Advances in machine learning technology have enabled real-time extraction of semantic information in signals which can revolutionize signal processing techniques and improve their performance significantly for the next generation of applications. With the objective of a concrete representation and efficient processing of the semantic information, we propose and demonstrate a formal graph-based semantic language and a goal filtering method that enables goal-oriented signal processing. The proposed semantic signal processing framework can easily be tailored for specific applications and goals in a diverse range of signal processing applications. To illustrate its wide range of applicability, we investigate several use cases and provide details on how the proposed goal-oriented semantic signal processing framework can be customized. We also investigate and propose techniques for communications where sensor data is semantically processed and semantic information is exchanged across a sensor network.

机器学习技术的进步使实时提取信号中的语义信息成为可能，这可以彻底改变信号处理技术并显着提高其在下一代应用程序中的性能。以语义信息的具体表示和有效处理为目标，我们提出并演示了一种基于形式图的语义语言和一种能够实现面向目标的信号处理的目标过滤方法。所提出的语义信号处理框架可以很容易地针对各种信号处理应用程序中的特定应用程序和目标进行定制。为了说明其广泛的适用性，我们研究了几个用例，并提供了有关如何定制所提出的面向目标的语义信号处理框架的详细信息。