论文标题
使用非线性动力学实现自适应和增强的声学感测
Enabling Adaptive and Enhanced Acoustic Sensing Using Nonlinear Dynamics
论文作者
论文摘要
由于传感器数据的远程处理，实时数据的传输正在强烈增加。满足这种需求的途径是自适应感应，其中传感器仅在传感器级别使用预处理获取相关信息。我们在这里提出基于具有集成感应和驱动的机械振荡器的自适应声传感器。他们的动力学通过反馈或耦合转移到非线性方案中。这可以增强动态范围，频率分辨率和信噪比。将可调的传感属性与声音分析相结合可以使仅获取相关信息，而不是通过后处理中从无关的数据中提取此信息。
Transmission of real-time data is strongly increasing due to remote processing of sensor data, among other things. A route to meet this demand is adaptive sensing, in which sensors acquire only relevant information using pre-processing at sensor level. We present here adaptive acoustic sensors based on mechanical oscillators with integrated sensing and actuation. Their dynamics are shifted into a nonlinear regime using feedback or coupling. This enhances dynamic range, frequency resolution and signal-to-noise ratio. Combining tunable sensing properties with sound analysis could enable acquiring of only relevant information rather than extracting this from irrelevant data by post-processing.
