• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5394-5409
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• 2019

The paper investigates a novel detector receiver with Kalman channel information estimator and iterative channel response equalization for MIMO (multi-input multi-output) OFDM (orthogonal frequency division multiplexing) communication systems in fast multipath fading environments. The performances of the existing linear equalizers (LE) are not good enough over most fast fading multipath channels. The existing adaptive equalizer with decision feedback structure (ADFE) can improve the performance of LE. But error-propagation effect seriously degrades the system performance of the ADFE, especially when operated in fast multipath fading environments. By considering the Kalman channel impulse response estimation for the fast fading multipath channels based on CE-BEM (complex exponential basis expansion) model, the paper proposes the iterative receiver with soft decision feedback equalization (SDFE) structure in the fast multipath fading environments. The proposed SDFE detector receiver combats the error-propagation effect for fast multipath fading channels and outperform the existing LE and ADFE. We demonstrate several simulations to confirm the ability of the proposed iterative receiver over the existing receivers.

• Journal of IKEEE
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• v.10 no.1 s.18
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• pp.62-68
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• 2006

In this paper, we propose an adaptation strategy for blind equalizers, which combines a blind algorithm based on high order statistics and the decision directed LMS algorithm. In contrast to 'Stop-and-Go' algorithm, where adaptation is stopped for unreliable signals, the proposed algorithm applies high order statistics (HOS) blind algorithm to the unreliable signals and applies DD-LMS for the reliable signals. The proposed algorithm, named 'Run-and-Go' algorithm, inherits minimum MSE performance of DD-LMS and acquisition ability of blind algorithms. Furthermore, by updating the reliable signal region according to signal quality in each iteration, the convergence speed and acquisition ability is further improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3704-3724
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• 2018

This paper proposes an advanced turbo receiver with joint inter-carrier interference (ICI) self cancellation and channel equalization for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems over rapidly time-varying channel environment. The ICI caused by impairment of local oscillators and carrier frequency offset (CFO) is the major problem for MIMO-OFDM communication systems. The existing schemes (conjugate cancellation (CC) and phase rotated conjugate cancellation (PRCC)) that deal with the ICI cancellation and channel equalization can't provide satisfactory performance over time-varying channels. In term of error rate performance and low computational complexity, ICI self cancellation is the best choice. So, this paper proposes a turbo receiver to deal with the problem of joint ICI self cancellation and channel equalization. We employ the adaptive phase rotations in the receiver to effectively track the CFO variations without feeding back the CFO estimate to the transmitter as required in traditional existing scheme. We also give some simulations to verify the proposed scheme. The proposed schene outperforms the existing schemes.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• ETRI Journal
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• v.37 no.1
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• pp.43-53
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• 2015

In this paper, a selective coefficient updating (SCU) approach at each branch of the per-tone equalization (PTEQ) structure has been applied for insufficient cyclic prefix (CP) length. Because of the high number of adaptive filters and their complex adaption process in the PTEQ structure, SCU has been proposed. Using this method leads to a reduction in the computational complexity, while the performance remains almost unchanged. Moreover, the use of set-membership filtering with variable step size is proposed for a sufficient CP case to increase convergence speed and decrease the average number of calculations. Simulation results show that despite the aforementioned algorithms having similar performance in comparison with conventional algorithms, they are able to reduce the number of calculations necessary. In addition, compensation of both the channel effect and the transmitter/receiver in-phase/quadrature-phase imbalances are achievable by these algorithms.

• Journal of Communications and Networks
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• v.12 no.5
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• pp.399-405
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• 2010

Blind equalization techniques have been used in broadcast and multipoint communications. In this paper, two criteria of minimizing Euclidian distance between two probability density functions (PDFs) for adaptive blind equalizers are presented. For PDF calculation, Parzen window estimator is used. One criterion is to use a set of randomly generated desired symbols at the receiver so that PDF of the generated symbols matches that of the transmitted symbols. The second method is to use a set of Dirac delta functions in place of the PDF of the transmitted symbols. From the simulation results, the proposed methods significantly outperform the constant modulus algorithm in multipath channel environments.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.4
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• pp.138-144
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• 2007

This paper introduces the design techniques of an IR module using the 2-D array un-cooled type infrared detector which is applied to the individual combat weapon. Considering the size and weight of the hand carried weapon system, we used a very small-sized detector and applied an adaptive temperature control algorithm so that the operation consumed with low power can be possible. We applied the AR(Auto Regressive) filter to improve the signal-to-noise ratio in a thermal image processing step. We also applied the plateau equalization and boundary enhancement techniques to improve the visibility for human visual system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9C
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• pp.876-881
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• 2006

In this paper, we propose a complex-valued IIR filter for digital VSB signals based on CMA in order to efficiently mitigate multipath distortions, especially the leakage from the quadrature component. The proposed equalizer overcomes the drawback of the conventional real-valued IIR equalizers that it attempts to equalize Hilbert transform of quadrature component. We demonstrate via simulation that the proposed complex IIR filter successfully mitigates the leakages from the quadrature component, while the conventional real IIR filter requires a longer IIR filter to achieve the same performance. We present cost function analysis for a simple two-tap case showing that the proposed IIR equalizer with CMA for VSB signals has a global minimum at the desired location.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.957-959
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• 1999

In this paper we proposed a new fuzzy modeling method by Fuzzy Equalization(FE) based on probability theory. FE concerns a process of building membership function without learning using back-propagation of neural network. Therefore, we compare the proposed method with Adaptive Network-based Inference System based on hybrid learning. Finally, we will show better performance and its usefulness for a new fuzzy modeling to automobile mpg prediction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.10A
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• pp.1639-1644
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• 2001

Two decision feedback equalizer structures employing recurrent neural network (RNN) used for non-linear channels with severe intersymbol interference (ISI) and non-linear distortion are proposed in this paper, which skillfully put the traditional decision feedback structure for linear channels equalization into RNN, replace decision feedback signal with training signal in the learning process and adaptively adjust the learning step. Simulative results of the first type of two new equalizer structures have shown that it has better equalization performances than traditional recurrent neural network equalizer (RNNE) under the same condition.