Segmentation via thresholding methodologies by using measure of fuzziness towards blind navigation

Blind navigation is specialized research directed towards the development of navigational aid for blind people to minimize assistance from sighted individuals during navigation. In this paper, two methodologies of segmentation are detailed and certain aspects of the methodologies are compared. Measure of fuzziness is applied in both the segmentation methodologies to find the threshold values. These methodologies are of an automated process resulting in the elimination of human circumvention. The segmentation methodologies have been found to work suitably for the purpose of blind navigation as shown by the results provided. The first methodology was developed for a single camera whereas the second was developed for a system of stereo cameras. A comparison in terms of results from both the methodologies is also discussed and finally, conclusions derived from the methodologies are presented

Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals

Recently, researchers have paid escalating attention to studying the emotional state of an individual from his/her speech signals as the speech signal is the fastest and the most natural method of communication between individuals. In this work, new feature enhancement using Gaussian mixture model (GMM) was proposed to enhance the discriminatory power of the features extracted from speech and glottal signals. Three different emotional speech databases were utilized to gauge the proposed methods. Extreme learning machine (ELM) and k-nearest neighbor (kNN) classifier were employed to classify the different types of emotions. Several experiments were conducted and results show that the proposed methods significantly improved the speech emotion recognition performance compared to research works published in the literature

Decision making using modified s-curve membership function in fuzzy linear programming problem

In order to develop approaches to solve a fuzzy linear programming problem, it is necessary to study first the formulation of membership functions and then the methodology for applying the solution to real life problems. A S-curve membership function is proposed in this paper. It is important to note that the S-curve membership function has to be flexible to describe the fuzziness in the problem. Fuzziness may occur in several levels of an industrial production management such as manpower requirements, resource availability such as software and the demand to be met. In order to show that the S-curve membership function works well for fuzzy problems, a numerical example is demonstrated. A thorough study on how the non linear membership function used in dealing with fuzzy parameters and fuzzy constraints is also presented. Only one case where all three coefficients (such as objective coefficients, technical coefficients and resource variables) that normally occur in production planning problem, are considered and fuzzified. However, there are several other cases. The result obtained from this paper is to provide confidence in using the proposed S-curve membership function in a real life production planning industrial problem

Elastic Characterization of Glass by Modal Analysis

Modal analysis is the study of dynamic characteristic of structures induced by vibrational excitation. Under modal excitation, three important parameters namely natural frequency, damping ratio and mode shape associated with the structural properties are acquired. This paper presents an experimental investigation of glass by experimental modal analysis. The specimen is excited by an impact hammer to perform resonant vibration where the characteristics of the resonance are acquired. One most important characteristic is the natural frequency where it is known that different material having undergone resonant vibration exhibit different specific natural frequencies to it. The natural frequencies are used as the parameters of determining the structural properties of the glass. The modal analysis is done using the LMS instruments and software where Frequency Response Function (FRF) measurement technique is employed in determining the natural frequencies. The structural properties are established based on the obtained natural frequencies and geometries of the materials using the expression from available literature. The results are then compared with the theoretical values for verification

Dual-Tree Complex Wavelet Packet Transform and Feature Selection Techniques for Infant Cry Classification

A Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) feature extraction has been used in infant cry signal classification to extract the feature. Total of 124 energy features and 124 Shannon entropy features were extracted from each sub-band after five level decomposition by DT-CWPT. Feature selection techniques used to deal with massive information obtained from DT-CWPT extraction. The feature selection techniques reduced the number of features by select and form feature subset for classification phase. ELM classifier with 10-fold cross-validation scheme was used to classify the infant cry signal. Three experiments were conducted with different feature sets for three binary classification problems (Asphyxia versus Normal, Deaf versus Normal, and Hunger versus Pain). The results reported that features selection techniques reduced the number of features and achieved high accuracy

Improved speaker-independent emotion recognition from speech using two-stage feature reduction

In the recent years, researchers are focusing to improve the accuracy of speech emotion recognition. Generally, high emotion recognition accuracies were obtained for two-class emotion recognition, but multi-class emotion recognition is still a challenging task.The main aim of this work is to propose a two-stage feature reduction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for improving the accuracy of the speech emotion recognition (ER) system. Short-term speech features were extracted from the emotional speech signals. Experiments were carried out using four different supervised classifi ers with two different emotional speech databases. From the experimental results, it can be inferred that the proposed method provides better accuracies of 87.48% for speaker dependent (SD) and gender dependent (GD) ER experiment, 85.15% for speaker independent (SI) ER experiment, and 87.09% for gender independent (GI) experiment

Development of Microcontroller-Based Inverter Control Circuit for Residential Wind Generator Application

The current usage level of wind power as alternative source of energy in Malaysia is very low. Ironically, some areas particularly coastal area has steady wind energy supply that is potential to generate electricity for residential use. There is urgent need to locally develop the low cost wind turbine generator that has the capability to not only supply electricity to respective household but can be connected to power grid so that excess power could be sold back to the local utility company. Recent developments of power electronic converters allow stable supply needed for grid transfer in respect to nature of wind dynamics, enhanced power extraction and low total harmonic distortion (THD). In this project, an inverter circuit with suitable control scheme design is developed to be used with a 500W permanent magnet type wind generator which is typical for residential use. Expected circuit output is single phase 240V sine wave voltage which is nominal grid voltage with the total harmonics distortion (THD) of voltage across load should not exceed 5% as recommended by IEEE Standard 519-1992. The simulation and experimental results are included in the paper

Modeling Of Lower Extremity For Joint Torques Determination By Performing A Lifting Task

Physical lifting tasks commonly involve two types of body postures,namely, squat lifting and stoop lifting. Studies shows improper bodyposture during lifting task has detrimental effect to human lower-backregion over extended period of time. This is because generally, stoop-liftingposture exerts relatively higher moments and compression forces on humanback than squat lifting posture. However, this claim was never thoroughlyexamined and validated from mathematical model approach. This paperproposes a mathematical model to represent the lower extremity of humanbody during lifting tasks, based on a two-link kinematic open chain in twodimensional spaces. Thus, all moment of torque and their effect to everypart of lower extremity of human body can be thoroughly analyzed