感知信息将被自动的收集、压缩和传输，用于对信息进行基于态势的维护。
Sensed information would be automatically collected, compressed, and forwarded for condition-based maintenance.
提出一种基于二维主成份分析(2dpca)和压缩感知的人脸识别方法。
A face recognition method based on 2d Principal Component Analysis (2dpca) and compressive sensing is introduced in this paper.
实验表明，语音信号在基于模板匹配的近似KLT域的压缩感知性能较好。
Simulation results demonstrate that compressed speech signal sensing in the approximate KLT using template matching has good performance.
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