scholarly journals Odor mixture perception: can molecular complexity be a factor determining elemental or configural perception?

2020 ◽  
Author(s):  
Masayuki Hamakawa ◽  
Hiroya Ishikawa ◽  
Yumika Kikuchi ◽  
Kaori Tamura ◽  
Tsuyoshi Okamoto
Keyword(s):  
Principal Component Analysis ◽  
Structural Complexity ◽  
Principal Component ◽  
Low Complexity ◽  
Olfactory Perception ◽  
High Complexity ◽  
Odor Mixture ◽  
Individual Odor ◽  
Odor Mixtures ◽  
Molecular Complexity

AbstractOdor mixtures can evoke smells that differ from those of their individual odor components. Research has revealed the existence of two perceptual modes, in which a mixture can be perceived as either the original smells of its individual components (elemental) or as a novel smell (configural). However, the factors underlying the perceptual transformation that occurs when smelling a mixture versus its original components remain unclear. Therefore, the present study aimed to identify the properties of odorants that affect olfactory perception of odor mixtures, focusing on the structural complexity of an odorant. We conducted psychophysical experiments in which different groups of participants were instructed to provide olfactory perceptual descriptions of low-, medium-, and high-complexity odor mixtures or components, respectively. To investigate the perceptual modes induced by the mixtures, we compared the participants’ evaluations between mixtures and components via two types of analyses. First, we compared each olfactory description following quantification via principal component analysis. We then compared data based on seven major olfactory perceptual groups. We observed that odor mixtures composed of low-complexity odorants were perceived as relatively novel smells with regard to both minor (olfactory descriptions) and major (perceptual community) odor qualities than medium- and high-complexity mixtures. Such information may further our understanding of the olfactory perceptual modes of odor mixtures.

Comparisons of Environmental Ordinations with Principal Component Vegetational Ordinations for Sets of Data Having Different Degrees of Complexity

1971 ◽  
Vol 1 (2) ◽  
pp. 99-112 ◽  
Author(s):  
J. K. Jeglum ◽  
C. F. Wehrhahn ◽  
J. M. A. Swan
Keyword(s):  
Principal Component Analysis ◽  
Water Level ◽  
Principal Component ◽  
Low Complexity ◽  
Component Analysis ◽  
Two Dimensional ◽  
High Complexity ◽  
Water Conductivity ◽  
Data Set ◽  
Total Data

Data from a survey of lowland, mainly peatland, vegetation were subjected to environmental ordination based on measurements of water level and water conductivity, and to vegetational ordination derived from principal component analysis (P.C.A.). Analyzed were the total set of the data ("all types"), half sets ("nonwoody" and "woody types") and quarter sets (stands of "marshes", "meadows", "shrub fens", and "other woody types"); the number of distinct physiognomic groups in a set of data, and presumably the amount of contained heterogeneity, decreased at each segmentation.The effectiveness of the ordination models was tested by correlating measured distances in two-dimensional ordination models with 2W/(A + B) indices of vegetational similarity for randomly selected pairs of types or stands. As the physiognomic complexity decreased, the effectiveness of the P.C.A. vegetational ordination increased whereas that of the environmental ordination decreased. The environmental ordination seemed most appropriate to the data encompassing high complexity (total data set), while the P.C.A. vegetational ordination seemed most appropriate to data with low complexity (quarter sets of the data).

scholarly journals PCA-Aided Linear Precoding in Massive MIMO Systems with Imperfect CSI

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Van-Khoi Dinh ◽  
Minh-Tuan Le ◽  
Vu-Duc Ngo ◽  
Chi-Hieu Ta
Keyword(s):  
Principal Component Analysis ◽  
Computational Complexity ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Principal Component ◽  
Low Complexity ◽  
Component Analysis ◽  
Linear Precoding ◽  
Block Diagonalization

In this paper, a low-complexity linear precoding algorithm based on the principal component analysis technique in combination with the conventional linear precoders, called Principal Component Analysis Linear Precoder (PCA-LP), is proposed for massive MIMO systems. The proposed precoder consists of two components: the first one minimizes the interferences among neighboring users and the second one improves the system performance by utilizing the Principal Component Analysis (PCA) technique. Numerical and simulation results show that the proposed precoder has remarkably lower computational complexity than its low-complexity lattice reduction-aided regularized block diagonalization using zero forcing precoding (LC-RBD-LR-ZF) and lower computational complexity than the PCA-aided Minimum Mean Square Error combination with Block Diagonalization (PCA-MMSE-BD) counterparts while its bit error rate (BER) performance is comparable to those of the LC-RBD-LR-ZF and PCA-MMSE-BD ones.

scholarly journals Examining conifer canopy structural complexity across forest ages and elevations with LiDAR data

2010 ◽  
Vol 40 (4) ◽  
pp. 774-787 ◽  
Author(s):  
Van R. Kane ◽  
Jonathan D. Bakker ◽  
Robert J. McGaughey ◽  
James A. Lutz ◽  
Rolf F. Gersonde ◽  
...  
Keyword(s):  
Pacific Northwest ◽  
Canopy Structure ◽  
Structural Complexity ◽  
Low Complexity ◽  
Primary Forest ◽  
Complexity Classes ◽  
High Complexity ◽  
Development Models ◽  
Management Actions ◽  
The Pacific

LiDAR measurements of canopy structure can be used to classify forest stands into structural stages to study spatial patterns of canopy structure, identify habitat, or plan management actions. A key assumption in this process is that differences in canopy structure based on forest age and elevation are consistent with predictions from models of stand development. Three LiDAR metrics (95th percentile height, rumple, and canopy density) were computed for 59 secondary and 35 primary forest plots in the Pacific Northwest, USA. Hierarchical clustering identified two precanopy closure classes, two low-complexity postcanopy closure classes, and four high-complexity postcanopy closure classes. Forest development models suggest that secondary plots should be characterized by low-complexity classes and primary plots characterized by high-complexity classes. While the most and least complex classes largely confirmed this relationship, intermediate-complexity classes were unexpectedly composed of both secondary and primary forest types. Complexity classes were not associated with elevation, except that primary Tsuga mertensiana (Bong.) Carrière (mountain hemlock) plots were complex. These results suggest that canopy structure does not develop in a linear fashion and emphasize the importance of measuring structural conditions rather than relying on development models to estimate structural complexity across forested landscapes.

scholarly journals Low-complexity carrier phase recovery based on principal component analysis for square-QAM modulation formats

2019 ◽  
Vol 27 (11) ◽  
pp. 15617 ◽  
Author(s):  
Júlio César Medeiros Diniz ◽  
Qirui Fan ◽  
Stenio Magalhães Ranzini ◽  
Faisal Nadeem Khan ◽  
Francesco Da Ros ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Carrier Phase ◽  
Principal Component ◽  
Low Complexity ◽  
Component Analysis ◽  
Modulation Formats ◽  
Phase Recovery ◽  
Qam Modulation

Scanning x-ray fluorescence microscopy and principal component analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1752-1753
Author(s):  
Brian Cross
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Microscopy ◽  
Spatial Resolution ◽  
High Speed ◽  
Image Acquisition ◽  
Principal Component ◽  
Fluorescence Microscope ◽  
Acquisition Rate ◽  
X Ray ◽  
Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

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