Innovation Starts with Education

Signal processing (SP) is at the very heart of our digital lives, owing to its role as the pivotal enabling technology for advancement across multiple disciplines. Its prominence in modern data science has created a necessity to supply industry, government labs, and academia with graduates who possess relevant SP expertise and are well equipped to deal with the manifold challenges in current and future applications. To this end, the ways to deliver both educational content and the core SP curriculum need to be revisited and integrated into current electrical engineering and computer science degrees to provide high-quality and hands-on multidisciplinary skills, experience, and inspiration for students at all levels.

SP education in today’s universities is largely influenced by three modern trends:

1. the availability of competing and complementary online and multimedia resources

2. the fact that we live in a world in which the amount and diversity of information we generate, process, and analyze are growing

3. the explosive growth of computing power and the rapid development of new technologies for implementing both analog and digital SP.

These trends offer both opportunities and challenges, which we can and must exploit in charting dynamically adjustable courses that attract a high level of student engagement while offering a mix of essential background physics, intuition, mathematical rigor, and practical applicability of the taught material.

With such initiatives underway worldwide, this special issue aims to facilitate both keeping abreast with SP education and exploring innovative and participatory ways to present the educational materials. In effect, we cannot assume that students will be able to appreciate the scope and relevance of their courses without explicitly building a bridge between the material presented in class and cutting-edge research and the societal and practical impact of their education. This includes the convergence of educational material with other disciplines (machine learning, data science, big data, bioengineering, artificial intelligence, finance, and many others).