Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves that improves accuracy while reducing the associated omputing time and power requirements. The system could provide a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing. It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data actually is. In a proof-of-principle study, the setup correctly identified a set of 3D numbers using tens of measurements instead of the hundreds or thousands typically required. The results appear in the journal Advanced Science and are a collaboration between David Smith, the James Duke Distinguished Professor of Electrical and Computer Engineering at Duke, and Roarke Horstmeyer, assistant professor of biomedical engineering at Duke. "Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate," said Horstmeyer. "But that's inefficient because the computer doesn't need to'look' at an image at all." "This approach circumvents that step and allows the program to capture details that an image-forming process might miss while ignoring other details of the scene that it doesn't need," added Aaron Diebold, a research assistant in Smith's lab.

Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves that improves accuracy while reducing the associated computing time and power requirements. The system could provide a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing. It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data actually is. In a proof-of-principle study, the setup correctly identified a set of 3-D numbers using tens of measurements instead of the hundreds or thousands typically required. The results appear online on December 6 in the journal Advanced Science and are a collaboration between David R. Smith, the James B. Duke Distinguished Professor of Electrical and Computer Engineering at Duke, and Roarke Horstmeyer, assistant professor of biomedical engineering at Duke. "Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate," said Horstmeyer. "But that's inefficient because the computer doesn't need to'look' at an image at all." "This approach circumvents that step and allows the program to capture details that an image-forming process might miss while ignoring other details of the scene that it doesn't need," added Aaron Diebold, a research assistant in Smith's lab.

Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves that improves accuracy while reducing the associated computing time and power requirements. The system could provide a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing. It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data actually is. In a proof-of-principle study, the setup correctly identified a set of 3D numbers using tens of measurements instead of the hundreds or thousands typically required. The results appear online on December 6 in the journal Advanced Science and are a collaboration between David R. Smith, the James B. Duke Distinguished Professor of Electrical and Computer Engineering at Duke, and Roarke Horstmeyer, assistant professor of biomedical engineering at Duke. "Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate," said Horstmeyer. "But that's inefficient because the computer doesn't need to'look' at an image at all." "This approach circumvents that step and allows the program to capture details that an image-forming process might miss while ignoring other details of the scene that it doesn't need," added Aaron Diebold, a research assistant in Smith's lab. "We're basically trying to see the object directly from the eyes of the machine."

U.S. and German engineers have developed a microscope that uses machine learning to co-optimize illumination from a bank of 1,500 programmable LEDs and image classification, for faster diagnosis of malaria. Nearly half the world's population is at risk of malaria, with an estimated 219 million cases of the mosquito-borne disease in 2017 alone, according to the World Health Organization. Early and accurate diagnosis of malaria allows clinicians to treat infected individuals quickly, which increases their chances of survival and prevents further transmission of the disease. Researchers in the United States and Germany have now brought optics and machine learning together in a bid to accelerate the disease's diagnosis (Biomed. The most common diagnostic test for malaria involves examining a drop of blood under an optical microscope to check for the presence of Plasmodium falciparum, a single-celled parasite that causes malaria.

Engineers at Duke University have developed a microscope that adapts its lighting angles, colors and patterns while teaching itself the optimal settings needed to complete a given diagnostic task. In the initial proof-of-concept study, the microscope simultaneously developed a lighting pattern and classification system that allowed it to quickly identify red blood cells infected by the malaria parasite more accurately than trained physicians and other machine learning approaches. The results appear online on November 19 in the journal Biomedical Optics Express. "A standard microscope illuminates a sample with the same amount of light coming from all directions, and that lighting has been optimized for human eyes over hundreds of years," said Roarke Horstmeyer, assistant professor of biomedical engineering at Duke. "But computers can see things humans can't," Hortmeyer said. "So not only have we redesigned the hardware to provide a diverse range of lighting options, we've allowed the microscope to optimize the illumination for itself."