• Title/Summary/Keyword: Moving average(MA)

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Design of 5GHz FIR filter LNA based on a Distribute Amplifier (분산증폭기 기반 5GHz FIR 필터 LNA 설계)

  • Yeo, Hyeopgoo;Jung, Seung-Min
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2012.10a
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    • pp.842-844
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    • 2012
  • This paper introduces a 5GHz FIR filter low noise amplifier (LNA) based on a distributed amplifier and analyzes the its characteristics. The proposed FIR filter-LNA has the MA(moving average) filter characteristic which improves the frequency selectivity of the amplifier. Proto-type circuits with FR4 and ${\varepsilon}_r=10.2$ PCB have been realized and simulated using ADS (Advanced Design System). The simulation results verified that the designed LNA had a gain of about 10dB and the frequency characteristic of the MA FIR filter. It is expected that the proposed FIR filter LNA can be applicable to the various applications using an amplifier and a filter in RF systems.

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Prediction of Covid-19 confirmed number of cases using ARIMA model (ARIMA모형을 이용한 코로나19 확진자수 예측)

  • Kim, Jae-Ho;Kim, Jang-Young
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.12
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    • pp.1756-1761
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    • 2021
  • Although the COVID-19 outbreak that occurred in Wuhan, Hubei around December 2019, seemed to be gradually decreasing, it was gradually increasing as of November 2020 and June 2021, and estimated confirmed cases were 192 million worldwide and approximately 184 thousand in South Korea. The Central Disaster and Safety Countermeasures Headquarters have been taking strong countermeasures by implementing level 4 social distancing. However, as the highly infectious COVID-19 variants, such as Delta mutation, have been on the rise, the number of daily confirmed cases in Korea has increased to 1,800. Therefore, the number of cumulative confirmed COVID-19 cases is predicted using ARIMA algorithms to emphasize the severity of COVID-19. In the process, differences are used to remove trends and seasonality, and p, d, and q values are determined and forecasted in ARIMA using MA, AR, autocorrelation functions, and partial autocorrelation functions. Finally, forecast and actual values are compared to evaluate how well it was forecasted.

Time Series Analysis of Wind Pressures Acting on a Structure (구조물에 작용하는 풍압력의 시계열 분석)

  • 정승환
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.13 no.4
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    • pp.405-415
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    • 2000
  • Time series of wind-induced pressure on a structure are modeled using autoregressive moving average (ARMA) model. In an AR process, the current value of the time series is expressed in terms of a finite, linear combination of the previous values and a white noise. In a MA process, the value of the time series is linearly dependent on a finite number of the previous white noises. The ARMA process is a combination of the AR and MA processes. In this paper, the ARMA models with several different combinations of the AR and MA orders are fitted to the wind-induced pressure time series, and the procedure to select the most appropriate ARMA model to represent the data is described. The maximum likelihood method is used to estimate the model parameters, and the AICC model selection criterion is employed in the optimization of the model order, which is assumed to be a measure of the temporal complexity of the pressure time series. The goodness of fit of the model is examined using the LBP test. It is shown that AR processes adequately fit wind pressure time series.

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Implementation of the Speech Emotion Recognition System in the ARM Platform (ARM 플랫폼 기반의 음성 감성인식 시스템 구현)

  • Oh, Sang-Heon;Park, Kyu-Sik
    • Journal of Korea Multimedia Society
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    • v.10 no.11
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    • pp.1530-1537
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    • 2007
  • In this paper, we implemented a speech emotion recognition system that can distinguish human emotional states from recorded speech captured by a single microphone and classify them into four categories: neutrality, happiness, sadness and anger. In general, a speech recorded with a microphone contains background noises due to the speaker environment and the microphone characteristic, which can result in serious system performance degradation. In order to minimize the effect of these noises and to improve the system performance, a MA(Moving Average) filter with a relatively simple structure and low computational complexity was adopted. Then a SFS(Sequential Forward Selection) feature optimization method was implemented to further improve and stabilize the system performance. For speech emotion classification, a SVM pattern classifier is used. The experimental results indicate the emotional classification performance around 65% in the computer simulation and 62% on the ARM platform.

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A Study on Robust Speech Emotion Feature Extraction Under the Mobile Communication Environment (이동통신 환경에서 강인한 음성 감성특징 추출에 대한 연구)

  • Cho Youn-Ho;Park Kyu-Sik
    • The Journal of the Acoustical Society of Korea
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    • v.25 no.6
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    • pp.269-276
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    • 2006
  • In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

Optimal Adjustment of Misestimated Control Model for a Process with Shift and White Noise (백색잡음과 Shift가 존재하는 공정에서 제어식이 부정확한 경우의 최적 보정)

  • Hwang, Ji-Bin;Kim, Ji-Hyun;Lee, Jae-Hyun;Kim, Sung-Shick
    • Journal of the Korea Society for Simulation
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    • v.16 no.4
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    • pp.43-55
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    • 2007
  • Moving average(MA) and exponentially weighted moving average(EWMA) are the two most popular control methods in manufacturing. Both methods are optimized under the assumption that the exact control equation is known. This paper focuses on the problems rising from estimation errors. Based on the accuracy of the estimated parameter and the range of the weight parameter $\lambda$, the limitations are identified and the performance of methods are evaluated. Optimal adjustment for process shift with misestimated control model and its application control methods to actual process is researched. The efficiency of proposed method is evaluated through simulation.

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Design of Acceptance Control Charts According to the Process Independence, Data Weighting Scheme, Subgrouping, and Use of Charts (프로세스의 독립성, 데이터 가중치 체계, 부분군 형성과 관리도 용도에 따른 합격판정 관리도의 설계)

  • Choi, Sung-Woon
    • Journal of the Korea Safety Management & Science
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    • v.12 no.3
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    • pp.257-262
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    • 2010
  • The study investigates the various Acceptance Control Charts (ACCs) based on the factors that include process independence, data weighting scheme, subgrouping, and use of control charts. USL - LSL > $6{\sigma}$ that used in the good condition processes in the ACCs are designed by considering user's perspective, producer's perspective and both perspectives. ACCs developed from the research is efficiently applied by using the simple control limit unified with APL (Acceptable Process Level), RLP (Rejectable Process Level), Type I Error $\alpha$, and Type II Error $\beta$. Sampling interval of subgroup examines i.i.d. (Identically and Independent Distributed) or auto-correlated processes. Three types of weight schemes according to the reliability of data include Shewhart, Moving Average(MA) and Exponentially Weighted Moving Average (EWMA) which are considered when designing ACCs. Two types of control charts by the purpose of improvement are also presented. Overall, $\alpha$, $\beta$ and APL for nonconforming proportion and RPL of claim proportion can be designed by practioners who emphasize productivity and claim defense cost.

Design of 2-D MA FIR Filters for Channel Estimation in OFDM Systems

  • Park, Ji-Woong;Lee, Seung-Woo;Lee, Yong-Hwan
    • Proceedings of the IEEK Conference
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    • 2003.07a
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    • pp.234-237
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    • 2003
  • The accuracy of channel estimation significantly affects the performance of coherent OFDM receiver. It is desirable to employ a good channel estimator while requiring low implementation complexity. In this paper, we propose a channel estimator that employs a simple two-dimensional (2-D) moving average (MA) filter as the channel estimation filter. The optimum tap size of the 2-D MA FIR filter is analytically designed in the time and frequency domain in association with the channel condition and pilot signal to interference power ratio. The analytic results can be applied to the design of adaptive channel estimator. Finally, the performance of the proposed channel estimator is verified by computer simulation.

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Dynamic bivariate correlation methods comparison study in fMRI

  • Jaehee Kim
    • Communications for Statistical Applications and Methods
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    • v.31 no.1
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    • pp.87-104
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    • 2024
  • Most functional magnetic resonance imaging (fMRI) studies in resting state have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant. However, increased interest has recently been in quantifying possible dynamic changes in FC during fMRI experiments. FC study may provide insight into the fundamental workings of brain networks to brain activity. In this work, we focus on the specific problem of estimating the dynamic behavior of pairwise correlations between time courses extracted from two different brain regions. We compare the sliding-window techniques such as moving average (MA) and exponentially weighted moving average (EWMA), dynamic causality with vector autoregressive (VAR) model, dynamic conditional correlation (DCC) based on volatility, and the proposed alternative methods to use differencing and recursive residuals. We investigate the properties of those techniques in a series of simulation studies. We also provide an application with major depressive disorder (MDD) patient fMRI data to demonstrate studying dynamic correlations.