Automated driving systems face challenges in GPS-denied situations. To address this issue, kinematic dead reckoning is implemented using measurements from the steering angle, steering rate, yaw rate, and wheel speed sensors onboard the vehicle. However, dead reckoning methods suffer from drift. This paper provides an arc-length-based map matching method that uses a digital 2D map of the scenario in order to correct drift in the dead reckoning estimate. The kinematic model's prediction is used to introduce a temporal notion to the spatial information available in the map data. Results show reliable improvement in drift for all GPS-denied scenarios tested in this study. This innovative approach ensures that automated vehicles can maintain continuous and reliable navigation, significantly enhancing their safety and operational reliability in environments where GPS signals are compromised or unavailable.

When it comes to the evolution of mobile robots, it may be a long time before legged robots can safely interact in the real world, according to a new study. Led by a team of researchers at The Ohio State University, the study published recently in the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022 describes a framework for testing and characterizing the safety of legged robots, machines that, unlike their wheeled counterparts, rely on mechanical limbs for movement. The study found that many current legged robotic models don't always act predictably in response to real-life situations, meaning it's hard to predict whether they'll fail – or succeed – at any given task that requires movement. "Our work reveals that these robotic systems are complex and, more importantly, anti-intuitive," said Bowen Weng, a Ph.D. student in electrical and computer engineering at Ohio State. "It means you can't rely on the robot's ability to know how to react in certain situations, so the completeness of the testing becomes even more important."

Combining satellite technology with machine learning may allow scientists to better track and prepare for climate-induced natural hazards, according to research presented last month at the annual meeting of the American Geophysical Union. Over the last few decades, rising global temperatures have caused many natural phenomena like hurricanes, snowstorms, floods and wildfires to grow in intensity and frequency. While humans can't prevent these disasters from occurring, the rapidly increasing number of satellites that orbit the Earth from space offers a greater opportunity to monitor their evolution, said C.K Shum, co-author of the study and a professor at the Byrd Polar Research Center and in earth sciences at The Ohio State University. He said that potentially allowing people in the area to make informed decisions could improve the effectiveness of local disaster response and management. "Predicting the future is a pretty difficult task, but by using remote sensing and machine learning, our research aims to help create a system that will be able to monitor these climate-induced hazards in a manner that enables a timely and informed disaster response," said Shum.

Machine learning technology has a range of applications in a range of industries in professions. For example, machine learning technology has become a popular fixture in the healthcare field. The ability to feed data into a machine and have an algorithm that can interpret the data, machine learning offers doctors and clinicians the ability to make diagnosis or spot information on an imaging scan, for example, that might not have been visible before. The general idea is that machine learning can take large quantities of data to solve problems that might be more difficult for humans to do alone. But what if machine learning were so good it could actually predict the future.

"We will use artificial intelligence and machine learning models to take all the complex information we collect and get insight out of the data, such as the …

Applications of artificial intelligence are growing every day in different sectors. There are numerous instances of AI applications in healthcare. The AI that occurs in hearing aids has actually been going on for years and the following is about how it happened. Hearing aids used to be relatively simple, he notes, but when hearing aids introduced a technology known as wide dynamic range compression (WDRC), the devices actually began to make a few decisions based on what is heard. For hearing aids to work effectively, they need to adapt to a person's individual hearing needs as well as all sorts of background noise environments. AI, machine learning, and neural networks are very good techniques to deal with such a complicated, nonlinear, multi-variable problem.

The traditional path of drug development is lengthy, expensive, and suffers from high failure rates – scientists test millions of molecules, however, only a handful progress to preclinical or clinical testing. Embracing innovation, particularly automated technologies, is essential to reduce the complexity associated with drug discovery and circumvent the high cost and time spent bringing a medicine to market. The subsequent sections will highlight examples of how ML can be used for drug repurposing and to discover novel antibiotics. The application of ML strategies to enhance image-based profiling and accelerate drug discovery will also be discussed. Drug discovery is often thought of as a complex jigsaw puzzle where connecting workflows and data are essential pieces.

Otologic Technologies, Inc., a Wisconsin-based health-tech startup developing an artificial intelligence (AI) system to improve treatment of ear disease, announced the issuance of US Patent No. 10,932,662, "System and Method of Otoscopy Image Analysis to Diagnose Ear Pathology." The patent explains a novel artificial intelligence system to help doctors better diagnose ear disease. "One of the biggest challenges in diagnosing ear disease is the difficult nature of an ear exam," said Aaron Moberly, MD, associate professor of otolaryngology at The Ohio State University and one of the inventors of the technology. "Even experienced doctors can have trouble with a live ear exam, as patients are usually uncomfortable and the view can be obstructed. In 2015, Dr. Moberly began an ongoing collaboration with Metin Gurcan, PhD, an artificial intelligence (AI) expert at The Ohio State University.

An insect-like machine with six individually powered legs that was intended for the battlefield received millions in US government funding during the 1980s. The project to create a fleet of real-life AT-AT walkers from Star Wars started in 1981 at Ohio State University, as the military searched for ways to traverse rough terrain that wheeled vehicles couldn't manage. Called the Adaptive Suspension Vehicle (ASV), the bizarre vehicle was part of a decade-long project which was eventually scrapped after receiving a reported $1million a year from Darpa between 1981 and 1990. The fate of the ASV is a mystery, with nobody knowing whether it is in storage somewhere or was scrapped decades ago. Professors Robert McGhee and Kenneth Waldron at Ohio State University wrote a scientific paper explaining their project in 1986.

There's still no completely safe and surefire method for locating unexploded ordinance after a war is over, but researchers at Ohio State University have found a way to harness image processing algorithms, powered by machine learning, to study satellite imagery and locate hot spots where UXO are likely to be located. The researchers focused their efforts on a 100-square-kilometre area near Kampong Trabaek, Cambodia, which was the target of carpet-bombing missions carried out by the United States Air Force during the Vietnam War. The team was given access to declassified military data that revealed that 3,205 bombs had been dropped in the area between 1970 and 1973. Determining exactly how many of those bombs didn't explode has gotten harder and harder as, six decades later, nature has slowly reclaimed the country's heaviest hit areas, hiding and obscuring the craters that are counted and used to make accurate estimates. The OSU study used a two-step process to come up with a more accurate estimate of how many bombs were still left in the area.