Signal Processing and Machine Learning for Sensor Systems (2nd Edition)

Dear Colleagues,

This Special Issue provides a platform for research on the processing of sensor signals, both at the edge and in large-scale data warehouses. Recent advances in machine learning and signal processing have driven significant progress in areas such as signal classification, fault detection, speech recognition, and industrial automation. With an estimated 14 billion Internet of Things (IoT) devices currently in use—ranging from smart lighting systems and cell phones to power tools and industrial equipment—the volume of sensor data being generated continues to expand exponentially. As this growth accelerates, efficient data processing closer to the device has become increasingly important. The integration of edge and embedded processing power, specialized signal processing hardware, and compact neural network architectures has enabled machine learning at the edge, resulting in intelligent devices capable of adapting to user needs. Additionally, processing sensor signals collected in data warehouses enables the application of sophisticated signal processing and deep machine learning algorithms. This Special Issue welcomes original research and review articles addressing the processing of sensor signals using machine learning techniques—both in edge and embedded environments and in data warehouse or cloud-based frameworks.

Dr. Richard J. J. Povinelli
Dr. Priya Deshpande
Dr. Cristinel Ababei
Guest Editors

Manuscript Submission Information

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