Hybrid Kalman / Minimax Filtering in Phase-Locked Loops

A method of combining Kahnan filtering and minimax filtering is proposed and demonstrated in an application to phase-locked loop design. Kalman filtering suffers from a lack of robustness to departures from the assumed noise statistics. Minimax filtering, however, has the drawback of ignoring the engineer\u27s (admittedly incomplete) knowledge of the noise statistics. It is shown in this paper that hybrid Kalman/minimax filtering can provide the “best of both worlds” . Phase-locked loop filter design is used in this paper to demonstrate an application of hybrid estimation

Fault Tolerant Training for Optimal Interpolative Nets

The optimal interpolative (OI) classification network is extended to include fault tolerance and make the network more robust to the loss of a neuron. The OI net has the characteristic that the training data are fit with no more neurons than necessary. Fault tolerance further reduces the number of neurons generated during the learning procedure while maintaining the generalization capabilities of the network. The learning algorithm for the fault-tolerant OI net is presented in a recursive formal, allowing for relatively short training times. A simulated fault-tolerant OI net is tested on a navigation satellite selection proble

Navigation Satellite Selection Using Neural Networks

The application of neural networks to optimal satellite subset selection for navigation use is discussed. The methods presented in this paper are general enough to be applicable regardless of how many satellite signals are being processed by the receiver. The optimal satellite subset is chosen by minimizing a quantity known as Geometric Dilution of Precision (GDOP), which is given by the trace of the inverse of the measurement matrix. An artificial neural network learns the functional relationships between the entries of a measurement matrix and the eigenvalues of its inverse, and thus generates GDOP without inverting a matrix. Simulation results are given, and the computational benefit of neural network-based satellite selection is discussed

Fault Tolerant Training for Optimal Interpolative Nets

The optimal interpolative (OI) classification network is extended to include fault tolerance and make the network more robust to the loss of a neuron. The OI net has the characteristic that the training data are fit with no more neurons than necessary. Fault tolerance further reduces the number of neurons generated during the learning procedure while maintaining the generalization capabilities of the network. The learning algorithm for the fault-tolerant OI net is presented in a recursive formal, allowing for relatively short training times. A simulated fault-tolerant OI net is tested on a navigation satellite selection proble

A Fault-Tolerant Optimal Interpolative Net

The optimal interpolative (OI) classification network is extended to include fault tolerance and make the network more robust to the loss of a neuron. The OI Net has the characteristic that the training data are fit with no more neurons than necessary. Fault tolerance further reduces the number of neurons generated during the learning procedure while maintaining the generalization capabilities of the network. The learning algorithm for the fault tolerant OI Net is presented in a recursive format, allowing for relatively short training times. A simulated fault tolerant OI Net is tested on a navigation satellite selective problem

A Fault-Tolerant Optimal Interpolative Net

The optimal interpolative (OI) classification network is extended to include fault tolerance and make the network more robust to the loss of a neuron. The OI Net has the characteristic that the training data are fit with no more neurons than necessary. Fault tolerance further reduces the number of neurons generated during the learning procedure while maintaining the generalization capabilities of the network. The learning algorithm for the fault tolerant OI Net is presented in a recursive format, allowing for relatively short training times. A simulated fault tolerant OI Net is tested on a navigation satellite selective problem

Fuzzy Logic for Digital Phase-Locked Loop Filter Design

The problem of robust phase-locked loop design has attracted attention for many years, particularly since the advent of the global positioning system. This paper proposes and demonstrates the use of a fuzzy PLL to estimate the time-varying phase of a sinusoidal signal. It is shown via simulation results that fuzzy PLL\u27s offer performance comparable to analytically derived PLL\u27s (e.g. Kalman filters and H∞ estimators) when the phase exhibits high dynamics and high noise. The fuzzy PLL rules are optimized using a gradient descent method and a genetic algorith

Pertussis seroimmunity in mother-neonate pairs and other pediatric age groups from Egypt

Background: Despite the widespread availability of 2 classes of effective vaccines, whole cell and acellular, pertussis has resurged as a serious public health problem. We sought to investigate the pertussis immune status of mother-neonate pairs and children in our country where pertussis vaccination is obligatory. Methods: This cross-sectional study included 75 healthy full-term neonates and their mothers, 100 infants (2-24 months), 170 children (2-12 years) and 80 adolescents (12-18 years). Serum pertussis IgG was measured in all enrolled subjects. A positive titre was defined as >24 U/ml. Results: Positive pertussis IgG levels were detected in 69 of the mothers (92%), in 63 of their newborns (84%). Seroimmunity to pertussis was positively noted in 55% of infants, 82.2% of preschool children, 77.5% of school-aged children and 75% in adolescents. Serum pertussis IgG titers among the neonates showed a significant positive correlation with the maternal titers (P=0.00001). Higher rates of pertussis seroimmunity was observed among residents in urban and suburban areas as compared to those living in rural areas (P<0.05) . Conclusion: This pilot study may suggest the presence of sufficient pertussis seroimmunity rates in the studied age groups. Still, there were some failures in immune acquisition probably due to inefficient vaccination in some localities or waning of immunity with age. Wider scale studies would allow better insight into the pertussis immune status in our country and hence the need for booster immunization

GPS Modeling for Designing Aerospace Vehicle Navigation Systems

The complexity of the design of a Global Positioning System (GPS) user segment, as well as the performance demanded of the components, depends on user requirements such as total navigation accuracy. Other factors, for instance the expected satellite/vehicle geometry or the accuracy of an accompanying inertial navigation system can also affect the user segment design. Models of GPS measurements are used to predict user segment performance at various levels. Design curves are developed which illustrate the relationship between user requirements, the user segment design, and component performance