Embedded LIB

This site is dedicated to software strategies and tools for developing state-of-the-art intelligent real-time computing systems, ranging from lightweight, resource-constrained embedded sensors and mobile platforms to more powerful applications on standard operating systems like Linux or Windows. Techniques that work for lightweight systems often also perform well in heavyweight environments, but the reverse is not typically true. Lightweight implementations are also often easily augmented for more standard platforms through conditional builds that add features not suitable for resource-constrained contexts.

While almost all aspects of embedded systems software are potential topics of discussion, the primary focus will be on software that enhances the capabilities of intelligent edge devices, such as medical monitoring wearables, advanced biosensors, and real-time scientific or medical instruments. Emphasis will be placed on designing, implementing, and using toolkits of practical, reusable, testable, manageable, and high-reliability software components, as well as the software required for database and cloud services network communication, vertical integration, and management. These components will leverage research and advanced methods in machine learning, deep learning, and signal processing with the intent to more rapidly achieve or extend state-of-the-art performance. Signal processing and machine learning components for biosensors or physiological signals is a particular focus.