Improving the performance and evaluation of computer-assisted semen analysis

Semen analysis is performed routinely in fertility clinics to analyze the quality of semen and sperm cells of male patients. The analysis is typically performed by trained technicians or by Computer-Assisted Semen Analysis (CASA) systems. Manual semen analysis performed by technicians is subjective, time-consuming, and laborious, and yet most fertility clinics perform semen analysis in this manner. CASA systems, which are designed to perform the same tasks automatically, have a considerable market share, yet many studies still express concerns about their accuracy and consistency. In this dissertation, the focus is on detection, tracking, and classification of sperm cells in semen images, key elements of CASA systems. The objective is to improve existing CASA algorithms and systems by applying validated computer vision, tracking, and computational intelligence algorithms. The first step of the study is the development of simulation models for generating synthetic images of semen samples. The images enable the assessment of CASA systems and their algorithms. Specifically, the simulation models generate time-lapse images of semen samples for various sperm image categories and include ground truth labels. The models exploit standard image processing operations such as point spread functions and 2D convolutions, as well as new models of sperm cell swimming, developed for this study. They embody multiple studies of sperm motility in the form of parameterized motion equations. Use cases are presented to use the swimming models and the simulated images to assess and compare algorithms for sperm cell segmentation, localization, and tracking. Second, a digital washing algorithm is presented for unwashed semen samples. Digital washing has the potential to replace the chemical washing techniques used by fertility clinics at present, which are costly, time-consuming, and unfriendly to the environment. The digital washing algorithm extracts features from moving sperm cells in an image, and uses these features to identify all sperm cells (moving and stationary) within each studied image (simulated or real). The effectiveness of the digital washing algorithm is demonstrated by comparing the performance of the proposed algorithm to other cell segmentation and detection techniques. Third, a classification algorithm for sperm cells is developed, based on their swimming patterns. The classification algorithm uses K-means clustering on a subset of motility parameters of sperm cells selected by the Artificial Bee Colony (ABC) algorithm. Results of classification and clustering are shown, using simulated and real semen images. Swimming pattern classification has the potential to increase understanding of the relationship between the distribution of sperm cell swimming modes in a patient’s semen image and the fertility of that patient. Lastly, a new method is presented to calculate motility parameters from sperm tracks. The movement of sperm cell is modeled as a sinusoidal traveling wave (“traveling sinusoid”). The amplitude and average path of a moving cell are estimated using an extended Kalman filter (EKF). The states estimated by the EKF include position, velocity, amplitude, and frequency of the traveling wave. The motility parameters calculated from this approach are shown to be superior to those calculated by other existing methods in terms of their accuracy and consistency. CASA developers will find in this study (and in the software made available) new tools to improve the performance of their designs, and to compare and contrast different proposed approaches and algorithms

The maximum time interval of time-lapse photography for monitoring construction operations

Many construction companies today utilize webcams on their jobsites to monitor and record construction operations. Jobsite monitoring is often limited to outdoor construction operations due to lack of mobility of wired webcams. A wireless webcam may help monitor indoor construction operations with enhanced mobility. The transfer time of sending a photograph from the wireless webcam, however, is slower than that of a wired webcam. It is expected that professionals may have to analyze indoor construction operations with longer interval time-lapse photographs if they want to use a wireless webcam. This research aimed to determine the maximum time interval for time-lapse photos that enables professionals to interpret construction operations and productivity. In order to accomplish the research goal, brickwork of five different construction sites was videotaped. Various interval time-lapse photographs were generated from each video. Worker?s activity in these photographs was examined and graded. The grades in one-second interval photographs were compared with the grades of the same in longer time interval photographs. Error rates in observing longer time-lapse photographs were then obtained and analyzed to find the maximum time interval of time-lapse photography for monitoring construction operations. Research has discovered that the observation error rate increased rapidly until the 60-second interval and its increasing ratio remained constant. This finding can be used to predict a reasonable amount of error rate when observing time-lapse photographs less than 60-second interval. The observation error rate with longer than 60-second interval did not show a constant trend. Thus, the 60-second interval could be considered as the maximum time interval for professionals to interpret construction operations and productivity

Forecasting potential project risks through leading indicators to project outcome

During project execution, the status of the project is periodically evaluated, using traditional methods or standard practices. However, these traditional methods or standard practices may not adequately identify certain issues, such as lack of sufficient identification of warning signs that predict potential project failure. Current methods may lack the ability to provide real time indications of emerging problems that impact project outcomes in a timely manner. To address this problem, the Construction Industry Institute (CII) formed a research team to develop a new tool that can forecast the potential risk of not meeting specific project outcomes based on assessing leading indicators. Thus, the leading indicators were identified and then the new tool was developed and validated. A screening process was conducted through industry surveys after identifying potential leading indicators. Each time, industry professionals were asked to evaluate the negative impact of leading indicators on project outcomes that were identified to measure the impact of leading indicators on project health. Through this process, forty-three leading indicators were acquired finally. Using descriptive statistics, the amount of negative impact of each leading indicator on project outcomes was identified after the analysis of the survey results. Based on these impacts, the tool development was initiated. The tool concept is that no indication of problems based on assessing leading indicators results in the tool output score high. To comply with this concept, specific weights were assigned to each leading indicator to reflect the impact on each project outcome. By this procedure, the Project Health Indicator (PHI) tool was developed. The validation process of the PHI tool was conducted using completed projects and finally negative correlation was observed between project outcomes and health scores generated by the PHI tool

Fabrication and surface plasmon coupling studies on the dielectric/Ag structure for transparent conducting electrode applications

The dielectric/Ag structures were fabricated on glass substrates using various metal oxides as dielectrics and their optical properties were studied through transmittance and ellipsometry measurements. The structures with 10 nm Ag film deposited on various metal oxides (Al2O3, ZrO2, SrTiO3, TiO2, CaCu3Ti4O12, WO3 and HfO2) of 30 nm showed enhancement in transmittance compared to bare Ag film in the visible region. This enhancement in transmittance was explained through suppression of surface plasmon coupling at the dielectric/Ag interface. The surface plasmon wave-vector (k(SP)) was calculated using the measured dielectric constants for the dielectric and Ag through ellipsometry and employed to analyze the transmittance data. The k(SP)/k(0) and delta(SP) values were estimated and used to interpret the enhanced visible transmittance for different dielectric/Ag structures. (C) 2014 Optical Society of Americ

From Text to Sign Language: Exploiting the Spatial and Motioning Dimension

PACLIC 19 / Taipei, taiwan / December 1-3, 200

Effects of Nd-doping on the structural, electrical, and multiferroic properties of Bi7Fe3Ti3O21 thin films

Aurivillius-phase six-layered Bi7Fe3Ti3O21 (BFTO21) and Nd-doped Bi6.4Nd0.6Fe3Ti3O21 (BNdFTO21) thin films were prepared on Pt(111)/Ti/SiO2/Si(100) substrates by using a chemical solution deposition method in order to investigate their structural, electrical, and multiferroic properties. Doping the Bi sites of the BFTO21 with Nd-ions led to remarkable improvements in the electrical and the multiferroic properties. The electrical study of the BNdFTO21 thin film showed a low leakage current density of 4.38 × 10-6 A/cm at an applied electric field of 100 kV/cm, which was about one order of magnitude lower than that of the BFTO21 thin film. The ferroelectric P - E hysteresis loop of the BNdFTO21 thin film exhibited a large remnant polarization (2Pr) of 24 μC/cm2 and a low coercive electric field (2Ec) of 154 kV/cm at an applied electric field of 239 kV/cm. Furthermore, the magnetization and the coercive magnetic field that were observed for the BNdFTO21 thin film at room temperature were drastically enhanced compared to those observed for the BFTO21 thin film