• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1641-1648
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• 2017

The main purpose of this paper is to compare the performances of various filters with color images to remove the noise. Furthermore, we suggest a modified de-noising process by the transformation of color model from RGB to another color models, such as HSV and $YC_BC_R$, to improve the quality of de-noising methods encompassing Median, Wiener, and Mean filters. Neither the performance comparison of the de-noising filters with color images nor the converting the color model for better de-noise on the degraded images haven't been performed before. Inspired to make improvements, we conduct experiments with new de-noising process on color images. The result of the experiments is shown that it could assist on certain filters being more reliable techniques.

• Journal of Advanced Navigation Technology
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• v.19 no.4
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• pp.318-322
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• 2015

In wireless communication networks, most of the prediction techniques are used for predicting the amount of resource required by user's calls for improving their demanding quality of service. However, we propose a channel allocation scheme based on predicting the resources of white spectrum holes for improving the QoS of rental user's spectrum handoff calls for cognitive radio networks in this paper. This method is supported by Wiener predictor to predict the amount of white spectrum holes of license user's free spectrum resources. We classify rental user's calls into initial calls and spectrum handoff calls, and some portion of predicted spectrum-hole resources is reserved for spectrum handoff calls' priority allocation. Simulations show that the performance of the proposed scheme outperforms in spectrum handoff call's dropping rate than an existing method without spectrum hole prediction(11% average improvement in 50% reservation).

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.889-898
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• 2013

Due to the popularization of high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy to make high-quality counterfeit money. However, the probability of detecting counterfeit money to the general public is extremely low and the detection device is expensive. In this paper, a counterfeit money detection algorithm using a general purpose scanner and computer system is proposed. First, the printing features of color printers are calculated using morphological operations and gray-level co-occurrence matrix. Then, these features are used to train a support vector machine classifier. This trained classifier is applied for identifying either original or counterfeit money. In the experiment, we measured the detection rate between the original and counterfeit money. Also, the printing source was identified. The proposed algorithm was compared with the algorithm using wiener filter to identify color printing source. The accuracy for identifying counterfeit money was 91.92%. The accuracy for identifying the printing source was over 94.5%. The results support that the proposed algorithm performs better than previous researches.