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.

The year 2023 marked the 75th anniversary of the IEEE Signal Processing Society (SPS), which was founded in 1948 as the “Professional Group on Audio” of the Institute of Radio Engineers (IRE), becoming the first IEEE Society. (The IRE, founded in 1912 with a focus on radio and then electronics, together with the American Institute of Electrical Engineers, founded in 1884 with an emphasis on power and utilities, were united in 1963 to form IEEE.)