The practicality gap between theoretical concepts, or even lab prototypes, and commercially viable products is quite often a bridge too far. The root causes for such shortcomings may simply be that a new technology is not well suited for real-world environments or economic variables, such as cost, may be to blame. A limiting factor often overlooked in technology transfer programs is a lack of high-quality, accessible technical standards that are also definitive and relevant. Standards are essential for driving high-technology products to market that are reasonably priced for the consumer.

In every imaging or sensing application, the physical hardware creates constraints that must be overcome or they will limit system performance. Techniques that leverage additional degrees of freedom can effectively extend performance beyond the inherent physical capabilities of the hardware. As these technologies mature beyond the conceptual and prototype phase they will ultimately transition to the commercial market. Here, standards play a critical role in ensuring success.

The goal of this paper is to provide an overview of recent methods for handling missing data in signal processing methods, from their origins to the challenges ahead. Missing data approaches are grouped by three main categories: i) missing-data imputation, ii) estimation with missing values and iii) prediction with missing values. We focus on methodological and experimental results through specific case studies on real-world applications. Promising and future research directions, including a better integration of informative missingness, are also discussed.

Deep learning (DL) is a field of study within machine learning and signal processing that has been around for nearly 40 years. In the last 10 years, its progress on problems including speech-to-text, image recognition, image generation, and language generation has been phenomenal. This exponential progress has been driven by exciting engineering and algorithmic developments.