The Experts below are selected from a list of 4884 Experts worldwide ranked by ideXlab platform

Hyung Joun Yoo - One of the best experts on this subject based on the ideXlab platform.

  • reconfigurable high order Moving Average Filter using inverter based variable transconductance amplifiers
    IEEE Transactions on Circuits and Systems Ii-express Briefs, 2014
    Co-Authors: Soonjae Kweon, Soohwan Shin, Hyung Joun Yoo
    Abstract:

    A charge sampler-based reconfigurable high-order Moving-Average (MA) Filter designed using a temporal MA method is proposed. The proposed Filter has a higher gain than conventional MA Filters. Moreover, the Filter supports variable sizes and orders of MA. That is, the Filter has a flexible frequency response by changing not only the sampling frequency but also the MA size $(N)$ and MA order $(M)$ . The $N$ and $M$ are easily controlled by changing the clock patterns; therefore, the Filter is suitable for multimode transceivers. To minimize the power consumption, inverter-based transconductance amplifiers are used. Here, our fabricated Filter using a 65-nm CMOS technology supports MA $(N=2,M=3)$ and MA $(N=3,M=2)$ without changing the hardware.

  • high order temporal Moving Average Filter using a multi transconductance amplifier
    Electronics Letters, 2012
    Co-Authors: Soonjae Kweon, Soohwan Shin, Hyung Joun Yoo
    Abstract:

    A temporal Moving-Average (TMA) Filter that has a high-order sinc-type frequency response is proposed. The proposed TMA Filter has a smaller size and higher gain than conventional Filters. To verify the proposed architecture, a third-order TMA Filter is designed using TSMC 0.13 µm CMOS technology and is compared with a theoretical analysis. Circuit level simulations show good agreement with the theoretical analysis.

  • A reconfigurable spatial Moving Average Filter in sampler-based discrete-time receiver
    2011 IEEE International Symposium on Radio-Frequency Integration Technology, 2011
    Co-Authors: Yong-ho Cho, Soonjae Kweon, Soohwan Shin, Hyung Joun Yoo
    Abstract:

    A non-decimation second-order spatial Moving Average (SMA) discrete-time (DT) Filter is proposed with reconfigurable null frequencies. The Filter coefficients are changeable, and it can be controlled by switching sampling capacitors. So, interferers can be rejected effectively by flexible nulls. Since it operates without decimation, it does not change the sample rate and aliasing problem can be avoided. The Filter is designed with variable weight of coefficients as 1:α:1 where a varies from 1 to 2. This corresponds to the change of null frequencies within the range of f s /3∼f s /2 and f s /2∼2f s /3. The proposed Filter is implemented in the TSMC 0.18-μm CMOS process. Simulation shows that null frequencies are changeable in the range of 0.38∼0.49 f s and 0.51∼0.62f s .

  • A novel high-order temporal Moving Average Filter in sampler-based discrete-time receiver
    2011 IEEE International Symposium on Radio-Frequency Integration Technology, 2011
    Co-Authors: Soohwan Shin, Yong-ho Cho, Hyung Joun Yoo
    Abstract:

    A discrete-time (DT) Filter with high-order temporal Moving Average (TMA) by actively-weighted charge sampling is proposed. The weight of sampled charge can be effectively controlled by digitally-controlled variable operational transconductance amplifier (OTA). By switching the control transistors of the OTA, the transconductance can be changed according to the desired weight ratio. Therefore, high-order TMA operation can be possible. It has small size, increased voltage gain, and low parasitic effects. The suggested high-order TMA Filter is implemented in the TSMC 0.18 μm CMOS process. DC current is about 9.7 mA.

Aimin Zhang - One of the best experts on this subject based on the ideXlab platform.

  • enhanced stability for local atomic clock ensemble time scale using weighted Moving Average Filter
    International Conference on Mechatronics and Automation, 2016
    Co-Authors: Yuzhuo Wang, Aimin Zhang
    Abstract:

    This paper presents a method to enhance the stability of atomic clock ensemble time scale by using weighted Moving Average Filter. Compared with Kalman Filter, the weighted Moving Average Filter has simpler algorithm structure, higher computational efficiency. Its convergence time is shorter than Kalman Filter. When the two Filters were applied to frequency data of Cs clocks, they have consistent Filtering performance. The preliminary results show the weighted Moving Average Filter can enhance the stability of local atomic clock ensemble time scale, especially the short- and middle-term stability.

Eswara B Reddy - One of the best experts on this subject based on the ideXlab platform.

  • a Moving Average Filter based hybrid arima ann model for forecasting time series data
    Applied Soft Computing, 2014
    Co-Authors: Narendra C Babu, Eswara B Reddy
    Abstract:

    A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated Moving Average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA-ANN model for the prediction of time series data. Many of the hybrid ARIMA-ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a Moving-Average Filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA-ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA-ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy.

Ni Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Correction of electrocardiogram signal baseline wander based on statistically weighted Moving Average Filter
    Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2012
    Co-Authors: Zhong Xiao, Ni Zhang, Xiaoming Han
    Abstract:

    Baseline wander (BW) is a common noise contaminating electrocardiogram (ECG). In order to effectively correct baseline of ECG signal and to preserve more latent components of ECG signal, this paper proposes a simple and novel Filter based on statistically weighted Moving Average. Firstly, after the arrange between the maximum and the minimum of these sampling values in a Moving window was divided into many sections with equal width, several segments [a(k), b(k)] including most samples were determined. Then, for every sample point in the Moving window its weight was decided according to the criterion: its weight was set as 1 if the sampling value belongs to [a(k), b(k)], otherwise, as 0. Lastly, all these ECG sampling points with 1 weight were Averaged to estimate the real baseline in the Moving window. The algorithm was tested by simulated signal and real signal from www. physionet. org. The results showed that compared to traditional Moving Average Filter and wavelet package (WP) translation, the proposed Filter could more effectively correct baseline in ECG signal and result in less distortion to ECG signal.

  • removal of baseline wander from ecg signal based on a statistical weighted Moving Average Filter
    Journal of Zhejiang University Science C, 2011
    Co-Authors: Zhong Xiao, Ni Zhang
    Abstract:

    Baseline wander is a common noise in electrocardiogram (ECG) results. To effectively correct the baseline and to preserve more underlying components of an ECG signal, we propose a simple and novel Filtering method based on a statistical weighted Moving Average Filter. Supposed a and b are the minimum and maximum of all sample values within a Moving window, respectively. First, the whole region [a, b] is divided into M equal sub-regions without overlap. Second, three sub-regions with the largest sample distribution probabilities are chosen (except M<3) and incorporated into one region, denoted as [a 0, b 0] for simplicity. Third, for every sample point in the Moving window, its weight is set to 1 if its value falls in [a 0, b 0]; otherwise, its weight is 0. Last, all sample points with weight 1 are Averaged to estimate the baseline. The algorithm was tested by simulated ECG signal and real ECG signal from www.physionet.org . The results showed that the proposed Filter could more effectively extract baseline wander from ECG signal and affect the morphological feature of ECG signal considerably less than both the traditional Moving Average Filter and wavelet package translation did.

  • Removal of baseline wander from ECG signal based on a statistical weighted Moving Average Filter
    Journal of Zhejiang University SCIENCE C, 2011
    Co-Authors: Zhong Xiao, Ni Zhang
    Abstract:

    Baseline wander is a common noise in electrocardiogram (ECG) results. To effectively correct the baseline and to preserve more underlying components of an ECG signal, we propose a simple and novel Filtering method based on a statistical weighted Moving Average Filter. Supposed a and b are the minimum and maximum of all sample values within a Moving window, respectively. First, the whole region [a, b] is divided into M equal sub-regions without overlap. Second, three sub-regions with the largest sample distribution probabilities are chosen (except M

Yuzhuo Wang - One of the best experts on this subject based on the ideXlab platform.

  • enhanced stability for local atomic clock ensemble time scale using weighted Moving Average Filter
    International Conference on Mechatronics and Automation, 2016
    Co-Authors: Yuzhuo Wang, Aimin Zhang
    Abstract:

    This paper presents a method to enhance the stability of atomic clock ensemble time scale by using weighted Moving Average Filter. Compared with Kalman Filter, the weighted Moving Average Filter has simpler algorithm structure, higher computational efficiency. Its convergence time is shorter than Kalman Filter. When the two Filters were applied to frequency data of Cs clocks, they have consistent Filtering performance. The preliminary results show the weighted Moving Average Filter can enhance the stability of local atomic clock ensemble time scale, especially the short- and middle-term stability.