Altı bacaklı bir robotun esnek bacaklarının yüzey etkileşimini kullanarak akustik yüzey algılaması.

A dynamically dexterous legged robot platform generates specific acoustic signals during the interaction with the ground and the environment. These acoustic signals are expected to contain rich information that is related to the interaction surface as a function of the position of the legs and the overall contact process mixed with the actuator sounds that initiate the movement. As the robot platform walks or runs in any environment, this convolved acoustic signal created can be processed and analyzed in real time operation and the interaction surface can be identified. Such an utilization of acoustic data can be possible for various indoor and outdoor surfaces and with this can be useful in adjusting gait parameters that play an essential role in dynamic dexterity. In this work, surface type identification is achieved with using the several popular signal processing and pattern classification methods not on the robot platform but off-line. The performances of the selected features and the algorithms are evaluated for the collected data sets and these outputs are compared with the expectations. Depending on the off-line training and experiment results, the applicability of the study to an embedded robot platform as a future application is found quite feasible and the surface type as an input to the robot sensing is expected to improve the mobility of the robot in both indoor and outdoor environment.M.S. - Master of Scienc

Single-image Bayesian Restoration and Multi-image Super-resolution Restoration for B-mode Ultrasound Using an Accurate System Model Involving Correlated Nature of the Speckle Noise

B-mode ultrasound is an essential part of radiological examinations due to its low cost, safety, and portability, but has the drawbacks of the speckle noise and output of most systems is two-dimensional (2D) cross sections. Image restoration techniques, using mathematical models for image degradation and noise, can be used to boost resolution (deconvolution) as well as to reduce the speckle. In this study, new single-image Bayesian restoration (BR) and multi-image super-resolution restoration (BSRR) methods are proposed for in-plane B-mode ultrasound images. The spatially correlated nature of the speckle was modeled, allowing for examination of two different models for BR and BSRR for uncorrelated Gaussian (BR-UG, BSRR-UG) and correlated Gaussian (BR-CG, BSRR-CG). The performances of these models were compared with common image restoration methods (Wiener filter, bilateral filtering, and anisotropic diffusion). Well-recognized metrics (peak signal-to-noise ratio, contrast-to-noise ratio, and normalized information density) were used for algorithm free-parameter estimation and objective evaluations. The methods were tested using superficial tissue (2D scan data collected from volunteers, tissue-mimicking resolutions, and breast phantoms). Improvement in image quality was assessed by experts using visual grading analysis. In general, BSRR-CG performed better than all other methods. A potential downside of BSRR-CG is increased computation time, which can be addressed by the use of high-performance graphics processing units (GPUs).Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [2211-C