Skull Sex Estimation Based on Wavelet Transform and Fourier Transform

Skull sex estimation is one of the hot research topics in forensic anthropology, and has important research value in the fields of criminal investigation, archeology, anthropology, and so on. Sex estimation of skull is crucial in forensic investigations, whether in legal situations that involve living people or to identify mortal remains. The aim of this study is to establish a skull-based sex estimation model in Chinese population, providing a scientific reference for the practical application of forensic medicine and anthropology. We take the superior orbital margin and frontal bone of the skull as the research object and proposed a technology of objective sex estimation of the skull using wavelet transform and Fourier transform. Firstly, the supraorbital margin and frontal bone were quantified by wavelet transform and Fourier transform, and then the extracted features were classified by SVM, and the model was tested. The experimental results show that the accuracy rate of male and female sex discrimination is 90.9% and 94.4%, respectively, which is higher than that of morphological and measurement methods. Compared with the traditional methods, the method has more theoretical basis and objectivity, and the correct rate is higher.

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Wavelet Analysis for Gas Turbine Fault Diagnostics

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.2817067 ◽

1997 ◽

Vol 119 (4) ◽

pp. 870-876 ◽

Cited By ~ 19

Author(s):

N. Aretakis ◽

K. Mathioudakis

Keyword(s):

Fourier Transform ◽

Wavelet Transform ◽

Wavelet Analysis ◽

Gas Turbines ◽

Diagnostic Techniques ◽

Diagnostic Information ◽

Practical Application ◽

Acoustic Measurements ◽

Time Signals ◽

Existing Data

The application of wavelet analysis to diagnosing faults in gas turbines is examined in the present paper. Applying the wavelet transform to time signals obtained from sensors placed on an engine gives information in correspondence to their Fourier transform. Diagnostic techniques based on Fourier analysis of signals can therefore be transposed to the wavelet analysis. In the paper the basic properties of wavelets, in relation to the nature of turbomachinery signals, are discussed. The possibilities for extracting diagnostic information by means of wavelets are examined, by studying the applicability to existing data from vibration, unsteady pressure, and acoustic measurements. Advantages offered, with respect to existing methods based on harmonic analysis, are discussed as well as particular requirements related to practical application.

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Wavelet Analysis for Gas Turbine Fault Diagnostics

Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; General ◽

10.1115/96-gt-343 ◽

1996 ◽

Cited By ~ 1

Author(s):

N. Aretakis ◽

K. Mathioudakis

Keyword(s):

Fourier Transform ◽

Wavelet Transform ◽

Wavelet Analysis ◽

Gas Turbines ◽

Diagnostic Techniques ◽

Diagnostic Information ◽

Practical Application ◽

Acoustic Measurements ◽

Time Signals ◽

Existing Data

The application of wavelet analysis to diagnosing faults in Gas Turbines is examined in the present paper. Applying the Wavelet Transform to time signals obtained from sensors placed on an engine, gives information which is in correspondence to their Fourier Transform. Diagnostic techniques based on Fourier analysis of signals can therefore be transposed to the Wavelet analysis. In the paper the basic properties of wavelets, in relation to the nature of turbomachinery signals, are discussed. The possibilities for extracting diagnostic information by means of wavelets are examined, by studying the applicability to existing data from vibration, unsteady pressure and acoustic measurements. Advantages offered, with respect to existing methods based on harmonic analysis, are discussed as well as particular requirements related to practical application.

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Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Animals ◽

10.3390/ani11051263 ◽

2021 ◽

Vol 11 (5) ◽

pp. 1263

Author(s):

Zhaojun Wang ◽

Jiangning Wang ◽

Congtian Lin ◽

Yan Han ◽

Zhaosheng Wang ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Digital Technology ◽

Rapid Development ◽

Image Data ◽

Image Database ◽

Test Accuracy ◽

Practical Application ◽

Accuracy Rate ◽

Deep Convolutional Neural Networks

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

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Identification of Static Unbalance Wheel of Passenger Car Carried out on a Road

Solid State Phenomena ◽

10.4028/www.scientific.net/ssp.214.48 ◽

2014 ◽

Vol 214 ◽

pp. 48-57 ◽

Cited By ~ 3

Author(s):

Krzysztof Prażnowski ◽

Sebastian Brol ◽

Andrzej Augustynowicz

Keyword(s):

Fourier Transform ◽

Wavelet Transform ◽

Constant Velocity ◽

Fourier Transforms ◽

Static Condition ◽

Rotational Frequency ◽

Acceleration Sensor ◽

Short Time Fourier Transform ◽

Road Roughness ◽

Short Time

This paper presents a method of identification of non-homogeneity or static unbalance of the structure of a car wheel based on a simple road test. In particular a method the detection of single wheel unbalance is proposed which applies an acceleration sensor fixed on windscreen. It measures accelerations cause by wheel unbalance among other parameters. The location of the sensor is convenient for handling an autonomous device used for diagnostic purposes. Unfortunately, its mounting point is located away from wheels. Moreover, the unbalance forces created by wheels spin are dumped by suspension elements as well as the chassis itself. It indicates that unbalance acceleration will be weak in comparison to other signals coming from engine vibrations, road roughness and environmental effects. Therefore, the static unbalance detection in the standard way is considered problematic and difficult. The goal of the undertaken research is to select appropriate transformations and procedures in order to determine wheel unbalance in these conditions. In this investigation regular and short time Fourier transform were used as well as wavelet transform. It was found that the use of Fourier transforms is appropriate for static condition (constant velocity) but the results proves that the wavelet transform is more suitable for diagnostic purposes because of its ability of producing clearer output even if car is in the state of acceleration or deceleration. Moreover it was proved that in the acceleration spectrum of acceleration measured on the windscreen a significant peak can be found when car runs with an unbalanced wheel. Moreover its frequency depends on wheel rotational frequency. For that reason the diagnostic of single wheel unbalance can be made by applying this method.

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Wavelet transform to quantify heart rate variability and to assess its instantaneous changes

Journal of Applied Physiology ◽

10.1152/jappl.1999.86.3.1081 ◽

1999 ◽

Vol 86 (3) ◽

pp. 1081-1091 ◽

Cited By ~ 145

Author(s):

Vincent Pichot ◽

Jean-Michel Gaspoz ◽

Serge Molliex ◽

Anestis Antoniadis ◽

Thierry Busso ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Nervous System ◽

Fourier Transform ◽

Wavelet Transform ◽

Wavelet Transforms ◽

Autonomous Nervous System ◽

Dynamic Changes ◽

Nonstationary Signals ◽

Higher Doses

Heart rate variability is a recognized parameter for assessing autonomous nervous system activity. Fourier transform, the most commonly used method to analyze variability, does not offer an easy assessment of its dynamics because of limitations inherent in its stationary hypothesis. Conversely, wavelet transform allows analysis of nonstationary signals. We compared the respective yields of Fourier and wavelet transforms in analyzing heart rate variability during dynamic changes in autonomous nervous system balance induced by atropine and propranolol. Fourier and wavelet transforms were applied to sequences of heart rate intervals in six subjects receiving increasing doses of atropine and propranolol. At the lowest doses of atropine administered, heart rate variability increased, followed by a progressive decrease with higher doses. With the first dose of propranolol, there was a significant increase in heart rate variability, which progressively disappeared after the last dose. Wavelet transform gave significantly better quantitative analysis of heart rate variability than did Fourier transform during autonomous nervous system adaptations induced by both agents and provided novel temporally localized information.

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