scholarly journals Effect of Resampling on the Performance and Execution Speed of Adaptive Marginalized Particle Filter

2019 ◽  
Vol 8 (3) ◽  
pp. 4005-4012
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Execution Time ◽  
Adaptive Filters ◽  
Mean Square ◽  
Different Types ◽  
Execution Speed ◽  
And Performance ◽  
Major Factors ◽  
Degeneracy Problem

One of the major factors that affects the performance of adaptive filters like Particle Filter (PF), Marginalized Particle Filter (MPF) and Adaptive Marginalized Particle Filter (AMPF) is sample degeneracy. Sample degeneracy occurs when the weights associated with particles converges to zero making them useless in state estimation. Resampling is the most common method used to avoid sample degeneracy problem, in which a new set of particles are generated and weights are assigned. Performance and execution time of these filter depends a lot on what type of resampling technique is employed. AMPF is the modified version of MPF which is typically faster than PF and MPF. The main aim of this paper is to find the effect of different types of resampling on the performance and execution time of AMPF. For this, a typical target tracking problem is simulated using MATLAB. AMPF with different types of resampling techniques is used for state estimation for the above-mentioned problem and the best in terms of performance and execution speed will be found out. From the simulation, it will be clear that AMPF with systematic resampling is found to be best in terms of execution speed and performance i.e. minimum Root Mean Square Error.

scholarly journals Simulação do comportamento estocástico do algoritmo KLMS com diferentes kernels

2020 ◽  
Author(s):  
Patrick Medeiros De Luca ◽  
Wemerson Delcio Parreira
Keyword(s):  
Monte Carlo Simulation ◽  
Adaptive Filtering ◽  
Adaptive Filters ◽  
Linear Filter ◽  
Mean Square ◽  
Least Mean Square ◽  
Model Order ◽  
Different Types ◽  
Kernel Least Mean Square ◽  
Nonlinear Adaptive Filtering

The kernel least-mean-square (KLMS) algorithm is a popular algorithmin nonlinear adaptive filtering due to its simplicity androbustness. In kernel adaptive filtering, the statistics of the inputto the linear filter depends on the kernel and its parameters. Moreover,practical implementations on systems estimation require afinite non-linearity model order. In order to obtain finite ordermodels, many kernelized adaptive filters use a dictionary of kernelfunctions. Dictionary size also depends on the kernel and itsparameters. Therefore, KLMS may have different performanceson the estimation of a nonlinear system, the time of convergence,and the accuracy using a different kernel. In order to analyze theperformance of KLMS with different kernels, this paper proposesthe use of the Monte Carlo simulation of both steady-state and thetransient behavior of the KLMS algorithm using different types ofkernel functions and Gaussian inputs.

Robust particle filter for state estimation using measurements with different types of gross errors

2017 ◽  
Vol 69 ◽  
pp. 281-295 ◽  
Author(s):  
Zhiliang Zhu ◽  
Zhiqiang Meng ◽  
Zhengjiang Zhang ◽  
Junghui Chen ◽  
Yuxing Dai
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Different Types

Characteristics of Physicians Referred for a Competence Assessment: A Comparison of State Medical Board and Hospital Referred Physicians

2010 ◽  
Vol 96 (3) ◽  
pp. 8-15 ◽  
Author(s):  
Elizabeth S. Grace ◽  
Elizabeth J. Korinek ◽  
Zung V. Tran
Keyword(s):  
Clinical Competence ◽  
Competence Assessment ◽  
Average Performance ◽  
Medical Board ◽  
Key Characteristics ◽  
Different Types ◽  
Specialty Board ◽  
And Performance ◽  
State Medical Boards ◽  
State Medical Board

ABSTRACT This study compares key characteristics and performance of physicians referred to a clinical competence assessment and education program by state medical boards (boards) and hospitals. Physicians referred by boards (400) and by hospitals (102) completed a CPEP clinical competence assessment between July 2002 and June 2010. Key characteristics, self-reported specialty, and average performance rating for each group are reported and compared. Results show that, compared with hospital-referred physicians, board-referred physicians were more likely to be male (75.5% versus 88.3%), older (average age 54.1 versus 50.3 years), and less likely to be currently specialty board certified (80.4% versus 61.8%). On a scale of 1 (best) to 4 (worst), average performance was 2.62 for board referrals and 2.36 for hospital referrals. There were no significant differences between board and hospital referrals in the percentage of physicians who graduated from U.S. and Canadian medical schools. The most common specialties referred differed for boards and hospitals. Conclusion: Characteristics of physicians referred to a clinical competence program by boards and hospitals differ in important respects. The authors consider the potential reasons for these differences and whether boards and hospitals are dealing with different subsets of physicians with different types of performance problems. Further study is warranted.

An Enhanced Two Stage MMSE Equalizer for Coded FBMC/OQAM Systems

2019 ◽  
Vol 10 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Mohammad Rizk Assaf ◽  
Abdel-Nasser Assimi
Keyword(s):  
Error Propagation ◽  
Channel Model ◽  
Error Correcting Codes ◽  
Two Stage ◽  
Probability Of Error ◽  
Second Stage ◽  
Different Types ◽  
Mmse Equalizer ◽  
And Performance ◽  
Ici Cancellation

In this article, the authors investigate the enhanced two stage MMSE (TS-MMSE) equalizer in bit-interleaved coded FBMC/OQAM system which gives a tradeoff between complexity and performance, since error correcting codes limits error propagation, so this allows the equalizer to remove not only ICI but also ISI in the second stage. The proposed equalizer has shown less design complexity compared to the other MMSE equalizers. The obtained results show that the probability of error is improved where SNR gain reaches 2 dB measured at BER compared with ICI cancellation for different types of modulation schemes and ITU Vehicular B channel model. Some simulation results are provided to illustrate the effectiveness of the proposed equalizer.

Alliance experience and performance outcomes: A meta-analysis

2021 ◽  
pp. 147612702098287
Author(s):  
Peng Wang ◽  
Xu Jiang ◽  
Maggie Chuoyan Dong
Keyword(s):  
Strategic Alliance ◽  
Meta Analysis ◽  
Group Analysis ◽  
Performance Outcomes ◽  
Country Level ◽  
Different Types ◽  
Performance Consequences ◽  
Controversial Topic ◽  
Alliance Experience ◽  
And Performance

Alliance experience has been a frequent topic in strategic alliance research in recent decades. Nonetheless, its performance consequences, either as a whole or differentiated into general versus partner-specific alliance experience, are neither theoretically clear nor empirically consistent. We use a range of meta-analytic techniques to integrate the empirical findings of 143 studies and provide a more conclusive assessment compared to prior research. Our study thus addresses a long-standing, understudied, and controversial topic: the distinction between the two types of alliance experiences. Going beyond traditional sub-group analysis, we reveal the contextual contingencies by examining how different types of alliance experiences and performance outcomes jointly affect the alliance experience–performance relationship. Moreover, we identify critical country-level institutional contingencies that moderate the focal effect.

scholarly journals The Ways of Knowing the Pandemic With the Help of Prompted Autoethnography

2020 ◽  
pp. 107780042096013
Author(s):  
Pille Pruulmann-Vengerfeldt
Keyword(s):  
Personal Experience ◽  
Self Care ◽  
Ways Of Knowing ◽  
Qualitative Inquiry ◽  
Global Pandemic ◽  
Different Types ◽  
And Performance

This article discusses how different forms of autoethnographic production prompted by diverse forms of academic self-expression can lead to different types of knowing. Utilizing five examples from the Massive_Microscopic project, where participants responded to 21 different prompts inviting autoethnographic reflections about COVID-19 global pandemic, the article explores the responses from the perspective of alternative ways of knowing, reflecting on questions of motherhood, self-care, and performance in academia. Whether visual, rhythmic, or text produced from the perspective of things, the different modalities of the prompts allowed unexpected knowledge to emerge and supported deeper and more colorful reflections. Exploring the personal experience with the pandemic is expanded by the qualitative inquiry supported by different (self-)expression formats.

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