• Proceedings of the IEEK Conference
• /
• 2003.07a
• /
• pp.418-421
• /
• 2003

In this thesis, a new channel estimation technique is proposed for orthogonal frequency division multiplexing (OFDM) over time varying channels. The channel estimation algorithm exploits the fact that the estimated channel impulse response (CIR) by using pilot signal is the average value of the CIR variation within an OFDM symbol period. With this fact, the CIR variation is simply estimated through lowpass interpolation of the CIRs of the adjacent OFDM symbols. For signal detection, a time domain equalizer is used in this thesis. Simulation results show that the proposed system improves the bit error rate (BER) over time varying channels.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.05a
• /
• pp.539-542
• /
• 2011

In this paper, we have implemented a performance analysis of $2{\times}1$ Alamouti scheme suggested by Alamouti, composed of the transmit space-time code and the simple linear decoding processing, in perfectly time-varying and spatially correlated channels. In addition, we derived the closed-form probability density function (PDF) of the output signal-to-noise ratio (SNR) and the outage probability of the Alamouti scheme as a function of the spatial correlation coefficient in the consideration of no correlation in time. As a result, it was found that the performance of the Alamouti scheme could be significantly degraded particularly in the case that the channels are time-varying and spatially correlated.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2000.12a
• /
• pp.201-204
• /
• 2000

This paper describes a study on the time-varying control of parameters in learning of the neural network. Elman recurrent neural network (RNN) is used to implement the control of parameters. The parameters of learning and momentum rates In the error backpropagation algorithm ate updated at every iteration using fuzzy rules based on performance index. In addition, the gain and slope of the neuron's activation function are also considered time-varying parameters. These function parameters are updated using the gradient descent algorithm. Simulation results show that the auto-tuned learning algorithm results in faster convergence and lower system error than regular backpropagation in the system identification.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10a
• /
• pp.842-846
• /
• 1987

This paper proves that a filter for scramble spectrum is realized by a periodically time-varying digital filter. Further, we propose a design method of this filter by using a bi-frequency map. As an example, we implemented this filter with Digital Signal Processor.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.585-587
• /
• 1998

It is suggested that the PWM inverter is controlled by Digital Software Programming. VVVF(Variable Voltage Variable Frequency) inverter control being used by PWM control for driving the motor with speed-varying, makes the PWM pattern with calculating the output voltage and frequency, and with controlling the carrier and signal, so actually this method is difficult to correspond with driving the motor by using voltage-varying and frequency-varying. Therefore this research suggested the new algorithm controlled by micro processor which is already stored by various PWM form of output voltage by using fundamental data of the carrier and signal. The PWM wave can be controlled with real time by using extra hardware and digital software and to speed up program processing, the control signals to switch the power semi-conductor of three phase PWM inverter, simultaneously use the output signal by microprocessor and extra hardware, and control signal by software. In the end, this method was proved by applying to Three Phase Voltage-type Inverter.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.14 no.2
• /
• pp.183-191
• /
• 2003

In this paper, the interference cancellation system, which is used to cancel the feedback signal In the wireless communication system with the same frequency, is studied. The time-varying feedback signal generated from transmitter antenna to receiver antenna reduces the performance of the receiver system. The interference cancellation system using adaptive feedback method(AF-ICS) is suggested to prevent the oscillation of the receiver system and maintain the maximum output power of the power amplifier by the reduction of time-varying feedback signal.

• Nuclear Engineering and Technology
• /
• v.31 no.2
• /
• pp.192-201
• /
• 1999

This paper proposes an improved signal processing strategy for accurate feedwater flowrate estimation in nuclear power plants. It is generally known that ∼2% thermal power errors occur due to fouling Phenomena in feedwater flowmeters. In the strategy Proposed, the noises included in feedwater flowrate signal are classified into rapidly varying noises and gradually varying noises according to the characteristics in a frequency domain. The estimation precision is enhanced by introducing a low pass filter with the wavelet analysis against rapidly varying noises, and an autoassociative neural network which takes charge of the correction of only gradually varying noises. The modified multivariate stratification sampling using the concept of time stratification and MAXIMIN criteria is developed to overcome the shortcoming of a general random sampling. In addition the multi-stage robust training method is developed to increase the quality and reliability of training signals. Some validations using the simulated data from a micro-simulator were carried out. In the validation tests, the proposed methodology removed both rapidly varying noises and gradually varying noises respectively in each de-noising step, and 5.54% root mean square errors of initial noisy signals were decreased to 0.674% after de-noising. These results indicate that it is possible to estimate the reactor thermal power more elaborately by adopting this strategy.

• Speech Sciences
• /
• v.12 no.4
• /
• pp.17-29
• /
• 2005

Time-frequency representation reveals some useful information about instantaneous frequency, instantaneous bandwidth and boundary of each AM-FM component of a speech signal. In many cases, the instantaneous frequency of each component is not constant. The variability of instantaneous frequency causes degradation of resolution in time-frequency representation. This paper presents a method of adaptively adjusting the transform kernel for preventing degradation of resolution due to time-varying instantaneous frequency. The transform kernel is the form of frequency modulated function. The modulation function in the transform kernel is determined by the estimate of instantaneous frequency which is approximated by first order polynomial at each time instance. Also, the window function is modulated by the estimated instantaneous. frequency for mitigation of fringing. effect. In the proposed method, not only the transform kernel but also the shape and the length of. the window function are adaptively adjusted by the instantaneous frequency of a speech signal.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.1
• /
• pp.8-15
• /
• 2005

The analysis of Rader signal that have non-linearity variable phase is signal that contact easily in several fields such as radar, telecommunication, seismic, sonar and biomedical applications. In generally, Non-stationary signal means that spectral characteristics are varying with time and instantaneous frequency is only one frequency or narrow range of frequencies varying as a function of time. Therefore, Instantaneous frequency is vary important variable that understanding physical characteristic of signal. This paper was describes continuous wavelet transform to determine instantaneous frequency at non-staionary signal and compare to existing method. When white noise or various frequency is overlapped each other in sign, existing method was can not decide corrected instantaneous frequency, but when used continuous wavelet transform, very well decide correctly frequency regardless of component of signal.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.10
• /
• pp.4835-4855
• /
• 2018

In this paper, we study the sparse signal recovery that uses information of both support and amplitude of the sparse signal. A convergent iterative algorithm for sparse signal recovery is developed using Majorization-Minimization-based Non-convex Optimization (MM-NcO). Furthermore, it is shown that, typically, the sparse signals that are recovered using the proposed iterative algorithm are not globally optimal and the performance of the iterative algorithm depends on the initial point. Therefore, a modified MM-NcO-based iterative algorithm is developed that uses prior information of both support and amplitude of the sparse signal to enhance recovery performance. Finally, the modified MM-NcO-based iterative algorithm is used to estimate the time-varying sparse wireless channels with temporal correlation. The numerical results show that the new algorithm performs better than related algorithms.