• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.549-555
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• 2006

An edge detection is an very important area in image processing and computer vision, General edge detection methods (Robert mask, Sobel mask, Kirsh mask etc) are a good performance to detect step edge in a image but are no good performance to detect step edge in a noses image. We suggested a step edge detection method based on image information measure and neutral network. Using these essential properties of step edges, which are directional and structural and whose gray level distribution in neighborhood, as a input vector to the BP neutral network we get the good result of proposed algorithm. And also we get the satisfactory experimental result using rose image and cell images an experimental and analysing image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.753-761
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• 2012

In this paper, we study a power allocation technique for Tomlinson-Harashima precoding (THP) in multi-user multiple input single output (MISO) downlink systems. In contrast to previous approaches, a mutual information based method is exploited for maximizing the sum rate of zero-forcing THP systems. Then, we propose a simple power allocation algorithm which assigns proper power level for modulo operated users. Simulation results show that the proposed scheme outperforms a conventional water-filling method, and it provides similar performance with near optimal method with much reduced complexity.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.229-237
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• 2021

Our modern 'information-hungry' age demands delivery of information at unprecedented fast rates. Timely delivery of noteworthy information about recent events can help people from different segments of life in number of ways. As world has become global village, the flow of news in terms of volume and speed demands involvement of machines to help humans to handle the enormous data. News are presented to public in forms of video, audio, image and text. News text available on internet is a source of knowledge for billions of internet users. Urdu language is spoken and understood by millions of people from Indian subcontinent. Availability of online Urdu news enable this branch of humanity to improve their understandings of the world and make their decisions. This paper uses available online Urdu news data to train machines to automatically categorize provided news. Various machine learning algorithms were used on news headline for training purpose and the results demonstrate that Bernoulli Naïve Bayes (Bernoulli NB) and Multinomial Naïve Bayes (Multinomial NB) algorithm outperformed other algorithms in terms of all performance parameters. The maximum level of accuracy achieved for the dataset was 94.278% by multinomial NB classifier followed by Bernoulli NB classifier with accuracy of 94.274% when Urdu stop words were removed from dataset. The results suggest that short text of headlines of news can be used as an input for text categorization process.

• Journal of KIISE:Software and Applications
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• v.36 no.6
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• pp.451-461
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• 2009

The Adaptive Random Testing(ART) aims to improve the performance of traditional Random Testing(RT) by reducing the number of test cases to find the failure region which is located in the input domain. Such enhancement can be obtained by efficient selection algorithms of test cases. The ART through Iterative Partitioning(IP-ART) is one of ART techniques and it uses an iterative input domain partitioning method to improve the performance of early-versions of ART which have significant drawbacks in computation time. And the IP-ART with Enlarged Input Domain(EIP-ART), an improved version of IP-ART, is known to make additional performance improvement with scalability by expanding to virtual test space beyond real input domain of IP-ART. The EIP-ART algorithm, however, have the drawback of heavy cost of computation time to generate test cases mainly due to the virtual input domain enlargement. For this reason, two algorithms are proposed in this paper to mitigate the computation overhead of the EIP-ART. In the experiments by simulations, the tiling technique of input domain, one of two proposed algorithms, showed significant improvements in terms of computation time and testing performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.675-682
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• 2000

In this paper, we propose a new scheduling algorithm called the Adaptive Weighted Round Robin with Delay Tolerance (AWRR/DT). The proposed scheme is based on the per-class queueing mechanism in which a number of connections of similar characteristics are multiplexed into one class-queue. Traffic classes of the proposed method are classified into a single non-real-time traffic class and other real-time traffic classes. The proposed scheme determines the weights of classes according to the input traffic and delay characteristics of each class at the beginning of every cycle. Furthermore, this scheme incorporates a cell discarding method to reduce the QoS degradation that may be incurred by congestion of networks. We have evaluated the proposed scheme through discrete-event simulation. Simulation results indicate that the proposed scheme can reduce the average delay of non-real-time class while maintaining the QoS of real-timeclasses. The proposed algorithm can be effectively applied to high-speed networks such as ATM networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2601-2619
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• 2012

A differencing multiuser detection (MUD) method is proposed for multiple-input multiple-output (MIMO) direct sequence (DS) ultra-wideband (UWB) system to cope with the multiple access interference (MAI) and the computational efficiency in Nakagami fading channel. The method, which combines a multiuser-interference-cancellation-based decision feedback equalizer using error feedback filter (MIC DFE-EFF), a coefficient optimization algorithm (COA) and a differencing algorithm (DA), is termed as MIC DFE-EFF (COA) with DA for short. In the paper, the proposed MUD method is illuminated from the rudimental MIC DFE-EFF to the advanced MIC DFE-EFF (COA) with DA step by step. Firstly, the MIC DFE-EFF system performance is analyzed by minimum mean square error criterion. Secondly, the COA is investigated for optimization of each filter coefficient. Finally, the DA is introduced to reduce the computational complexity while sacrificing little performance. Simulations show a significant performance gain can be achieved by using the MIC DFE-EFF (COA) with DA detector. The proposed MIC DFE-EFF (COA) with DA improves both bit error rate performance and computational efficiency relative to DFE, DFE-EFF, parallel interference cancellation (PIC), MIC DFE-EFF and MIC DFE-EFF with DA, though it sacrifices little system performance, compared with MIC DFE-EFF (COA) without DA.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.107-115
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• 2014

The reduced-bit transform based bit-plane matching algorithm (BPM) can obtain the block matching result through its simple calculation and hardware design compared to the conventional block matching algorithms (BMAs), but the block matching accuracy of BPMs is somewhat low. In this paper, reduced-bit transform based sum of the absolute difference (R-SAD) is proposed to improve the block matching accuracy in comparison with the conventional BPMs and it is shown that the matching process can be obtained using the logical operations. Firstly, this method transforms the current and the reference images into their respective 2-bit images and then a truth table is obtained from the relation between input and output 2-bit images. Next, a truth table is simplified by Karnaugh map and the absolute difference is calculated by using simple logical operations. Finally, the simulation results show that the proposed R-SAD can obtain higher accuracy in block matching results compared to the conventional BPMs through the PSNR analysis in the motion compensation experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1673-1678
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• 2016

In the area of the speech recognition processing, background noises are caused the incorrect response to the speech input, therefore the speech recognition rates are decreased by the background noises. Accordingly, a more high level noise processing techniques are required since these kinds of noise countermeasures are not simple. Therefore, this paper proposes an algorithm to distinguish between the stationary background noises or non-stationary background noises and the speech signal having short time duration in the noisy environments. The proposed algorithm uses the characteristic parameter of the improved speech signal as an important measure in order to distinguish different types of the background noises and the speech signals. Next, this algorithm estimates various kinds of the background noises using a multi-layer perceptron neural network. In this experiment, it was experimentally clear the estimation of the background noises and the speech signals.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2169-2177
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• 2015

This paper proposes a new pattern matching module to overcome the increased runtime of previous algorithm using RAM, which was designed to overcome cost limitation of hash-based algorithm using CAM (Content Addressable Memory). By adopting Merge FSM algorithm to reduce the number of state, the proposed module contains state block and entry block to use in RAM. In the proposed module, one input string is compared with multiple entry strings simultaneously using entry block. The effectiveness of the proposed pattern matching unit is verified by executing Snort 2.9 rule set. Experimental results show that the number of memory reads has decreased by 15.8%, throughput has increased by 47.1%, while memory usage has increased by 2.6%, when compared to previous methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1033-1039
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• 2012

In this paper, a simple and robust algorithm is proposed for detecting each steel plate from a image which contains several steel plates. Steel plate is characterized by line edge, so line detection is a fundamental task for analyzing and understanding of steel plate images. To detect the line edge, the proposed algorithm uses the small eigenvalue analysis. The proposed approach scans an input edge image from the top left corner to the bottom right corner with a moving mask. A covariance matrix of a set of edge pixels over a connected region within the mask is determined and then the statistical and geometrical properties of the small eigenvalue of the matrix are explored for the purpose of straight line detection. Using the detected line edges, each plate is determined based on the directional information and the distance information of the line edges. The results of the experiments emphasize that the proposed algorithm detects each steel plate from a image effectively.