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1
Reduced rank technique for joint channel estimation and joint data detection in TD-SCDMA systems
Published 2013“…Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. Simulation results of the normalized mean square error for the above mentioned estimators showed the superiority of reduced rank estimators. …”
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2
Enhancement of Space-Time Receiver Structure with Multiuser Detection for Wideband CDMA Communication Systems
Published 2006“…We consider two different pilot symbol assisted adaptive beamforming algorithms, Least Mean Square (LMS) and Recursive Least Square (RLS). …”
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3
Reduced Rank Technique for Joint Channel Estimation and Joint Data Detection in TD-SCDMA Systems
Published 2012“…Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. Simulation results of the normalized mean square error for the above mentioned estimators showed the superiority of reduced rank estimators. …”
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4
Comparative study of clustering-based outliers detection methods in circular-circular regression model
Published 2021“…The performances of the algorithms have been demonstrated using the simulation studies that consider several outlier scenarios with a certain degree of contamination. …”
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Comparative study of clustering-based outliers detection methods in circularcircular regression model
Published 2021“…The performances of the algorithms have been demonstrated using the simulation studies that consider several outlier scenarios with a certain degree of contamination. …”
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6
Comparative study of clustering-based outliers detection methods in circular-circular regression model
Published 2021“…The performances of the algorithms have been demonstrated using the simulation studies that consider several outlier scenarios with a certain degree of contamination. …”
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7
Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
Published 2009“…Kallel et al. ( 2002 ) proposed utilizing the bootstrap technique for model selection. They used the classical bootstrap method to estimate the bootstrap location and the scale parameters based on calculating the Mean of Squared Residual (MSR). …”
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8
Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari
Published 2015“…We first look at the concentration parameter of von Mises distribution. The von Mises distribution is the most commonly used probability distribution of a circular random variable, and the concentration of a circular data set is measured using the mean resultant length. …”
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9
Reduced-rank technique for joint channel estimation in TD-SCDMA systems.
Published 2013“…Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. Simulation results of the normalized mean square error for the above mentioned estimators showed the superiority of reduced rank estimators. …”
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10
Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation
Published 2019“…The error measurements of the proposed method such as Mean Absolute Percentage Error, Mean Absolute Error, And Root Mean Square Error for islanding detection are less than 0.02% for ideal and noisy conditions which shows that the algorithm is not sensitive to noise. …”
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11
Application of neural networks in early detection and diagnosis of parkinson's disease
Published 2014“…This MLFNN with BP algorithm is simulated using MATLAB software. The dataset information used in this study was taken from the Oxford Parkinson’s Disease Detection Dataset. …”
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12
Numerical Analysis of structural batteries response with the presence of uncertainty
Published 2023“…The proposed approach could detect the results that are not necessarily detected through deterministic means. …”
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Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model
Published 2000“…From the simulation study for this particular case, we can conclude that the EM algorithm proved to be more superior in terms of mean value of parameter estimates, bias and root mean square error. …”
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14
Numerical Analysis of Structural Batteries Response with the Presence of Uncertainty
Published 2023“…The proposed approach could detect the results that are not necessarily detected through deterministic means. …”
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15
Modified least trimmed squares method for face recognition / Nur Azimah Abdul Rahim
Published 2018“…It can be concluded that the modified algorithm decreases the biases, the variances and the mean squared errors of the LTS estimators. …”
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Development of a phantom and metal artifact correction (MAC) algorithm for post-operative spine computed tomography (CT) imaging / Noor Diyana Osman
Published 2014“…The missing projection data due to metal region is detected and extracted using an improved technique of dual-step adaptive thresholding. …”
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18
Integrated geophysical, hydrogeochemical and artificial intelligence techniques for groundwater study in the Langat Basin, Malaysia / Mahmoud Khaki
Published 2014“…These results confirm that, for all the networks the Levenberg-Marquardt algorithm is the most effective algorithm to model the groundwater level. …”
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19
Analytical simulation of non-linearity effects compensation in all-optical orthogonal frequency division multiplexing systems
Published 2020“…The digital processing of the detected signals and Bit Error Rate (BER) calculation are performed by using DSP algorithm in Matlab software. …”
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20
Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis
Published 2004“…From the simulation study for this particular case, we can conclude that Weibull distribution describes well the nature of the model concerned as compared to the exponential distribution in terms of the mean value of parameter estimates, bias, and the root means square error. …”
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