Utilizing sensor data to model students’ creativity in a digital environment

While creativity is essential for developing students’ broad expertise in Science, Technology, Engineering, and Math (STEM) fields, many students struggle with various aspects of being creative. Digital technologies have the unique opportunity to support the creative process by (1) recognizing elements of students’ creativity, such as when creativity is lacking (modeling step), and (2) providing tailored scaffolding based on that information (intervention step). However, to date little work exists on either of these aspects. Here, we focus on the modeling step. Specifically, we explore the utility of various sensing devices, including an eye tracker, a skin conductance bracelet, and an EEG sensor, for modeling creativity during an educational activity, namely geometry proof generation. We found reliable differences in sensor features characterizing low vs. high creativity students. We then applied machine learning to build classifiers that achieved good accuracy in distinguishing these two student groups, providing evidence that sensor features are valuable for modeling creativity.     

Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator

Train driving is a highly visual task. The visual capabilities of the train driver affects driving safety and driving performance. Understanding the effects of train speed and background image complexity on the visual behavior of the high-speed train driver is essential for optimizing performance and safety. This study investigated the role of the apparent image velocity and complexity on the dynamic visual field of drivers. Participants in a repeated-measures experiment drove a train at nine different speeds in a state-of-the-art high-speed train simulator. Eye movement analysis indicated that the effect of image velocity on the dynamic visual field of high-speed train driver was significant while image complexity had no effect on it. The fixation range was increasingly concentrated on the middle of the track as the speed increased, meanwhile there was a logarithmic decline in fixation range for areas surrounding the track. The extent of the visual search field decreased gradually, both vertically and horizontally, as the speed of train increased, and the rate of decrease was more rapid in the vertical direction. A model is proposed that predicts the extent of this tunnel vision phenomenon as a function of the train speed.Relevance to industryThis finding can be used as a basis for the design of high-speed railway system and as a foundation for improving the operational procedures of high-speed train driver for safety.     

UV-LIGA深度光刻机平滑衍射技术研究

UV-LIGA深度光刻技术是加工微型机电系统的一项重要的微细加工技术。介绍了一种平滑衍射效应的位错强度叠加原理，它采用了蝇眼积分透镜去平滑衍射，并模拟数值计算了硅片表面的光强分布，最后给出了光刻结果。     

A Class of Band-limited Chip Waveforms for DS-CDMA Systems

The conventional frequency domain square-root raised cosine （Nyquist） chip waveform has much poorer anti-multiple-access-interference （anti-MAI） capability than the optimal band-limited waveform in direct sequence code division multiple access （DS-CDMA） systems. However, the digital implementation of the optimal chip pulse is very costly due to the slow decaying rate of the time waveform. In addition, its eye diagram and envelope uniformity arc worse than those of the Nyquist pulse for a wide range of roll-off factors, which will incur performance degradation due to timing jitters and post non-linear processing. In this paper, based on an elementary density function of a second order polynomial, a class of second order continuous pulses is proposed. From this class of pulses, we ban find some members have faster decaying rate, bigger eye opening, more uniform envdope and stronger anti-MAI capability than the Nyquist waveform. The normalized-bandwidth-pulse-shape-factor product, the decaying rate of the tail of the time waveform, the opening of the eye diagram and the envelope uniformity of the second order continuous pulses are addressed in this paper which provides the basic information for the selection of the chip pulse for CDMA systems.     

Measuring the likelihood of VR visual fatigue through ocular biomechanics

Immersion in virtual reality is still linked to symptoms of visual fatigue such as eye strain, dizziness, and overall discomfort. Studies have investigated visual fatigue through pre- and post-immersion tests of the visual function. In this work, we extend on our previous study and derive a visual fatigue likelihood metric using biomechanical analysis. Previously, we have investigated the effect of VR on the vergence system during immersion. The proposed visual fatigue metric exhibited a significant correlation to vergence angle variability which was previously linked to vergence accommodation conflict in VR. We also discuss subjective feedback and its relationship with the proposed visual fatigue metric.     

Improving SEM Inspection Performance in Semiconductor Manufacturing Industry

Scanning electron microscope (SEM) station is one of the major quality control tasks in wafer manufacturing process. During the process of examining defects on a screen for a certain period of time, SEM inspectors frequently complain about visual fatigue problems. The purpose of the study was to evaluate the effect of wafer‐coating condition (noncoating treatment and gold‐coating treatment) and liquid crystal display (LCD) monitor size (14‐inch and 19‐inch monitor) from the aspects of objective visual fatigue measures (NPA and CFF), subjective eye fatigue rating, and inspection performance. Twelve SEM inspectors participated in this experiment. The results indicated that the treatment of gold‐coating wafer showed significant difference on reducing objective visual fatigue in NPA and CFF change. Using a 19‐inch LCD monitor also demonstrated the effect on reducing eye fatigue as well as improving inspection performance. The improvement of using a gold‐coating treatment wafer and a 19‐inch LCD monitor to reduce SEM inspectors' visual fatigue and to increase inspection performance is thus recommended. © 2012 Wiley Periodicals, Inc.     

Using space windowing for a preliminary analysis of complex time data in human component system studies. Examples with eye-tracking in advertising and car/head movements in driving

Empirical studies of human systems often involve recording multidimensional signals because the system components may require physical measurements (e.g., temperature, pressure, body movements and/or movements in the environment) and physiological measurements (e.g., electromyography or electrocardiography). Analysis of such data becomes complex if both the multifactor aspect and the multivariate aspect are retained. Three examples are used to illustrate the role of fuzzy space windowing and the large number of data analysis paths. The first example is a classic simulated data set found in the literature, which we use to compare several data analysis paths generated with principal component analysis and multiple correspondence analysis with crisp and fuzzy windowing. The second example involves eye-tracking data based on advertising, with a focus on the case of one category variable, but with the possibility of several space windowing models and time entities. The third example concerns car and head movement data from a driving vigilance study, with a focus on the case involving several quantitative variables. The notions of analysis path multiplicity and information are discussed both from a general perspective and in terms of our two real examples.     

Human eye sclera detection and tracking using a modified time-adaptive self-organizing map

This paper investigates the use of time-adaptive self-organizing map (TASOM)-based active contour models (ACMs) for detecting the boundaries of the human eye sclera and tracking its movements in a sequence of images. The task begins with extracting the head boundary based on a skin-color model. Then the eye strip is located with an acceptable accuracy using a morphological method. Eye features such as the iris center or eye corners are detected through the iris edge information. TASOM-based ACM is used to extract the inner boundary of the eye. Finally, by tracking the changes in the neighborhood characteristics of the eye-boundary estimating neurons, the eyes are tracked effectively. The original TASOM algorithm is found to have some weaknesses in this application. These include formation of undesired twists in the neuron chain and holes in the boundary, lengthy chain of neurons, and low speed of the algorithm. These weaknesses are overcome by introducing a new method for finding the winning neuron, a new definition for unused neurons, and a new method of feature selection and application to the network. Experimental results show a very good performance for the proposed method in general and a better performance than that of the gradient vector field (GVF) snake-based method.     

Estimation of behavioral user state based on eye gaze and head pose—application in an e-learning environment

Most e-learning environments which utilize user feedback or profiles, collect such information based on questionnaires, resulting very often in incomplete answers, and sometimes deliberate misleading input. In this work, we present a mechanism which compiles feedback related to the behavioral state of the user (e.g. level of interest) in the context of reading an electronic document; this is achieved using a non-intrusive scheme, which uses a simple web camera to detect and track the head, eye and hand movements and provides an estimation of the level of interest and engagement with the use of a neuro-fuzzy network initialized from evidence from the idea of Theory of Mind and trained from expert-annotated data. The user does not need to interact with the proposed system, and can act as if she was not monitored at all. The proposed scheme is tested in an e-learning environment, in order to adapt the presentation of the content to the user profile and current behavioral state. Experiments show that the proposed system detects reading- and attention-related user states very effectively, in a testbed where children’s reading performance is tracked.