HoloRadar helps give the vehicles a more complete picture of their surroundings. Breakthroughs, discoveries, and DIY tips sent six days a week. In late January, an Alphabet-owned Waymo self-driving car was cruising near an elementary school in Santa Monica, California, when a young child suddenly darted into the street . Waymo's LiDAR sensors detected the student, who had just emerged from behind a parked SUV, but it was too late. Despite slamming on the brakes and slowing from 17 to six mph, the driverless car struck the child, knocking them to the pavement.

How Does the Hive Mind Work in? The "Joining" seems to connect people via radio waves. Let's dig into the physics at play. Carol Sturka (left) and her chaperone," Zosia, in the Apple TV show . You know what's great about a show like?

Researchers have developed a method for diagnosing autism which could spare families years of uncertainty and spur crucial earlier treatments. The new AI analysis can identify the genetic markers of autism via biological activity in the brain, they report, with 89 to 95 percent accuracy. This new method starts out with standard brain-mapping via magnetic resonance imaging (MRI) before re-analyzing those scans via AI to detect the movements of proteins, nutrients and other processes within the brain that may indicate autism. 'Autism is traditionally diagnosed behaviorally,' via a person's speech, for example, as the medical team behind the process noted. 'But [it] has a strong genetic basis.'

The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.

Researchers from MIT and elsewhere have built a wake-up receiver that communicates using terahertz waves, which enabled them to produce a chip more than 10 times smaller than similar devices. Their receiver, which also includes authentication to protect it from a certain type of attack, could help preserve the battery life of tiny sensors or robots. Scientists are striving to develop ever-smaller internet-of-things devices, like sensors tinier than a fingertip that could make nearly any object trackable. These diminutive sensors have miniscule batteries which are often nearly impossible to replace, so engineers incorporate wake-up receivers that keep devices in low-power "sleep" mode when not in use, preserving battery life. Researchers at MIT have developed a new wake-up receiver that is less than one-tenth the size of previous devices and consumes only a few microwatts of power.

The proliferation of sensitive information being stored online highlights the pressing need for secure and efficient user authentication methods. To address this issue, this paper presents a novel zero-effort two-factor authentication (2FA) approach that combines the unique characteristics of a users environment and Machine Learning (ML) to confirm their identity. Our proposed approach utilizes Wi-Fi radio wave transmission and ML algorithms to analyze beacon frame characteristics and Received Signal Strength Indicator (RSSI) values from Wi-Fi access points to determine the users location. The aim is to provide a secure and efficient method of authentication without the need for additional hardware or software. A prototype was developed using Raspberry Pi devices and experiments were conducted to demonstrate the effectiveness and practicality of the proposed approach. Results showed that the proposed system can significantly enhance the security of sensitive information in various industries such as finance, healthcare, and retail. This study sheds light on the potential of Wi-Fi radio waves and RSSI values as a means of user authentication and the power of ML to identify patterns in wireless signals for security purposes. The proposed system holds great promise in revolutionizing the field of 2FA and user authentication, offering a new era of secure and seamless access to sensitive information.

The Israeli military is using AI-powered detection system that lets soldiers see through walls before attacking. Designed in part with Camero-Tech, Xaver 1000 uses algorithms to track targets behind an obstacle, which are then displayed on a screen fitted in the center of the device. Xaver 1000, which users place directly on the wall, produces such high resolution displays that users can determine if a person is sitting, standing or lying down. The system is also capable of providing measurements of targets and determining if the image is of an adult, child or animal, allowing soldiers or police officers to know what they are up against on the other side of the wall. The device is designed like a diamond with four flaps that open outward.

The Israeli military is using AI-powered detection system that lets soldiers see through walls before attacking. Designed in part with Camero-Tech, Xaver 1000 uses algorithms to track targets behind an obstacle, which are then displayed on a screen fitted in the center of the device. Xaver 1000, which users place directly on the wall, produces such high resolution displays that users can determine if a person is sitting, standing or lying down. The system is also capable of providing measurements of targets and determining if the image is of an adult, child or animal, allowing soldiers or police officers to know what they are up against on the other side of the wall. The device is designed like a diamond with four flaps that open outward.

Google has unveiled technology that can read people's body movements to let devices'understand the social context around them' and make decisions. Developed by Google's Advanced Technology and Products division (ATAP) in San Francisco, the technology consists of chips built into TVs, phones and computers. But rather than using cameras, the tech uses radar – radio waves that are reflected to determine the distance or angle of objects in the vicinity. If built into future devices, the technology could turn down the TV if you nod off or automatically pause Netflix when you leave the sofa. Assisted by machine learning algorithms, it would also generally allow devices to know that someone is approaching or entering their'personal space'. Google has unveiled technology that can read people's body movements to let devices'understand the social context around them' and make decisions, such as flashing up information when you walk by or turning down volume on Radar is an acronym, which stands for Radio detection and ranging.

Can't get your partner to ever tell you how they really feel? There may be an app for that…one day. Scientists can now predict how someone is feeling using radio waves to measure heart rate and breathing. The wireless signals can detect a person's feelings even in the absence of any other visual cues such as facial expressions. This AI technology could be used to help reveal our inner emotions.