• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.3 s.345
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• pp.10-17
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• 2006

For orthogonal frequency division multiplexing (OFDM), cyclic prefix (CP) should be longer than the length of channel impulse response, resulting in a loss of bandwidth efficiency. In this letter, the CP reconstruction (CPR) technique is first applied to a multi-input multi-output (MIMO)-OFDM system with insufficient CP. The intercarrier interference (ICI) from multiple transmit antennas is so large for MIMO system that it can not be sufficiently suppressed with the conventional CPR procedure used in single-input single-output (SISO) system. A new minimum mean-square error (MMSE) equalization and ordering process is proposed for MIMO system to suppress the ICI during the CPR procedure. By applying the proposed CPR algerian to MIMO-OFDM system, we can obtain both the benefits of multiplexing gai and spectral efficiency gain.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.450-463
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• 2018

Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sounds being inevitably corrupted by random disturbance, the most important part of the diagnosis consists of discovering the hidden elements inside the data that can reveal the faulty patterns. This paper presents a novel feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by the drills. Using the Fourier analysis, the magnitude spectrum of the sounds are extracted, converted into two-dimensional vectors and uniformly normalized in such a way that they can be represented as 8-bit grayscale images. Histogram equalization is then performed over the obtained images in order to adjust their very poor contrast. The obtained contrast enhanced images will be used as the features of our diagnosis system. Finally, principal component analysis is performed over the image features for reducing their dimensions and a nonlinear classifier is adopted to produce the final response. Unlike the conventional features, the results demonstrate that the proposed feature extraction method manages to capture the hidden health patterns of the sound.