Navigation Sensor Stochastic Error Modeling and Nonlinear Estimation for Low-Cost Land Vehicle Navigation

The increasing use of low-cost inertial sensors in various mass-market applications necessitates their accurate stochastic modeling. Such task faces challenges due to outliers in the sensor measurements caused by internal and/or external factors. To optimize the navigation performance, robust estimation techniques are required to reduce the influence of outliers to the stochastic modeling process. The Generalized Method of Wavelet Moments (GMWM) and its Multi-signal extensions (MS-GMWM) represent the latest trend in the field of inertial sensor error stochastic analysis, they are capable of efficiently modeling the highly complex random errors displayed by low-cost and consumer-grade inertial sensors and provide very advantageous guarantees for the statistical properties of their estimation products. On the other hand, even though a robust version exists (RGMWM) for the single-signal method in order to protect the estimation process from the influence of outliers, their detection remains a challenging task, while such attribute has not yet been bestowed in the multi-signal approach. Moreover, the current implementation of the GMWM algorithm can be computationally intensive and does not provide the simplest (composite) model. In this work, a simplified implementation of the GMWM-based algorithm is presented along with techniques to reduce the complexity of the derived stochastic model under certain conditions. Also, it is shown via simulations that using the RGMWM every time, without the need for contamination existence confirmation, is a worthwhile trade-off between reducing the outlier effects and decreasing the estimator efficiency. Generally, stochastic modeling techniques, including the GMWM, make use of individual static signals for inference. However, it has been observed that when multiple static signal replicates are collected under the same conditions, they maintain the same model structure but exhibit variations in parameter values, a fact that called for the MS-GMWM. Here, a robust multi-signal method is introduced, based on the established GMWM framework and the Average Wavelet Variance (AWV) estimator, which encompasses two robustness levels: one for protection against outliers in each considered replicate and one to safeguard the estimation against the collection of signal replicates with significantly different behaviour than the majority. From that, two estimators are formulated, the Singly Robust AWV (SR-AWV) and the Doubly Robust (DR-AWV) and their model parameter estimation efficiency is confirmed under different data contamination scenarios in simulation and case studies. Furthermore, a hybrid case study is conducted that establishes a connection between model parameter estimation quality and implied navigation performance in those data contamination settings. Finally, the performance of the new technique is compared to the conventional Allan Variance in a land vehicle navigation experiment, where the inertial information is fused with an auxiliary source and vehicle movement constraints using the Extended and Unscented Kalman Filters (EKF/UKF). Notably, the results indicate that under linear-static conditions, the UKF with the new method provides a 16.8-17.3% improvement in 3D orientation compared to the conventional setting (AV with EKF), while the EKF gives a 7.5-9.7% improvement. Also, in dynamic conditions (i.e., turns), the UKF demonstrates an 14.7-17.8% improvement in horizontal positioning and an 11.9-12.5% in terms of 3D orientation, while the EKF has an 8.3-12.8% and an 11.4-11.7% improvement respectively. Overall, the UKF appears to perform better but has a significantly higher computational load compared to the EKF. Hence, the EKF appears to be a more realistic option for real-time applications such as autonomous vehicle navigation

External validation and calibration of IVFpredict:A national prospective cohort study of 130,960 in vitro fertilisation Cycles

© 2015 Smith et al. Background Accurately predicting the probability of a live birth after in vitro fertilisation (IVF) is important for patients, healthcare providers and policy makers. Two prediction models (Templeton and IVFpredict) have been previously developed from UK data and are widely used internationally. The more recent of these, IVFpredict, was shown to have greater predictive power in the development dataset. The aim of this study was external validation of the two models and comparison of their predictive ability. Methods and Findings 130,960 IVF cycles undertaken in the UK in 2008-2010 were used to validate and compare the Templeton and IVFpredict models. Discriminatory power was calculated using the area under the receiver-operator curve and calibration assessed using a calibration plot and Hosmer-Lemeshow statistic. The scaled modified Brier score, with measures of reliability and resolution, were calculated to assess overall accuracy. Both models were compared after updating for current live birth rates to ensure that the average observed and predicted live birth rates were equal. The discriminative power of both methods was comparable: the area under the receiver-operator curve was 0.628 (95% confidence interval (CI): 0.625-0.631) for IVFpredict and 0.616 (95% CI: 0.613-0.620) for the Templeton model. IVFpredict had markedly better calibration and higher diagnostic accuracy, with calibration plot intercept of 0.040 (95% CI: 0.017-0.063) and slope of 0.932 (95% CI: 0.839 - 1.025) compared with 0.080 (95% CI: 0.044-0.117) and 1.419 (95% CI: 1.149-1.690) for the Templeton model. Both models underestimated the live birth rate, but this was particularly marked in the Templeton model. Updating the models to reflect improvements in live birth rates since the models were developed enhanced their performance, but IVFpredict remained superior. Conclusion External validation in a large population cohort confirms IVFpredict has superior discrimination and calibration for informing patients, clinicians and healthcare policy makers of the probability of live birth following IVF

Text to speech synthesis based on hidden Markov models

106 σ.Η συγκεκριμένη διπλωματική πραγματεύεται το θέμα της Σύνθεσης φωνής από κείμενο με Στατιστική Μοντελοποίηση Κρυφών Μαρκοβιανών Μοντέλων. Το συγκεκριμένο θέμα εντάσσεται στη συνδυαστική ερευνητική περιοχή της επεξεργασίας φωνής και της αναγνώρισης προτύπων. Σταδιακά στα πρώτ6α κεφάλαια ανλύεται όλο το απαιτούμενο θεωρητικό υπόβαθρο που αφορά το συγκεκριμένο πρόβλημα. Ιδιαίτερη έμφαση δίνεται στη μελέτη και υλοποίηση διαφορετικών vocoders για την επιλογή των κατάλληλων χαρακτηριστικών. Στο τελευταίο Κεφάλαιο περιγράφονται αναλυτικά τα στάδια που ακολουθήθηκαν για την υλοποίηση του συνθέτη φωνής από κείμενο περιλαμβάνοντας και τα πειραματικά αποτελέσματα τα οποία είναι ιδιαίτερα ενθαρρυντικά.The goal of this Thesis is the study and the implementation of an HMM Text to Speech Synthesizer. This applications is part of the combinational research area of Speech Processing and Pattern Recognition. In the first part the thesis focuses on the theoretical framework of dealing with the problem of HMM Text to Speech Synthesis. Additionally, there is a special research on implementing different Vocoders, in order to figure out which are the best characteristics. Finally, this thesis describes the main steps of the HMM TTS system implementation, including very encouraging experimental results.Χρήστος Α. Μιναρετζή

LABOR CHARACTERISTICS OF UNCOMPLICATED PROLONGED PREGNANCIES AFTER INDUCTION WITH INTRACERVICAL PROSTAGLANDIN-E2 GEL VERSUS INTRAVENOUS OXYTOCIN

Labor characteristics after intracervical application of 0.5 mg prostaglandin (PG) E2 gel (n = 83)versus intravenous administration of oxytocin (n = 82) for labor induction were investigated in uncomplicated prolonged pregnancies with unripe cervix. The induction to delivery time as well as the total oxytocin dose were significantly reduced in the PGE2 group (p < 0.001). Cesarean sections, instrumental deliveries and fetal distress had the same frequency, but the failures of trial were significantly higher in the oxytocin group than in the PGE, group (20.7 vs. 6%, p < 0.01). Twenty-four percent of women needed a second PGE2 dose, and almost half of the women in the PGE2 group experienced ‘spontaneous’ labor. More neonates in the oxytocin group had 5-min Apgar scores < 7 (p < 0.05). Intracervical PGE2 gel application is superior to intravenous oxytocin in terms of shortening the induction-delivery interval and increasing the frequency of successful vaginal delivery. In addition, it is safe for mother and fetus

Administration of methylprednisolone to prevent severe ovarian hyperstimulation syndrome in patients undergoing in vitro fertilization

Objective: To determine whether administration of methylprednisolone to high-risk women undergoing IVF/ICSI helps reduce the development of OHSS. Design: Retrospective clinical controlled study. Setting: lVF unit. Patient(s): One thousand ten women who underwent IVF/ICSI from January 9, 1997, to December 31, 1999. Ninety-one patients who were at high risk for OHSS were identified by using standard criteria. Intervention(s): Methylprednisolone, 16 mg/d starting on day 6 of the stimulation and tapered after the first pregnancy test (day 13 after embryo transfer). Main Outcome Measure(s): Occurrence of OHSS. Result(s): A significantly lower proportion of methylprednisolone recipients than untreated participants developed OHSS (10.0% vs. 43.9%). Treatment recipients had more oocytes retrieved and more embryos fertilized than did untreated participants. Methylprednisolone treatment was equally effective in preventing OHSS in all causes of infertility and was effective independent of the number of IVF trials and pregnancy rates. Conclusion(s): Treatment with methylprednisolone appears to reduce the risk for OHSS. This treatment thus helps to avoid hospitalization, reduces cycle cancellations, and improves the cost-effectiveness of IVF cycles. (C) 2002 by American Society for Reproductive Medicine

OVARIAN-TUMORS - PREDICTION OF THE PROBABILITY OF MALIGNANCY BY USING PATIENTS AGE AND TUMOR MORPHOLOGIC FEATURES WITH A LOGISTIC MODEL

An attempt was made to predict the probability of malignancy of a given ovarian tumor in a certain patient by using the age and simple morphologic features of the tumor. A cohort of 959 patients with ovarian tumors was analysed retrospectively according to the patient’s age and tumor characteristics such as greatest diameter, consistency, bilaterality and diagnosis as malignant (271 patients) or benign (688 patients). All variables were entered unconditionally in a logistic regression. The presence of solid/multilocular elements has a 9.6-fold increased risk of malignancy, where a bilateral tumor has a 2.8-fold increase. Significant increase in risk of malignancy was observed in ages under 20 and over 40 years, as well as in tumors with a diameter larger than 9 cm. All variables were highly significant associated with the discrimination between benign and malignant. A formula including all variables has been developed so that the probability of malignancy can be estimated by a scientific calculator. In conclusion, simple, easily determined by ultrasound and reproducible criteria such as patient’s age, tumor size, consistency and bilaterality were assembled in a logistic model in order to predict the probability of malignancy for a given ovarian tumor, in an individual patient