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.

Are you looking to energize signal processing students, early stage researchers, and industry practitioners in your area? Consider hosting a Seasonal School for young engineers near you!

The list of tables of contents (TOCs) are now available for February 2019. The TOCs are formatted to the style of its publication and offers not only links to the full issue, but also to the individual articles in IEEEXplore®. 

Subspace learning theory for dimensionality reduction was initiated with the Principal Component Analysis (PCA) formulation proposed by Pearson in 1901. 

Applications of semidefinite optimization in signal processing are often derived from the Kalman-Yakubovich-Popov lemma and its extensions, which give sum-of-squares theorems of nonnegative trigonometric polynomials and generalized polynomials.

Large-scale networks are becoming more prevalent, with applications in healthcare systems, financial networks, social networks, and traffic systems. The detection of normal and abnormal behaviors (signals) in these systems presents a challenging problem.

This work investigates two different digital signal processing (DSP) approaches that rely on signal-derived timing: continuous-time (CT) DSP and variable-rate DSP. Both approaches enable designs of energy-efficient signal processing systems by relating their operation rates to the input activity.

Lotfi A. Zadeh was one of the most impressive researchers of our modern era. Not only did he create a groundbreaking theory, but he also paved the way for an incredible variety of its applications, ranging from medicine and economy to large-scale scientific, industrial, and commercial applications.