Browsing by Subject "Artificial Neural Networks"
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Item Artificial Neural Network Circuit for Spectral Pattern Recognition(2013-09-04) Rasheed, FarahArtificial Neural Networks (ANNs) are a massively parallel network of a large number of interconnected neurons similar to the structure of biological neurons in the human brain. ANNs find applications in a large number of fields, from pattern classification problems in Computer Science like handwriting recognition to cancer classification problems in Biomedical Engineering. The parallelism inherent in neural networks makes hardware a good choice to implement ANNs compared to software implementations. The ANNs implemented in this thesis have feedforward architecture and are trained using backpropagation learning algorithm. Different neural network models are trained offline using software and the prediction algorithms are implemented using Verilog and compared with the software models. The circuit implementation of feedforward neural networks is found to be much faster than its software counterpart because of the parallel and pipelined structure as well as the presence of a large number of computations that makes the software simulations slower in comparison. The time taken from input to output by the circuit implementing the feedforward prediction algorithm is measured from the waveform diagram, and it is seen that the circuit implementation of the ANNs provides an increase of over 90% in processing speeds obtained via post-synthesis simulation compared to the software implementation. The ANN models developed in this thesis are plant disease classification, soil clay content classification and handwriting recognition for digits. The accuracy of the ANN model is found to be 75% to 97% for the three different problems. The results obtained from the circuit implementation show a < 1% decrease in accuracy compared with the software simulations because of the use of fixed-point representation for the real numbers. Fixed-point representation of numbers is used instead of floating-point representation for faster computational speed and better resource utilization.Item Estimation and prediction of travel time from loop detector data for intelligent transportation systems applications(Texas A&M University, 2005-11-01) Vanajakshi, Lelitha DeviWith the advent of Advanced Traveler Information Systems (ATIS), short-term travel time prediction is becoming increasingly important. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as loop detectors. Because of their wide spread deployment, travel time estimation from loop detector data is one of the most widely used methods. However, the major criticism about loop detector data is the high probability of error due to the prevalence of equipment malfunctions. This dissertation presents methodologies for estimating and predicting travel time from the loop detector data after correcting for errors. The methodology is a multi-stage process, and includes the correction of data, estimation of travel time and prediction of travel time, and each stage involves the judicious use of suitable techniques. The various techniques selected for each of these stages are detailed below. The test sites are from the freeways in San Antonio, Texas, which are equipped with dual inductance loop detectors and AVI. ?? Constrained non-linear optimization approach by Generalized Reduced Gradient (GRG) method for data reduction and quality control, which included a check for the accuracy of data from a series of detectors for conservation of vehicles, in addition to the commonly adopted checks. ?? A theoretical model based on traffic flow theory for travel time estimation for both off-peak and peak traffic conditions using flow, occupancy and speed values obtained from detectors. ?? Application of a recently developed technique called Support Vector Machines (SVM) for travel time prediction. An Artificial Neural Network (ANN) method is also developed for comparison. Thus, a complete system for the estimation and prediction of travel time from loop detector data is detailed in this dissertation. Simulated data from CORSIM simulation software is used for the validation of the results.