A temporal-frequency variant on robust-principle component analysis for segmentation of motile cilia in optical coherence tomography images (Conference Presentation)

2018 ◽  
Author(s):  
James P. McLean ◽  
Yuye Ling ◽  
Christine Hendon
Keyword(s):  
Optical Coherence Tomography ◽  
Principle Component Analysis ◽  
Temporal Frequency ◽  
Component Analysis ◽  
Optical Coherence ◽  
Principle Component ◽  
Motile Cilia

Speckle Noise Reduction of Optical Coherence Tomography Based on Robust Principle Component Analysis Algorithm

2018 ◽  
Vol 38 (5) ◽  
pp. 0511002
Author(s):  
袁治灵 Yuan Zhiling ◽  
陈俊波 Chen Junbo ◽  
黄伟源 Huang Weiyuan ◽  
魏波 Wei Bo ◽  
唐志列 Tang Zhilie
Keyword(s):  
Optical Coherence Tomography ◽  
Noise Reduction ◽  
Principle Component Analysis ◽  
Speckle Noise ◽  
Component Analysis ◽  
Optical Coherence ◽  
Analysis Algorithm ◽  
Principle Component ◽  
Speckle Noise Reduction

ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

2019 ◽  
Vol 12 ◽  
Author(s):  
Basavaraj N Hiremath ◽  
Malini M Patilb
Keyword(s):  
Feature Extraction ◽  
Computer Program ◽  
Principle Component Analysis ◽  
Voice Recognition ◽  
Component Analysis ◽  
Recognition System ◽  
Principle Component ◽  
Whole Process ◽  
The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

Four challenges for cognitive neuroscience and the cortico-hippocampal division of memory

2003 ◽  
Vol 26 (6) ◽  
pp. 681-682
Author(s):  
Harry Howard
Keyword(s):  
Independent Component Analysis ◽  
Recent Work ◽  
Cognitive Neuroscience ◽  
Principle Component Analysis ◽  
Pattern Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Principle Component ◽  
Complementary Methods ◽  
Current State

Jackendoff's criticisms of the current state of theorization in cognitive neuroscience are defused by recent work on the computational complementarity of the hippocampus and neocortex. Such considerations lead to a grounding of Jackendoff's processing model in the complementary methods of pattern analysis effected by independent component analysis (ICA) and principle component analysis (PCA).

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