Kıvrak bacaklı robotlarda gürbüz görsel algının işitsel algı desteğinde geliştirilmesi ve otonom navigasyon amaçlı uygulanması

TÜBİTAK EEEAG Proje01.10.201

Lagrangian based mathematical modeling and experimental validation of a planar stabilized platform for mobile systems

Typical operating conditions for mobile sensor systems, and in particular mobile robots, exhibit a wide range of mechanical disturbances due their ego-motion. Sensor systems mounted on these mobile platforms often suffer to varying degrees from these disturbances. The quality of acquired data is degraded as a result. For instance, the quality of captured video frames from an onboard camera greatly depends on the angular velocity of the body on which the camera is mounted. Motion blur degradation results if large angular motions are present. In order to compensate for such disturbances, stabilization platforms are used. A common approach is measuring body movements using inertial sensors and attempting their cancellation with actuators and control systems. Design of high performance control systems often requires analytical system models. In this article, a planar stabilization platform is considered, to develop and study its kinematic and simple-to-complex dynamic model. The mathematical derivation of the model is presented with and without neglect of the actuator mass components as well as friction effects. This is followed by the comparative validation of these model alternatives against a realistic numerical model fitted to physical experimental data. The results demonstrate that the analytical model, in particular with the actuator mass and friction components included, provides a high degree of fit to the actual behavior

Scan-line quality inspection of strip materials using 1-D radial basis function network

There exist a variety of manufacturing quality inspection tasks where the inspection of a continuous strip of material using a scan-line camera is involved. Here the image is very short in one dimension but unlimited in the other dimension. In this study, a method of image event detection for this class of applications based on adaptive radial-basis function networks is presented. The architecture of the system and the adaptation methodology is presented in detail together with a detailed discussion on parameter selection. Promising detection results are illustrated for an application to grinded glass edge inspection problem

A unifying theory for rank-based multiple classifier systems, with applications in speaker identification and speech recognition

Ph.D. - Doctoral Progra

Performance Comparison of Target Tracking Algortihms in Underwater Environment

Target tracking is one the most fundamental elements of a radar system. The aim of target tracking is the reliable estimation of a target's true state based on a time history of noisy sensor observations. In real life, the sensor data may include substantial noise. This noise can render the raw sensor data unsuitable to be used directly. Instead, we must filter the noise, preferably in an optimal manner. For land, air and surface marine vehicles, very successful filtering methods are developed. However, because of the significant differences in the underwater propagation environment and the associated differences in the corresponding sensors, the successful use of similar principles and techniques in an underwater scenario is still an active topic of research. A comparative study of the effects of the underwater environment on a number of tracking algorithms is the focus of the present thesis. The tracking algorithms inspected are: the Kalman Filter, the Extended Kalman Filter and the Particle Filter. We also investigate in particular the IMM (1)extension to these filters. These algorithms are tested under two representative scenarios

A study on improving decisions in closed set speaker identification

In this study, closed-set, text-independent speaker identification is considered and the problem of improving the reliability of the decisions made by available algorithms is addressed. The work presented here is based on the idea of combining the evidences from different algorithms or decision strategies to improve the recognition performance and the reliability. For this purpose, the models generated by a single algorithm for 17 speakers from the SPIDRE; database are considered and a matrix of speaker-to-model fitness values is processed by two different decision strategies. Ideas from the Mathematical Theory of Evidence are applied to combine the decisions produced by these two strategies to generate a better decision on the speaker identity. The combined decision show an improved degree of corectness hence suggesting a promising way of combining the decisions from partially successful algorithms

Complexity reduction in radial basis function (RBF) networks by using radial B-spline functions

In this paper, new basis consisting of radial cubic and quadratic B-spline functions are introduced together with the CORDIC algorithm, within the context of RBF networks as a means of reducing computational complexity in real-time signal-processing applications. The new basis are compared with two other existing and popularly used basis families, namely the Gaussian functions and the inverse multiquadratic functions (IVMQ) in terms of approximation performance and computational requirements. The new basis are shown to achieve approximation performance very similar to the Gaussian basis functions and are better than the IVMQ functions with less computational load and without any need for approximation methods such as table-lookup

An FPGA based high performance optical flow hardware design for computer vision applications

Optical Flow (OF) information is used in higher level vision tasks in a variety of computer vision applications. However, its use in resource constrained applications such as small-scale mobile robotic platforms is limited because of the high computational complexity involved. The inability to compute the OF vector field in real-time is the main drawback which prevents these applications to efficiently utilize some successful techniques from the computer vision literature. In this work, we present the design and implementation of a high performance FPGA hardware with a small footprint and low power consumption that computes OF at a speed exceeding real-time performance. A well known OF algorithm by Horn and Schunck is selected for this baseline implementation. A detailed multiple-criteria performance analysis of the proposed hardware is presented with respect to computation speed, resource usage, power consumption and accuracy compared to a PC based floating-point implementation. The implemented hardware computes OF vector field on 256 x 256 pixels images in 3.89 ms i.e. 257 fps. Overall, the proposed implementation achieves a superior performance in terms of speed, power consumption and compactness while there is minimal loss of accuracy. We also make the FPGA design source available in full for research and academic use

A unified view of rank-based decision combination

This study presents a theoretical investigation of the rank-based multiple classifier decision problem for closed-set pattern classification. The case with classifier raw outputs in the form of candidate class rankings is considered and formulated as a discrete optimization problem with the objective function being the total probability of correct decision. The problem has a global optimum solution but is of prohibitive dimensionality. We present a partitioning formalism under which this dimensionality can be reduced by incorporating our prior knowledge about the problem domain and the structure of the training data. The formalism can effectively explain a number of rank-based combination approaches successfully used in the literature one of which is discussed

Improved inspection of TFT LCD panels using on-demand automated optical inspection sub-system

In an inspection system for electrical and electro-optical inspection of TFT-LCD panels, a fine resolution area imaging camera with a pulse illumination source disposed to scan the region and operative capture images of the region illuminated with pulses of short illumination and automatically maintained in focus while continuously scanning in order to resolve points of defects