Birsel Ayrulu

Fractional Fourier transform pre-processing for neural networks and its application to object recognition

This study investigates fractional Fourier transform pre-processing of input signals to neural networks. The fractional Fourier transform is a generalization of the ordinary Fourier transform with an order parameter a. Judicious choice of this parameter can lead to overall improvement of the neural network performance. As an illustrative example, we consider recognition and position estimation of different types of objects based on their sonar returns. Raw amplitude and time-of-flight patterns acquired from a real sonar system are processed, demonstrating reduced error in both recognition and position estimation of objects.

Neural network based target differentiation using sonar for robotics applications

This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor environments. The neural network can differentiate more targets, and achieves high differentiation and localization accuracy, improving on previously reported methods. It achieves this by exploiting the identifying features in the differential amplitude and time-of-flight characteristics of these targets. An important observation follows from the robustness tests, which indicate that the amplitude information is more crucial than time-of-flight for reliable operation. The study suggests wider use of neural networks and amplitude information in sonar-based mobile robotics.

Identification of target primitives with multiple decision-making sonars using evidential reasoning

In this study, physical models are used to model reflections from target primitives commonly encountered in a mobile robots envi ronment. These targets are differentiated by employing a multi- transducer pulse/echo system that relies on both time-of-flight data and amplitude in the feature-fusion process, allowing more robust differentiation. Target features are generated as being evidentially tied to degrees of belief, which are subsequently fused by employ ing multiple logical sonars at geographically distinct sites. Feature datafrom multiple logical sensors arefused with Dempsters rule of combination to improve the performance of classification by reduc ing perception uncertainty. Using three sensing nodes, improvement in differentiation is between 10% and 35% withoutfalse decision, at the cost of additional computation. The method is verified by exper iments with a real sonar system. The evidential approach employed here helps to overcome the vulnerability of the echo amplitude to noise, and enables the modeling of nonparametric uncertainty in real time.

Neural networks for improved target differentiation and localization with sonar

This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. Differentiation of such features is of interest for intelligent systems in a variety of applications. Different representations of amplitude and time-of-flight measurement patterns acquired from a real sonar system are processed. In most cases, best results are obtained with the low-frequency component of the discrete wavelet transform of these patterns. Modular and non-modular neural network structures trained with the back-propagation and generating-shrinking algorithms are used to incorporate learning in the identification of parameter relations for target primitives. Networks trained with the generating-shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. Neural networks can differentiate more targets employing only a single sensor node, with a higher correct differentiation percentage (99%) than achieved with previously reported methods (61-90%) employing multiple sensor nodes. A sensor node is a pair of transducers with fixed separation, that can rotate and scan the target to collect data. Had the number of sensing nodes been reduced in the other methods, their performance would have been even worse. The success of the neural network approach shows that the sonar signals do contain sufficient information to differentiate all target types, but the previously reported methods are unable to resolve this identifying information. This work can find application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems.

Reliability measure assignment to sonar for robust target differentiation

This article addresses the use of evidential reasoning and majority voting in multi-sensor decision making for target di#erentiation using sonar sensors. Classi/cation of target primitives which constitute the basic building blocks of typical surfaces in uncluttered robot environments has been considered. Multiple sonar sensors placed at geographically di#erent sensing sites make decisions about the target type based on their measurement patterns. Their decisions are combined to reach a group decision through Dempster–Shafer evidential reasoning and majority voting. The sensing nodes view the targets at di#erent ranges and angles so that they have di#erent degrees of reliability. Proper accounting for these di#erent reliabilities has the potential to improve decision making compared to simple uniform treatment of the sensors. Consistency problems arising in majority voting are addressed with a view to achieving high classi/cation performance. This is done by introducing preference ordering among the possible target types and assigning reliability measures (which essentially serve as weights) to each decision-making node based on the target range and azimuth estimates it makes and the belief values it assigns to possible target types. The results bring substantial improvement over evidential reasoning and simple majority voting by reducing the target misclassi/cation rate. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Voting as Validation in Robot Programming

This paper investigates the use of voting as a conflict-resolution technique for data analysis in robot programming. Voting represents an information-abstraction technique. It is argued that in some cases a voting approach is inherent in the nature of the data being analyzed: where multiple, independent sources of information must be reconciled to give a group decision that reflects a single outcome rather than a consensus average. This study considers an example of target classification using sonar sensors. Physical models of reflections from target primitives that are typical of the indoor environment of a mobile robot are used. Dispersed sensors take decisions on target type, which must then be fused to give the single group classification of the presence or absence and type of a target. Dempster-Shafer evidential reasoning is used to assign a level of belief to each sensor decision. The decisions are then fused by two means. Using Dempster’s rule of combination, conflicts are resolved through a group measure expressing dissonance in the sensor views. This evidential approach is contrasted with the resolution of sensor conflict through voting. It is demonstrated that abstraction of the level of belief through voting proves useful in resolving the straightforward conflicts that arise in the classification problem. Conflicts arise where the discriminant data value, an echo amplitude, is most sensitive to noise. Fusion helps to overcome this vulnerability: in Dempster-Shafer reasoning, through the modeling of nonparametric uncertainty and combination of belief values; and in voting, by emphasizing the majority view. The paper gives theoretical and experimental evidence for the use of voting for data abstraction and conflict resolution in areas such as classification, where a strong argument can be made for techniques that emphasize a single outcome rather than an estimated value. Methods for making the vote more strategic are also investigated. The paper addresses the reduction of dimension of sets of decision points or decision makers. Through a consideration of combination order, queuing criteria for more strategic fusion are identified.

Target identification with multiple logical sonars using evidential reasoning and simple majority voting

In this study, physical models are used to model reflections from target primitives commonly encountered in a mobile robots environment. These targets are differentiated by employing a multi-transducer pulse/echo system which relies on both amplitude and time-of-flight data, allowing more robust differentiation. Target features are generated as being evidentially tied to degrees of belief which are subsequently fused by employing multiple logical sonars at different geographical sites. Feature data from multiple logical sensors are fused with the Dempster-Shafer rule of combination to improve the performance of classification by reducing perception uncertainty. Dempster-Shafer fusion results are contrasted with the results of combination of sensor beliefs through a simple majority vote. The method is verified by experiments with a real sonar system. The evidential approach employed here helps to overcome the vulnerability of the echo amplitude to noise and enables the modeling of non-parametric uncertainty in real time.

Comparative analysis of different approaches to target differentiation and localization with sonar

This study compares the performances of di erent methods for the di erentiation and localization of commonly encountered features in indoor environments. 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 identi/cation, map building, navigation, obstacle avoidance, and target tracking. Di erent representations of amplitude and time-of-2ight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target di erentiation algorithm, Dempster–Shafer evidential reasoning, di erent kinds of voting schemes, statistical pattern recognition techniques ( k-nearest neighbor classi/er, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and arti/cial neural networks. The neural networks are trained with di erent input signal representations obtained using pre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen’s self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect di erentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Fuzzy clustering and enumeration of target type based on sonar returns

The fuzzy c-means (FCM) clustering algorithm is used in conjunction with a cluster validity criterion, to determine the number of di2erent types of targets in a given environment, based on their sonar signatures. The class of each target and its location are also determined. The method is experimentally veri4ed using real sonar returns from targets in indoor environments. A correct di2erentiation rate of 98% is achieved with average absolute valued localization errors of 0:5 cm and 0:8 ◦ in range

Comparative analysis of different approaches to target classification and localization with sonar

This study compares the performances of different classification and fusion techniques for the differentiation and localization of commonly encountered features in indoor environments. Differentiation of such features is of interest for intelligent systems in a variety of applications.