Orientation coordination of multiple robots based on a nonlinear consensus algorithm

2012 ◽  
Author(s):  
Guansheng Xing ◽  
Hexu Sun ◽  
Haiyong Chen ◽  
Ning Liu
Keyword(s):  
Consensus Algorithm ◽  
Multiple Robots

Cognitive analysis methods for control of multiple robots: Robotics on $5 a day

2004 ◽  
Author(s):  
Roger A. Chadwick ◽  
Douglas J. Gillan ◽  
Dominic Simon ◽  
Skye Pazuchanics
Keyword(s):  
Multiple Robots ◽  
Cognitive Analysis ◽  
Analysis Methods

MOLECULAR AND GENETIC SUBTYPES OF OVARIAN CANCER: PROSPECTS FOR FURTHER STUDIES

2019 ◽  
Vol 65 (1) ◽  
pp. 56-62
Author(s):  
Alisa Villert ◽  
Larisa Kolomiets ◽  
Natalya Yunusova ◽  
Yevgeniya Fesik
Keyword(s):  
Ovarian Cancer ◽  
Ovarian Carcinoma ◽  
Molecular Subtypes ◽  
Survival Rates ◽  
Consensus Algorithm ◽  
Histopathological Diagnosis ◽  
Low Grade ◽  
High Grade ◽  
Ovarian Carcinomas ◽  
Genetic Subtypes

High-grade ovarian carcinoma is a histopathological diagnosis, however, at the molecular level, ovarian cancer represents a heterogeneous group of diseases. Studies aimed at identifying molecular genetic subtypes of ovarian cancer are conducted in order to find the answer to the question: can different molecular subgroups influence the choice of treatment? One of the achievements in this trend is the recognition of the dualistic model that categorizes various types of ovarian cancer into two groups designated high-grade (HG) and low-grade (LG) tumors. However, the tumor genome sequencing data suggest the existence of 6 ovarian carcinoma subtypes, including two LG and four HG subtypes. Subtype C1 exhibits a high stromal response and the lowest survival. Subtypes C2 and C4 demonstrate higher number of intratumoral CD3 + cells, lower stromal gene expression and better survival than sybtype C1. Subtype C5 (mesenchymal) is characterized by mesenchymal cells, over-expression of N-cadherin and P-cadherin, low expression of differentiation markers, and lower survival rates than C2 and C4. The use of a consensus algorithm to determine the subtype allows identification of only a minority of ovarian carcinomas (approximately 25%) therefore, the practical importance of this classification requires additional research. There is evidence that it makes sense to randomize tumors into groups with altered expression of angiogenic genes and groups with overexpression of the immune response genes, as in the angiogenic group there is a comparative superiority in terms of survival. The administration of bevacizumab in the angiogenic group improves survival, while the administration of bevacizumab in the immune group even worsens the outcome. Molecular subtypes with worse survival rates (proliferative and mesenchymal) also benefit most from bevacizumab treatment. This review focuses on some of the advances in understanding molecular, cellular, and genetic changes in ovarian carcinomas with the results achieved so far regarding the formulation of molecular subtypes of ovarian cancer, however further studies are needed.

Control and Random Searching with Multiple Robots

2005 ◽  
Author(s):  
A. J. Healey ◽  
Y. Kim
Keyword(s):  
Multiple Robots

Implementation of a Distributed Consensus Algorithm with OMNeT++

2009 ◽  
Vol 19 (1) ◽  
pp. 41-42
Author(s):  
Andreas Dielacher ◽  
Thomas Handl ◽  
Christian Widtmann
Keyword(s):  
Consensus Algorithm ◽  
Distributed Consensus

scholarly journals RANSAC algorithm for instantaneous frequency estimation and reconstruction of frequency-modulated undersampled signals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Igor Djurović
Keyword(s):  
Instantaneous Frequency ◽  
Matching Pursuit ◽  
Frequency Estimation ◽  
Consensus Algorithm ◽  
Multidimensional Space ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency ◽  
Polynomial Phase ◽  
Fm Signals ◽  
Fifth Order

AbstractFrequency modulated (FM) signals sampled below the Nyquist rate or with missing samples (nowadays part of wider compressive sensing (CS) framework) are considered. Recently proposed matching pursuit and greedy techniques are inefficient for signals with several phase parameters since they require a search over multidimensional space. An alternative is proposed here based on the random samples consensus algorithm (RANSAC) applied to the instantaneous frequency (IF) estimates obtained from the time-frequency (TF) representation of recordings (undersampled or signal with missing samples). The O’Shea refinement strategy is employed to refine results. The proposed technique is tested against third- and fifth-order polynomial phase signals (PPS) and also for signals corrupted by noise.

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