Radio Frequency Fingerprinting (RFF) has evolved as an effective solution for authenticating devices by leveraging the unique imperfections in hardware components involved in the signal generation process. In this work, we propose a Convolutional Neural Network (CNN) based framework for detecting rogue devices and identifying genuine ones using softmax probability thresholding. We emulate an attack scenario in which adversaries attempt to mimic the RF characteristics of genuine devices by training a Generative Adversarial Network (GAN) using In-phase and Quadrature (IQ) samples from genuine devices. The proposed approach is verified using IQ samples collected from ten different ADALM-PLUTO Software Defined Radios (SDRs), with seven devices considered genuine, two as rogue, and one used for validation to determine the threshold.

The first wireless brain-computer interface (BCI) system is not only giving people with paralysis the ability to type on computer screens with their minds, but the innovation is also giving them freedom to do so anywhere. Traditional BCIs are tethered to a large transmitter with long cables, but a team from Brown University has cut the cords and replaced them with a small transmitter that sits atop the user's head. The redesigned equipment is just two inches in diameter and connects to an electrode array within the brain's motor cortex by means of the same port used by wired systems. The trials, dubbed BrainGate,' showed two men paralyzed by spinal injuries were able to type and click on a tablet just by thinking of the action, and did so with similar point-and-click accuracy and typing speeds as those with a wired system. A participant in the BrainGate clinical trial uses wireless transmitters that replace the cables normally used to transmit signals from sensors inside the brain.

The team has been focusing on improving a brain-computer interface, a device implanted beneath the skull on the surface of a patient's brain. This implant connects the human nervous system to an electronic device that might, for instance, help restore some motor control to a person with a spinal cord injury, or someone with a neurological condition like amyotrophic lateral sclerosis, also called Lou Gehrig's disease. The current generation of these devices record enormous amounts of neural activity, then transmit these brain signals through wires to a computer. But when researchers have tried to create wireless brain-computer interfaces to do this, it took so much power to transmit the data that the devices would generate too much heat to be safe for the patient. Now, a team led by electrical engineers and neuroscientists Krishna Shenoy, PhD, and Boris Murmann, PhD, and neurosurgeon and neuroscientist Jaimie Henderson, MD, have shown how it would be possible to create a wireless device, capable of gathering and transmitting accurate neural signals, but using a tenth of the power required by current wire-enabled systems.

As 5G networks continue to expand in cities and countries across the globe, key researchers have already started to lay the foundation for 6G deployments roughly a decade from now. This time, they say, the key selling point won't be faster phones or wireless home internet service, but rather a range of advanced industrial and scientific applications -- including wireless, real-time remote access to human brain-level AI computing. That's one of the more interesting takeaways from a new IEEE paper published by NYU Wireless's pioneering researcher Dr. Ted Rappaport and colleagues, focused on applications for 100 gigahertz (GHz) to 3 terahertz (THz) wireless spectrum. As prior cellular generations have continually expanded the use of radio spectrum from microwave frequencies up to millimeter wave frequencies, that "submillimeter wave" range is the last collection of seemingly safe, non-ionizing frequencies that can be used for communications before hitting optical, x-ray, gamma ray, and cosmic ray wavelengths. Dr. Rappaport's team says that while 5G networks should eventually be able to deliver 100Gbps speeds, signal densification technology doesn't yet exist to eclipse that rate -- even on today's millimeter wave bands, one of which offers access to bandwidth that's akin to a 500-lane highway. Consequently, opening up the terahertz frequencies will provide gigantic swaths of new bandwidth for wireless use, enabling unthinkable quantities and types of data to be transferred in only a second.

Pillar cofounders Alex Schwarzkopf and Matt Joyal, Forbes 30 Under 30 alumni, developed rugged, wireless devices for construction sites to flag environmental risks like fires or leaks early. In February 2017, AvalonBay's 235-unit development in Maplewood, New Jersey, burned down six weeks before its planned opening. The homebuilder rebuilt the $55 million project from scratch--then searched for new ways to prevent fires, a constant threat when building with woodframe construction. The solution: Pillar Technologies, whose devices monitor temperature, smoke and other signs of pending disaster, and which will soon use artificial intelligence to flag environmental risks even earlier. "It's like medical monitoring for buildings," says Michael Feigin, chief construction officer and executive vice president at AvalonBay.

Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario. We believe advances in wireless localization, working in unison with cameras, can produce automated annotation of targets on images and videos captured in the wild. Using pedestrian and vehicle detection as examples, we demonstrate the feasibility, benefits, and challenges of an automatic image annotation system. Our work calls for new technical development on passive localization, mobile data analytics, and error-resilient ML models, as well as design issues in user privacy policies.