into the motion planner, cutting the average computation time from 29 seconds to 80 milliseconds, or less than one-tenth of a second.  Goldberg predicts that, with this and other advances in robotic technology, robots could be assisting in warehouse environments in the next few years.

Jake Tibbetts wins Bulletin of the Atomic Scientists’ 2020 Leonard M. Rieser Award

EECS grad student and alumnus Jake Tibbetts (B.S. EECS/Global Studies '20) has won the Bulletin of the Atomic Scientists’ 2020 Leonard M. Rieser Award.   Winners of the award have published essays in the Bulletin's Voices of Tomorrow column, and are selected by the Bulletin’s editorial team for recognition as "outstanding emerging science and security experts passionate about advancing peace and security in our time."  Tibbetts received the award for his article “Keeping classified information secret in a world of quantum computing,” published in the Bulletin on February 11, 2020.  “In his piece, Jake Tibbetts accomplished the kind of deep, thoughtful, and well-crafted journalism that is the Bulletin's hallmark," said editor-in-chief John Mecklin. "Quantum computing is a complex field; many articles about it are full of strange exaggerations and tangled prose. Tibbetts' piece, on the other hand, is an exemplar of clarity and precision and genuinely worthy of the Rieser Award.”  Tibbetts is a fellow at the NNSA-supported Nuclear Science and Security Consortium, and has previously worked as a research assistant at the LBNL Center for Global Security Research.  He has made contributions to the Nuclear Policy Working Group and the Project on Nuclear Gaming at Cal, and made the EECS news last year for his involvement in creating the online three-player experimental wargame "SIGNAL," which was named the Best Student Game of 2019 by the Serious Games Showcase and Challenge (SGS&C).  The Rieser Award comes with a $1K prize.

New "spin-orbit torque" switching technique breaks magnetic memory speed record

EECS Chair Jeffrey Bokor is among an international team of researchers who have published a paper in the journal Nature Electronics that describes a new technique for magnetization switching — the process used to “write” information into magnetic memory — that is nearly 100 times faster than state-of-the-art spintronic devices. The advance could lead to the development of ultrafast magnetic memory for computer chips that would retain data even when there is no power.  In "Spin–orbit torque switching of a ferromagnet with picosecond electrical pulses," researchers report using extremely short, 6-picosecond electrical pulses to switch the magnetization of a thin film in a magnetic device with great energy efficiency. A picosecond is one-trillionth of a second.  The project began at UC Berkeley when Jon Gorchon, now a researcher at the French National Centre for Scientific Research (CNRS) working at the University of Lorraine L’Institut Jean Lamour in France, and Richard Wilson, now assistant professor of both mechanical engineering and materials science & engineering at UC Riverside, were postdoctoral researchers in Bokor’s lab.

"Extreme MRI" chosen as ISMRM Reproducible Research pick

"Extreme MRI: Large‐scale volumetric dynamic imaging from continuous non‐gated acquisitions,” a paper by EECS alumnus Frank Ong (B.S. '13, Ph.D. '18) and his advisor, Prof. Miki Lustig, has been chosen as October's Reproducible Research pick by the International Society for Magnetic Resonance in Medicine (ISMRM).  The paper, in which the researchers attempt to reconstruct a large-scale dynamic image dataset while pushing reconstruction resolution to the limit, was chosen "because, in addition to sharing their code, the authors also shared a demo of their work in a Google Colab notebook."  Lustig and Ong, now a research engineer at Stanford, participated in a Q&A in which they discussed how they became interested in MRI, what makes Extreme MRI "extreme," the culture and value of open science, and why Lustig's grad school paper on compressed sensing became the most cited paper in MRM.  ISMRM is an international nonprofit association that promotes research development in the field of magnetic resonance in medicine to help facilitate continuing education in the field.

Cecilia Aragon: Flying Free

CS alumna Cecilia Aragon (Ph.D. '04, advisors: Shankar Sastry and Marti Hearst) has written a memoir titled "Flying Free," which describes how she shook off the tethers of discrimination and her debilitating fear of heights to become the first Latina pilot to win a spot on the United States Unlimited Aerobatic Team, which represented the U.S. at the World Aerobatic Championships in 1991.  The daughter of a Chilean father and Filipina mother, Aragon earned her B.S. in Mathematics at Caltech before coming to Berkeley.  She was president of the student organization Women in Computer Science and Engineering (WICSE) in 1985 before dropping out.  After conquering her fears, she returned to Berkeley to complete her dissertation, "Improving Aviation Safety with Information Visualization:  Airflow Hazard Display for Helicopter Pilots," in 2004.  Aragon then spent nine years at the NASA Ames Research Center designing software for projects that included missions to Mars, before leaving to be a staff scientist/visiting faculty at LBNL for another 15 years. She then became the first Latina full professor at the University of Washington (UW), where has worked for the past ten years in the Department of Human Centered Design and Engineering, founding and co-directing the UW Data Science Masters Degree program.  Aragon was named Berkeley Computer Science Distinguished Alumna in 2013.  She co-authored a previous book, "Writers in the Secret Garden:  Fanfiction, Youth, and New Forms of Mentoring," released by MIT Press in 2019.

Victor Han selected runner-up for ISMRM I.I. Rabi Award

Third year EECS PhD candidate Victor Han (advisor: Prof. Chunlei Liu) was selected as a finalist for the International Society of Magnetic Resonance in Medicine (ISMRM) I.I. Rabi Young Investigator Award for original basic research.  He was chosen for his paper entitled “Multiphoton Magnetic Resonance Imaging,” in which he developed a novel technique that excites multiphoton resonances to generate signal for MRI by using multiple magnetic field frequencies, none of which is near the Larmor frequency. Only the total energy absorbed by a spin must correspond to the Larmor frequency. In contrast, today’s MRI exclusively relies on single-photon excitation. He was named runner-up at the ISMRM annual conference in early August.  Han will continue to develop his multiphoton technique and is exploring its applications in medicine and neuroscience as a part of his PhD dissertation research.  The ISMRM is a multi-disciplinary nonprofit professional association that promotes innovation, development, and application of magnetic resonance techniques in medicine and biology throughout the world. 

Ava Tan wins DRC 2020 Best Paper Award

EECS graduate student Ava Jiang Tan (advisor: Sayeef Salahuddin) has won the 2020 Best Paper Award at the 78th Device Research Conference (DRC) for "Reliability of Ferroelectric HfO2-based Memories: From MOS Capacitor to FeFET."  The paper, co-authored by Profs. Salahuddin and Chenming Hu, grad student Yu-Hung Liao, postdoc Jong-Ho Bae, and Li-Chen Wang of MSE, introduces nonvolatile ferroelectric field-effect transistors (FeFETs) which boast impressive programmability and a strong potential for further scalability.  The paper also demonstrates for the first time a systematic, reliable, and rapid method to qualitatively predict the FE endurance of prospective gate stack designs prior to running a full FeFET fabrication process.  Tan works in the Laboratory for Emerging and Exploratory Devices (LEED), and is particularly interested in the architectural potential of nonvolatile ferroelectric CMOS-compatible memories for realizing brain-inspired computing paradigms and energy-efficient hardware for deep learning. The DRC, which is the longest-running device research meeting in the world,  was held in June.