AN INVESTIGATION OF MOTOR VEHICLE DRIVER INATTENTION AND ITS EFFECTS AT HIGHWAY-RAIL GRADE CROSSINGS

The relationship between accident injury severity and drivers’ inattentive behavior requires an in-depth investigation – this is especially needed in the case of motor vehicle drivers at highway-rail grade crossings (HRGCs). The relationship between drivers’ personality/ socioeconomic characteristics and inattentive behavior at HRGCs is another topic requiring research. Past educational programs about safe driving at HRGCs have often not been designed to target people who may be in urgent need of such information, which may limit the effectiveness of those programs. This dissertation thus focuses on the following four objectives: to investigate the association between motor vehicle inattentive driving and the severity of drivers’ injuries sustained in crashes reported at or near HRGCs; to investigate the association between drivers’ self-reported inattentive driving experience and a series of factors such as drivers’ knowledge of safe driving, attitudes towards safe driving, etc.; to identify driver groups that have lower or higher levels of knowledge of correct rail crossing negotiation; and to investigate the direct and indirect effects between drivers’ characteristics and their knowledge level as well as their involvement with inattentive driving behavior at HRGCs. The research obtained 12 years of police-reported crash data from the Nebraska Department of Roads and collected data in a statewide random-sample mail questionnaire survey. Statistical analysis methods, including random parameters binary logit model, confirmatory factor analysis, robust linear regression, multinomial logit model, and structural equation models were utilized in this research. Conclusions are that inattentive driving plays a significant role in contributing to more severe injuries in accidents reported in proximity of HRGCs in Nebraska; Nebraska motor vehicle drivers’ personality traits, knowledge levels of negotiating HRGCs and driving experience are associated with inattentive driving; drivers with lower levels of knowledge of correct HRGC negotiation are: drivers who drive vehicles other than passenger cars, have received less safety information, have a shorter driving history, are older, have lower household income, and have higher intent to violate rules at rail crossings; inattentive driving behavior at HRGCs is directly and indirectly affected by their personality traits while drivers’ knowledge of correct HRGC negotiation appears to only have an indirect effect. Advisor: Aemal J.Khatta

Design of a 10GHz RF power amplifier in 130nm CMOS technology based on Wilkinson combiner methodology

There is a growing demand today to design and fabricate RF power amplifiers at high frequencies above 5GHz that can directly drive a 50Ω antenna with sufficiently high transmission power to meet the needs of various wireless communication applications. This has typically been done by using GaN or other III-V technologies to build the power amplifier transistor, in order to allow for the use of much higher power supply voltages, than are used in today’s silicon technologies. For example, a 5W GaN power amplifier at 5GHz would typically make use of a VDD of 5V to 10V, and would be done as a discrete device on a separate module from the RF analog circuitry built out of silicon. With the continuing evolution of Moore’s Law, silicon technologies in use today for high frequency wireless communications typically are using VDD of 1.5V or less. There is a desire, however, in many wireless applications to be able to place the RF power amplifier on the same silicon chip as all the other RF/analog IC circuitry, in order to save chip fabrication cost. Consequently, research in improved methods of RF power amplifier design in silicon technology is being done in many IC design laboratories in order to increase the RF power output of power amplifiers built in silicon. This MS Thesis proposes the complete design of a four channel RF power amplifier by using the Wilkinson combiner with 27dBm output power. All the circuits are designed and implemented based on the Global Foundries 130nm SiGe BiCMOS technology and design kit at a frequency of 10GHz with a VDD = 1.5V, to provide 0.5W of RF output signal power into a 50Ω antenna

Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning

As an important and challenging problem in computer vision, learning based optical flow estimation aims to discover the intrinsic correspondence structure between two adjacent video frames through statistical learning. Therefore, a key issue to solve in this area is how to effectively model the multi-scale correspondence structure properties in an adaptive end-to-end learning fashion. Motivated by this observation, we propose an end-to-end multi-scale correspondence structure learning (MSCSL) approach for optical flow estimation. In principle, the proposed MSCSL approach is capable of effectively capturing the multi-scale inter-image-correlation correspondence structures within a multi-level feature space from deep learning. Moreover, the proposed MSCSL approach builds a spatial Conv-GRU neural network model to adaptively model the intrinsic dependency relationships among these multi-scale correspondence structures. Finally, the above procedures for correspondence structure learning and multi-scale dependency modeling are implemented in a unified end-to-end deep learning framework. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed approach.Comment: 7 pages, 3 figures, 2 table

Industrial wireless sensor networks 2016

The industrial wireless sensor network (IWSN) is the next frontier in the Industrial Internet of Things (IIoT), which is able to help industrial organizations to gain competitive advantages in industrial manufacturing markets by increasing productivity, reducing the costs, developing new products and services, and deploying new business models. The IWSN can bridge the gap between the existing industrial systems and cyber networks to offer both new challenges and opportunities for manufacturers

Indole contributes to tetracycline resistance via the outer membrane protein OmpN in Vibrio splendidus

As an interspecies and interkingdom signaling molecule, indole has recently received attention for its diverse effects on the physiology of both bacteria and hosts. In this study, indole increased the tetracycline resistance of Vibrio splendidus. The minimal inhibitory concentration of tetracycline was 10 mu g/mL, and the OD600 of V. splendidus decreased by 94.5% in the presence of 20 mu g/mL tetracycline; however, the OD600 of V. splendidus with a mixture of 20 mu g/mL tetracycline and 125 mu M indole was 10- or 4.5-fold higher than that with only 20 mu g/mL tetracycline at different time points. The percentage of cells resistant to 10 mu g/mL tetracycline was 600-fold higher in the culture with an OD600 of approximately 2.0 (higher level of indole) than that in the culture with an OD600 of 0.5, which also meant that the level of indole was correlated to the tetracycline resistance of V. splendidus. Furthermore, one differentially expressed protein, which was identified as the outer membrane porin OmpN using SDS-PAGE combined with MALDI-TOF/TOF MS, was upregulated. Consequently, the expression of the ompN gene in the presence of either tetracycline or indole and simultaneously in the presence of indole and tetracycline was upregulated by 1.8-, 2.54-, and 6.01-fold, respectively, compared to the control samples. The combined results demonstrated that indole enhanced the tetracycline resistance of V. splendidus, and this resistance was probably due to upregulation of the outer membrane porin OmpN

Blockchain enabled industrial Internet of Things technology

The emerging blockchain technology shows promising potential to enhance industrial systems and the Internet of things (IoT) by providing applications with redundancy, immutable storage, and encryption. In the past a few years, many more applications in industrial IoT (IIoT) have emerged and the blockchain technologies have attracted huge amounts of attention from both industrial and academic researchers. In this paper we address the integration of blockchain and IIoT from the industrial prospective. A blockchain enabled IIoT framework is introduced and involved fundamental techniques are presented. Moreover, main applications and key challenges are addressed. A comprehensive analysis for the most recent research trends and open issues is provided associated with the blockchain enabled IIoT

Deep 3D Information Prediction and Understanding

3D information prediction and understanding play significant roles in 3D visual perception. For 3D information prediction, recent studies have demonstrated the superiority of deep neural networks. Despite the great success of deep learning, there are still many challenging issues to be solved. One crucial issue is how to learn the deep model in an unsupervised learning framework. In this thesis, we take monocular depth estimation as an example to study this problem through exploring the domain adaptation technique. Apart from the prediction from a single image or multiple images, we can also estimate the depth from multi-modal data, such as RGB image data coupled with 3D laser scan data. Since the 3D data is usually sparse and irregularly distributed, we are required to model the contextual information from the sparse data and fuse the multi-modal features. We examine the issues by studying the depth completion task. For 3D information understanding, such as point clouds analysis, due to the sparsity and unordered property of 3D point cloud, instead of the conventional convolution, new operations which can model the local geometric shape are required. We design a basic operation for point cloud analysis through introducing a novel adaptive edge-to-edge interaction learning module. Besides, due to the diversity in configurations of the 3D laser scanners, the captured 3D data often varies from dataset to dataset in object size, density, and viewpoints. As a result, the domain generalization in 3D data analysis is also a critical problem. We study this issue in 3D shape classification by proposing an entropy regularization term. Through studying four specific tasks, this thesis focuses on several crucial issues in deep 3D information prediction and understanding, including model designing, multi-modal fusion, sparse data analysis, unsupervised learning, domain adaptation, and domain generalization

Iterative Geometry-Aware Cross Guidance Network for Stereo Image Inpainting

Currently, single image inpainting has achieved promising results based on deep convolutional neural networks. However, inpainting on stereo images with missing regions has not been explored thoroughly, which is also a significant but different problem. One crucial requirement for stereo image inpainting is stereo consistency. To achieve it, we propose an Iterative Geometry-Aware Cross Guidance Network (IGGNet). The IGGNet contains two key ingredients, i.e., a Geometry-Aware Attention (GAA) module and an Iterative Cross Guidance (ICG) strategy. The GAA module relies on the epipolar geometry cues and learns the geometry-aware guidance from one view to another, which is beneficial to make the corresponding regions in two views consistent. However, learning guidance from co-existing missing regions is challenging. To address this issue, the ICG strategy is proposed, which can alternately narrow down the missing regions of the two views in an iterative manner. Experimental results demonstrate that our proposed network outperforms the latest stereo image inpainting model and state-of-the-art single image inpainting models.Comment: Accepted by IJCAI 202

Safety and Economic Assessment of Converting Two-Way Stop-Controlled Intersections to Roundabouts on High Speed Rural Highways

This research addressed two questions: “Are roundabouts on rural high-speed roadways safer than two-way stop controlled (TWSC) intersections?” and “What economic benefits can be expected from converting TWSC intersections to roundabouts in terms of safety improvement?” Crash and traffic data on four TWSC intersections that were converted to roundabouts in Kansas were analyzed using the empirical Bayes before-after evaluation method and crash costs were applied to evaluate economic benefits. Analysis showed that fatal, non-fatal, and property-damage-only crashes were reduced by 100%, 76.47%, and 35.49%, respectively. The annual monetary value from this reduction was between $1.0—$1.6 million in 2014 dollars