SPS Blog Article

Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases, has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [2], if not two [3], it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the Machine Learning (ML) community.

Artificial intelligence (AI) and machine learning (ML) as an application of AI, has today become an inevitable part of major industries such as healthcare, financial trending, and transportation. Future urgent need to intelligently utilize wireless resources to meet the need of ever-increasing diversity in services and user behavior, has actuated the wireless communication industry to deploy AI and ML techniques.

Erik Meijering, the current Chair of SPS’s Bio Imaging and Signal Processing (BISP) Technical Committee, has been pursuing his passion in signal processing from the onset of his education. Realizing early on the vast and evolving ways that signal processing can unlock solutions for the advancement of technology, Erik’s studies began in digital image processing and eventually focused on applications in biomedical imaging for his PhD.

If you’ve ever been to the emergency room due to an injury, chances are you’ve had a routine encounter with what is in fact a wonder of the modern age: Magnetic Resonance Imaging, or MRI. Advancements in medical imaging allow us to study parts of the body, including the brain, to an extent unprecedented in the history of medical science.

This past Star Wars Day (May the Fourth), fans had reason to celebrate. The new prequel, Solo: A Star Wars Story, was less than a week away from release, and more broadly, the entire franchise is undergoing a renaissance with new films more faithful in style and storyline to the beloved original trilogy. But beyond the actors and writers, Star Wars fans should be thanking the technology that makes such films possible: signal processing.

Technologies that at first seem like magic soon become so commonplace that we no longer even think about how amazing they are – or wonder how they operate. Our digital home assistants – Amazon’s Alexa, Google Home and their emerging rivals – have been with us long enough that we take for granted they can understand our unique voices, commands and questions, even with other noise occurring around them.

Will autonomous vehicles kill people? The answer is almost certainly yes. But, human drivers kill people too. In 2016 the U.S. had 37,461 deaths in 34,436 human-operated motor vehicle crashes, a staggering average of 102 per day, according to the National Highway Traffic Safety Administration. An additional 2,350,000 are injured or disabled. These figures are alarming, but there’s a solution on the horizon.

So much of our daily lives are powered by signal processing technologies. They go unnoticed mostly because that’s the purpose – to help our lives run more seamlessly, with little to no impact. For example, you’re probably reading this blog on your computer. Guess what? Signal processing is responsible for your computer’s ability to store data and run smoothly.

Have you had an experience in an MRI scanner? Chances are you have or know someone who has. Magnetic Resonance Imaging produces detailed pictures of soft tissue in the human body – but, unlike many other techniques, it does not require any radiation.