A new support identification technique based on factor graphs and belief propagation is proposed for Compressive Sensing (CS) aided Wireless Sensor Networks (WSNs), which reliably estimates the locations of non-zero entries in a sparse signal through an iterative process. Our factor graph based approach achieves a support identification error rate of 10% at an Signal to Noise Ratio (SNR) that is 6 dB lower than that required by the state-of-the-art relative frequency based support identification approach, as well as by the Orthogonal Matching Pursuit (OMP) algorithm. We also demonstrate that our support identification technique is capable of mitigating the signal reconstruction noise by as much as 4 dB upon pruning the sparse sensing matrix. Furthermore, by intrinsically amalgamating the relative frequency based and the proposed factor graph based approach, we conceived a hybrid support identification technique for reducing communication between the sensor nodes and the Fusion Center (FC), while maintaining highaccuracy support identification and simultaneously mitigating the noise contaminating signal reconstruction.
Fourier transformation is a powerful analytical tool with wide-ranging applications in many fields. In certain cases, some of the inputs to the transformation function are zero, while the others are real or complex. For the case, where the nonzero inputs are complex, the transform decomposition (TD) method enables a significant reduction in the computational complexity. This letter proposes a modified TD (MTD) algorithm to further reduce the complexity when the nonzero input data are consecutive and real-valued. The analytical and numerical results confirm that the complexity of the MTD scheme is not only significantly lower than that of the original TD method, but also lower than that of the traditional split-radix fast Fourier transform (FFT) method when the length of the input sequence is short.
Index Terms-Discrete Fourier transform (DFT), fast Fourier transform (FFT), transform decomposition (TD).
The transient effect of electromagnetic interference (EMI) produced by electronic equipments has been extensively studied. However, for the electric vehicles, speed changing also introduces considerable amount of interference. In this paper, we study the EMI influences, which include the interferences caused by the electronic switch and the vehicular engine, on the electric vehicular communications systems. The transmission quality of various standards is investigated, including FlexRay, and Control Area Network (CAN).
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