On the optimal frame size of linear prediction techniques

Khan, Faheem Ullah; Khan, Naeem; Khan, Latif U.; Khan, Muhammad Naeem; Pirzada, Bilal

doi:10.1109/iccpct.2013.6529006

Cited by 5 publications

(3 citation statements)

References 9 publications

(9 reference statements)

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“…75 While there is a potential dimension of covert indirect participation, in the form of allegations of a possible a secret deal between the PML-N and TTP, 76 the TTP targeting of the ANP, MQM, and PPP's rallies, workers, and candidates was clear and consistent. 77 This was a violent campaign trying to tip the electoral balance against particularly dangerous parties, without necessarily actively supporting its mainstream rivals.…”

mentioning

confidence: 99%

How and Why Armed Groups Participate in Elections

Matanock

Staniland

2017

SSRN Journal

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Armed actors are often involved in electoral politics, from the fusing of ballots and bullets in armed political parties to insurgents covertly backing politicians. This article develops new concepts and theory to better understand these complex relationships between violent actors and democratic practice. It first offers a novel conceptualization of armed groups' electoral strategies that systematically maps out variation in the organizational directness and public openness of groups' involvement in elections. The article then uses comparative case studies to develop theory about the conditions under which each of these electoral strategies is most likely, and what can trigger changes between them. The interaction of armed groups' power and expectations of popular support with governments' policies of toleration or repression determines the strategies of electoral participation that groups pursue. These concepts and arguments lay the foundation for a systematic research agenda on when and how "normal" and armed politics become intertwined.Word Count: 13,042 2

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confidence: 99%

How and Why Armed Groups Participate in Elections

Matanock

Staniland

2017

SSRN Journal

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“…A general review of Kalman filter and linear prediction theory is discussed in this section, suitable for an article level. Each iteration of the Kalman filter consists of two cycles without loss of generality: prediction or time update, and filtration or measurement update [6,17].…”

Section: Filtering With Data Lossmentioning

confidence: 99%

Compensation of Data Loss Using ARMAX Model in State Estimation for Control and Communication Systems Applications

et al. 2021

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Compensation of data loss in the state estimation plays an indispensable role in efficient and stable control and communication systems. However, accurate compensation of data loss in the state estimation is extremely challenging issue. To cater this challenging issue, two techniques such as the open-loop Kalman filter and the compensating closed-loop Kalman filter have emerged. The closed-loop technique compensates for the missing data using the autoregressive model. However, the autoregressive model used only past measurements for data loss compensation. Considering only one parameter, i.e., the past measurements, is insufficient and leads to inaccurate state estimation. Thus, in this work, autoregressive moving average with exogenous inputs model considers three parameters, i.e., the past measurements, the input signal, and the sensor noise, simultaneously to compensate data loss in state estimation. To endorse the effectiveness and applicability of the proposed model, a standard mass-spring-damper is employed in the case study. Simulation results show that the proposed model outperforms the existing autoregressive models in terms of performance parameters.

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“…However, if the threshold τ is too small, the overfitting phenomenon can occur, which would not only decrease the predictive accuracy but also lead to the divergence of average error power. According to an earlier research, the optimum threshold τ usually ranges from 10% to 30% 25 …”

Section: Modified Linear Predictive Coding Algorithmmentioning

confidence: 99%

Modified linear predictive coding approach for moving target tracking by Doppler radar

Ding

Lin

Sun

et al. 2016

J. Appl. Remote Sens

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.Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

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On the optimal frame size of linear prediction techniques

Cited by 5 publications

References 9 publications

How and Why Armed Groups Participate in Elections

How and Why Armed Groups Participate in Elections

Compensation of Data Loss Using ARMAX Model in State Estimation for Control and Communication Systems Applications

Modified linear predictive coding approach for moving target tracking by Doppler radar

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