• KIPS Transactions on Computer and Communication Systems
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• v.2 no.2
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• pp.59-66
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• 2013

In this paper, we proposed an optimal ILP algorithm on memory address code generation for DSP in embedded system. This paper using 0-1 ILP formulations DSP address generation units should minimize the memory variable data layout. We identify the possibility of the memory assignment of variable based on the constraints condition, and register the address code which a variable instructs in the program pointer. If the process sequence of the program is declared to the program pointer, then we apply the auto-in/decrement mode about the address code of the relevant variable. And we minimize the loads on the address registers to optimize the data layout of the variable. In this paper, in order to prove the effectiveness of the proposed algorithm, FICO Xpress-MP Modeling Tools were applied to the benchmark. The result that we apply a benchmark, an optimal memory layout of the proposed algorithm then the general declarative order memory on the address/modify register to reduce the number of loads, and reduced access to the address code. Therefor, we proved to reduce the execution time of programs.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.2
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• pp.83-92
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• 2013

RSA-CRT is a widely used algorithm that provides high performance implementation of the RSA-signature algorithm. Many previous studies on each operation step have been published to verify the physical leakages of RSA-CRT when used in smart devices. This paper proposes SAED (subtraction algorithm analysis on equidistant data), which extracts sensitive information using the event information of the subtraction operation in a reduction algorithm. SAED is an attack method that uses algorithm-dependent power signal changes. An adversary can extract a key using differential power analysis (DPA) of the subtraction operation. This paper indicates the theoretical rationality of SAED, and shows that its results are better than those of other methods. According to our experiments, only 256 power traces are sufficient to acquire one block of data. We verify that this method is more efficient than those proposed in previously published studies.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.311-318
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• 2013

Recently, there have been many studies on RFID-based SCM. Yet, studies of synchronizing errors caused by tracking logistics information in supply chain, and activating & monitoring RFID infra is still insufficient. Also, there is no case of developing the intelligent SCM system enabling total monitoring and controlling RFID Infra by applying these technologies. Logistics information synchronization based Smart SCM model is intelligent supply chain service model to monitor the status of the RFID equipments in supply chain and the synchronization of the logistics process in each logistics point through one integrated view, as well as to react instantly by providing the information to help the various decision makings, when the emergency occurs. By adopting global logistics standard, RFID related standard specification, EPCIS standard, and SSI middleware platform, this model provides the domestic standard specification.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.341-346
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• 2013

In this paper, we propose a rule extraction method using a modified Fuzzy Min-Max (FMM) neural network. The suggested method supplements the hyperbox definition with a frequency factor of feature values in the learning data set. We have defined a relevance factor between features and pattern classes. The proposed model can solve the ambiguity problem without using the overlapping test process and the contraction process. The hyperbox membership function based on the fuzzy partitions is defined for each dimension of a pattern class. The weight values are trained by the feature range and the frequency of feature values. The excitatory features and the inhibitory features can be classified by the proposed method and they can be used for the rule generation process. From the experiments of sign language recognition, the proposed method is evaluated empirically.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.347-352
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• 2013

In impulse noise filtering techniques window size play an important role. Usually, an appropriate window is determined according to the noise density. A small window may not be able to suppress noise properly whereas a large window may remove edges and fine image details. Moreover, the value of the central pixel is estimated by considering all pixels within the window. In this work, contrary to the previous approaches, we propose an iterative impulse noise removal scheme that emphasizes on noise-free pixels within a small neighborhood. The iterative process continues until all noisy pixels are replaced with the estimated pixels. In order to estimate the optimal value for a noisy pixel, a genetic programming (GP) based estimator is evolved that takes few noise-free pixels as input. The estimator is constituent of noise-free pixels, arithmetic operators and random constants. Experimental results show that theproposed scheme is capable of removing impulse noise effectively while preserving the fine image details. Especially, our approach has shown effectiveness against high impulse noise density.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.483-488
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• 2016

In this paper, we propose a prototype-based classification learning by using the nearest-neighbor rule. The nearest-neighbor is applied to segment the class area of all the training data with hyperspheres, and a hypersphere must cover the data from the same class. The radius of a hypersphere is computed by the mid point of the two distances to the farthest same class point and the nearest other class point. And we transform the prototype selection problem into a set covering problem in order to determine the smallest set of prototypes that cover all the training data. The proposed prototype selection method is designed by a greedy algorithm and applicable to process a large-scale training set in parallel. The prediction rule is the nearest-neighbor rule and the new training data is the set of prototypes. In experiments, the generalization performance of the proposed method is superior to existing methods.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.503-510
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• 2016

FsGr model, which has been proposed recently, is an approach of accelerometer-based gesture recognition by applying DTW algorithm in two steps, which improved recognition success rate. In FsGr model, sets of similar gestures will be produced through training phase, in order to define the notion of a set of similar gestures. At the 1st attempt of gesture recognition, if the result turns out to belong to a set of similar gestures, it makes the 2nd recognition attempt to feature-strengthened parts extracted from the set of similar gestures. However, since a same gesture show drastically different characteristics according to physical traits such as body size, age, and sex, FsGr model may not be good enough to apply to multi-user environments. In this paper, we propose FsGrM model that extends FsGr model for multi-user environment and present a program which controls channel and volume of smart TV using FsGrM model.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.489-496
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• 2016

Thousands of pathological images are produced daily per hospital and they are stored and managed by a pathology information system (PIS). Since image edge detection is one of fundamental analysis tools for pathological images, many researches are targeted to improve accuracy and performance of image edge detection algorithm of HIS. In this paper, we propose a novel image edge detection method. It is based on Canny algorithm with adaptive threshold configuration. It also uses a dividing ruler to configure the two threshold instead of whole image to improve the detection ratio of ruler itself. To verify the effectiveness of our proposed method, we conducted empirical experiments with real pathological images(randomly selected image group, image group that was unable to detect by conventional methods, and added noise image group). The results shows that our proposed method outperforms and better detects compare to the conventional method.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.159-168
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• 2018

In recent, mobile augmented reality game which has been attracting high attention is considered to be an good approach to increase immersion. In conventional augmented reality-based games that recognize target objects using a mobile camera and show the matching game characters, touch-based interaction is mainly used. In this paper, we propose an intuitive interaction method which manipulates a deformable game object by moving a target image of augmented reality in order to enhacne the immersion of the game. In the proposed method, the deformable object is intuitively manipulated by calculating the distance and direction between the target images and by adjusting the external force applied to the deformable object using them. In this paper, we focus on the cloth deformable object which is widely used for natural object animation in game contents and implement natural cloth simulation interacting with game objects represented by wind and rigid objects. In the experiments, we compare the previous commercial cloth model with the proposed method and show the proposed method can represent cloth animation more realistically.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.135-144
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• 2018

The domestic pigsty environment is highly vulnerable to the spread of respiratory diseases such as foot-and-mouth disease because of the small space. In order to manage this issue, a variety of studies have been conducted to automatically analyze behavior of individual pigs in a pig pen through a video surveillance system using a camera. Even though it is required to correctly segment touching pigs for tracking each pig in complex situations such as aggressive behavior, detecting the correct boundaries among touching pigs using Kinect's depth information of lower accuracy is a challenging issue. In this paper, we propose a segmentation method using motion information of the touching pigs. In addition, our proposed method can be applied for detecting tracking errors in case of tracking individual pigs in the complex environment. In the experimental results, we confirmed that the touching pigs in a pig farm were separated with the accuracy of 86%, and also confirmed that the tracking errors were detected accurately.