Gait Identification Considering Body Tilt byWalking Direction Changes

Gait identification has recently gained attention as a method of identifying individuals at a distance. Thought most of the previous works mainly treated straight-walk sequences for simplicity, curved-walk sequences should be also treated considering situations where a person walks along a curved path or enters a building from a sidewalk. In such cases, person's body sometimes tilts by centrifugal force when walking directions change, and this body tilt considerably degrades gait silhouette and identification performance, especially for widely-used appearance-based approaches. Therefore, we propose a method of body-tilted silhouette correction based on centrifugal force estimation from walking trajectories. Then, gait identification process including gait feature extraction in the frequency domain and learning of a View Transformation Model (VTM) follows the silhouette correction. Experiments of gait identification for circular-walk sequences demonstrate the effectiveness of the proposed method

Load Estimation, Structural Identification and Human Comfort Assessment of Flexible Structures

Stadiums, pedestrian bridges, dance floors, and concert halls are distinct from other civil engineering structures due to several challenges in their design and dynamic behavior. These challenges originate from the flexible inherent nature of these structures coupled with human interactions in the form of loading. The investigations in past literature on this topic clearly state that the design of flexible structures can be improved with better load modeling strategies acquired with reliable load quantification, a deeper understanding of structural response, generation of simple and efficient human-structure interaction models and new measurement and assessment criteria for acceptable vibration levels. In contribution to these possible improvements, this dissertation taps into three specific areas: the load quantification of lively individuals or crowds, the structural identification under non-stationary and narrowband disturbances and the measurement of excessive vibration levels for human comfort. For load quantification, a computer vision based approach capable of tracking both individual and crowd motion is used. For structural identification, a noise-assisted Multivariate Empirical Mode Decomposition (MEMD) algorithm is incorporated into the operational modal analysis. The measurement of excessive vibration levels and the assessment of human comfort are accomplished through computer vision based human and object tracking, which provides a more convenient means for measurement and computation. All the proposed methods are tested in the laboratory environment utilizing a grandstand simulator and in the field on a pedestrian bridge and on a football stadium. Findings and interpretations from the experimental results are presented. The dissertation is concluded by highlighting the critical findings and the possible future work that may be conducted

Caution, Drivers! Children Present: Traffic, Pollution, and Infant Health

Since the Clean Air Act Amendments of 1990 (CAAA), atmospheric concentration of local pollutants has fallen drastically. A natural question is whether further reductions will yield additional health benefits. We further this research by addressing two related research questions: (1) what is the impact of automobile driving (and especially congestion) on ambient air pollution levels, and (2) what is the impact of modern air pollution levels on infant health? Our setting is California (with a focus on the Central Valley and Southern California) in the years 2002-2007. Using an instrumental variables approach that exploits the relationship between traffic, ambient weather conditions, and various pollutants, our findings suggest that ambient pollution levels, specifically particulate matter, still have large impacts on weekly infant mortality rates. Our results also illustrate the importance of weather controls in measuring pollution’s impact on infant mortality.University of California Energy Institute and UC Davis Institute of Transportation Studies. UC Davis Institute of Governmental Affairs, the UC Davis Institute of Transportation Studies, and the TSR&TP through the Atmospheric Aerosols and Health (AAH) Lead Campus program

Modelling bid-ask spreads in competitive dealership markets

pricing;estimation;asset valuation

A Software Engineered Voice-Enabled Job Recruitment Portal System

The inability of job seekers to get timely job information regarding the status of the application submitted via conventional job portal system which is usually dependent on accessibility to the Internet has made so many job applicants to lose their placements. Worse still, the epileptic services offered by Internet Service Providers and the poor infrastructures in most developing countries have greatly hindered the expected benefits from Internet usage. These have led to cases of online vacancies notifications unattended to simply because a job seeker is neither aware nor has access to the Internet. With an increasing patronage of mobile phones, a self-service job vacancy notification with audio functionality or an automated job vacancy notification to all qualified job seekers through mobile phones will simply provide a solution to these challenges. In this paper, we present a Voice-enabled Job Recruitment Portal (JRP) System. The system is accessed through two interfaces – the voice user’s interface (VUI) and web interface. The VUI was developed using VoiceXML and the web interface using PHP, and both interfaces integrated with Apache and MySQL as the middleware and back-end component respectively. The JRP proposed in this paper takes the hassle of job hunting from job seekers, provides job status information in real-time to the job seeker and offers other benefits such as, cost, effectiveness, speed, accuracy, ease of documentation, convenience and better logistics to the employer in seeking the right candidate for a job

Time-dependent metabolic phenotyping of inflammatory dysregulation

A rich and functional description of a patient health status is the fundamental basis for the personalisation of treatment and the targeting of interventions. The function of inflammation in the healing process as well as its involvement in most major diseases is well established, yet the specific mechanism by which it contributes to the pathogenesis is still not fully understood. If conditions arising from a dysregulation of the inflammatory process are to be treated before they become irreversible, a novel understanding of these pathologies must be achieved and a stratification of patients based on their inflammatory status undertaken. The work presented in this thesis aims to deliver new analytical and statistical approaches to support the investigation of the time-dependent dysregulation of inflammation. Lipid mediators have been described as exerting a major role in the initiation and regulation of the inflammatory response, yet analytical platforms for their large-scale characterisation in human biofluids are lacking. This thesis reports the validation of an assay for the simultaneous quantification of pro- and anti-inflammatory signalling molecules in multiple human biofluids. The coverage of the assay in each biofluid is subsequently established, characterising inflammatory signalling across biological compartments. A second study explores the assay’s applicability in a clinical context; investigating the relationship between lipid mediators, current clinical markers of inflammation and post-operative complications. Characterising the interplay between signalling and regulatory networks is key to understanding a living system’s response to perturbations, yet few statistical approaches are suited for the detection of time-dependent patterns in short and irregularly sampled longitudinal datasets. This thesis reports the development of a statistical approach to support the identification of altered time-trajectories in such studies. The method’s wide applicability is subsequently demonstrated on two investigations covering the diversity of metabolic phenotyping data generation platforms. This thesis is a proof of concept for the characterisation of patient-specific inflammatory status in a clinical context and the identification of altered time-dependent patterns. Both analytical and statistical developments have been motivated by the needs of real world applications and provide a template for the characterisation and analysis of the molecular basis for treatment.Open Acces