• Journal of KIISE
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• v.43 no.12
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• pp.1334-1341
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• 2016

In recent years, the importance of managing software defects in the implementation stage has emerged because of the rapid development and wide-range usage of intelligent smart devices. Even if not a few studies have been conducted on the prediction models for software defects, their outcomes have not been widely shared. This paper proposes an efficient probabilistic management model of software metrics based on the Bayesian network, to overcome limits such as binary defect prediction models. We expect the proposed model to configure the Bayesian network by taking advantage of various software metrics, which can help in identifying improvements for refactoring. Once the source code has improved through code refactoring, the measured related metric values will also change. The proposed model presents probability values reflecting the effects after defect removal, which can be achieved by improving metrics through refactoring. This model could cope with the conclusive binary predictions, and consequently secure flexibilities on decision making, using indeterminate probability values.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.3
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• pp.134-142
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• 2002

Quadtree, which is a hierarchical data structure, is a very important data structure to represent binary images. The linear quadtree representation as a way to store a quadtree is efficient to save space compared with other representations. Therefore, it has been widely studied to develop efficient algorithms to execute operations related with quadtrees. The window operation is one of important geometry operations in image processing, which extracts a sub-image indicated by a window in the image. In this paper, we present an algorithm to perform the window operation of binary images represented by quadtrees, using three-dimensional $n{\times}n{\times}n$ processors on RMESH(Reconfigurable MESH). This algorithm has constant-time complexity by using efficient basic operations to route the locational codes of quardtree on the hierarchical structure of $n{\times}n{\times}n$ RMESH.

• Journal of KIISE
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• v.42 no.6
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• pp.699-706
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• 2015

In recent years, as near-duplicate image has been increasing explosively by the spread of Internet and image-editing technology that allows easy access to image contents, related research has been done briskly. However, BoF (Bag-of-Feature), the most frequently used method for near-duplicate image detection, can cause problems that distinguish the same features from different features or the different features from same features in the quantization process of approximating a high-level local features to low-level. Therefore, a post-verification method for BoF is required to overcome the limitation of vector quantization. In this paper, we proposed and analyzed the performance of a post-verification method for BoF, which converts SIFT (Scale Invariant Feature Transform) descriptors into 128 bits binary codes and compares binary distance regarding of a short ranked list by BoF using the codes. Through an experiment using 1500 original images, it was shown that the near-duplicate detection accuracy was improved by approximately 4% over the previous BoF method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.34-43
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• 2010

Texture information plays an important role in object recognition and classification. To perform an accurate classification, the texture feature used in the classification must be highly discriminative. This paper presents a novel texture descriptor for texture-based image retrieval and its application in Computer-Aided Diagnosis (CAD) system for Emphysema classification. The texture descriptor is based on the combination of local surrounding neighborhood difference and centralized neighborhood difference and is named as Combined Neighborhood Difference (CND). The local differences of surrounding neighborhood difference and centralized neighborhood difference between pixels are compared and converted into binary codewords. Then binomial factor is assigned to the codewords in order to convert them into high discriminative unique values. The distribution of these unique values is computed and used as the texture feature vectors. The texture classification accuracies using Outex and Brodatz dataset show that CND achieves an average of 92.5%, whereas LBP, LND and Gabor filter achieve 89.3%, 90.7% and 83.6%, respectively. The implementations of CND in the computer-aided diagnosis of Emphysema is also presented in this paper.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.54-61
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• 2008

This paper presents a new TIQ based CMOS flash 6-bit ADC to process digital signal in real time. In order to improve the conversion speed of ADC by designing new logic or layout of ADC circuits, a new design method is proposed in encoding logic circuits. The proposed encoding circuits convert analog input into digitally encoded double base number system(DBNS), which uses two bases unlike the normal binary representation scheme. The DBNS adopts binary and ternary radix to enhance digital arithmetic processing capability. In the DBNS, the addition and multiplication can be processed with just shift operations only. Finding near canonical representation is the most important work in general DBNS. But the main disadvantage of DBNS representation in ADC is the fan-in problem. Thus, an equal distribution algorithm is developed to solve the fan-in problem after assignment the prime numbers first. The conversion speed of simulation result was 1.6 GSPS, at 1.8V power with the Magna $0.18{\mu}m$ CMOS process, and the maximum power consumption was 38.71mW.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.741-743
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• 2017

The visible light communication is a communication method of outputting binary data based on the illumination threshold value at the light receiving diode of the receiving unit, when the LED of the transmitting unit transmits light. However, separate receiver is necessary to receive the optical signal, there is a problem that a device not equipped with such a receiver can not utilize visible light communication. To solve this problem, this paper proposes a portable visible light receiver applicable to devices using USB OTG. Implemented portable visible light receiver converts the binary data received from the LED into a character string of ASCII code and transmits it to another device. Through data transmission experiments using smart phone confirmed that it is possible to transmit ASCII codes in the proposed method.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.485-490
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• 2003

In this paper, we propose the possibility of introducing chaotic sequences into digital watermarking systems as potential substitutes to commonly used pseudo noise sequences. Chaotic sequences have several good properties including the availability of a great number of them, the ease of their generation, as well as their sensitive dependence on their initial conditions. And the quantization does not destroy the good property. So this paper proposes a method that transforms Hangul text to chaotic sequence. And we presents how the Hangul text is expressed by an implied data and the implied data is regenerated into the original text. In this paper, we use this implied Hangul text for watermarking.

• The KIPS Transactions:PartA
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• v.10A no.3
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• pp.207-214
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• 2003

Quadtree, which is a hierarchical data structure, is a very important data structure to represent binary images. The linear quadtree representation as a way to store a quadtree is efficient to save space compared with other representations. Therefore, it has been widely studied to develop efficient algorithms to execute operations related with quadtrees. The linear translation is one of important operations in image processing, which moves the image by a given distance. In this paper, we present an algorithm to perform the linear translation of binary images represented by quadtrees, using three-dimensional $n{\times}n{\times}n$ processors on RMESH (Reconfigurable MESH). This algorithm has constant-time complexity by using efficient basic operations to route the locational codes of quardtree on the hierarchical structure of n${\times}$n${\times}$n RMESH.

• Journal of Biomedical Engineering Research
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• v.24 no.3
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• pp.217-231
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• 2003

Coded excitation with complementary Golay sequences is an effective means to increase the SNR and penetration of ultrasound imaging. in which the two complementary binary codes are transmitted successively along each scan-line, reducing the imaging frame rate by half. This method suffers from low frame rate particularly when multiple transmit focusing is employed, since the frame rate will be further reduced in proportion to the number of focal zones. In this paper. a new ultrasound imaging technique based on simultaneous multiple transmit focusing using modified orthogonal Golay codes is proposed to improve lateral resolution with no accompanying decrease in the imaging frame rate, in which a pair of orthogonal Golay codes focused at two different focal depths are transmitted simultaneously. On receive, these modified orthogonal Golay codes are separately compressed into two short pulses and individually focused. These two focused beams are combined to form a frame of image with improved lateral resolution. The Golay codes were modified to improve the transmit power efficiency (TPE) for practical imaging. Computer simulations and experimental results show that the proposed method improves significantly the lateral resolution and penetration of ultrasound imaging compared with the conventional method.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.41-49
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• 2021

All application programs, including malware, call the Application Programming Interface (API) upon execution. Recently, using those characteristics, attempts to detect and classify malware based on API Call information have been actively studied. However, datasets containing API Call information require a large amount of computational cost and processing time. In addition, information that does not significantly affect the classification of malware may affect the classification accuracy of the learning model. Therefore, in this paper, we propose a method of extracting a essential feature set after reducing the dimensionality of API Call information by applying various feature selection methods. We used CICAndMal2020, a recently announced Android malware dataset, for the experiment. After extracting the essential feature set through various feature selection methods, Android malware classification was conducted using CNN (Convolutional Neural Network) and the results were analyzed. The results showed that the selected feature set or weight priority varies according to the feature selection methods. And, in the case of binary classification, malware was classified with 97% accuracy even if the feature set was reduced to 15% of the total size. In the case of multiclass classification, an average accuracy of 83% was achieved while reducing the feature set to 8% of the total size.