2 research outputs found
A comparison of different approaches to target differentiation with sonar
Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2001.Thesis (Ph.D.) -- Bilkent University, 2001.Includes bibliographical references leaves 180-197This study compares the performances of di erent classication schemes and fusion techniques
for target di erentiation and localization of commonly encountered features in indoor robot
environments using sonar sensing Di erentiation of such features is of interest for intelligent
systems in a variety of applications such as system control based on acoustic signal detection
and identication map building navigation obstacle avoidance and target tracking The
classication schemes employed include the target di erentiation algorithm developed by
Ayrulu and Barshan statistical pattern recognition techniques fuzzy c means clustering
algorithm and articial neural networks The fusion techniques used are Dempster Shafer
evidential reasoning and di erent voting schemes To solve the consistency problem arising in
simple ma jority voting di erent voting schemes including preference ordering and reliability
measures are proposed and veried experimentally To improve the performance of neural
network classiers di erent input signal representations two di erent training algorithms
and both modular and non modular network structures are considered The best classication
and localization scheme is found to be the neural network classier trained with the wavelet
transform of the sonar signals This method is applied to map building in mobile robot
environments Physically di erent sensors such as infrared sensors and structured light systems
besides sonar sensors are also considered to improve the performance in target classication
and localization.Ayrulu (Erdem), BirselPh.D
Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals
We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWT decomposition and reconstruction