Channel Sampling

Channel sampling involves strategically selecting subsets of available channels (e.g., sensor modalities, frequency bands, or network layers) to optimize performance while minimizing resource consumption. Current research focuses on developing adaptive sampling strategies, often employing reinforcement learning or trainable parameters to dynamically adjust channel selection based on input data or environmental conditions, with applications ranging from energy-efficient wireless communication to improved efficiency in convolutional neural networks. These techniques aim to improve data efficiency, reduce computational costs, and enhance robustness in various applications, including human activity recognition, signal processing, and video classification. The impact spans diverse fields, offering improvements in energy efficiency, computational speed, and data representation.