• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2347-2352
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• 2012

In this paper, a new alternating equalizer and its differential adjustment algorithm are proposed. The proposed alternating equalizer achieves equalization effectively using an algorithm performing symbol decisions based on soft/hard decision. In addition, it is possible to improve the initial blind convergence speed and steady-state error performance simultaneously by adjusting the equalizer differentially according to the relative reliability of the symbol decisions by soft/hard decision devices. The simulation results on 16/64-QAM constellations under multipath propagation channel and additive noise conditions confirmed to support usefulness of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.12C
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• pp.1262-1267
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• 2005

Elliptical basis function (EBF) neural network works stably under high-level background noise environment and makes the nonlinear processing possible. It can be adapted real time VAD with simple design. This paper introduces VAD implementation using EBF and the experimental results show that EBF VAD outperforms G729 Annex B and RBF neural networks. The best error rates achieved by the EBF networks were improved more than $70\%$ in speech and $50\%$ in silence while that achieved by G.729 Annex B and RBF networks respectively.

• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.153-162
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• 2003

This paper describes a structure of adaptive equalizer to improve reception performance of ATSC digital television (DTV) for 8-VSB receivers. There are many strong and dynamic echoes affecting reliable reception of DTV signal. Conventional DFE based least mean square (LMS) algorithm is readily implemented and has good Performance. There are still problems to be solved, however, in handling strong echoes and indoor reception. In this paper, structure of adaptive equalizer to mitigate these Problems in strong multipath interference conditions and indoor reception environment is first presented. Methods to reduce error propagation effects on DFE and initialization scheme of filter coefficients for fast convergence are then introduced. Computer simulation results prove that an adaptive equalizer with proposed design methods can combat with Brazil Ensemble and the Threshold of Visibility(TOV) is improved.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.179-184
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• 2019

In this paper, we propose a design method of Active Noise Control (ANC) that reduces perceived level of the residual noise. A FELMS (Filtered-E Least Mean Squares) algorithm is used for the ANC system and the NC (noise criteria) is applied as an evaluation criterion of the residual noise. With this structure, we present the allowable spectral shape of the noise shaping filter that minimizes the NC index within the effective operating frequency band of the ANC, and showed that the filter satisfying in the criterion has a lower NC value than the psychoacoustic-based filter used in the previous studies.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.154-162
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• 2003

In this paper, Associative Memory with Cellular Neural Networks classifies and recognizes image patterns as an operator applied to image process. CNN processes nonlinear data in real-time like neural networks, and made by cell which communicates with each other directly through its neighbor cells as the Cellular Automata does. It is applied to the optimization problem, associative memory, pattern recognition, and computer vision. Image processing with CNN is appropriate to 2-D images, because each cell which corresponds to each pixel in the image is simultaneously processed in parallel. This paper shows the method for designing the structure of associative memory based on CNN and getting output image by choosing the most appropriate weight pattern among the whole learned weight pattern memories. Each template represents weight values between cells and updates them by learning. Hebbian rule is used for learning template weights and LMS algorithm is used for classification.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.285-291
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• 2017

Many methods have been studied to estimate the time delay between incoming signals to two receivers. In the case of the method based on the channel estimation technique, the relative delay between the input signals of the two receivers is estimated as an impulse response of the channel between the two signals. In this case, the characteristic of the channel has sparsity. Most of the existing methods do not take advantage of the channel sparseness. In this paper, we propose a time delay estimation method using BPD (Basis Pursuit Denoising) optimization technique, which is one of the sparse signal optimization methods, in order to utilize the channel sparseness. Compared with the existing GCC (Generalized Cross Correlation) method, adaptive eigen decomposition method and RZA-LMS (Reweighted Zero-Attracting Least Mean Square), the proposed method shows that it can mitigate the threshold phenomenon even under a white Gaussian source, a colored signal source and oceanic mammal sound source.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.1
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• pp.35-47
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• 2002

This paper proposes a self organizing RBF neural network equalizer for the equalization of digital communications. It is the most important for the equalizer using the RBF neural network to estimate the RBF centers correctly and quickly, which are the desired channel states. However, the previous RBF equalizers are not used in the actual communication system because of some drawbacks that the number of channel states has to be known in advance and many centers are necessary. Self organizing neural network equalizer proposed in this paper can implement the equalization without prior information regarding the number of channel states because it selects RBF centers among the signals that are transmitted to the equalizer by the new addition and removal criteria. Furthermore, the proposed equalizer has a merit that is able to make a equalization with fewer centers than those of prior one by the course of the training using LMS and clustering algorithm. In the linear, nonlinear and standard telephone channel, the proposed equalizer is compared with the optimal Bayesian equalizer for the BER performance, the symbol decision boundary and the number of centers. As a result of the comparison, we can confirm that the proposed equalizer has almost similar performance with the Bavesian enualizer.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9A
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• pp.1332-1339
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• 2000

This paper presents the architecture and design of a high speed asymmetric data transmission baseband MODEM ASIC chip for CATV networks. The implemented MODEM chip supports the physical layer of the DOCSIS(Data Over Cable Service Interface Specification) standard in MCNS(Multimedia Cable Network System) The chip consists of a QPSK/16-QAM transmitter and a 64/256-QAM receiver which contain a symbol timing recovery circuit, a carrier recovery circuit, a blind equalizer using MMA and LMS algorithms. The chip can support data rates of 64Mbps at 256 QAM and 48Mbps at 64-QAM and can provide symbol rates up to 8MBaud. This symbol rate is faster than existing QAM receivers. We have performed logic synthesis using the $0.35\mu\textrm{m}$ standard cell library. The total number of gates is about 290,000 and the implemented chip is being fabricated and will be delivered soon.

• Journal of The Korean Association of Information Education
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• v.23 no.2
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• pp.159-167
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• 2019

Recently, a learning management system incorporating ICT technology into learning has helped students improve self-directed learning skills. Self-directed learning using LMS promotes and stimulates learners' participation in learning, focusing on the advantages of efficient use of learning resources and the spread of communication. In this study, we study the impact of self-directed learning using the learning management system on elementary school students' motivation and academic performance. We expect learners will be able to achieve effective academic achievement by learning problems that fit their level through the algorithms of the proposed learning management system. For this study, a total of 16 classes were conducted for eight weeks using the proposed learning management system for 21 elementary school students. Research has shown significant improvement in the learning orientation and interest areas of the learners who participated in the experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.456-463
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• 2021

In this paper, Ultra Wide-Band(UWB) radar sensor is employed to detect flying drones and avoid collision in dense clutter environments. UWB signal is preferred when high resolution range measurement is required for moving targets. However, the time varying motion of flying drones may increase clutter noises in return signals and deteriorates the target detection performance, which lead to the performance degradation of anti-collision radars. We adopt a dynamic clutter suppression algorithm to estimate the time-varying distances to the moving drones with enhanced accuracy. A modified Constant False Alarm Rate(CFAR) is developed using an adaptive filter algorithm to suppress clutter while the false detection performance is well maintained. For this purpose, a velocity dependent CFAR algorithm is implemented to eliminate the clutter noise against dynamic target motions. Experiments are performed against flying drones having arbitrary trajectories to verify the performance improvement.