For reasons beyond our control, the issues of IEEE Signal Processing Magazine arrive to you with delays this year. As you receive the current March issue, we are back from another edition of our flagship conference, the IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP), which took place in Seoul, Korea, 14–19 April 2024.

Vector-valued signals are crucial in science and engineering. The evolving field of hypercomplex signal processing, particularly quaternion algebra, offers a concise and natural approach to handling vectorial data. In multicomponent seismology, for instance, vector-valued signal processing finds a natural fit that has been exploited in several applications.

Novel computational signal and image analysis methodologies based on feature-rich mathematical/computational frameworks continue to push the limits of the technological envelope, thus providing optimized and efficient solutions. Hypercomplex signal and image processing is a fascinating field that extends conventional methods by using hypercomplex numbers in a unified framework for algebra and geometry. 

How did an “old dog” signal processing professor approach learning and teaching the “new tricks” of generative artificial intelligence (AI)? This article overviews my recent experience in preparing and delivering a new course called “Computational Creativity,” reflecting on the methods I adopted compared to a traditional equations-on-a-whiteboard course.

This article aims to identify core research directions and provide a comprehensive overview of major advancements in the field of hypercomplex signal and image processing techniques using network graph theory. The methodology employs community detection algorithms on research networks to uncover relationships among researchers and topic fields in the hypercomplex domain.