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Magnetic induction tomography modeling in biological tissue imaging using two-port network technique
(International Frequency Sensor Association (IFSA), 2013)
Magnetic Induction Tomography (MIT) is a non-invasive and non-intrusive imaging technique which interested in passive electrical properties of a material that are permittivity, permeability and conductivity. MIT applies ...
A comparative study of wavelet families for classification of wrist motions
(Elsevier Ltd., 2012-11)
The selection of most suitable mother wavelet function is still an open research problem in various signal and image processing applications. This paper presents a comparative study of different wavelet families (Daubechies, ...
EEG based detection of conductive and sensorineural hearing loss using artificial neural networks
(Advanced Institute of Convergence IT, 2013-05)
In this paper, a simple method has been proposed to distinguish the normal and abnormal hearing subjects (conductive or sensorineural hearing loss) using acoustically stimulated EEG signals. Auditory Evoked Potential (AEP) ...
Structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks
(Elsevier Ltd., 2013)
This paper discusses about the detection of damages present in the steel plates using nondestructive vibration testing. A simple experimental model has been developed to hold the steel plate complying with the simply ...
Impact response of thin-walled tubes: A prospective review
(Trans Tech Publications, 2012-04)
Thin-walled structures have been widely used in various structural applications asimpact energy absorbing devices. During an impact situation, thin-walled tubesdemonstrate excellent capability in absorbing greater energy ...
Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks
(Elsevier Ltd, 2012-08)
A cry is the first verbal communication of infants and it is described as a loud, high-pitched sound made by infants in response to certain situations. Infant cry signals can be used to identify physical or psychological ...
Analysis of infant cry through weighted linear prediction cepstral coefficients and probabilistic neural network
(Springer Science+Business Media, LLC., 2012)
Acoustic analysis of infant cry signals has been proven to be an excellent tool in the
area of automatic detection of pathological status of an infant. This paper investigates
the application of parameter weighting for ...
Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network
(Elsevier Ireland Ltd., 2012-11)
Crying is the most noticeable behavior of infancy. Infant cry signals can be used to identify physical or psychological status of an infant. Recently, acoustic analysis of infant cry signal has shown promising results and ...
Classification of speech dysfluencies with MFCC and LPCC features
(Elsevier Ltd., 2012-02)
The goal of this paper is to discuss comparison of speech parameterization methods: Mel-Frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstrum Coefficients (LPCC) for recognizing the stuttered events. Speech ...
Asynchronous brain machine interface-based control of a wheelchair
(Springerlink, 2011)
A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is ...