This stunning x-ray of a Callimico monkey skeleton, posed as if preparing to jump, was collected by visiting Professor Hesham Sallam at the Duke SMIF lab. In the wild, these pint-sized monkeys can be found in the dense underbrush of the upper Amazon rainforest, leaping from branch to branch in search of tasty berries or bugs.
Duke University researchers believe they have overcome a longstanding hurdle to producing cheaper, more robust ways to print and image across a range of colors extending into the infrared.
As any mantis shrimp will tell you, there are a wide range of "colors" along the electromagnetic spectrum that humans cannot see but which provide a wealth of information. Sensors that extend into the infrared can, for example, identify thousands of plants and minerals, diagnose cancerous melanomas and predict weather patterns, simply by the spectrum of light they reflect.
The sewer gnat is a common nuisance around kitchen and bathroom drains that’s no bigger than a pea. But magnified thousands of times, its compound eyes and bushy antennae resemble a first place winner in a Movember mustache contest.
Like the regular-sized copper wires that power our lamps and computers, miniscule copper nanowires are great at conducting electricity. Duke Professor Benjamin Wiley and his team are investigating how to brew up films thin sheets of copper nanowires that are precisely tailored to work as inexpensive, transparent electrodes in devices like touch screens, light-emitting diodes, and solar cells.
For David Johnston, drones are the perfect surveillance tool to spy on marine wildlife. Johnston and his team of ecologists, stationed at the Duke Marine Lab in Beaufort, NC, use unmanned aerial systems rigged with cameras and infrared sensors to map coastal habitats, like oyster reefs and seagrass, and to track ocean species like seals, sea turtles and sharks.
Turbulent storms in stock price and demand are illuminated in this Mahato-winning illustration by Ashleigh Swingler. Created using software developed by Laurens Howle, the zig-zagging line shows swings in the price of Nasdaq’s QQQ stock during January 2016: a zig to the left signals a price drop, and a zag to the right signals a price increase.
It takes a well-trained eye to spot an irregular heartbeat in the peaks and valleys of an electrocardiogram. The same goes for identifying an extinct ape from a single fossilized tooth, or telling an original van Gogh from a fake.
But in recent years, applied mathematician Ingrid Daubechies has been training computers to churn through ECG tracings, high-resolution scans of fossils, paintings and other complex digital data and work things out automatically.
Once they've mastered the skills of toddlerhood, humans are pretty good at what roboticists call "motion planning"—reaching around obstacles to precisely pick up a soda in a crowded fridge, or slipping their hands around a screen to connect an unseen cable.
But for robots with multi-jointed arms, motion planning is a hard problem that requires time-consuming computation. Simply picking an object up in an environment that has not been pre-engineered for the robot may require several seconds of computation.