Sensor Data Compression

Sensor data compression aims to reduce the size of data streams from various sensors, improving efficiency in storage, transmission, and processing. Current research focuses on developing efficient compression algorithms, including autoencoders (often incorporating transforms like the discrete cosine Stockwell transform for improved sparsity), and spiking neural networks for dynamic selection of relevant data points. These advancements are crucial for enabling real-time processing of high-resolution sensor data in applications like autonomous vehicles and IoT devices, addressing limitations in bandwidth and computational resources.