IEEE University of Lahore

July 9th, 2019

Starting out together and then splitting apart makes these bio-inspired drones fly farther and more precisely

Journal Watch report logo, link to report landing page

As useful as conventional fixed-wing and quadrotor drones have become, they still tend to be relatively complicated, expensive machines that you really want to be able to use more than once. When a one-way trip is all that you have in mind, you want something simple, reliable, and cheap, and we’ve seen a bunch of different designs for drone gliders that more or less fulfill those criteria. 

For an even simpler gliding design, you want to minimize both airframe mass and control surfaces, and the maple tree provides some inspiration in the form of samara, those distinctive seed pods that whirl to the ground in the fall. Samara are essentially just an unbalanced wing that spins, and while the natural ones don’t steer, adding an actuated flap to the robotic version and moving it at just the right time results in enough controllability to aim for a specific point on the ground.

Roboticists at the Singapore University of Technology and Design (SUTD) have been experimenting with samara-inspired drones, and in a new paper in IEEE Robotics and Automation Letters they explore what happens if you attach five of the drones together and then separate them in mid air.

July 9th, 2019

If you are interested in learning how to model a medical device interacting with physiology, then tune into this webinar

If you are interested in learning how to model a medical device interacting with physiology, then tune into this webinar featuring guest speaker Paul Belk from Boston Scientific Corporation.

Modeling physiologic systems uses the same principles applied to other multiphysics applications, but it is often complicated by the challenges in characterizing the properties of the biological tissues and processes involved. These challenges make it even more important to be able to analyze quantitatively through numerical simulation the interactions between the variable biological phenomena and the device.

In this webinar, we will present a model of catheter ablation from a large vessel. We will begin by setting up the coupled physics, including electric currents, laminar flow of blood, and heat transfer by conduction and convection. We will then show how to characterize the properties of the tissues involved and how the COMSOL Multiphysics® software can be used to simulate a closed-loop control system to stabilize the energy flow delivered to the surrounding tissues. The simulation results will be used to characterize how intended physiologic results can be affected by uncontrolled physiologic changes and which control systems are most robust.

You can ask questions at the end of the webinar during the Q&A session.



Paul Belk, Fellow, Process Engineering, Boston Scientific Corporation

Paul Belk has a PhD in medical physics and is a Fellow in process engineering at Boston Scientific Corporation, where he works on the development of diagnostic and therapeutic medical devices. He has been using simulation of all types for more than 20 years as an integral part of the research and development process. For the past six years, he has been using the COMSOL Multphysics® software (whenever he gets a chance) to study problems including heat transfer and fluid dynamics in tissue, field distributions, and electrochemical processes at metal surfaces.


Aline Tomasian, Applications Engineer, COMSOL

Aline Tomasian is an applications engineer at COMSOL, specializing in high- and low-frequency electromagnetics. She holds a BS in physics from Worcester Polytechnic Institute.

Attendees of this IEEE Spectrum webinar have the opportunity to earn PDHs or Continuing Education Certificates!  To request your certificate you will need to get a code. Once you have registered and viewed the webinar send a request to for a webinar code. To request your certificate complete the form here:

Attendance is free. To access the event please register.

NOTE: By registering for this webinar you understand and agree that IEEE Spectrum will share your contact information with the sponsors of this webinar and that both IEEE Spectrum and the sponsors may send email communications to you in the future.​

July 4th, 2019

How NASA’s Jet Propulsion Laboratory designed the robot arm for the Mars 2020 rover

Last month, engineers at NASA’s Jet Propulsion Laboratory wrapped up the installation of the Mars 2020 rover’s 2.1-meter-long robot arm. This is the most powerful arm ever installed on a Mars rover. Even though the Mars 2020 rover shares much of its design with Curiosity, the new arm was redesigned to be able to do much more complex science, drilling into rocks to collect samples that can be stored for later recovery.

JPL is well known for developing robots that do amazing work in incredibly distant and hostile environments. The Opportunity Mars rover, to name just one example, had a 90-day planned mission but remained operational for 5,498 days in a robot unfriendly place full of dust and wild temperature swings where even the most basic maintenance or repair is utterly impossible. (Its twin rover, Spirit, operated for 2,269 days.) 

To learn more about the process behind designing robotic systems that are capable of feats like these, we talked with Matt Robinson, one of the engineers who designed the Mars 2020 rover’s new robot arm.

July 4th, 2019

Startup Biobot Analytics monitors wastewater to identify at-risk neighborhoods

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THE INSTITUTEMost people don’t spend time thinking about what’s in the waste they flush down the toilet. But health officials do. The urine in sewer water is a surprisingly rich source of information about the health of communities. Epidemiologists can analyze wastewater to check for viruses, chemicals, and both illegal and prescription drugs.

Armed with that information, public health officials can stock up on vaccines, equip ambulances with life-saving medications, and run awareness campaigns.

But testing wastewater samples can be an expensive, time-consuming job. Biobot Analytics, a startup in Somerville, Mass., that was spun out of MIT in 2017, is working to improve the process with its collection, measurement, and analysis service.

Biobot uses portable devices to collect wastewater samples, which it analyzes in the laboratory. The company uses the resulting data to create spatial maps and charts that can illustrate which neighborhoods have high concentrations of a particular substance.

Biobot’s approach can be used to look at lots of different compounds. But so far the company is focusing on one target: opioid metabolites from prescription pain relievers and synthetic opioids such as fentanyl.

Metabolites are byproducts of the body metabolizing a drug. They are reliable indicators of whether a person has ingested or injected an opioid.

“Right now our focus is just analyzing for opioids, because opioid addiction is a major public health crisis,” says IEEE Member Irene Hu, a hardware electronics engineer at Biobot.

Around 68 percent of the more than 70,200 U.S. drug-overdose deaths in 2017 involved an opioid, according to the federal Centers for Disease Control and Prevention.

Biobot has partnered with Cary, N.C., to help town officials assess the scope of its opioid epidemic, allocate resources, and then gauge the effectiveness of their efforts over time.

Hu talked about the project at the IoT–Smart Networks and Social Innovations panel, held in May during the IEEE Vision, Innovation, and Challenges Summit in San Diego. You can watch the session on


Biobot last year began a pilot with Cary, North Carolina’s seventh-largest municipality, with about 162,000 citizens. Last year 11 people in Cary died and about 60 others overdosed on opioids—a 70 percent increase from the previous year.

Biobot works with the town to identify which catchment basins and associated manholes they want to survey. At the chosen manholes, a sampling bot is suspended by a rope so it sits just above the water. The bot houses filters, a pump, sensors, and other hardware. Sewage is pumped through a series of filters—which bind the compounds of interest—and then out again during a 24-hour period.

A city worker collects the filters, which are sent back to the company. Back in the Biobot lab, analysts use mass spectrometry and other techniques to scan the filters for 16 different opioid metabolites.

In Cary, samples were extracted from 200,000 gallons of wastewater that flowed through 10 sample areas—gathering information from neighborhoods of about roughly 5,000 homes each. The results helped researchers determine a baseline level of opioid consumption.

The reports that Biobot provides to Cary officials include, for example, comparisons of reported overdoses—which the city already collects from first responders—and the levels of opioids that were found in the sewers. Presented as spatial maps of the city with blocks corresponding to the sampled catchment areas, the comparisons allow the city to visualize and identify “hidden” areas of consumption that are not captured by the officially reported overdoses, Hu says. For example, preliminary results showed that opioids were found all throughout Cary, not just in areas with reported overdoses.

In addition, Biobot found that prescription opioids were driving much of the consumption. The town used the information to tailor outreach programs around prescription opioids, resulting in a threefold increase in people using drop-off points to dispose of their leftover prescribed opioid medication.

Biobot also measures levels of naloxone (Narcan), a medication that can rapidly reverse opioid overdose. Preliminary results showed that Narcan usage correlates with reported overdoses in Cary, but the levels found in the sewers were much higher than expected, implying many unreported overdoses. The city is now digging into potential barriers that might exist to reporting overdoses, Hu reports. And the town is conducting awareness campaigns about opioid use.

Hu says first responders have expressed interest in data about trends on emerging drugs, such as marijuana and cocaine, so Biobot is now measuring for those as well.


After graduating from Princeton with a degree in electrical engineering, Hu spent a few years working for a financial consulting company. She decided to pursue a graduate degree in environmental engineering. “I wanted to do something that helped the world,” she says, “and pursue a cause I believed in.”

She earned a Ph.D. in environmental engineering from MIT. Her research thesis involved building sensors to measure naturally occurring chemicals in water.

Hu joined IEEE as a grad student because of the discounted rates members receive on conferences. She remains a member, she says, because she finds that “IEEE isn’t just for electrical engineers; it’s very interdisciplinary. There’s a conference for everyone, and it’s nice to have this professional community.

“Being on the IoT–Smart Networks and Social Innovations panel was one of my first forays into branching out in IEEE and talking about my work to a broader audience,” she says.

A friend who works at Biobot persuaded her to join the startup. The company, which was founded by two MIT graduate students, has almost a dozen employees. “It was a really good fit for me,” Hu says.

Biobot, which plans to expand its client base to more cities, counties, and states, has raised nearly US $2.5 million in seed funding from 22 investors.

July 3rd, 2019

Individual experimental accelerator chips can be ganged together in a single module to tackle both the small jobs and the big ones without sacrificing efficiency

There’s no doubt that GPU-powerhouse Nvidia would like to have a solution for all size scales of AI—from massive data center jobs down to the always-on, low-power neural networks that listen for wakeup words in voice assistants.

Right now, that would take several different technologies, because none of them scale up or down particularly well. It’s clearly preferable to be able to deploy one technology rather than several. So, according to Nvidia chief scientist Bill Dallythe company has been seeking to answer the question: “Can you build something scalable… while still maintaining competitive performance-per-watt across the entire spectrum?” 

It looks like the answer is yes. Last month at the VLSI Symposia in Kyoto, Nvidia detailed a tiny test chip that can work on its own to do the low-end jobs or be linked tightly together with up to 36 of its kin in a single module to do deep learning’s heavy lifting. And it does it all while achieving roughly the same top-class performance.

The individual accelerator chip is designed to perform the execution side of deep learning rather than the training part. Engineers generally measure the performance of such “inferencing” chips in terms of how many operations they can do per joule of energy or millimeter of area. A single one of Nvidia’s prototype chips peaks at 4.01 tera-operations per second (1000 billion operations per second) and 1.29 TOPS per millimeter. Compared to prior prototypes from other groups using the same precision the single chip was at least 16 times as area efficient and 1.7 times as energy efficient. But linked together into a 36-chip system it reached 127.8 TOPS. That’s a 32-fold performance boost. (Admittedly, some of the efficiency comes from not having to handle higher-precision math, certain DRAM issues, and other forms of AI besides convolutional neural nets.)

Companies have mainly been tuning their technologies to work best for their particular niches. For example, Irvine, Calif.,-startup Syntiant uses analog processing in flash-memory to boost performance for very-low power, low-demand applications. While Google’s original tensor processing unit’s powers would be wasted on anything other than the data center’s high-performance, high-power environment.

With this research Nvidia is trying to demonstrate that one technology can operate well in all those situations. Or at least it can if the chips are linked together with Nvidia’s mesh network in a multichip module. These modules are essentially small printed circuit boards or slivers of silicon that hold multiple chips in a way that they can be treated as one large IC. They are becoming increasingly popular, because they allow systems composed of a couple of smaller chips—often called chiplets—instead of a single larger and more expensive chip.

“The multichip module option has a lot of advantages not just for future scalable [deep learning] accelerators but for building version of our products that have accelerators for different functions,” explains Dally.

Key to the Nvidia multichip module’s ability to bind together the new deep learning chips is an interchip network that uses a technology called ground-referenced signaling. As its name implies, GRS uses the difference between a voltage signal on a wire and a common ground to transfer data, while avoiding many of the known pitfalls of that approach. It can transmit 25 gigabits/s using a single wire, whereas most technologies would need a pair of wires to reach that speed. Using single wires boosts how much data you can stream off of each millimeter of the edge of the chip to a whopping terabit per second. What’s more, GRS’s power consumption is a mere picojoule per bit.

“It’s a technology that we developed to basically give the option of building multichip modules on an organic substrate, as opposed to on a silicon interposer, which is much more expensive technology,” says Dally.

The accelerator chip presented at VLSI is hardly the last word on AI from Nvidia. Dally says they’ve already completed a version that essentially doubles this chip’s TOPS/W. “We believe we can do better than that,” he says. His team aspires to find inferencing accelerating techniques that blow past the VLSI prototype’s 9.09 TOPS/W and reaches 200 TOPS/W while still being scalable.

July 3rd, 2019

Researchers used deep reinforcement learning to teach these strange robots how to move

Designing robots is a finicky process, requiring an exhaustive amount of thought and care. It’s usually necessary to have a very clear idea of what you want your robot to do and how you want it to do it, and then you build a prototype, discover everything that’s wrong with it, build something different and better, and repeat until you run out of time and/or money.

But robots don’t necessarily have to be this complicated, as long as your expectations for what they should be able to do are correspondingly low. In a paper presented at a NeurIPS workshop last December, a group of researchers from the University of Tokyo and Preferred Networks experimented with building mobile robots out of a couple of generic servos plus stuff you can find on the ground, like tree branches. 

July 2nd, 2019

Register for our Application Note “Tips and Tricks on how to verify control loop stability”

The Application Note explains the main measurement concept and will guide the user during the measurements and mention the main topics in a practical manner. Wherever possible, a hint is given where the user should pay attention.


July 2nd, 2019

Field tests affirm the energy boost long promised by optimized ‘wake steering’ of wind turbines

“Logic clearly dictates that the needs of the many outweigh the needs of the few.” So declares Spock, Star Trek’s Vulcan hero, as he sacrifices himself to save the Starship Enterprise and its crew in the 1982 film Star Trek: The Wrath of Khan. Today Stanford University researchers presented the clearest proof to date that self-sacrifice can also benefit wind farms. In their demonstration at an Alberta wind farm, one turbine sacrifices a fifth of its generating potential to enable better performance by neighboring turbines, boosting the group’s collective output.

And while Spock’s heroics necessitated a major plot twist to revive his character for the next Star Trek sequel, teaching turbines to behave altruistically requires just a small (but intelligent) tweak to their control systems. What they learn is how to share the wind.

Like parasols casting shadows, spinning rotors in a wind farm cast an energy-depleted “wake” that can slow downstream turbines. The resulting lost energy can be 10 percent or more of a wind farm’s annual power generation. At Denmark’s Horns Rev offshore wind farm, wake losses cut annual energy production by a hefty 20 percent [photo above].

This week’s report in the Proceedings of the National Academy of Sciences proves out a coordinated control scheme to cut the losses. It is called “wake steering” because rotors are turned about their towers to point slightly away from the oncoming wind and thus deflect their wakes away from downstream turbines [see artist’s rendition].

Modeling and wind tunnel experiments have shown for more than a decade that wake steering should boost overall output, but it’s been hard to test at wind farms according to John Dabiri, the Stanford fluid mechanics expert behind the Alberta test. Wind producers are understandably reluctant to risk losing revenue during a test or, worse still, damaging multi-million-dollar turbines by placing them slightly off-kilter. “When you have technology that’s seen as mature, people just want to operate it as is,” says Dabiri.

Through a friend of a friend Dabiri found one company willing to give wake steering a try: Calgary-based wind operator TransAlta Renewables. Last October TransAlta made a row of six turbines at its Summerview 1 wind farm at Pincher Creek, Alberta available to Dabiri and his team for ten days. The plant’s turbines are laid out to face Pincher Creek’s strong southeast winds. But during the summer and fall winds can also blow from the northwest, flowing straight down its tightly-packed rows of turbines [see photo below]. The northwest wind is no bother for the rows’ lead turbines, but thanks to wind wakes each following turbine captures 30-40 percent less energy than its upstream neighbor.

To determine the best yaw angle for their experiment, the Stanford team fed five years of wind speed, wind direction and power generation data from the six test turbines to their proprietary optimization algorithm. Combining that data with a simple wind model, the algorithm projected that yawing each of the five upstream turbines about 20 degrees to the north would maximize the group’s generation from the northwest winds.

Next they had to find a way to command the turbines. All commercial turbines are programed to always turn or ‘yaw’ their rotors to face the wind. Teaching them a new angle would require just a few days of coding work on most contemporary turbines, says Dabiri, but that was not an option for the relatively inflexible control systems running at Pincher Creek. Dabiri’s team got it done with a manual work-around: repositioning the direction-tracking wind vanes atop the turbines’ nacelles during the 10-day test and thereby tricking the control system to turn 20 degrees off the wind.

The resulting power gains were significant. Power generation rose 13 percent under northwest winds blowing into the wind farm at 7-8 meters per second (mps)—average speeds for Pincher Creek. Steering had a still greater impact amidst slower northwest winds by reducing the times when the wind hitting turbines fell below the 5 mps—the threshold at which they automatically shut down. For 5-6 mps winds wake steering boosted generation by up to 47 percent.

Dabiri says in its commercial incarnation wake steering should yield even better results by adjusting turbines dynamically, based on a table of optimal yaw angles for each turbine under a range of wind conditions. He says the group is readying a workaround to make such dynamic tuning possible for turbines with older control systems: a small logic circuit to tweak the data feed from the wind vane, spoofing the control system. “We intercept and change that number,” says Dabiri.

Meanwhile they are planning with TransAlta for a larger wake steering run next year at an Ontario wind farm. Job one will be measuring mechanical loads to ensure that wake steering is not straining the turbines. Dabiri says there is reason to expect the opposite, since wakes often hit rotors unevenly, torquing their components.

The U.S. National Renewable Energy Lab (NREL) is simultaneously pushing to improve its own wake steering algorithms, and testing them at commercial wind farms. NREL struck first in 2017 in a collaboration with Shanghai-based Envision Energy. In that test steering one turbine at an offshore farm in China boosted output for several neighboring turbines. Two months ago NREL reported successful steering of two turbines at a Colorado wind farm operated by Juno Beach, FL-based NextEra Energy Resources.

The NREL team says its results suggest that wake steering can boost annual wind farm output by at least 1-2 percent, lifting revenues at a typical 300-megawatt U.S. wind farm by $1 million or more. Dabiri says his results suggest that wake steering could yield 7 percent more energy annually from those six Alberta turbines. Applied across the more than 600 gigawatts of wind power capacity installed worldwide, either estimate represents an impressive bolus of clean energy that can be seized with little extra investment.

Dabiri predicts that wake steering’s greatest impact will be on future installations. While wind farms installed today are among the cheapest power sources available, he says decarbonizing economies in the decades ahead requires a multiplication of global wind capacity that will lead to more densely-packed wind farms tapping lower-quality winds. “There’s a misconception that good winds are unlimited. That’s just not the case,” says Dabiri. “We’re going to need to do a better job of extracting wind energy.”

June 29th, 2019

Did getting that masters or doctorate degree help your career? Fewer than half of tech professionals who have an advanced degree say yes

Was getting that advanced degree worth it? Job search site Hired asked 1,800 technology professionals that question recently, and 48 percent who hold such a degree think they owe their current job or salary to their degree, with 21 percent not sure and 31 percent indicating that they could have had their same job and salary without it.

That’s a pretty mixed review of the benefits of advanced degrees. And, going forward, the Hired survey found that 54 percent of survey respondents were uninterested in continuing on to another credential.

June 29th, 2019

Your weekly selection of awesome robot videos

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!):

MARSS 2019 – July 1-5, 2019 – Helsinki, Finland
ICRES 2019 – July 29-30, 2019 – London, UK
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, PA, USA

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