This window film (held in fingers at top left) keeps rooms bright and cool by allowing visible light to pass in while reflecting invisible infrared and ultraviolet sunlight and radiating heat into outer space. Credit: Adapted from ACS Energy Letters 2022, DOI: 10.1021/acsenergylett.2c01969 Demand is growing for effective new technologies to cool buildings, as climate change intensifies summer heat. Now, scientists have just designed a transparent window coating that could lower the temperature inside buildings, without expending a single watt of energy. They did this with the help of advanced computing technology and artificial intelligence. The researchers report the details today (November 2) in the journal ACS Energy Letters.

Infrared night-vision systems that see in colour could be a reality thanks to researchers in the US, who have used machine learning to create colour images of photographs that are illuminated with just infrared light. The team hope their technique could be further developed to create imaging systems that operate where the use of visible light is impossible, such as retinal surgery. Traditional night vision systems work by illuminating an area with near infrared radiation and detecting the reflections or by using ultrasensitive cameras to detect the small amount of light present even at night. Both, however, usually produce monochromatic images, so researchers are seeking ways to produce multi-colour images of objects without having to bathe them in visible light. Computer scientist Pierre Baldi of University of California, Irvine (UCI), explains that this would be very useful in medical applications where use of visible light is problematic.

Night vision is typically monotone--everything the wearer can see is colored in the same hue, which is mostly shades of green. But by using varying wavelengths of infrared light and a relatively simple AI algorithm, scientists from the University of California, Irvine have been able to bring back some color into these desaturated images. Their findings are published in the journal PLOS ONE this week. Light in the visible spectrum, similar to an FM radio, consists of many different frequencies. Both light and radio are part of the electromagnetic spectrum.

Scientists from the University of Irvine have developed a camera system that combines artificial intelligence (AI) with an infrared camera to capture full-color photos even in complete darkness. Human vision perceives light on what is known as the "visible spectrum," wavelengths of light between about 300 and 700 nanometers. Infrared light exists beyond 700 nanometers and is invisible to humans without the help of special technology, and many night vision systems can detect infrared light and transpose it into a digital display that provides humans with a monochromatic view. Scientists endeavored to take that process one step further and combined that infrared data with an AI algorithm that predicts color to render images in the same way they would appear if the light existed in the visible spectrum. Typical night vision systems render scenes as a monochromatic green display, and newer night vision systems use ultrasensitive cameras to detect and amplify visible light. The scientists say that computer vision tasks with low illuminance imaging have employed image enhancement and deep learning to aid in object detection and characterization from the infrared spectrum, but not with accurate interpretation of the same scene in the visible spectrum.

An artifact-free computational approach to extract the phase of light from noisy intensity signals improves imaging of transparent objects, such as biological cells, under low light conditions. Deep neural networks are trained to operate on these two frequency bands, before a final algorithm recombines them into a full-band phase image. This method avoids the tendency of automatic phase extraction programs to over-represent low frequencies. The retrieval of phase of electromagnetic fields is one of the most important problems in optics as it allows the shape of transparent objects, including cells, to be quantified using visible light. Phase is a quantity that relates to the wave nature of light; it is not directly detectable by our eyes or common cameras, and yet carries important information about objects the light went through.

Humans can spot patterns of activity, but we can't see through walls. Advanced neural networks that use radio wave imaging to see have the exact opposite problem. Now, a new technique developed by researchers at Massachusetts Institute of Technology is helping the neural networks see the world a little more clearly. The new method uses radio waves to train a neural network to spot patterns of activity that can't be viewed in visible light, according to a paper, titled "Making the Invisible Visible: Action Recognition Through Walls and Occlusions," recently posted to the preprint server arXiv. The researchers say the tech is especially helpful in difficult conditions, such as when someone is obscured in darkness or fog or around a corner.

Overcoming the visual barrier and developing "see-through vision" has been one of mankind's long-standing dreams. However, visible light cannot travel through opaque obstructions (e.g. walls). Unlike visible light, though, Radio Frequency (RF) signals penetrate many common building objects and reflect highly off humans. This project creates a breakthrough artificial intelligence methodology by which the skeletal structure of a human can be reconstructed with RF even through visual occlusion. In a novel procedural flow, video and RF data are first collected simultaneously using a co-located setup containing an RGB camera and RF antenna array transceiver. Next, the RGB video is processed with a Part Affinity Field computer-vision model to generate ground truth label locations for each keypoint in the human skeleton. Then, a collective deep-learning model consisting of a Residual Convolutional Neural Network, Region Proposal Network, and Recurrent Neural Network 1) extracts spatial features from RF images, 2) detects and crops out all people present in the scene, and 3) aggregates information over dozens of time-steps to piece together the various limbs that reflect signals back to the receiver at different times. A simulator is created to demonstrate the system. This project has impactful applications in medicine, military, search & rescue, and robotics. Especially during a fire emergency, neither visible light nor infrared thermal imaging can penetrate smoke or fire, but RF can. With over 1 million fires reported in the US per year, this technology could save thousands of lives and tens-of-thousands of injuries.

UK police forces are increasingly experimenting with controversial new facial recognition (FR) technology for crowd control and locating suspects. Critics, however, have labeled the trials a shambles, pointing to the high error rate and even higher cost of the program. Documents released under Freedom of Information Act requests have shown that collectively South Wales Police and London's Metropolitan Police have spent millions of pounds on trials of the technology, despite the fact that both systems have been shown to have an error rate over 90 per cent. Similar trials around the world have raised concerns around the technology, including in San Francisco where privacy advocates are calling for a ban on the use of FR by law enforcement. It's not just the police who are interested in the potential use of FR.

I have been an electronics enthusiast ever since I was in elementary school. To put together an electronic device that interacts with the physical world in some way has been my passion and I still remember the excitement I felt when I built my first circuit in 6th grade – even though it was simply something that periodically turned an LED on and off. After earning an undergraduate degree in electronic systems engineering in my home country of Mexico, I came to the U.S. to study for a PhD in Electrical Engineering, before joining IBM in 2006 to work on silicon integrated millimeter wave circuits and systems. I had the honor of joining a team of IBM scientists who were pioneers of the first monolithic millimeter wave radio that exploited portions of the radio spectrum to boost wireless communications. And since then I have been researching how to engineer more and more complex millimeter wave systems.

A working lightsaber became a dream for most Star Wars enthusiasts when they were first introduced to a galaxy far, far away. And Disney could finally turn this sci-fi device into reality at its 14-acre Star Wars Land, as a new patent describes plans for visitors to interact with what looks, feels and behaves like the real thing. The patent describes an'audience interaction projection system' that uses drones to send down beams of light towards the audience, who are given'faux lightsabers' to deflect the laser bolts back at the machine. Disney's latest patent describes an'audience interaction projection system' that uses drones to send down beams of light towards the audience, who are given'faux lightsabers' to deflect the laser bolts. Disney's latest patent describes plans for visitors to interact with what looks, feels and behaves like a real lightsaber.