Industry 4.0 applications involve more number of sensors or Internet of Things (IoT) devices to support automation in the industry. It involves more number of computations to analyze the sensor data collected from several critical parts of the processing units. Sparse signal processing is one which has numerous applications in area of communication and signal processing. This paper presents a novel approach to reduce the computations with the help of level cross sampling (LCS) and a backtracking based iterative hard thresholding (BIHT) algorithm for reconstruction. The process involves, an information signal is converted to a random sparse signal using non-uniform sampling at the transmitter side and then it can be reconstructed back using BIHT algorithm at receiver side. Simulation results exhibit the superior performance of the proposed BIHT reconstruction in comparison with the literature.

中文翻译：

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使用稀疏信号和回溯迭代硬阈值重构水平交叉采样信号

工业4.0应用程序涉及更多数量的传感器或物联网（IoT）设备，以支持工业自动化。它涉及大量计算，以分析从处理单元几个关键部分收集的传感器数据。稀疏信号处理是在通信和信号处理领域中具有众多应用的一种。本文提出了一种新的方法，该方法借助级交叉采样（LCS）和基于回溯的迭代硬阈值（BIHT）算法进行重构，从而减少了计算量。该过程涉及在发送器端使用非均匀采样将信息信号转换为随机稀疏信号，然后在接收器端使用BIHT算法将其重构回去。