The progression of IBM Watson from gameshow fame to perennial problem solving, signifies the untapped potential for cognitive computing in industrial electronics applications. Transition from the programming era, where explicit instructions dictate function within a constrained environment, into the cognitive era, is generally identified by three characteristics; incremental learning from data and insights, hypothesis generation and contextual insight based on learned models. Programmed systems generate deterministic outcomes by processing well-defined structured data. In contrast, cognitive computing systems process and integrate both structured and unstructured data from multiple sources to generate probabilistic outcomes. Thereby, data sourced from multiple systems, regardless structure and format, are learned and optimized to generate a number of potential outcomes with varying degrees of confidence.

The provision of a selection of informed outcomes is an unprecedented enrichment of decision-support in industrial applications. For instance, the gradual adaptation of smart cities as cognitive cities indicates the significance of this transition. Cognitive computing is an emerging field of study. Therefore, it is important to ensure substantial peer-reviewed research outcomes provide a strong foundation for the proliferation and development of this field. To this end, we cordially invite original research articles that focus on cognitive computing theory and applications in Internet of Things (IoT), Big Data, smart energy, industrial electronics, controls, communications and instrumentation.