• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6C
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• pp.625-631
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• 2002

This paper presents a MSB-first digit-serial systolic array for computing modular multiplication of A(x)B(x) mod G(x) in finite fields $GF(2^m)$. From the MSB-first multiplication algorithm in $GF(2^m)$, we obtain a new data dependence graph and design an efficient digit-serial systolic multiplier. For circuit synthesis, we obtain VHDL code for multiplier, If input data come in continuously, the implemented multiplier can produce multiplication results at a rate of one every [m/L] clock cycles, where L is the selected digit size. The analysis results show that the proposed architecture leads to a reduction of computational delay time and it has much more simple structure than existing digit-serial systolic multiplier. Furthermore, since the propose architecture has the features of unidirectional data flow and regularity, it shows good extension characteristics with respect to m and L.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.24-32
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• 2010

In this paper we proposed an object tracking system that detects the abandoned and removed objects, which is to be used in the intelligent surveillance applications. After the GMM based background subtraction and by using histogram method, the static region is identified to detect abandoned and removed objects. Since the system is implemented on DSP chip, it operates in realtime and is programmable. The input videos used in the experiment contain various indoor and outdoor scenes, and they are categorized into three different complexities; low, midium and high. By 10 times of experiment, we obtained high detection ratio at low and medium complexity sequences. On the high complexity video, successful detection ratio was relatively low because the scene contains crowdedness and repeated occlusion. In the future work, these complicated situation should be solved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1155-1163
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• 2010

An algorithm of the conventional wavelet shift keying is carried out that the scaling function and wavelet are encoded to 1(mark) and 0(space) for the input binary data, respectively. Two bit modulation technique which uses four carrier frequencies is existed. Four carrier frequencies are defined as scaling function, inversed scaling function, wavelet, and inversed wavelet, which are encoded to 10, 11, 00 and 01, respectively. In this paper, we defined 4-ary SWSK (4-ary scaling wavelet shift keying) which is two bit modulation, and it is derived to the probability of bit error and symbol error of the defined system from QPSK. In order to analyze to the performance of 4-ary SWSK, we are obtained in terms of the probability of bit error and symbol error for QPSK (quadrature phase shift keying), MFSK(M-ary frequency shift keying) and proposed method. As a results of simulation, we confirmed that the proposed method was superior to the performance in terms of the probability of bit error and symbol error.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1631-1642
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• 1999

In this paper, we propose a new fault tolerant ATM Switch and a new adaptive self-routing scheme used to make the switch to be fault tolerant. It can provide more multiple paths than the related previous switches between an input/output pair of a switch by adding extra links between switching elements in the same stage and extending the self-routing scheme of the Banyan network. Our routing scheme is as simple as that of the banyan network, which is based on the topological relationships among the switching elements (SE’s) that render a packet to the same destination with the regular self-routing. These topological properties of the Banyan network are discovered in this paper. We present an algebraic proof to show the correctness of this scheme, and an analytic reliability analysis to provide quantitative comparisons with other switches, which shows that the new switch is more cost effective than the Banyan network and other augmented MIN’s in terms of the reliability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.765-774
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• 2013

In this paper, we present a cooperative compressed spectrum sensing scheme for correlated signals in decentralized wideband cognitive radio networks. Compressed sensing is a signal processing technique that can recover signals which are sampled below the Nyquist rate with high probability, and can solve the necessity of high-speed analog-to-digital converter problem for wideband spectrum sensing. In compressed sensing, one of the main issues is to design recovery algorithms which accurately recover original signals from compressed signals. In this paper, in order to achieve high recovery performance, we consider the multiple measurement vector model which has a sequence of compressed signals, and propose a cooperative sparse Bayesian recovery algorithm which models the temporal correlation of the input signals.

• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2070-2079
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• 1997

In this paper, we proposed the FNCAC (fuzzy neural call admission control) scheme of the ATM networks which used the benefits of fuzzy logic controller and the learning abilities of the neural network to solve the call admission control problems. The new call in ATM networks is connected if QoS(quality of service) of the current calls is not affected due to the connection of a new call. The neural network CAC(call admission control) system is predictable system because the neural network is able to learn by the input/output pattern. We applied the fuzzy inference on the learning rate and momentum constant for improving the learning speed of the fuzzy neural network. The excellence of the proposed algorithm was verified using measurement of learning numbers in the traditional neural network method and fuzzy neural network method by simulation. We found that the learning speed of the FNCAC based on the fuzzy learning rules is 5 times faster than that of the CAC method based on the traditional neural network theory.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.465-472
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• 2005

In this paper, we propose the method of manipulating the gaze direction of 3D FPS game's character by using eye gaze detection from the successive images captured by USB camera, which is attached beneath HMB. The proposed method is composed of 3 parts. At first, we detect user's pupil center by real-time image processing algorithm from the successive input images. In the second part of calibration, when the user gaze on the monitor plane, the geometric relationship between the gazing position of monitor and the detected position of pupil center is determined. In the last part, the final gaze position on the HMD monitor is tracked and the 3D view in game is controlled by the gaze position based on the calibration information. Experimental results show that our method can be used for the handicapped game player who cannot use his(or her) hand. Also, it can Increase the interest and the immersion by synchronizing the gaze direction of game player and the view direction of game character.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.69-76
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• 2008

This paper concerns Fuzzy Radial Basis Function Neural Network (FRBFNN) and automatic rule generation of extraction of the FRBFNN by means of mountain clustering. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values (degree of membership) directly rely on the computation of the relevant distance between data points. Also, we consider high-order polynomial as the consequent part of fuzzy rules which represent input-output characteristic of sup-space. The number of clusters and the centers of clusters are automatically generated by using mountain clustering method based on the density of data. The centers of cluster which are obtained by using mountain clustering are used to determine a degree of membership and weighted least square estimator (WLSE) is adopted to estimate the coefficients of the consequent polynomial of fuzzy rules. The effectiveness of the proposed model have been investigated and analyzed in detail for the representative nonlinear function.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.1-14
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• 2010

The back-propagation neural network (BPN) has long been successfully applied in bankruptcy prediction problems. Despite its wide application, some major issues must be considered before its use, such as the network topology, learning parameters and normalization methods for the input and output vectors. Previous studies on bankruptcy prediction with BPN have shown that many researchers are interested in how to optimize the network topology and learning parameters to improve the prediction performance. In many cases, however, the benefits of data normalization are often overlooked. In this study, a genetic algorithm (GA)-based normalization transform, which is defined as a linearly weighted combination of several different normalization transforms, will be proposed. GA is used to extract the optimal weight for the generalization. From the results of an experiment, the proposed method was evaluated and compared with other methods to demonstrate the advantage of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2137-2142
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• 2016

The electrical current generated by heart creates not only electric potential but also a magnetic field. In this study, the signals obtained magnetocardiogram(MCG) using 61 channel superconducting quantum interference device(SQUID) system, and the clinical significance of various feature parameters has been developed MCG. Neural network algorithm was used to perform the classification of ischemic heart disease. The MCG signal was obtained to facilitate the extraction of parameters through a process of pre-processing. The data used to research the normal group 10 and ischemic heart disease group 10 with visible signs of stable angina patients. The available clinical indicators were extracted by characteristic point, characteristic interval parameter, and amplitude ratio parameter. The extracted parameters are determined to analysis the significance and clinical parameters were defined. It is possible to classify ischemic heart disease using the MCG feature parameters as a neural network input.