Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

Harnessing Spatial Intensity Fluctuations for Optical Imaging and Sensing

Properties of light such as amplitude and phase, temporal and spatial coherence, polarization, etc. are abundantly used for sensing and imaging. Regardless of the passive or active nature of the sensing method, optical intensity fluctuations are always present! While these fluctuations are usually regarded as noise, there are situations where one can harness the intensity fluctuations to enhance certain attributes of the sensing procedure. In this thesis, we developed different sensing methodologies that use statistical properties of optical fluctuations for gauging specific information. We examine this concept in the context of three different aspects of computational optical imaging and sensing. First, we study imposing specific statistical properties to the probing field to image or characterize certain properties of an object through a statistical analysis of the spatially integrated scattered intensity. This offers unique capabilities for imaging and sensing techniques operating in highly perturbed environments and low-light conditions. Next, we examine optical sensing in the presence of strong perturbations that preclude any controllable field modification. We demonstrate that inherent properties of diffused coherent fields and fluctuations of integrated intensity can be used to track objects hidden behind obscurants. Finally, we address situations where, due to coherent noise, image accuracy is severely degraded by intensity fluctuations. By taking advantage of the spatial coherence properties of optical fields, we show that this limitation can be effectively mitigated and that a significant improvement in the signal-to-noise ratio can be achieved even in one single-shot measurement. The findings included in this dissertation illustrate different circumstances where optical fluctuations can affect the efficacy of computational optical imaging and sensing. A broad range of applications, including biomedical imaging and remote sensing, could benefit from the new approaches to suppress, enhance, and exploit optical fluctuations, which are described in this dissertation

Segmentation of neuroanatomy in magnetic resonance images

Segmentation in neurological Magnetic Resonance Imaging (MRI) is necessary for volume measurement, feature extraction and for the three-dimensional display of neuroanatomy. This thesis proposes several automated and semi-automated methods which offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. Work has concentrated on the use of dual echo multi-slice spin-echo data sets in order to take advantage of the intrinsically multi-parametric nature of MRI. Such data is widely acquired clinically and segmentation therefore does not require additional scans. The literature has been reviewed. Factors affecting image non-uniformity for a modem 1.5 Tesla imager have been investigated. These investigations demonstrate that a robust, fast, automatic three-dimensional non-uniformity correction may be applied to data as a pre-processing step. The merit of using an anisotropic smoothing method for noisy data has been demonstrated. Several approaches to neurological MRI segmentation have been developed. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing, two threshold based techniques and a fast radial CSF identification approach are proposed to identify the intracranial region contour in each slice of the data set. Once isolated, the intracranial region is further processed to identify CSF, and, depending upon the MRI pulse sequence used, the brain itself may be sub-divided into grey matter and white matter using semiautomatic contrast enhancement and clustering methods. The segmentation of Multiple Sclerosis (MS) plaques has also been considered. The utility of the stack, a data driven multi-resolution approach to segmentation, has been investigated, and several improvements to the method suggested. The factors affecting the intrinsic accuracy of neurological volume measurement in MRI have been studied and their magnitudes determined for spin-echo imaging. Geometric distortion - both object dependent and object independent - has been considered, as well as slice warp, slice profile, slice position and the partial volume effect. Finally, the accuracy of the approaches to segmentation developed in this thesis have been evaluated. Intracranial volume measurements are within 5% of expert observers' measurements, white matter volumes within 10%, and CSF volumes consistently lower than the expert observers' measurements due to the observers' inability to take the partial volume effect into account