• Journal of Digital Contents Society
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• v.10 no.2
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• pp.189-198
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• 2009

In this paper, we propose a method to improve the performance of orthogonal frequency division multiplexing (OFDM) receivers by using oversampling the received signals. Demodulation of the received OFDM signals is to detect the amplitude and phase components of the subcarriers. From the oversampled OFDM signals, we can get redundant informations in frequency domain for the data, which are different in phase but the same in amplitude. By using these properties, we can obtain signal to noise ratio (SNR) gain by the oversampling ratio compared to the receivers which sampled with symbol rate. In this paper, we propose oversampled receivers whose oversampling ratio is expanded from integer to general rational number. Through computer simulations, we show the validity of the proposed methods by comparing the performance of the receivers with nonideal band-limiting filters.

• Information Systems Review
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• v.18 no.2
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• pp.173-198
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• 2016

Financial distress can damage stakeholders and even lead to significant social costs. Thus, financial distress prediction is an important issue in macroeconomics. However, most existing studies on building a financial distress prediction model have only considered idiosyncratic risk factors without considering systematic risk factors. In this study, we propose a prediction model that considers both the idiosyncratic risk based on a financial ratio and the systematic risk based on a business cycle. Ultimately, we build several IT artifacts associated with financial ratio and add them to the idiosyncratic risk factors as well as address the imbalanced data problem by using an oversampling technique and synthetic minority oversampling technique (SMOTE) to ensure good performance. When considering systematic risk, our study ensures that each data set consists of both financially distressed companies and financially sound companies in each business cycle phase. We conducted several experiments that change the initial imbalanced sample ratio between the two company groups into a 1:1 sample ratio using SMOTE and compared the prediction results from the individual data set. We also predicted data sets from the subsequent business cycle phase as a test set through a built prediction model that used business contraction phase data sets, and then we compared previous prediction performance and subsequent prediction performance. Thus, our findings can provide insights into making rational decisions for stakeholders that are experiencing an economic crisis.