• ETRI Journal
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• v.37 no.1
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• pp.32-42
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• 2015

Orthogonal frequency-division multiplexing (OFDM) suffers from spectral nulls of frequency-selective fading channels. Linear precoded (LP-) OFDM is an effective method that guarantees symbol detectability by spreading the frequency-domain symbols over the whole spectrum. This paper proposes a computationally efficient and low-cost implementation for discrete Hartley transform (DHT) precoded OFDM systems. Compared to conventional DHT-OFDM systems, at the transmitter, both the DHT and the inverse discrete Fourier transform are replaced by a one-level butterfly structure that involves only one addition per symbol to generate the time-domain DHT-OFDM signal. At the receiver, only the DHT is required to recover the distorted signal with a single-tap equalizer in contrast to both the DHT and the DFT in the conventional DHT-OFDM. Theoretical analysis of DHT-OFDM with linear equalizers is presented and confirmed by numerical simulation. It is shown that the proposed DHT-OFDM system achieves similar performance when compared to other LP-OFDMs but exhibits a lower implementation complexity and peak-to-average power ratio.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2701-2722
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• 2017

Key generation is essential for protecting wireless networks. Based on wireless channel reciprocity, transceivers can generate shared secret keys by measuring their communicating channels. However, due to non-simultaneous measurements, asymmetric noises and other interferences, channel measurements collected by different transceivers are highly correlated but not identical and thus might have some discrepancies. Further, these discrepancies might lead to mismatches of bit sequences after quantization. The referred mismatches significantly affect the efficiency of key generation. In this paper, an efficient key generation scheme leveraging wireless channel reciprocity is proposed. To reduce the bit mismatch rate and enhance the efficiency of key generation, the involved transceivers separately apply discrete cosine transform (DCT) and inverse discrete cosine transform (IDCT) to pre-process their measurements. Then, the outputs of IDCT are quantified and encoded to establish the bit sequence. With the implementations of information reconciliation and privacy amplification, the shared secret key can be generated. Several experiments in real environments are conducted to evaluate the proposed scheme. During each experiment, the shared key is established from the received signal strength (RSS) of heterogeneous devices. The results of experiments demonstrate that the proposed scheme can efficiently generate shared secret keys between transceivers.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.841-844
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• 2001

This paper introduces a new technique for estimating the speed of a mobile station in a wireless system. The proposed method is based on the feature extraction of the received signal envelope. The undecimated discrete wavelet transform via lifting captures local minimum points of the received signal, which is used for the speed estimation. This technique requires neither knowledge of the average received power of the nonstationary signal nor adaptation of a temporal observation window, in contrast to other speed estimators given in the literature. Simulations show that the proposed speed estimator tracks the variable speed of the mobile station.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.269-273
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• 2000

This paper describes Active Noise Control (ANC) using Discrete Wavelet Transform (DWT) Domain Least Mean Square (LMS) Method. DWT-LMS is one of the transform domain input decorrelation LMS and improves the convergence speed of adaptive filter especially when the input signal is highly correlated. Conventional transform domain LMS's use Discrete Cosine Transform (DCT) because it offers linear band signal decomposition and fast transform algorithm. Wavelet transform can project the input signal into the several octave band subspace and offers more efficient sliding fast transform algorithm. In this paper, we propose Wavelet transform domain LMS algorithm and shows its performance is similar to DCT LMS in some cases using ANC simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.364-364
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• 2000

Timed Petri Net(TPN) is one of methods to model and to analyze Discrete Event Dynamic Systems(DEDSs) with real time values. It has two time values, earliest firing time ($\alpha$$_{i}$) and latest firing time ($\beta$$_{I}$) for the each transition. A transition of TPN is fired at arbitrary time of time interval ($\alpha$$_{I}$, $\beta$$_{i}$). Uncertainty of firing time gives difficulty to analyze and estimate a modeled system. In this paper, we proposed the Fuzzy Transition Timed Petri Net(FTTPN) with fuzzy theory to determine the optimal transition time (${\gamma}$$_{i}$). The transition firing time (${\gamma}$$_{i}$) of FTTPN is determined from fuzzy controller which is modeled with information of state transition. Each of the traffic signal controllers are modeled using the proposed method and timed petri net. And its Performance is evaluated by simulation of traffic signal controller. controller.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.84-93
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• 2010

This paper describes a method of visualization and fast estimation of parameter in continuous time signal. The parameter estimation method of this paper directly estimate the parameters on the basis of the discrete Fourier transform. Also, this paper present to efficient visualization method of dominant parameters obtained in continuous time signal. The proposed methods are applied to test functions with three dominant modes. The results show that the proposed methods are highly applicable to parameter estimation and visualization in continuous time signal.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.157-160
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• 1996

Physical systems axe generally continuous-time in nature. However as the data measured from these systems is generally in the form of discrete samples, and most modern signal processing is performed in the discrete-time domain, discrete-time models are employed. This paper describes methods for estimating the coefficients of continuous-time system within a closed loop control system. The method employs a recursive estimation algorithm to identify the coefficients of a discrete-time bilinear-operator model. The coefficients of the discrete-time bilinear-operator model closely approximate those of the corresponding continuous-time Laplace transform transfer function.

• Journal of Korean Society of Transportation
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• v.14 no.1
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• pp.101-116
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• 1996

Increased attention has been paid in recent years to the need of traffic management for alleviating urban traffic congestion. This paper presents a discrete event modeling and simulation framework for analyzing the traffic flow. Traffic simulation models can be classified as being either microscopic and macroscopic models. The discrete event modeling and simulation technique can be basically employed to describe the macroscopic traffic simulation model. To do this, we have employed the System Entity Structure/Model Base (SES/MB) framework which integrates the dynamic-based formalism of simulation with the symbolic formalism of AI. The SES/MB framework supports to hierarchical, modular discrete event modeling and simulation environment. We also adopt the Symbolic DEVS (Discrete Event System Specification) to developed the automated analysis methodology for generating optimal signal light policy. Several simulation tests will demonstrates the techniques.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.315-318
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• 2008

Numerical calculation for the 1MW class horizontal axis wind turbine blade has been carried out to estimate the magnitude between discrete noise and random noise. Farassat formula 1A was adopted to get the discrete noise signal, and blade element momentum theory was used to obtain the distribution of the aerodynamic data along the blade span. Fukano's approach was also adopted to calculate the unsteady aerodynamic random noise due to the Karman vortex generation at the trailing edge of the wind turbine blade. From the noise prediction for the 1MW class horizontal axis wind turbine, the frequency band of the discrete noise lies in the infrasound region, and that of the random noise lies in the audible band region.