Sensory processing and world modeling for an active ranging device

In this project, we studied world modeling and sensory processing for laser range data. World Model data representation and operation were defined. Sensory processing algorithms for point processing and linear feature detection were designed and implemented. The interface between world modeling and sensory processing in the Servo and Primitive levels was investigated and implemented. In the primitive level, linear features detectors for edges were also implemented, analyzed and compared. The existing world model representations is surveyed. Also presented is the design and implementation of the Y-frame model, a hierarchical world model. The interfaces between the world model module and the sensory processing module are discussed as well as the linear feature detectors that were designed and implemented

Recognizing targets from infrared intensity scan patterns using artificial neural networks

This study investigates the use of simple, low-cost infrared sensors for the recognition of geometry and surface type of commonly encountered features or targets in indoor environments, such as planes, corners, and edges. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and recognition process. We employ artificial neural networks to determine the geometry and the surface type of targets and provide experimental verification with three different geometries and three different surface types. The networks are trained with the Levenberg-Marquardt algorithm and pruned with the optimal brain surgeon technique. The geometry and the surface type of targets can be correctly classified with rates of 99 and 78.4%, respectively. An average correct classification rate of 78% is achieved when both geometry and surface type are differentiated. This indicates that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor in recognizing the patterns. The results demonstrate that processing the data from simple infrared sensors through suitable techniques can help us exploit their full potential and extend their usage beyond well-known applications. © 2009 Society of Photo-Optical Instrumentation Engineers

Coded Pulse Transmission and Correlation for Robust Ultrasound Ranging from a Long-Cane Platform

The objective of this research was to increase the independence and safety of the sight impaired by developing an enhanced travel aid in the form of a sensor embedded long-cane to reduce the risk of injury from walking into suspended or overhanging objects while providing the sight impaired community with a familiar and well accepted tool. Prior research at the Electromechanical Systems Laboratory had established a theoretical framework for ultrasound-based ranging and spatial obstacle localization from the moving reference frame of a long-cane. A prototype was implemented using analog threshold detection techniques. This research focused on a new approach. A coded pulse was transmitted and correlation techniques were used to identify echoes and determine time of flight. Compared to the prior effort this new approach was more sensitive, had greater noise immunity, and provide greater spatial resolution for obstacle detection. The first step in the coded pulse approach was to generate a transmit pulse with an embedded binary code that is highly distinguishable. A transmit pulse generated by phase modulating a 40 kHz carrier signal with a 13-bit Barker code word, with each bit consisting of 4 cycles of the 40 kHz carrier was used. Digitized representative echoes were used as reference vectors for correlation to account for the effect of the impulse responses of the transducers, the air, and the reflection, on the transmitted pulse. In a detection cycle, the coded pulse was transmitted, the A/D converters took 2600 samples at the 150 kHz sampling rate to capture any echoes from objects between 1 and 4 meters in front of the cane. The receiver data was cross-correlated with the stored echo image to find echoes in the received signal. The correlation peak positions from the upper receiver were then compared to the peak positions from the lower receiver and if they collaborated within the synthetic aperture, the range and height were calculated annunciation was made by a synthesized voice. The new obstacle detection system described above was designed and a prototype was constructed and embedded into the shaft of an 18 mm diameter body of a long cane

Neural networks for improved target differentiation and localization with sonar

Cataloged from PDF version of article.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-¯ight 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 identi®cation 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 ®xed 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 suf®cient information to differentiate all target types, but the previously reported methods are unable to resolve this identifying information. This work can ®nd application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identi®cation, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems. q 2001 Elsevier Science Ltd. All rights reserved

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

Radius of curvature estimation and localization of targets using multiple sonar sensors

Acoustic sensors have been widely used in time-of-flight ranging systems since they are inexpensive and convenient to use. One of the most important limitations of these sensors is their low angular resolution. To improve the angular resolution and the accuracy, a novel, flexible, and adaptive three- dimensional (3-D) multi-sensor sonar system is described for estimating the radius of curvature and location of cylindrical and spherical targets. Point, line, and planar targets are included as limiting cases which are important for the characterization of typical environments. Sensitivity analysis of the curvature estimate with respect to measurement errors and certain system parameters is provided. The analysis and the simulations are verified by experiments in 2-D with specularly reflecting cylindrical and planar targets, using a real sonar system. Typical accuracies in range and azimuth are 0.18 mm and 0.1°, respectively. Accuracy of the curvature estimation depends on the target type and system parameters such as transducer separation and operating range. The adaptive configuration brings an improvement varying between 35% and 45% in the accuracy of the curvature estimate. The presented results are useful for target differentiation and tracking applications.A flexible and adaptive three-dimensional multisensor sonar system capable of estimating the location and radius of curvature of spherical and cylindrical targets is presented. The performance radius of curvature estimation is analyzed to provide information for differentiating reflectors with different radii. Results showed that the adaptive configuration improved the accuracy of the curvature estimate between 35% and 45%

TECHNIKI ROZPOZNAWANIA TWARZY

The problem of face recognition is discussed. The main methods of recognition are considered. The calibrated stereo pair for the face and calculating the depth map by the correlation algorithm are used. As a result, a 3D mask of the face is obtained. Using three anthropomorphic points, then constructed a coordinate system that ensures a possibility of superposition of the tested mask.Omawiany jest problem rozpoznawania twarzy. Rozważane są główne metody rozpoznawania. Użyta zostaje skalibrowana para stereo dla twarzy oraz obliczanie mapy głębokości poprzez algorytm korelacji. W wyniku takiego, uzyskiwana jest maska twarzy w wymiarze 3D. Użycie trzech antropomorficznych punktów, a następnie skonstruowany systemu współrzędnych zapewnia możliwość nakładania się przetestowanej maski

The Cleo Rich Detector

We describe the design, construction and performance of a Ring Imaging Cherenkov Detector (RICH) constructed to identify charged particles in the CLEO experiment. Cherenkov radiation occurs in LiF crystals, both planar and ones with a novel ``sawtooth''-shaped exit surface. Photons in the wavelength interval 135--165 nm are detected using multi-wire chambers filled with a mixture of methane gas and triethylamine vapor. Excellent pion/kaon separation is demonstrated.Comment: 75 pages, 57 figures, (updated July 26, 2005 to reflect reviewers comments), to be published in NIM