Compressive sensing (CS) is applied to sparse signal transmission so that it can be transmitted efficiently over lossy wireless links. By exploiting the commonly sparse property of measured signal within wireless sensor networks (WSNs), we pro pose a CS-reconstruction based efficient information transmission framework. According to CS theory, if the sensed information has some sparsity, it can be reconstructed with only a few sensed data. In this case, we argue that, by using CS technique, information transmission can tolerate a certain degree of link lossy without requiring all of the data being successfnlly transmit ted, thus avoiding the expensive data retransmission. Moreover,
CS-based information transmission framework is established,where the lossy link transmission is modeled as compressive sampling process. Data packets are directly transmitted after signal sampling, then the sensing matrix is obtained through the original sequence of received broken data and finally signal is reconstructed through optimization algorithm. Through experi mental verification, we first show the lossy link and sparsity of signal. Further, aiming at two distinct links, we make a couple of comparison tests, which shows our method achieves the same good reconstruction performance as conventional mnltiple data retransmission scheme does in good link. While in bad link our method outperforms conventional method even it adopts multiple retransmission. Results verify that during lossy link information transmission, the proposed CS-based method obtains high information transmission quality, also significantly reduces the energy cost and latency.
This paper presents a novel ECG signal measuring approach using compressive sensing method. The signal representing sparsity in any orthogonal basis can be well recovered using minimize L 1 norm optimization, while satisfying the RIP condition for the measurement matrix and orthogonal basis . First, based on this theorem, an analysis for evaluating the sparsity of ECG signal in orthogonal basis domain is proposed. A set of ECG samples from MIT-BIH medical database are adopted into Fast Fourier Transformation (FFT) process. The results indicate these signals can be well represented in sparsity using conventional orthogonal transforms. Second, the lightweight recovery algorithm is proposed based on orthogonal matching pursuit using iterative function and least squared approximation. The simulation results show that the special features in ECG including QRS complex, R-R interval, PQ and ST duration, can be well recovered with negligible norm error. It also indicates that using this approach can significantly save the total power of ECG acquisition.
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