The Department of Electrical & Systems Engineering has a unique and long tradition of excellence in advancing basic science and solving cutting-edge engineering problems relevant to society. The second-oldest electrical engineering department in the country, it is dedicated to providing high-quality education and research.



Imaging Science Seminar: Ulugbek Kamilov Science Seminar: Ulugbek Kamilov2019-11-22T06:00:00Z
ESE Seminar: Ryan Caverly, PhD,-PhD.aspxESE Seminar: Ryan Caverly, PhD2019-11-22T06:00:00Z
ESE Seminar: Keith Hengen, PhD,-PhD-.aspxESE Seminar: Keith Hengen, PhD 2019-12-06T06:00:00Z

Research Highlights
 WiFi is weak, send noise instead<img alt="" src="/news/PublishingImages/silent%20send%20noise.jpg?RenditionID=1" style="BORDER:0px solid;" /><div id="__publishingReusableFragmentIdSection"><a href="/ReusableContent/36_.000">a</a></div><p>​When WiFi was designed, it was intended for high speed data communications. The Institute of Electrical and Electronics Engineers (IEEE) set the standards for communications — that’s the 802.11 protocol, a familiar number on many wireless routers.</p><p>According to the protocol, once a device is unable to send at least one megabit per second (Mbps), it is “out of range.” Even if it were physically possible to send, say, a half megabit per second, the protocol won’t allow it.</p><p>Electrical and systems engineer and computer scientist <a href="/Profiles/Pages/Neal-Patwari.aspx">Neal Patwari</a> of the McKelvey School of Engineering at Washington University in St. Louis has been working with a group using sensors to continuously collect indoor air quality data from the homes of volunteers, in a project sponsored by the National Institute of Biomedical Imaging and Bioengineering (NIBIB).</p><p>But when researchers stopped receiving data, there wasn’t a way to determine whether a sensor had been unplugged, or if something was interfering with the WiFi signal. They just needed to send a small ping, a tiny bit of data, but that was the problem — the protocol wouldn’t allow it.<br/></p><p></p><p>“We were trying to figure out, can we send lower rate data from a WiFi device even though it’s not part of the protocol, using the same hardware?” said Patwari, professor of electrical and systems engineering and of computer science and engineering.</p><p>Indeed, they found a way.</p><p>Patwari and the team presented <a href="">the results of their research</a> Oct. 22 at ACM MobiCom 2019, the 25th International Conference on Mobile Computing and Networking.</p><p>For their study regarding how indoor air quality affected asthma rates, the researchers needed lots of data from lots of homes with asthmatic children over a long period of time.</p><p>Research participants agreed to have air quality sensors in their homes. The sensors transmitted data to the researchers via WiFi, and were expected to do so for a year.</p><blockquote>“This is a problem,” Patwari said. “If you’ve ever had to set up and maintain a wireless network, you know that it requires some work every once in a while if something goes wrong.”</blockquote><p>Something will always go wrong, and, after lots of communication back and forth with participants to fix things, researchers were worried the challenges would cause participants to drop out.</p><p>Patwari experienced this frustration himself, when he put a sensor in his bedroom, across the house from his wireless router. His own student, Philip Lundrigan, also an author of the study, called when the link went down. When he went to check on the router, he had to move a basket of laundry out of the way.</p><p>Suddenly, the connection to the sensor was restored.</p><p>“It was the laundry basket,” he said, “and it was clean laundry!”</p><p>It wasn’t that the laundry had formed an impenetrable wall and the WiFi signal was stopped dead in its tracks. Rather, since the sensor was far away from the router, any small perturbation kicked the data transfer rate below 1 Mbps — the lowest transfer rate allowed by the protocol. So communication was cut off.</p><p>The situation the researchers were trying to address didn’t require that much data, though. They were just trying to find a way to figure out if the connection had been terminated, or if the sensor had been unplugged. For this purpose, instead of treating the transmitter as something that sent data, Patwari decided to consider it as something that sent noise.</p><p>Modern homes are awash in wireless noise — from computers to televisions to stereos to cell phones — the signals are everywhere. The team, led by Phil Lundrigan, assistant professor at Brigham Young University, thought they could use this to their advantage. They programmed into the WiFi sensor a series of 1s and 0s, essentially turning the signal on and off in a specific pattern. The router was able to distinguish this pattern from the surrounding wireless noise.</p><p>So even if the sensor’s data wasn’t being received, the router could pick out that pattern in the ambient noise and know that the sensor was still transmitting something.</p><p>The process isn’t entirely straightforward; some noise is louder than other noise, so the team had to devise a way to quiet some of the loudest noise in order to spot the sensor’s hidden message. Nearby signals — say, the television next to the router — were canceled out. By analyzing just a few weaker signals, it becomes much easier to pick out the pattern being sent by the sensor.</p><p>“If the access point hears this code, it says, ‘OK, I know the sensor is still alive and trying to reach me, it’s just out of range,’” Patwari said. “It’s basically sending one bit of information that says it’s alive.”</p><p>The team, which also included <a href="">Sneha K. Kasera</a>, professor at the University of Utah, eventually showed that the code could be transmitted even further than the edge of the WiFi data range — twice as far away, in fact.</p><p>“Even when the laundry basket is in the way and the link can’t send data at the 1 Mbps rate, it can still send this code,” Patwari said, “and your router then knows that the sensor is alive and transmitting. The researcher can rest easy knowing that the sensor is still collecting data, and eventually they’ll get their air quality data.”</p><p>This is just the beginning for the new innovation. It might be able to make so-called “long range” wireless protocols even longer range, according to Lundrigan, or be used on top of other wireless technology such as bluetooth or cellular.</p><p>“We can send and receive data regardless of what WiFi is doing,” Lundrigan said. “All we need is the ability to transmit energy and then receive noise measurements.”</p><SPAN ID="__publishingReusableFragment"></SPAN><p><br/></p><div><div class="cstm-section"><h3>Neal Patwari<br/></h3><div style="text-align: center;"> <strong> <a href="/Profiles/Pages/Neal-Patwari.aspx"> </a> <img src="/Profiles/PublishingImages/Neal%20Patwari_03.jpg?RenditionID=3" alt="" style="margin: 5px;"/> <br/></strong></div><ul style="text-align: left;"><li>Professor<br/></li><li>Research: The intersection of statistical signal processing and wireless networking, for improving wireless sensor networking and RF sensing. <br/></li></ul><p style="text-align: center;"> <a href="/Profiles/Pages/Neal-Patwari.aspx">>> View Bio</a><br/></p></div></div> <span> <div class="cstm-section"><h3>Media Coverage<br/></h3><div> <strong>Engadget: </strong><a href="">BYU researchers extend WiFi range by 200 feet with a software upgrade</a></div><div><br/></div><div><strong style="caret-color: #343434; color: #343434;">TechRadar: </strong><a href="">This new technology could make your Wi-Fi instantly better</a><br/></div></div></span>Brandie Jefferson wireless noise can be key to sending information, researchers find<p>​Recognizing wireless noise can be key to sending information, researchers find<br/></p>’s my car? WashU researchers to study short-term working memory<img alt="" src="/news/PublishingImages/iStock-1133618377.jpg?RenditionID=2" style="BORDER:0px solid;" /><div id="__publishingReusableFragmentIdSection"><a href="/ReusableContent/36_.000">a</a></div><p>When we drive to a place and park the car, most of us walk away without giving any thought about how to find the car when we want to leave. A team of researchers at Washington University in St. Louis plans to study how and where the brain stores this type of information so that it can be retrieved when needed.</p><p>ShiNung Ching, associate professor of electrical & systems engineering in the McKelvey School of Engineering, and Lawrence Snyder, MD, PhD, professor of neuroscience at the School of Medicine, will study short-term working memory in the brain — part of a broader effort to understand the link between the dynamics and function of neural circuits — with a three-year, $1.1 million grant from the National Institutes of Health (NIH)'s National Institute of Biomedical Imaging and Bioengineering and the National Institute of Mental Health. The grant is part of the NIH's Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative aimed at revolutionizing the understanding of the human brain.</p><p>"Our goal is to take mathematical and computational theories about how resources are optimally allocated in different engineered settings, then use these ideas as a framework to think about how the brain might be allocating its resources to achieve working memory," Ching said. </p><p>Working memory is critical for normal human reasoning, and problems with it stem from normal aging as well as neuropsychiatric illnesses. A clearer understanding of how it works could lead to improvements in diagnosing and treating these illnesses.</p><p>When we do a task that requires working memory, such as parking a car or remembering a phone number, we are receiving and storing information from the periphery. We can't always anticipate when we are going to be faced with something new that we need to remember, so there is a problem of allocating resources between current and future memory needs that the brain has to solve, Ching said. </p><p>"Let's say we are observing an area of the brain that we thought was important for working memory," Ching said. "If I looked at it one minute after you parked your car, I might see some activity that suggests that it is storing the memory of your car's location. If I looked four hours later, I might see a weaker signal there. Eight hours later I may see no signal there, or something totally different, but you might nonetheless still figure out where your car is. How does the brain manage the use of those circuits? We'd like to understand how this type of prioritization is accomplished in the brain." </p><p>Ching said optimal resource allocation is also used in many engineered systems, such as power grids or smart grids, which have to determine how to distribute finite resources across a network. </p><p>"This work will take the mathematical engineering theory that we use to study those problems and see if we can use it to predict what we observe in the brain," he said. </p><p>With this information, Ching will develop models of neuronal activity in the brain, then Snyder will then examine whether the activity precited by the model is present in actual brain activity. </p><p>"We've observed that when memories first become engaged, certain neurons will become active, seemingly representing the memory, but over time, that activity will drop away, so if you look 5 or 10 seconds later, the cells appear to be doing nothing," Ching said. "Despite this, the ability to recall the memory persists. Which begs the question, why were those neurons turned 'off' and where, ultimately, in the circuit does the memory reside?"</p><SPAN ID="__publishingReusableFragment"></SPAN><br/><span> <div class="cstm-section"><h3>Collabo​rators​</h3><div style="text-align: center;"> <strong><a href="/Profiles/Pages/ShiNung-Ching.aspx"><img src="/Profiles/PublishingImages/Ching_ShiNung.jpg?RenditionID=3" class="ms-rtePosition-3" alt="" style="margin: 5px;"/>​</a> </strong></div><div style="text-align: center;"> <a href="/Profiles/Pages/ShiNung-Ching.aspx"><strong>ShiNung Ching</strong></a><br/></div><div style="text-align: center;"><div style="caret-color: #343434; color: #343434;"> <span style="font-size: 12px;">Associate Professor</span></div><div style="caret-color: #343434; color: #343434;"> <span style="font-size: 12px;">Electrical & Systems Engineering</span></div></div><div style="text-align: center;"> <span style="font-size: 12px;"> <br/></span></div><div style="text-align: center;"><div style="color: #343434; text-align: center;"> <span style="font-size: 12px;"> <a href=""> <img src="/news/PublishingImages/Pages/Where%E2%80%99s-my-car-WashU-researchers-to-study-short-term-working-memory/lawrence-snyder.jpg?RenditionID=3" alt="" style="margin: 5px;"/></a></span></div><div style="color: #343434; text-align: center;"> <strong> <a href=""> <strong>Lawrence Snyder</strong></a></strong><br/></div><div style="color: #343434; text-align: center;"> <span style="font-size: 12px;"><span style="caret-color: #343434; color: #343434; font-size: 12px;">Professor of Neuroscience</span></span></div><div style="color: #343434; text-align: center;"> <span style="font-size: 12px;"><span style="caret-color: #343434; color: #343434; font-size: 12px;">School of Medicine</span></span></div></div></div></span>Beth Miller 2019-09-25T05:00:00ZShiNung Ching and Larry Snyder at the School of Medicine plan to study how the brain allocates resources for working memory with funding from the NIH's BRAIN Initiative. for Quantum Sensors awarded NSF Quantum Leap Challenge seed grant<img alt="" src="/news/PublishingImages/IMG_9062.jpg?RenditionID=1" style="BORDER:0px solid;" /><p>​</p><p>The <a href="">Center for Quantum Sensors</a> (CQS) was awarded a Quantum Leap Challenge Institute (QLCI) conceptualization grant from the National Science Foundation. QLCIs are large-scale, interdisciplinary research projects that aim to advance applications of quantum information science. This conceptualization grant will provide WashU's CQS the resources it needs to potentially develop into a fully-fledged QLCI, validate the center's work on quantum sensors as one of the most compelling areas of quantum research, and give the center a voice in shaping national collaborations around quantum sensing. The grant will support planning and team-building activities over the coming year, in preparation for the next stage of NSF funding and development.</p><p>"Our conceptualization grant aims to focus the community's conversation around quantum sensing to try to identify the most compelling questions and the most compelling ideas for addressing those questions," said <a href="">Kater Murch</a>, associate professor in the Department of Physics in Arts & Sciences, who led the proposal. "Our goal is to identify challenges where we can make a transformative advance in the next five years."</p><p>Housed within Arts & Sciences, the Center for Quantum Sensors aims to engage the physics, chemistry, engineering, medical, and industrial communities to tackle quantum sensing problems that can only be addressed collaboratively. In addition to Murch, the center includes collaborators from several departments in Arts & Sciences and from the McKelvey School of Engineering: <a href="">Sophia Hayes</a>, professor of chemistry in Arts & Sciences; <a href="">James H. Buckley</a>, professor of physics in Arts & Sciences; <a href="/Profiles/Pages/Ron-Cytron.aspx">Ron K. Cytron</a>, professor and associate department chair of computer science and engineering in McKelvey Engineering; <a href="">Erik A. Henriksen</a>, assistant professor of physics in Arts & Sciences; <a href="">Henric Krawczynski</a>, professor of physics in Arts & Sciences; <a href="/Profiles/Pages/Matthew-Lew.aspx">Matthew Lew</a>, assistant professor of electrical and systems engineering in McKelvey Engineering; and <a href="/Profiles/Pages/Lan-Yang.aspx">Lan Yang</a>, Edwin H. and Florence G. Skinner Professor of electrical and systems engineering in McKelvey Engineering. The cast of collaborators is set to expand this year as the center brings on graduate students from multiple disciplines and broadens its network of researchers beyond WashU.</p><p> </p><p> </p><p><br/></p><p><br/></p>Shawn Ballard2019-09-20T05:00:00ZThree McKelvey School of Engineering faculty are collaborators in the university's new Center for Quantum Sensors. first principles last: A new control methodology<img alt="" src="/news/PublishingImages/ESE%20control%20methods%20919.jpg?RenditionID=1" style="BORDER:0px solid;" /><div id="__publishingReusableFragmentIdSection"><a href="/ReusableContent/36_.000">a</a></div><p>​If an aeronautical engineer wants to make sure a plane stays steady in the air, she might first build a computational model, feeding into it variables such as information about the plane’s weight or the thrust that can be achieved, together with the laws of aerodynamics, which account for how a plane moves through the air.</p><p>She can then build a controller — a program designed to monitor and regulate the system. In this case, the system is the airplane and the surrounding atmosphere. The controller receives information from sensors on the plane, and then continuously and fully automatically makes necessary adjustments to meet a goal. If the plane is pitching down, the controller compensates, directing the plane to pull up its nose.</p><p>A biological system might be understood in the same way: a brain might be modeled by its neural connections the speed at which neurons fire. And there are controllers. Hormones are released according to brain states in order to meet a goal (such as the release of cortisol to manage blood pressure).</p><p style="text-align: right;"> </p><p>Can systems engineers, then, use control theory to better understand the workings of the brain?</p><p>Not in the traditional sense, according to<a href="/Profiles/Pages/Shen-Zeng.aspx"> Shen Zeng</a>, assistant professor of electrical & systems engineering in the McKelvey School of Engineering at Washington University in St. Louis.<br/></p><p>“Such a task is feasible if your system is not too complicated,” Zeng said. But a biological system such a brain or a cell is complicated. Even if it could be reproduced as a model, that model would be too big and too complicated to design a feedback control system.</p><p> </p><p></p><p>Zeng and<a href="/Profiles/Pages/Jr-Shin-Li.aspx"> Jr-Shin Li</a>, professor of electrical & systems engineering, have been developing a new, data- and computationally-driven method to understand and bring under control more complex systems. They have recently received a $488,811 grant from the National Science Foundation for projects focused on the analyzing and controlling aspects in the dynamics of the brain and cancer cells populations.</p><p>Traditionally, systems engineers first build a model of the system they are working on. Only then do they move on to the controllers, programs that rely on feedback to continuously monitor the state of the system and adjust according to the desired outcome.<br/></p><p>This method is not perfect, but it works well when the system is something like an airplane with well-understood underlying rules — aerodynamics, laws of motion, etc. In a more complex system such as a brain, however, it’s a different story.</p><p>“Even if a model could be built,” Zeng said, “it would be very detailed and large, but also so complicated that you won’t be able to do feedback control design with it.”</p><blockquote>“Also, in many scientific domains conducting research on highly complex systems, researchers trust their data much more than any mathematical model,” Li said. Therefore, instead, Li and Zeng are developing a novel, more holistic data-driven approach to analyzing complex dynamical systems and designing control mechanisms.</blockquote><p>They can do that because of the vast amounts of data now available; data in the form of brain scans, improved microscopy and other high tech innovations. They plan to use this data along with a kind of generic, or primitive model they have developed, and build control methodologies while at the same time, building out and enhancing the model.</p><p>“It will be the data and models informing each other,” Zeng said. The more data, the more powerful and precise the model may be established. With a more powerful model, Li and Zeng will be able to design more effective and holistic controllers. Along the way, they expect to also uncover new, fundamental principles about systems design — new control design paradigms, for example.</p><p>And there might be another benefit.</p><p>A model of an airplane is based on those principles that underlie the system, but researchers know of nothing as certain or comprehensive when it comes to more complex, biological systems, like a brain.</p><p>But if such principles exist, the data is beholden to them nonetheless.</p><p>Marrying massive quantities of data with their model has the potential to draw out these as-of-yet-unknown principles, shedding light on biological fundamentals through systems engineering.</p><p>“Of course we like to build upon knowledge and use fundamental principles,” Zeng said, “but sometimes there are limits. There are places we cannot get to just from looking at fundamental things. But data, that can take us to uncharted territory.”</p><SPAN ID="__publishingReusableFragment"></SPAN><p><br/></p>From left: Zeng, LiBrandie Jefferson​New data- and computationally-driven approach to systems engineering may shed light on fundamental biological principles<p>​New data- and computationally-driven approach to systems engineering may shed light on fundamental biological principles<br/></p>

Research Areas

Applied Physics
  • Nano-photonics
  • Quantum Optics
  • Engineered Materials
  • Electrodynamics
Devices & Circuits
  • Computer Engineering
  • Integrated Circuits
  • Radiofrequency Circuits
  • Sensors
Systems Science
  • Optimization
  • Applied Mathematics
  • Control
  • Financial Engineering
Signals & Imaging
  • Computational Imaging
  • Signal Processing
  • Optical Imaging
  • Data Sciences