Amphetamine-like discrimination stimulus effects of ephedrine and its stereoisomers in pigeons.

This study assessed the discriminative stimulus effects of (±)-ephedrine and its stereoisomers in pigeons discriminating 1.0 mg/kg of amphetamine from saline. Amphetamine, (±)-, (-)-, and (+)-ephedrine, and cocaine occasioned greater than 80% drug-key responding with the following rank order of potency: amphetamine > cocaine > (-)-ephedrine ≥ (±)-ephedrine ≥ (+)-ephedrine. Neither the α-adrenergic antagonist, phentolamine, nor the β-adrenergic antagonist, propranolol, antagonized the effects of amphetamine or (±)-ephedrine. In contrast, the dopamine receptor antagonist, haloperidol, antagonized the discriminative stimulus effects of amphetamine and (±)-ephedrine as well as those of (-)- and (+)-ephedrine. These results indicate that, like cocaine, (±)-ephedrine and its stereoisomers share discriminative stimulus effects with amphetamine. Moreover, these effects appear to be the result of increased activity in dopaminergic systems. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Classical Theory of Invariants and Object Recognition Using Algebraic Curve and Surfaces

Combining implicit polynomials and algebraic invariants for representing and recognizing complicated objects proves to be a powerful technique. In this paper, we explore the findings of the classical theory of invariants for the calculation of algebraic invariants of implicit curves and surfaces, a theory largely disregarded in the computer vision community by a shadow of skepticism. Here, the symbolic method of the classical theory is described, and its results are extended and implemented as an algorithm for computing algebraic invariants of projective, affine, and Euclidean transformations. A list of some affine invariants of 4th degree implicit polynomials generated by the proposed algorithm is presented along with the corresponding symbolic representations, and their use in recognizing objects represented by implicit polynomials is illustrated through experiments. An affine invariant fitting algorithm is also proposed and the performance is studied.     

Defect detection in textile fabric images using wavelet transforms and independent component analysis

In this paper, a new method based on the use of wavelet transformation prior to independent component analysis for solving the problem of defect detection in textile fabric images is presented. Different subbands of the wavelet packet tree scheme of the defect-free subwindows are obtained and independent components of these subbands are calculated as basis vectors. The true feature vectors corresponding to these basis vectors are computed. The test subwindow is labeled as defective, or not according to the Euclidean distance between the true feature vector representing the non-defective regions and the feature vector of the subwindow under test. The advantage of adding wavelet analysis prior to the independent component analysis is presented. The text was submitted by the authors in English.     

The Underlying Reasons of the Navigation Control Effect on Performance in a Virtual Reality Endoscopic Surgery Training Simulator

Navigation control skills of surgeons become very critical for surgical procedures. Strategies improving these skills are important for developing higher-quality surgical training programs. In this study, the underlying reasons of the navigation control effect on performance in a virtual reality-based navigation environment are evaluated. The participants’ performance is measured in conditions: navigation control display and paper-map display. Performance measures were collected from 45 beginners and experienced residents. The results suggest that navigation display significantly improved performance of the participants. Also, navigation was more beneficial for beginners than experienced participants. The underlying reason of the better performance in the navigation condition was due to lower number of looks to the map, which causes attention shifts between information sources. Accordingly, specific training scenarios and user interfaces can be developed to improve the navigation skills of the beginners considering some strategies to lower their number of references to the information sources.     

Using perceptual relation of regularity and anisotropy in the texture with independent component model for defect detection

This paper addresses the raw textile defect detection problem using independent components approach with insights from human vision system. Human vision system is known to have specialized receptive fields that respond to certain type of input signals. Orientation-selective bar cells and grating cells are examples of receptive fields in the primary visual cortex that are selective to periodic- and aperiodic-patterns, respectively. Regularity and anisotropy are two high-level features of texture perception, and we can say that disruption in regularity and/or orientation field of the texture pattern causes structural defects. In our research, we observed that independent components extracted from texture images give bar or grating cell like results depending on the structure of the texture. For those textures having lower regularity and dominant local anisotropy (orientation or directionality), independent components look similar to bar cells whereas textures with high regularity and lower anisotropy have independent components acting like grating cells. Thus, we will expect different bar or grating cell like independent components to respond to defective and defect-free regions. With this motivation, statistical analysis of the structure of the texture by means of independent components and then extraction of the disturbance in the structure can be a promising approach to understand perception of local disorder of texture in human vision system. In this paper, we will show how to detect regions of structural defects in raw textile data that have certain regularity and local orientation characteristics with the application of independent component analysis (ICA), and we will present results on real textile images with detailed discussions.     

Comparison of the discussion sections of PhD dissertations in educational technology: the case of Turkey and the USA

In academic studies, the discussion section is important in which the effect of the results of the study on the existing literature is highlighted and interpreted. The aim of the current paper is to compare the discussion sections of Turkish and American PhD dissertations in the field of Educational Technologies, taking into consideration the basic features that should be found in an effective discussion section. For this purpose, a content analysis was carried out using a checklist developed to examine the basic components, the presentation of the results, the interpretation of the results, and the overall language of 120 PhD dissertations published in this field. The study showed although there were strong aspects, the contribution to the literature and limitations of studies were only included in some of the Turkish PhD dissertations; however, they were included in the majority of American PhD dissertations. Almost all of the dissertations explained the findings and their significance; however, there was no emphasis on the significant and interesting findings in Turkish PhD dissertations. The number of pages of the discussion section in Turkish and American PhD dissertations was almost the same, and the relationship between the number of pages of the discussion section and the quality of the discussion section was rather weak in the dissertations from both countries. The results obtained from the study were evaluated in general and suggestions to consider when writing the discussion section are given.     

Insights from surgeons’ eye-movement data in a virtual simulation surgical training environment: effect of experience level and hand conditions

Today, with the advancements in the eye-tracking technology, it has become possible to follow surgeons’ eye movements while performing surgical tasks. Despite the availability of studies providing a better understanding of surgeons’ eye movements, research in the particular field of endoneurosurgery is very limited. Analysing surgeons’ eye-movement data can provide general insights into how to improve surgical education programmes. In this study, four simulation-based task-oriented endoscopic surgery training scenarios were developed and implemented by 23 surgical residents using three different hand conditions; dominant, non-dominant, and both. The participants’ recorded eye data comprised fixation number, fixation duration, saccade number, saccade duration, pursuit number, pursuit duration, and pupil size. This study has two main contributions: First, it reports on the eye-movement behaviours of surgical residents, demonstrating that novice residents tended to make more fixations and saccades than intermediate residents. They also had a higher fixation duration and followed the objects more frequently compared to the intermediates. Furthermore, hand conditions significantly affected the eye movements of the participants. Based on these results, it can be concluded that eye-movement data can be used to assess the skill levels of surgical residents and would be an important measure to better guide trainees in surgical education programmes. The second contribution of this study is the eye-movement event classifications of 10 different algorithms. Although the algorithms mostly provided similar results, there were a few conflicted values for some classifications, which offers a clue as to how researchers can utilise these algorithms with low sampling frequency eye trackers.     

Multimodal person recognition for human-vehicle interaction

The authors combine two different biometric modalities for next-generation vehicles that use biometric person recognition. Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies.     

Defect detection in textile fabric images using subband domain subspace analysis

In this work, a new model that combines the concepts of wavelet transformation and subspace analysis tools, like independent component analysis (ICA), topographic independent component analysis (TICA), and Independent Subspace Analysis (ISA), is developed for the purpose of defect detection in textile images. In previous works, it has been shown that reduction of the textural components of the textile image by preprocessing has increased the performance of the system. Based on this observation, in the present work, the aforementioned subspace analysis tools are applied to subband images. The feature vector of a subwindow of a test image is compared with that of a defect-free image in order to make a decision. This decision is based on a Euclidean distance classifier. The increase performance that results from using wavelet transformation prior to subspace analysis has been discussed in detail. While it has been found that all subspace analysis methods lead to the same detection performances, as a further step, independent subspace analysis is used to classify the detected defects according to their directionalities.     

Real-time feature-based image morphing for memory-efficient impostor rendering and animation on GPU

Real-time rendering of large animated crowds consisting of thousands of virtual humans is important for several applications including simulations, games, and interactive walkthroughs but cannot be performed using complex polygonal models at interactive frame rates. For that reason, methods using large numbers of precomputed image-based representations, called impostors, have been proposed. These methods take advantage of existing programmable graphics hardware to compensate for computational expense while maintaining visual fidelity. Thanks to these methods, the number of different virtual humans rendered in real time is no longer restricted by computational power but by texture memory consumed for the variety and discretization of their animations. This work proposes a resource-efficient impostor rendering methodology that employs image morphing techniques to reduce memory consumption while preserving perceptual quality, thus allowing higher diversity or resolution of the rendered crowds. Results of the experiments indicated that the proposed method, in comparison with conventional impostor rendering techniques, can obtain 38 % smoother animations or 87 % better appearance quality by reducing the number of key-frames required for preserving the animation quality via resynthesizing them with up to 92 % similarity on real time.