• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1106-1113
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• 2000

In this, paper, a DFT based polyphase filter bank for the fractional ratio oversampling is proposed. Proper fractional oversampling ratio gives lower aliasing than the critical sampling and, at the same time, lower computational load than the integer ratio oversampling. In addition, filter bank design becomes easier by the reduced aliasing effect of fractional ratio oversampling. Proposed fractional ratio oaversampling polyphase structure is applied to a subband adaptive filter for acoustic echo cancellation where long adaptive filter are ofter required. Echo cancellation results show that fractional ratio oversampling gives comparable performance to the integer ratio oversampling with less computational load.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.6 s.360
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• pp.1-7
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• 2007

This paper proposes CMOS semi-digital data recovery with 2X converse oversampling to reduce power consumption and chid area of high definition multimedia interface (HDMI) receivers. Proposed recovery can reduce its power and the effective area by using nt converse oversampling algorithm and semi-digital architecture. Proposed circuit is fabricated using 0.18um CMOS process and measured results demonstrated the power consumption of 14.4mW, the effective area of $0.152mm^2$ and the jitter tolerance of 0.7UIpp with 1.8V supply voltage.)

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.189-198
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• 2009

In this paper, we propose a method to improve the performance of orthogonal frequency division multiplexing (OFDM) receivers by using oversampling the received signals. Demodulation of the received OFDM signals is to detect the amplitude and phase components of the subcarriers. From the oversampled OFDM signals, we can get redundant informations in frequency domain for the data, which are different in phase but the same in amplitude. By using these properties, we can obtain signal to noise ratio (SNR) gain by the oversampling ratio compared to the receivers which sampled with symbol rate. In this paper, we propose oversampled receivers whose oversampling ratio is expanded from integer to general rational number. Through computer simulations, we show the validity of the proposed methods by comparing the performance of the receivers with nonideal band-limiting filters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10A
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• pp.1590-1597
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• 2000

This paper describes a CMOS data recovery circuit using oversampling technique. Digital oversampling is done using a delay locked loop circuit locked to multiple clock periods. The delay locked loop circuit generates the vernier delay resolution less than the gate delay of the delay chain. The transition and non-transition counting algorithm for 4x oversampling was implemented for data recovery and verified through FPGA. The chip has been fabricated with 0.6um CMOS technology and measured results are presented.

• Information Systems Review
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• v.24 no.4
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• pp.97-118
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• 2022

As generative adversarial network (GAN) based oversampling techniques have achieved impressive results in class imbalance of unstructured dataset such as image, many studies have begun to apply it to solving the problem of imbalance in structured dataset. However, these studies have failed to reflect the characteristics of structured data due to changing the data structure into an unstructured data format. In order to overcome the limitation, this study adapted CycleGAN to reflect the characteristics of structured data, and proposed hybridization of synthetic minority oversampling technique (SMOTE) and the adapted CycleGAN. In particular, this study tried to overcome the limitations of existing studies by using a one-dimensional convolutional neural network unlike previous studies that used two-dimensional convolutional neural network. Oversampling based on the method proposed have been experimented using various datasets and compared the performance of the method with existing oversampling methods such as SMOTE and adaptive synthetic sampling (ADASYN). The results indicated the proposed hybrid oversampling method showed superior performance compared to the existing methods when data have more dimensions or higher degree of imbalance. This study implied that the classification performance of oversampling structured data can be improved using the proposed hybrid oversampling method that considers the characteristic of structured data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1872-1879
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• 2022

Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.

• Proceedings of the KSMRM Conference
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• 2002.11a
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• pp.96-96
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• 2002

목적： MRI 시스템의 비약적인 발전으로 인하여, 시스템에서 발생되는 noise가 상당히 줄었다. 따라서 시스템에서 발생되는 random noise뿐만 아니라 sampling 과정에서 발생되는 quantization noise도 중요하게 고려하여야 할 요소가 되었다. 특히, MRI 신호의 경우 dynamic range가 크기 때문에 bit 수가 큰 ADC를 이용하여 데이터를 얻어야 한다. 그러나, bit 수가 크고 높은 sampling rate를 갖는 ADC의 경우 가격이 높을 뿐만 아니라, 기존의 장비를 교체해야하는 어려움이 있다. 본 연구는 oversampling과 quantization noise와의 관계를 컴퓨터 시뮬레이션을 통하여 알아보고, MRI영상에서 oversampling을 통하여 quantization noise를 줄임으로써 영상의 질을 개선하고자 한다.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.1193-1196
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• 2003

This paper proposes a 2x oversampling method with a smart sampling for a clock and data recovery(CDR) circuit in a 2.5Gbps serial data link. In the conventional 2x oversampling method, the "bang-bang" operation of the phase detection produces a systematic jitter in CDR. The smart sampling in phase detection helps the CDR to remove the "bang-bang" operation and to improve the jitter performance. The CDR with the proposed 2x oversampling method is designed using Samsung 0.25${\mu}{\textrm}{m}$ process parameters and verified by simulation. Simulation result shows the proposed 2x oversampling method removes the systematic jitter.e systematic jitter.

• Progress in Medical Physics
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• v.27 no.2
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• pp.55-63
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• 2016

We studied the method to gain a clear LSF using a thick aluminum sheet and to acquire the spatial resolution value with a high accuracy for a low spatial resolution imaging modality. In this study, aluminum sheets with thicknesses varying from 0.3 mm to 1.2 mm were tested to derive a modulation transfer function (MTF) for the oversampling and non-oversampling methods. The results were evaluated to verify the feasibility of the use of thick sheets for periodic quality assurance. Oversampling was more accurate than non-oversampling, and an aluminum sheet with a correction factor less than 2 at the cut-off frequency, which was less than 0.8 mm in this case, was confirmed to be suitable for MTF measurements. Therefore, MTF derivation from a thick aluminum sheet with thickness correction is plausible for a medical imaging modality.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.49-55
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• 2021

Recently, with the development of database, it is possible to store a lot of data generated in finance, security, and networks. These data are being analyzed through classifiers based on machine learning. The main problem at this time is data imbalance. When we train imbalanced data, it may happen that classification accuracy is degraded due to over-fitting with majority class data. To overcome the problem of data imbalance, oversampling strategy that increases the quantity of data of minority class data is widely used. It requires to tuning process about suitable method and parameters for data distribution. To improve the process, In this study, we propose a strategy to explore and optimize oversampling combinations and ratio based on various methods such as synthetic minority oversampling technique and generative adversarial networks through genetic algorithms. After sampling credit card fraud detection which is a representative case of data imbalance, with the proposed strategy and single oversampling strategies, we compare the performance of trained classifiers with each data. As a result, a strategy that is optimized by exploring for ratio of each method with genetic algorithms was superior to previous strategies.