Sampling continuous-time signals constitutes a fundamental process in the modern digital technology era. This research investigates a novel class of event-triggered samplers, in which the sampling times are dictated in a dynamic way by the actual signals to be sampled. The richness of the event-triggered sampling schemes allows for the mathematical formulation of meaningful optimization problems that can lead to sampling strategies with optimal characteristics and resulting in potentially significant performance gains. The theories and techniques to be developed in this project can potentially make significant contributions to important application areas such as sensor network systems for event surveillance and monitoring, and networked control systems for smart-grid-based power generation and distribution.
This research focuses on three major problems in the general areas of decentralized statistical signal processing, namely, sequential detection (hypothesis testing and changepoint detection), parameter estimation, and signal estimation and control. In all three problems, optimal event-triggered mechanisms will be investigated that can optimize the corresponding detection or estimation performance. In addition, a special class of event-triggered samplers will be analyzed and the corresponding design methods for digital signal processing (DSP) will be developed .This research will have transformative impacts on the fields of statistical signal processing and digital signal processing. In particular, the use of event-triggered sampling in detection, estimation and control can significantly improve the performance in a decentralized setup and simplify considerably the communication requirements in terms of power and bandwidth.
