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Full-Text Articles in Engineering
Detection Of Parkinson Disease Rest Tremor, Matthew J. Johnson
Detection Of Parkinson Disease Rest Tremor, Matthew J. Johnson
McKelvey School of Engineering Theses & Dissertations
Parkinson Disease (PD) is a debilitating and progressive movement disorder that is estimated to affect over six million worldwide. One of the most characteristic symptoms of PD is resting tremor, which involves unintentional and rhythmic muscle oscillations of an afflicted extremity while the muscles of said extremity are relaxed. This study involved measuring the rest tremor of 10 PD subjects, 10 Essential Tremor subjects, and 10 healthy control subjects using two devices. One device was an FDA approved accelerometry system to measure human tremor known as the TremorometerTM and the other was a consumer three-dimensional camera known as the …
Design And Implementation Of A Byzantine Fault Tolerance Framework For Non-Deterministic Applications, H. Zhang, Wenbing Zhao, Louise E. Moser, P. Michael Melliar-Smith
Design And Implementation Of A Byzantine Fault Tolerance Framework For Non-Deterministic Applications, H. Zhang, Wenbing Zhao, Louise E. Moser, P. Michael Melliar-Smith
Wenbing Zhao
State-machine-based replication is an effective way to increase the availability and dependability of mission-critical applications. However, all practical applications contain some degree of non-determinism. Consequently, ensuring strong replica consistency in the presence of application non-determinism has been one of the biggest challenges in building dependable distributed systems. In this Study, the authors propose a classification of common types of application non-determinism with respect to the requirement of achieving Byzantine fault tolerance (BFT), and present the design and implementation of a BFT framework that controls these types of non-determinism in a systematic manner.
A Ga-Svm Hybrid Classifier For Multiclass Fault Identification Of Drivetrain Gearboxes, Dingguo Lu, Wei Qiao
A Ga-Svm Hybrid Classifier For Multiclass Fault Identification Of Drivetrain Gearboxes, Dingguo Lu, Wei Qiao
Department of Electrical and Computer Engineering: Faculty Publications
This paper presents a genetic algorithm (GA)- support vector machine (SVM) hybrid classifier for multiclass fault identification of drivetrain gearboxes in variable-speed operational conditions. An adaptive feature extraction algorithm is employed to effectively extract the features of gearbox faults from the stator current signal of an AC machine connected to the gearbox. The multiclass GA-SVM classifier is used to identify the faults in the gearbox according to the fault features extracted. A GA is designed to find the optimal parameters of the SVM to obtain the best classification accuracy. The proposed hybrid classifier is validated on a gearbox connected with …
Automated Classification Of Malignant Melanoma Based On Detection Of Atypical Pigment Network In Dermoscopy Images Of Skin Lesions, Nabin K. Mishra
Automated Classification Of Malignant Melanoma Based On Detection Of Atypical Pigment Network In Dermoscopy Images Of Skin Lesions, Nabin K. Mishra
Doctoral Dissertations
“Melanoma causes more deaths than any other form of skin cancer. Early melanoma detection is important to prevent progression to a more deadly stage. Automated computer-based identification of melanoma from dermoscopic images of skin lesions is the most efficient method in early diagnosis. An automated melanoma identification system must include multiple steps, involving lesion segmentation, feature extraction, feature combination and classification. In this research, a classifier-based approach for automatically selecting a lesion border mask for segmentation of dermoscopic skin lesion images is presented. A logistic regression based model selects a single lesion border mask from multiple border masks generated by …
Feature Selection On Single-Lead Ecg For Obstructive Sleep Apnea Diagnosis, Hüseyi̇n Gürüler, Mesut Şahi̇n, Abdullah Feri̇koğlu
Feature Selection On Single-Lead Ecg For Obstructive Sleep Apnea Diagnosis, Hüseyi̇n Gürüler, Mesut Şahi̇n, Abdullah Feri̇koğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Many articles that appeared in the literature agreed upon the feasibility of diagnosing obstructive sleep apnea (OSA) with a single-lead electrocardiogram. Although high accuracies have been achieved in detection of apneic episodes and classification into apnea/hypopnea, there has not been a consensus on the best method of selecting the feature parameters. This study presents a classification scheme for OSA using common features belonging to the time domain, frequency domain, and nonlinear calculations of heart rate variability analysis, and then proposes a method of feature selection based on correlation matrices (CMs). The results show that the CMs can be utilized in …
A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou
A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou
Turkish Journal of Electrical Engineering and Computer Sciences
The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly for classification problems. Due to the size of the network after training, this type of network is usually used for problems with a small-sized training dataset. In this paper, a new training algorithm is presented for use with large training databases. Application to the handwritten digit database shows that the reduced PNN performs better than the standard PNN for all of the studied cases with a big gain in size and processing speed. This new type of neural network can be used easily for …
Comparison Of Different Methods For Determining Diabetes, Mehmet Recep Bozkurt, Ni̇lüfer Yurtay, Zi̇ynet Yilmaz, Cengi̇z Sertkaya
Comparison Of Different Methods For Determining Diabetes, Mehmet Recep Bozkurt, Ni̇lüfer Yurtay, Zi̇ynet Yilmaz, Cengi̇z Sertkaya
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, the Pima Indian Diabetes dataset was categorized with 8 different classifiers. The data were taken from the University of California Irvine Machine Learning Repository's web site. As a classifier, 6 different neural networks [probabilistic neural network (PNN), learning vector quantization, feedforward networks, cascade-forward networks, distributed time delay networks (DTDN), and time delay networks], the artificial immune system, and the Gini algorithm from decision trees were used. The classifier's performance ratios were studied separately as accuracy, sensitivity, and specificity and the success rates of all of the classifiers are presented. Among these 8 classifiers, the best accuracy and …
A Penalty Function Method For Designing Efficient Robust Classifiers With Input Space Optimal Separating Surfaces, Ayşegül Uçar, Yakup Demi̇r, Cüneyt Güzeli̇ş
A Penalty Function Method For Designing Efficient Robust Classifiers With Input Space Optimal Separating Surfaces, Ayşegül Uçar, Yakup Demi̇r, Cüneyt Güzeli̇ş
Turkish Journal of Electrical Engineering and Computer Sciences
This paper considers robust classification as a constrained optimization problem. Where the constraints are nonlinear, inequalities defining separating surfaces, whose half spaces include or exclude the data depending on their classes and the cost, are used for attaining robustness and providing the minimum volume regions specified by the half spaces of the surfaces. The constraints are added to the cost using penalty functions to get an unconstrained problem for which the gradient descent method can be used. The separating surfaces, which are aimed to be found in this way, are optimal in the input data space in contrast to the …
Online Feature Selection And Classification With Incomplete Data, Habi̇l Kalkan
Online Feature Selection And Classification With Incomplete Data, Habi̇l Kalkan
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a classification system in which learning, feature selection, and classification for incomplete data are simultaneously carried out in an online manner. Learning is conducted on a predefined model including the class-dependent mean vectors and correlation coefficients, which are obtained by incrementally processing the incoming observations with missing features. A nearest neighbor with a Gaussian mixture model, whose parameters are also estimated from the trained model, is used for classification. When a testing observation is received, the algorithm discards the missing attributes on the observation and ranks the available features by performing feature selection on the model that …
A Method For Non-Invasive, Automated Behavior Classification In Mice, Using Piezoelectric Pressure Sensors, Steven R. Gooch
A Method For Non-Invasive, Automated Behavior Classification In Mice, Using Piezoelectric Pressure Sensors, Steven R. Gooch
Theses and Dissertations--Electrical and Computer Engineering
While all mammals sleep, the functions and implications of sleep are not well understood, and are a strong area of investigation in the research community. Mice are utilized in many sleep studies, with electroencephalography (EEG) signals widely used for data acquisition and analysis. However, since EEG electrodes must be surgically implanted in the mice, the method is high cost and time intensive. This work presents an extension of a previously researched high throughput, low cost, non-invasive method for mouse behavior detection and classification. A novel hierarchical classifier is presented that classifies behavior states including NREM and REM sleep, as well …
End-To-End Classification Process For The Exploitation Of Vibrometry Data, Ashley Nicole Smith
End-To-End Classification Process For The Exploitation Of Vibrometry Data, Ashley Nicole Smith
Browse all Theses and Dissertations
Laser vibrometry provides a method to identify running vehicles' unique signatures using non-contact measurements. A vehicle's engine, size, materials, shape, and other attributes affect its vibration signature. To develop the capability to classify and identify these signatures, a robust aided target recognition (AiTR) end-to-end process is evaluated and expanded. The main challenge in classifying a vehicle's vibration signatures is presented by the operating conditions and parameters that vary as a function of sensor, environment, and collection locations on the target, among others. Some of the parameters affecting the vibration signatures include weather, terrain, sensor location, sensor type, and engine speed. …