SPM Articles

The flexibility and dexterity of human limbs rely on the processing of a vast quantity of signals within the sensory-motor networks in the brain and spinal cord, distilled into stimuli that govern the commands and movements. Hence, the use of assistive devices, such as robotic limbs or exoskeletons, is critically dependent on the processing of a large number of heterogeneous signals to mimic natural movements.

Deep learning, in general, focuses on training a neural network from large labeled datasets. Yet, in many cases, there is value in training a network just from the input at hand. This is particularly relevant in many signal and image processing problems where training data are scarce and diversity is large on the one hand, and on the other, there is a lot of structure in the data that can be exploited.

By “social learning,” in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents’ decisions evolve dynamically through interactions with neighbors and the environment. The study of social learning strategies is critical for at least two reasons.

As I take on the President of the Signal Processing Society (SPS) role, I am excited to connect with you through this column. I look forward to introducing myself and inviting you, the members, to join our volunteers in shaping our shared future.

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.

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.

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.