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Articles 151 - 156 of 156

Full-Text Articles in Electrical and Computer Engineering

Landmine Detection And Discrimination Using High-Pressure Waterjets, Daryl G. Beetner, R. Joe Stanley, Sanjeev Agarwal, Deepak R. Somasundaram, Kopal Nema, Bhargav Mantha Oct 2004

Landmine Detection And Discrimination Using High-Pressure Waterjets, Daryl G. Beetner, R. Joe Stanley, Sanjeev Agarwal, Deepak R. Somasundaram, Kopal Nema, Bhargav Mantha

Electrical and Computer Engineering Faculty Research & Creative Works

Methods of locating and identifying buried landmines using high-pressure waterjets were investigated. Methods were based on the sound produced when the waterjet strikes a buried object. Three classification techniques were studied, based on temporal, spectral, and a combination of temporal and spectral approaches using weighted density distribution functions, a maximum likelihood approach, and hidden Markov models, respectively. Methods were tested with laboratory data from low-metal content simulants and with field data from inert real landmines. Results show that the sound made when the waterjet hit a buried object could be classified with a 90% detection rate and an 18% false …

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Real-Time Classification Algorithm For Recognition Of Machine Operating Modes By Use Of Self-Organizing Maps, Gancho Vachkov, Yuhiko Kiyota, Koji Komatsu, Satoshi Fujii Jan 2004

Real-Time Classification Algorithm For Recognition Of Machine Operating Modes By Use Of Self-Organizing Maps, Gancho Vachkov, Yuhiko Kiyota, Koji Komatsu, Satoshi Fujii

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper a new algorithm for classification and real-time recognition of different a-priorily assumed operating modes for construction machines is proposed. This algorithm utilizes the effectiveness of the Self-Organizing Maps (SOM) for creating the so called Separation Models, that are able to distinguish each operating mode separately. After training, these models are used in a real-time procedure, which calculates at each sampling time the minimal Euclidean distances from the current data point to a certain node of each SOM. Then the separation model (represented by a respective SOM) that has the least minimal distance to this data point defines …

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Polar Sea Ice Mapping For Seawinds, Hyrum Spencer Anderson May 2003

Polar Sea Ice Mapping For Seawinds, Hyrum Spencer Anderson

Theses and Dissertations

In recent years, the scientific community has expressed interest in the ability to observe global climate indicators such as polar sea ice. Advances in microwave remote sensing technology have allowed a large-scale and detailed study of sea ice characteristics. This thesis provides the analysis and development of sea ice mapping algorithms for the SeaWinds scatterometer. First, an in-depth analysis of the Remund Long (RL) algorithm for SeaWinds is performed. From this study, several improvements are made to the RL algorithm which enhance its performance. In addition, a new method for automated polar sea ice mapping is developed for the SeaWinds …

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Differentiating Type Of Muscle Movement Via Ar Modeling And Neural Network Classification, Beki̇r Karlik Jan 1999

Differentiating Type Of Muscle Movement Via Ar Modeling And Neural Network Classification, Beki̇r Karlik

Turkish Journal of Electrical Engineering and Computer Sciences

The aim of this study is to classify electromyogram (EMG) signals for controlling multifunction proshetic devices. An artificial neural network (ANN) implementation was used for this purpose. Autoregressive (AR) parameters of $a_1, a_2, a_3, a_4$ and their signal power obtained from different arm muscle motions were applied to the input of ANN, which is a multilayer perceptron. At the output layer, for 5000 iterations, six movements were distinguished at a high accuracy of 97.6%.

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Efficient Training Techniques For Classification With Vast Input Space, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian Jan 1999

Efficient Training Techniques For Classification With Vast Input Space, Donald C. Wunsch, Emad W. Saad, J. J. Choi, J. L. Vian

Electrical and Computer Engineering Faculty Research & Creative Works

Strategies to efficiently train a neural network for an aerospace problem with a large multidimensional input space are developed and demonstrated. The neural network provides classification for over 100,000,000 data points. A query-based strategy is used that initiates training using a small input set, and then augments the set in multiple stages to include important data around the network decision boundary. Neural network inversion and oracle query are used to generate the additional data, jitter is added to the query data to improve the results, and an extended Kalman filter algorithm is used for training. A causality index is discussed …

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Navigation Satellite Selection Using Neural Networks, Daniel J. Simon, Hossny El-Sherief May 1995

Navigation Satellite Selection Using Neural Networks, Daniel J. Simon, Hossny El-Sherief

Electrical and Computer Engineering Faculty Publications

The application of neural networks to optimal satellite subset selection for navigation use is discussed. The methods presented in this paper are general enough to be applicable regardless of how many satellite signals are being processed by the receiver. The optimal satellite subset is chosen by minimizing a quantity known as Geometric Dilution of Precision (GDOP), which is given by the trace of the inverse of the measurement matrix. An artificial neural network learns the functional relationships between the entries of a measurement matrix and the eigenvalues of its inverse, and thus generates GDOP without inverting a matrix. Simulation results …

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