Resource Allocation, Scheduling and Feedback Reduction in Multiple Input Multiple Output (MIMO) Orthogonal Frequency-Division Multiplexing (OFDM) Systems

The number of wireless systems, services, and users are constantly increasing and therefore the bandwidth requirements have become higher. One of the most robust modulations is Orthogonal Frequency-Division Multiplexing (OFDM). It has been considered as an attractive solution for future broadband wireless communications. This dissertation investigates bit and power allocation, joint resource allocation, user scheduling, and limited feedback problem in multi-user OFDM systems. The following dissertation contributes to improved OFDM systems in the following manner. (1) A low complexity sub-carrier, power, and bit allocation algorithm is proposed. This algorithm has lower computational complexity and results in performance that is comparable to that of the existing algorithms. (2) Variations of the proportional fair scheduling scheme are proposed and analyzed. The proposed scheme improves system throughput and delay time, and achieves higher throughput without sacrificing fairness which makes it a better scheme in terms of efficiency and fairness. (3) A DCT feedback compression algorithm based on sorting is proposed. This algorithm uses sorting to increase the correlation between feedback channel quality information of frequency selective channels. The feedback overhead of system is successfully reduced

Metabolic Characteristics of Porcine LA-MRSA CC398 and CC9 Isolates from Germany and China via Biolog Phenotype MicroArrayTM

Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) is an important zoonotic pathogen, often multi-resistant to antimicrobial agents. Among swine, LA-MRSA of clonal complex (CC) 398 dominates in Europe, Australia and the Americas, while LA-MRSA-CC9 is the main epidemic lineage in Asia. Here, we comparatively investigated the metabolic properties of rare and widespread porcine LA-MRSA isolates from Germany and China using Biolog Phenotype MicroArray technology to evaluate if metabolic variations could have played a role in the development of two different epidemic LA-MRSA clones in swine. Overall, we were able to characterize the isolates’ metabolic profiles and show their tolerance to varying environmental conditions. Sparse partial least squares discriminant analysis (sPLS-DA) supported the detection of the most informative substrates and/or conditions that revealed metabolic differences between the LA-MRSA lineages. The Chinese LA-MRSA-CC9 isolates displayed unique characteristics, such as a consistently delayed onset of cellular respiration, and increased, reduced or absent usage of several nutrients. These possibly unfavorable metabolic properties might promote the ongoing gradual replacement of the current epidemic LA-MRSA-CC9 clone in China with the emerging LA-MRSA-CC398 lineage through livestock trade and occupational exposure. Due to the enhanced pathogenicity of the LA-MRSA-CC398 clone, the public health risk posed by LA-MRSA from swine might increase further

SENP1 regulates IFN-γ−STAT1 signaling through STAT3−SOCS3 negative feedback loop

Interferon-γ (IFN-γ) triggers macrophage for inflammation response by activating the intracellular JAK−STAT1 signaling. Suppressor of cytokine signaling 1 (SOCS1) and protein tyrosine phosphatases can negatively modulate IFN-γ signaling. Here, we identify a novel negative feedback loop mediated by STAT3−SOCS3, which is tightly controlled by SENP1 via de-SUMOylation of protein tyrosine phosphatase 1B (PTP1B), in IFN-γ signaling. SENP1-deficient macrophages show defects in IFN-γ signaling and M1 macrophage activation. PTP1B in SENP1-deficient macrophages is highly SUMOylated, which reduces PTP1B-induced de-phosphorylation of STAT3. Activated STAT3 then suppresses STAT1 activation via SOCS3 induction in SENP1-deficient macrophages. Accordingly, SENP1-deficient macrophages show reduced ability to resist Listeria monocytogenes infection. These results reveal a crucial role of SENP1-controlled STAT1 and STAT3 balance in macrophage polarization

LoRa-based Internet-of-Things: A Water Quality Monitoring System

Rapid convergence of traditional research areas of embedded systems, wireless sensor networks, control systems, automation, and sensors has enabled the evolution of Internet of Things (IoT). The concept of IoT is based on a self-configuring and adaptive system consisting of networks of sensors that interconnect in such a way as to make them intelligent and programmable. An important application of IoT is in water quality monitoring through distributed sensors and controllers that stream data for cloud storage and web access. Lake Dardanelle is the 3rd largest publicly-owned lake in Arkansas covering 138.8 km2 (34,300 acres). Apart from being a recreational destination, it supports large populations of fish. Currently, the lake does not have any water quality monitoring station that can record data for public or private users. The main goal of this research is to leverage the power of IoT for design and development of a mobile water quality monitoring system (WQ MS) for Lake Dardanelle. We survey the available wireless technologies, and then deploy the LoRa (Long Range) network and evaluate its performance. We also present the detailed design and implementation of the mobile WQMS based on an unmanned surface vehicle (USV). © 2019 IEEE

Measuring self-efficacy in engineering courses - Impact of learning style preferences

Self-efficacy is an important outcome of engineering education as it relates to students\u27 feelings, thoughts, motivations and behaviors. The key element of self-efficacy construct is a self-belief in one\u27s abilities and has been described in detail in terms of Bandura\u27s Social Cognitive Theory. Measuring self-efficacy of students in engineering courses is an important element of evaluating the overall effectiveness of engineering education. Traditional methods of judging student learning outcomes include quizzes, homework, exams, and course projects, with a primary focus on measuring student skills. It is important that, along with mastering the skills, students should also possess self-belief that they will be able to perform required tasks with those skills. An important research question is: How should self-efficacy be measured in engineering courses? This paper addresses this question by highlighting the results of a longitudinal study conducted on students in engineering modeling and design (junior-level) courses at Arkansas Tech University. This course is selected because the teaching method is based on project-based learning activities. Using the collected data, we have analyzed the effect of learning style preference on the perception of self-efficacy. Previous research has demonstrated that students have different preferred learning styles, and they approach learning new information in different ways. Our collected data includes student responses on their learning styles, including lectures/discussions, books/related written material, video/movies/media, hands-on activities, and a hybrid method. Paired sample t-tests and one-way analysis of variance (ANOVA) are used to analyze the collected data. These methods allow us to determine any statistically significant differences between the self-efficacy scores at the start and end of the course. We also determine the impact of learning style preference on students\u27 perception of self-efficacy. Based on the collected data, results indicated that the self-efficacy of students improved equally using project-based learning techniques, regardless of their learning style preferences

A study on measuring self-efficacy in engineering modeling and design courses

Preparing future engineers to model and design engineering systems is one of the primary objectives of engineering education. Rapid advances in technologies such as high performance computing, rapid prototyping through additive manufacturing, robotics, automation, nanotechnology and instrumentation have increased the complexity of engineering systems. The engineering design process involves knowledge of multiple domains of engineering and collaborative work among multi-disciplinary teams. The design process is also complicated by the safety, practicality and cost constraints. In light of these challenges, the engineering education needs to maintain its focus on principles of engineering design that can effectively prepare engineering graduates to meet the challenges posed by rapid technological growth in engineering and manufacturing technologies. The effectiveness of engineering education in modeling and design courses, traditionally, is measured through quizzes, exams and course projects that are aimed at measuring level of developed skills. For engineering students to be successful, it is not only essential that they possess required skills and competencies but should also have the belief that they will be able to perform with those skills. This self-belief in one\u27s ability to perform assigned tasks for attainment of a specified objective has been described as self-efficacy construct in terms of Bandura\u27s Social Cognitive Theory. An important research question is how to measure the developed self-confidence of engineering students in modeling and design courses. To address this question, present study proposes development of a self-efficacy measure. The proposed measure has been used to collect pre and post course data on self-efficacy through student surveys in engineering modeling and design courses at Arkansas Tech University. The collected data is analyzed with Statistical Package for the Social Sciences (SPSS). The data analysis involves computation of correlations and reliability coefficients, t-tests and analysis of variance (ANOVA)

Introducing AI to Undergraduate Students via Computer Vision Projects

Computer vision, as a subfield in the general artificial intelligence (AI), is a technology can be visualized and easily found in a large number of state-of-art applications. In this project, undergraduate students performed research on a landmark recognition task using computer vision techniques. The project focused on analyzing, designing, configuring, and testing the two core components in landmark recognition: feature detection and description. The project modeled the landmark recognition system as a tour guide for visitors to the campus and evaluated the performance in the real world circumstances. By analyzing real-world data and solving problems, student’s cognitive skills and critical thinking skills were sharpened. Their knowledge and understanding in mathematical modeling and data processing were also enhanced

Learn to Rank Images: A Unified Probabilistic Hypergraph Model for Visual Search

In visual search systems, it is important to address the issue of how to leverage the rich contextual information in a visual computational model to build more robust visual search systems and to better satisfy the user’s need and intention. In this paper, we introduced a ranking model by understanding the complex relations within product visual and textual information in visual search systems. To understand their complex relations, we focused on using graph-based paradigms to model the relations among product images, product category labels, and product names and descriptions. We developed a unified probabilistic hypergraph ranking algorithm, which, modeling the correlations among product visual features and textual features, extensively enriches the description of the image. We conducted experiments on the proposed ranking algorithm on a dataset collected from a real e-commerce website. The results of our comparison demonstrate that our proposed algorithm extensively improves the retrieval performance over the visual distance based ranking

Robotics and Deep Learning Framework for Structural Health Monitoring of Utility Pipes

A critical modern-day challenge for utility operators is condition monitoring of underground sewer infrastructure. Existing industry standard for underground sewer line inspection is based on sending a wire-guided robot with a closed circuit television (CCTV) camera through a pipe. A trained operator observes the video feed from the camera, and annotates it to record defects such as cracks, sags, offsets, root infiltrations, grease build up, and lateral protrusions. The success of a CCTV based robot system depends on visual observation and alertness of the operator. There is a likelihood that the operator fatigue and distraction may lead to missed observations. The CCTV based systems are expensive and man-hour intensive. We propose a deep learning based method to make the defect detection process automated without the need for an onsite operator to visually observe the video. The system is based on passing an autonomous camera-mounted robot through the pipe. The recorded video is analyzed using deep learning based algorithms. Our initial focus is to detect presence of cracks in polyvinyl chloride pipes, which are industry standard for sewer installations. We propose a deep learning framework including network architecture to detect presence or absence of a crack in a pipe sample. We also collect empirical data using an autonomous robot during laboratory trials to validate our approach. The data analysis indicates an accuracy of 89.42% in training and 83.3 % in validation. Further data collection and analysis is currently in progress and results will be reported in future. © 2019 IEEE