Extracting nonlinear features for multispectral images by FCMC and KPCA

2005 ◽  
Vol 15 (4) ◽  
pp. 331-346 ◽  
Author(s):  
Zhan-Li Sun ◽  
De-Shuang Huang ◽  
Yiu-Ming Cheun
Keyword(s):  
Multispectral Images ◽  
Nonlinear Features

scholarly journals A novel automated autism spectrum disorder detection system

2021 ◽  
Author(s):  
Shu Lih Oh ◽  
V. Jahmunah ◽  
N. Arunkumar ◽  
Enas W. Abdulhay ◽  
Raj Gururajan ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Developmental Disorder ◽  
Detection System ◽  
Brain Activity ◽  
Limited Range ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Nonlinear Features ◽  
Genetic And Environmental Factors ◽  
Future Work

AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

scholarly journals Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1994
Author(s):  
Qian Ma ◽  
Wenting Han ◽  
Shenjin Huang ◽  
Shide Dong ◽  
Guang Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Confusion Matrix ◽  
Object Oriented ◽  
Low Complexity ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Multispectral Images ◽  
High Complexity ◽  
Structure Complexity

This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

Nonlinear Features of Fano Resonance: A QM/EM Study

2021 ◽  
Author(s):  
Jin Sun ◽  
Zongling Ding ◽  
Yuanqin Yu ◽  
WanZhen Liang
Keyword(s):  
Hybrid Systems ◽  
Fano Resonance ◽  
Hybrid Approach ◽  
Nonlinear Features ◽  
Fano Effects ◽  
Dye Molecule

The nonlinear Fano effects on the absorption of hybrid systems composed of a silver nanosphere and an indoline dye molecule have been systematically investigated by the hybrid approach, which combines...

scholarly journals Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1333
Author(s):  
Giuseppe Francesco Cesare Lama ◽  
Mariano Crimaldi ◽  
Vittorio Pasquino ◽  
Roberta Padulano ◽  
Giovanni Battista Chirico
Keyword(s):  
Riparian Vegetation ◽  
Vegetation Index ◽  
Arundo Donax ◽  
Water Bodies ◽  
Giant Reed ◽  
Multispectral Images ◽  
Drainage Channel ◽  
Area Index ◽  
Riparian Plants ◽  
The Impact

Estimating the main hydrodynamic features of real vegetated water bodies is crucial to assure a balance between their hydraulic conveyance and environmental quality. Riparian vegetation stands have a high impact on vegetated channels. The present work has the aim to integrate riparian vegetation’s reflectance indices and hydrodynamics of real vegetated water flows to assess the impact of riparian vegetation morphometry on bulk drag coefficients distribution along an abandoned vegetated drainage channel fully covered by 9–10 m high Arundo donax (commonly known as giant reed) stands, starting from flow average velocities measurements at 30 cross-sections identified along the channel. A map of riparian vegetation cover was obtained through digital processing of Unnamed Aerial Vehicle (UAV)-acquired multispectral images, which represent a fast way to observe riparian plants’ traits in hardly accessible areas such as vegetated water bodies in natural conditions. In this study, the portion of riparian plants effectively interacting with flow was expressed in terms of ground-based Leaf Area Index measurements (LAI), which easily related to UAV-based Normalized Difference Vegetation Index (NDVI). The comparative analysis between Arundo donax stands NDVI and LAI map enabled the analysis of the impact of UAV-acquired multispectral imagery on bulk drag predictions along the vegetated drainage channel.

scholarly journals Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model

2021 ◽  
Vol 11 (2) ◽  
pp. 787
Author(s):  
Bartłomiej Ambrożkiewicz ◽  
Grzegorz Litak ◽  
Anthimos Georgiadis ◽  
Nicolas Meier ◽  
Alexander Gassner
Keyword(s):  
Mathematical Model ◽  
Degrees Of Freedom ◽  
Ball Bearing ◽  
Hertzian Contact ◽  
Two Degrees Of Freedom ◽  
Wide Range ◽  
Shape Errors ◽  
Nonlinear Features ◽  
Fourier Transform Phase ◽  
Hertzian Contact Theory

Often the input values used in mathematical models for rolling bearings are in a wide range, i.e., very small values of deformation and damping are confronted with big values of stiffness in the governing equations, which leads to miscalculations. This paper presents a two degrees of freedom (2-DOF) dimensionless mathematical model for ball bearings describing a procedure, which helps to scale the problem and reveal the relationships between dimensionless terms and their influence on the system’s response. The derived mathematical model considers nonlinear features as stiffness, damping, and radial internal clearance referring to the Hertzian contact theory. Further, important features are also taken into account including an external load, the eccentricity of the shaft-bearing system, and shape errors on the raceway investigating variable dynamics of the ball bearing. Analysis of obtained responses with Fast Fourier Transform, phase plots, orbit plots, and recurrences provide a rich source of information about the dynamics of the system and it helped to find the transition between the periodic and chaotic response and how it affects the topology of RPs and recurrence quantificators.

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