• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.2A
• /
• pp.155-163
• /
• 2009

OFDM System is a promising transmission technique due to its spectral efficiency But, a major disadvantage of the OFDM system is its sensitivity to frequency offset and phase noise that makes intercarrier interference (ICI), which degrades the system performance severely The ICI self-cancellation method has a good performance with frequency offset or phase noise. This paper proposed the N/2 spacing data-conjugate method that works well in large frequency offset and phase noise (normalized frequency offset=0.2-0.4, phase noise standard deviation=about lodes). Also, an efficiency ICI cancellation method using pilot was proposed. Simulation results confirm that performance of the proposed scheme is better than conventional schemes.

• MALSORI
• /
• no.52
• /
• pp.121-132
• /
• 2004

This paper is aiming at real-time implementation of adaptive noise cancellation system using DSP processor. ACHARF algorithm, which guarantees stability and fast convergence by adaptive compensator, is used on this DSP system. For the experiments, TLV320AIC23 stereo CODEC of TI Inc. is used with TMS320C6413 DSP processor. Signals of primary input and reference input are obtained by two microphones. The primary input is the voice plus noise signal and the reference input is white noise or real noise. The experimental results show that ANC system using DSP processor with ACHARF is verified to be an effective speech enhancement method for various speech processing units.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.05a
• /
• pp.525-529
• /
• 2002

ANC technic can overcome the limited performance of passive noise control at the low frequency range. But it has the local quiet control region in general. In this paper, it is discussed that the global noise control in a circular duct using a ring type smart foam and a porous material. LMS algorithm and RLS algorithm are used to find optimal orders of cancellation path. Experiments are performed to compare the efficiency of RLS algorithm with that of LMS algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.4
• /
• pp.29-33
• /
• 2022

In this paper, we propose the deep learning based pre interference cancellation scheme algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information by applying a deep learning model at the transmitter. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.2
• /
• pp.41-45
• /
• 2019

In this paper, we propose the interference cancellation scheme of end-to-end method algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information of receiver by applying a deep learning model at the receiver. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2244-2247
• /
• 2003

In this paper, we present a method which can minimize distortion from desired signal in thoracic sound signal processing. We firstly chose the proper wavelet mother function to reduce noise components. Secondly, we chose a clean thoracic sound, then added Gaussian noise and 3 step(10, 15, 20db) uniform noise to it. Finally, the various wavelet functions are applied for noise cancellation. To evaluate the efficiency of this study, we computed SNR and RSE value. Then we found the optimal mother wavelet function for thoracic sound.

• Journal of Digital Convergence
• /
• v.10 no.11
• /
• pp.377-382
• /
• 2012

In this paper, the echo noise robust CHMM learning model using echo cancellation average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise. For improving the performance of a continuous speech recognition, CHMM models were constructed using echo noise cancellation average estimator LMS algorithm. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 1.93dB, recognition rate improved as 2.1%.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.5
• /
• pp.351-359
• /
• 2003

In this paper, we proposed a method for acoustic echo cancellation based on independent component analysis. When the large acoustic noise is picked up by the microphone, the performance of echo cancellation decreased. We used two microphones that received echo signal which is linearly mixed with the noise, then separated the echo signals from the received signals with independent component analysis algorithm. The separated echo signal is used for the reference signal of adaptive algorithm which leads to better performance of the echo cancellation. Computer simulation results show the validity of the proposed method.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.2 no.1
• /
• pp.35-43
• /
• 2009

In this paper, we introduce a reading training program offering visual-auditory cue with noise cancellation function (RT program) developed by us. The RT program provides some training sentences with visual-auditory cues. Motor speech disorder patients can use the visual and/or auditory cues for reading training. To provide convenient estimation of training result, we developed a noise cancellation algorithm. The function of the algorithm is to remove noise and auditory-cues which are recorded with reading speech at the same time while patient read the sentences in PC monitor. In addition, we developed a function for finding out the first starting time of reading sound after a patient sees a sentence and begins to read the sentence. The recorded speeches are acquired from six people(three male, three female) in four noisy environments (interior noise, white noise, car interior noise, babble noise). We evaluated the timing error for starting time between original recorded speech and processed speech in condition of executing noise cancellation function and not executing. The timing error was improved as much as $4.847{\pm}2.4235[ms]$ as the effect of noise cancellation. It is expected that the developed RT program helps motor speech disorder patient in reading training and symptom evaluation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2002.11b
• /
• pp.611-616
• /
• 2002

Bearing diagnostics is difficult in a gearbox because bearing signals are masked by the strong gear signals. Self adaptive noise cancellation(SANC) is useful technique to seperate bearing signals from gear signals. While gear signals are correlated with a long correlation length, bearing signals are not correlated with a short length. SANC seperates two components on the basis of correlation length. Then we can find defect frequency component in the envelope spectrum of the bearing signals.