@article{luo_perng_2011, title={Avances of mechatronics and robotics}, volume={5}, number={3}, journal={IEEE Industrial Electronics Magazine}, author={Luo, R. C. and Perng, Y. W.}, year={2011}, pages={27–34} }
 @article{potlapalli_luo_1998, title={Fractal-based classification of natural textures}, volume={45}, ISSN={["1557-9948"]}, DOI={10.1109/41.661315}, abstractNote={Texture classification is an important first step in image segmentation and image recognition. The classification algorithm must be able to overcome distortions, such as scale, aspect and rotation changes in the input texture. In this paper, a new fractal model for texture classification is presented. The model is based on fractional Brownian motion (FBM). It is also shown that this model is invariant to changes in incident light; empirical results are also given. The isotropic nature of Brownian motion is particularly useful for outdoor applications, where the viewing direction may change. Classification results of this model are presented; comparisons with other texture measurement models indicate that the incremental FBM (IFBM) model has better performance for the samples tested.}, number={1}, journal={IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS}, author={Potlapalli, H and Luo, RC}, year={1998}, month={Feb}, pages={142–150} }
 @article{gutierrez-osuna_janet_luo_1998, title={Modeling of ultrasonic range sensors for localization of autonomous mobile robots}, volume={45}, ISSN={["1557-9948"]}, DOI={10.1109/41.704895}, abstractNote={This paper presents a probabilistic model of ultrasonic range sensors using backpropagation neural networks trained on experimental data. The sensor model provides the probability of detecting mapped obstacles in the environment, given their position and orientation relative to the transducer. The detection probability can be used to compute the location of an autonomous vehicle from those obstacles that are more likely to be detected. The neural network model is more accurate than other existing approaches, since it captures the typical multilobal detection pattern of ultrasonic transducers. Since the network size is kept small, implementation of the model on a mobile robot can be efficient for real-time navigation. An example that demonstrates how the credence could be incorporated into the extended Kalman filter (EKF) and the numerical values of the final neural network weights are provided in the appendices.}, number={4}, journal={IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS}, author={Gutierrez-Osuna, R and Janet, JA and Luo, RC}, year={1998}, month={Aug}, pages={654–662} }