Serial Quantization for Sparse Time Sequences

Sparse signals are encountered in a broad range of applications. In order to process these using digital hardware, they must first be sampled and quantized an analog-to-digital convertor (ADC), which typically operates serial scalar manner. this work, we propose quantization sparse time sequences (SQuaTS) method inspired by group testing theory. This is designed reliably accurately quantize acquired sequential manner the ADCs. Unlike previously proposed approaches that combine compressed sensing (CS), our SQuaTS scheme updates its representation on each incoming analog sample does not require complete signal observed or stored prior quantization. We characterize asymptotic tradeoff between accuracy rate as well computational burden. also variation trades for efficiency. Next, show how can naturally extended distributed scenarios, where set jointly individually processed jointly. Our numerical results demonstrate capable achieving substantially improved over previous CS-based schemes without requiring samples quantization, making attractive approach acquiring sequences.