• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.104-113
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• 2022

In this paper, we propose deep learning-based target distance and velocity estimation technique for OFDM radar systems. In the proposed technique, the 2D periodogram is obtained via 2D fast Fourier transform (FFT) from the reflected signal after removing the modulation effect. The periodogram is the input to the conventional and proposed estimators. The peak of the 2D periodogram represents the target, and the constant false alarm rate (CFAR) algorithm is the most popular conventional technique for the target's distance and speed estimation. In contrast, the proposed method is designed using the multiple output convolutional neural network (CNN). Unlike the conventional CFAR, the proposed estimator is easier to use because it does not require any additional information such as noise power. According to the simulation results, the proposed CNN improves the mean square error (MSE) by more than 5 times compared with the conventional CFAR, and the proposed estimator becomes more accurate as the number of transmitted OFDM symbols increases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1666-1671
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• 2022

The structure of a high-power gain-flattened long wavelength band (L-band) optical amplifier was optimized, which was implemented for 64-channel wavelength division multiplexed optical signals with a channel spacing of 50 GHz. The output characteristics of this L-band amplifier were measured and analyzed. The amplifier of the optimized two-stage amplification configuration had a flattened gain of 20 dB within 1 dB deviation between 1570 and 1600 nm for -2 dBm input power condition. The noise figure under this condition was minimized to within 6 dB in the amplification bandwidth. The gain flattening was realized by considering only the characteristics of gain medium in the amplifier without using additional optical or electrical devices. The proposed amplifier consisted of two stages of amplification stages, each of which was based on the erbium-doped fiber amplifier (EDFA) structure. The erbium-doped fiber length and pumping structures in each stage of the amplifier were optimized through experiments.

• KNOM Review
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• v.24 no.2
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• pp.13-23
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• 2021

Recently, as networks become more complex due to the activation of IoT devices, research on long-term traffic prediction beyond short-term traffic prediction is being activated to predict and prepare for network congestion in advance. The recursive strategy, which reuses short-term traffic prediction results as an input, has been extended to multi-step traffic prediction, but as the steps progress, errors accumulate and cause deterioration in prediction performance. In this paper, an LSTM-based multi-step traffic prediction method using a multi-output strategy is introduced and its performance is evaluated. As a result of experiments based on actual DNS request traffic, it was confirmed that the proposed LSTM-based multiple output strategy technique can reduce MAPE of traffic prediction performance for non-stationary traffic by 6% than the recursive strategy technique.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.175-182
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• 2023

Differential cryptanalysis is one of the analysis techniques for block ciphers, and uses the property that the output difference with respect to the input difference exists with a high probability. If random data and differential data can be distinguished, data complexity for differential cryptanalysis can be reduced. For this, many studies on deep learning-based neural distinguisher have been conducted. In this paper, a deep learning-based neural distinguisher for PIPO 64/128 is proposed. As a result of experiments with various input differences, the 3-round neural distinguisher for the differential characteristics for 0, 1, 3, and 5-rounds achieved accuracies of 0.71, 0.64, 0.62, and 0.64, respectively. This work allows distinguishing attacks for up to 8 rounds when used with the classical distinguisher. Therefore, scalability was achieved by finding a distinguisher that could handle the differential of each round. To improve performance, we plan to apply various neural network structures to construct an optimal neural network, and implement a neural distinguisher that can use related key differential or process multiple input differences simultaneously.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.75-87
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• 2009

We propose in this paper a novel approach to image fusion in which the fusion rule is guided by optimizing an image clarity function. A Genetic Algorithm is used to stochastically select, comparative to the clarity function, the optimum block from among the source images. A novel nested Genetic Algorithm with gifted individuals found through bombardment of genes by the mutation operator is designed and implemented. Convergence of the algorithm is analytically and empirically examined and statistically compared (MANOVA) with the canonical GA using 3 test functions commonly used in the GA literature. The resulting GA is invariant to parameters and population size, and a minimal size of 20 individuals is found to be sufficient in the tests. In the fusion application, each individual in the population is a finite sequence of discrete values that represent input blocks. Performance of the proposed technique applied to image fusion experiments, is characterized in terms of Mutual Information (MI) as the output quality measure. The method is tested with C=2 input images. The results of the proposed scheme indicate a practical and attractive alternative to current multi-focus image fusion techniques.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.11C
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• pp.1139-1151
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• 2002

In this paper, we propose the novel module-type optical space switch, employing time division multiplexing (TDM) method and multihop structure, in order to enlarge the capacity of the switching system. And we show that the proposed structure is superior over conventional ones, in terms of power loss, the number of the devices used, and signal to crosstalk (SXR). We also analyze the saturation throughput with the number of module M. As a result, the saturation throughput of the switching system with M modules is M+ 1-√(M$^2$+1), when the number of input port in a module (N) is large. Finally, we confirmed the cell loss rate (CLR) performance with the proposed switch through simulation. For example, when p=0.9, M=8 and N=32, to get the CLR that is less than or equal to 10$\^$-6/, the number of input buffers storage unit is greater than or equal to 6 and output buffers storage unit is greater than or equal to 52.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.511-520
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• 2009

In this paper, we present a new iso-density surface reconstruction scheme based on a hierarchy on the input volume data and the output mesh data. From the input volume data, we construct a hierarchy of volumes, called a volume pyramid, based on a 3D dilation filter. After constructing the volume pyramid, we extract a coarse base mesh from the coarsest resolution of the pyramid with the Cell-boundary representation scheme. We iteratively fit this mesh to the iso-points extracted from the volume data under O(3)-adjacency constraint. For the surface fitting, the shrinking process and the smoothing process are adopted as in the SWIS (Shrink-wrapped isosurface) algorithm[6], and we subdivide the mesh to be able to reconstruct fine detail of the isosurface. The advantage of our method is that it generates a mesh which can be utilized by several multiresolution algorithms such as compression and progressive transmission.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.5
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• pp.333-340
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• 2003

Doppler processing is the heart of pulsed Doppler radar. It gives a clutter elimination and coherent integration. With the improvement of digital signal processors (DPSs), the implementation using them is more widely used in radar systems. Generally, so as for Doppler processor to process the input data in real time, a parallel processing concept using multiple DSPs should be used. This paper implements a programmable Doppler processor, which consists of MTI filter, DFB and square-law detector, using 8 ADSP21060s. Formulating the distribution time of the input data, the transfer time of the output data and the time required to compute each algorithm, it estimates total processing time and the number of required DSP. Finally, using the TSG that provides radar control pulses and simulated target signals, performances of the implemented Doppler processor are evaluated.

• Journal of Internet Computing and Services
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• v.5 no.1
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• pp.41-49
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• 2004

Analytical performance evaluation is crucial for justifying the merit of the design of Multistage Interconnection Networks(MINs) in different operational conditions. While several analytical models have been proposed for the performance evaluation of MlNs, they are mainly for uniform traffics. Even the models for nonuniform traffics have various shortcomings. In this paper, an accurate model for the performance evaluation of multi buffered banyan-type MIN's under nonuniform traffic condition is obtained. The accuracy of proposed models are conformed by comparing with the results from simulation.