Clustering pavement aggregate particles based on shape and texture properties

Aggregates are the major component of pavements. Physical characteristics of aggregates significantly affect the properties of pavements. Different pavement construction projects may require different characteristics of aggregate. Proper selection of aggregate with consistent shape properties ensures high performance of pavements. The available test methods for evaluating the aggregate physical properties and classifying them are laborious, time-consuming, and subjective. This study presents the development of an objective system which evaluates the shape properties of aggregate particles and classifies them into distinct groups regarding their sphericity, form, angularity and texture features. By using this system, the heterogeneity in an aggregate sample based on a given feature could be assessed. This system includes a laser scanner developed at the University of Texas at Austin to scan aggregate particles. Total of 1398 aggregate particles, from eight different quarries in the state of Texas, were scanned. The scanned data were analyzed using a MATLAB algorithm for measuring the sphericity, form, angularity, and texture of particles. All the measurements were stored in an Excel file and were imported to another algorithm developed in R software and OpenBUGS package to cluster the aggregate particles. Several methods of clustering were reviewed and finally, model-based clustering approach was selected. The model-based cluster analysis was applied to the measurements aiming to detect subclasses in aggregate particles based on each feature. This study shows how to use this clustering approach to group the particles based on their sphericity, form, angularity, and texture features.Statistic

Laboratory Resistivity Measurements for Soil Characterization

Field based electrical resistivity measurements, such as electrical resistivity tomography (ERT) and capacitively coupled resistivity (CCR), are geophysical methods that offer a non-destructive and rapid means to collect continuous data. As such, ERT and CCR are becoming increasingly popular tools for geotechnical engineers; however, it is challenging to derive geotechnical information such as soil type, density, and water content from the data. A laboratory geophysical investigation was carried out to gain a better understanding of the parameters that affect the electrical resistivity of soils and devise a relationship between resistivity and soil type or classification. In this study, a soil box attached to a resistance meter in a 4-electrode Wenner array was used for the resistivity measurements. Nine different benchmark soils were tested, representing most of the major soil groups according to the unified soil classification system. The effects of water quality, water content, degree of saturation, bulk density, dry density, Atterberg limits and temperature on the measured electrical resistivity of the soils were investigated. Although there is an apparent correlation between all of these parameters and the electrical resistivity of soils, the parameters that are most effective in the identification of soil type are bulk density and degree of saturation. The laboratory results indicate that if the soil is saturated, a reasonable estimate of the soil group classification can likely be made from resistivity alone. For unsaturated samples, the range of possible resistivity values is much larger; however, the estimate of soil group can be significantly narrowed down if an approximation of saturation or density can be made. To assess the feasibility of the developed approach, a series of verification studies using samples acquired from the field and other processed soils were also conducted

Local Binary Patterns as a Feature Descriptor in Alignment-free Visualisation of Metagenomic Data

Shotgun sequencing has facilitated the analysis of complex microbial communities. However, clustering and visualising these communities without prior taxonomic information is a major challenge. Feature descriptor methods can be utilised to extract these taxonomic relations from the data. Here, we present a novel approach consisting of local binary patterns (LBP) coupled with randomised singular value decomposition (RSVD) and Barnes-Hut t-stochastic neighbor embedding (BH-tSNE) to highlight the underlying taxonomic structure of the metagenomic data. The effectiveness of our approach is demonstrated using several simulated and a real metagenomic datasets

Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis

Open Radio Access Network (O-RAN) has introduced an emerging RAN architecture that enables openness, intelligence, and automated control. The RAN Intelligent Controller (RIC) provides the platform to design and deploy RAN controllers. xApps are the applications which will take this responsibility by leveraging machine learning (ML) algorithms and acting in near-real time. Despite the opportunities provided by this new architecture, the progress of practical artificial intelligence (AI)-based solutions for network control and automation has been slow. This is mostly because of the lack of an endto-end solution for designing, deploying, and testing AI-based xApps fully executable in real O-RAN network. In this paper we introduce an end-to-end O-RAN design and evaluation procedure and provide a detailed discussion of developing a Reinforcement Learning (RL) based xApp by using two different RL approaches and considering the latest released O-RAN architecture and interfaces.Comment: This article has been accepted for publication in IEEE GLOBECOM 202

Doctor of Philosophy

dissertationThis dissertation explores the role of professional self-conceptions on ethical behavior. Relying on recent literature on licensing, contrary to conventional wisdom, I suggest that professional self-conceptions lead individuals to engage in unethical behaviors. The results of Study 1 demonstrated that professional self-conceptions license individuals to act unethically. Study 2 tested for differential effect of accessibility of professional self-conceptions versus concept of professionalism and showed that seeing oneself as a professional, and not the accessibility of the concept of professionalism per se, is needed to license unethical acts. Study 3, a field study, showed that membership in occupations traditionally associated with professions compared to other occupations led to higher unethical behaviors and professional selfconceptions mediated the effect of occupational membership on unethical behaviors. Together, the results of three studies demonstrate that professional self-conceptions, either measured or manipulated, can license individuals to act unethically. The theoretical and practical implications of these findings are discussed

Guilt Enhances the Sense of Control and Drives Risky Judgments

The present studies investigate the hypothesis that guilt influences risk-taking by enhancing one's sense of control. Across multiple inductions of guilt, we demonstrate that experimentally induced guilt enhances optimism about risks for the self (Study 1), preferences for gambles versus guaranteed payoffs (Studies 2, 4, and 6), and the likelihood that one will engage in risk-taking behaviors (Study 5). In addition, we demonstrate that guilt enhances the sense of control over uncontrollable events, an illusory control (Studies 3, 4, and 5), and found that a model with illusory control as a mediator is consistent with the data (Studies 5 and 6). We also found that a model with feelings of guilt as a mediator, but not generalized negative affect, fits the data (Study 4). Finally, we examined the relative explanatory power of different appraisals and found that appraisals of illusory control best explain the influence of guilt on risk-taking (Study 6). These results provide the first empirical demonstration of the influence of guilt on sense of control and risk-taking, extend previous theorizing on guilt, and more generally contribute to our understanding of how specific emotions influence cognition and behavior

The Contaminating Effects of Building Instrumental Ties: How Networking Can Make Us Feel Dirty

To create social ties to support their professional or personal goals, people actively engage in instrumental networking. Drawing from moral psychology research, we posit that this intentional behavior has unintended consequences for an individual's morality. Unlike personal networking in pursuit of emotional support or friendship, and unlike social ties that emerge spontaneously, instrumental networking in pursuit of professional goals can impinge on an individual's moral purity—a psychological state that results from viewing the self as clean from a moral standpoint—and thus make an individual feel dirty. We theorize that such feelings of dirtiness decrease the frequency of instrumental networking and, as a result, work performance. We also examine sources of variability in networking-induced feelings of dirtiness by proposing that the amount of power people have when they engage in instrumental networking influences how dirty this networking makes them feel. Three laboratory experiments and a survey study of lawyers in a large North American law firm provide support for our predictions. We call for a new direction in network research that investigates how network-related behaviors associated with building social capital influence individuals' psychological experiences and work outcomes

An adaptive filtering approach using supervised SSA for identification of sleep stages from EEG

Purpose: Sleep is a complex physiological state and an indicator of the changes in the brain function similar to those occurring in many psychiatric and neurological conditions. Since visual sleep scoring consuming process, automatic sleep staging methods, also called scoring, hold promise in diagnosing alterations in the sleep process and the sleep EEG more effectively. Method: In this paper, a supervised approach for sleep scoring from single channel EEG signals is proposed. First, a supervised singular spectrum analysis (SSA) which is a subspace based method is used to extract the desired signal for each stage of sleep. Then, two recursive least squares (RLS) adaptive filters are trained and used to identify first and deep sleep stages. Result: The proposed system which can be considered as a filter bank for separating multiple signal subbands is tested using real EEG where the results verify the accuracy of the proposed method. Conclusion: The overall result show the effectiveness of algorithm for detection of sleep stages from EEG signals often characterised by a sharp increase in delta and a rapid decrease in alpha as sleep deepens

Improving time–frequency domain sleep EEG classification via singular spectrum analysis

Background: Manual sleep scoring is deemed to be tedious and time consuming. Even among automatic methods such as Time-Frequency (T-F) representations, there is still room for more improvement. New method: To optimise the efficiency of T-F domain analysis of sleep electroencephalography (EEG) a novel approach for automatically identifying the brain waves, sleep spindles, and K-complexes from the sleep EEG signals is proposed. The proposed method is based on singular spectrum analysis (SSA). The single-channel EEG signal (C3-A2) is initially decomposed and then the desired components are automatically separated. In addition, the noise is removed to enhance the discrimination ability of features. The obtained T-F features after preprocessing stage are classified using a multi-class support vector machines (SVM) and used for the identification of four sleep stages over three sleep types. Furthermore, to emphasize on the usefulness of the proposed method the automatically-determined spindles are parameterised to discriminate three sleep types. Result: The four sleep stages are classified through SVM twice: with and without preprocessing stage. The mean accuracy, sensitivity, and specificity for before the preprocessing stage are: 71.5 ± 0.11%, 56.1 ± 0.09% and 86.8 ± 0.04% respectively. However, these values increase significantly to 83.6 ± 0.07%, 70.6 ± 0.14% and 90.8 ± 0.03% after applying SSA. Comparison with existing method: The new T-F representation has been compared with the existing benchmarks. Our results prove that, the proposed method well outperforms the previous methods in terms of identification and representation of sleep stages. Conclusion: Experimental results confirm the performance improvement in terms of classification rate and also representative T-F domain