Systems reliability issues for future aircraft

The reliability of adaptive controls for future aircraft are discussed. The research, formulation, and experimentation for improved aircraft performance are considered

Robust and Optimal Methods for Geometric Sensor Data Alignment

Geometric sensor data alignment - the problem of finding the rigid transformation that correctly aligns two sets of sensor data without prior knowledge of how the data correspond - is a fundamental task in computer vision and robotics. It is inconvenient then that outliers and non-convexity are inherent to the problem and present significant challenges for alignment algorithms. Outliers are highly prevalent in sets of sensor data, particularly when the sets overlap incompletely. Despite this, many alignment objective functions are not robust to outliers, leading to erroneous alignments. In addition, alignment problems are highly non-convex, a property arising from the objective function and the transformation. While finding a local optimum may not be difficult, finding the global optimum is a hard optimisation problem. These key challenges have not been fully and jointly resolved in the existing literature, and so there is a need for robust and optimal solutions to alignment problems. Hence the objective of this thesis is to develop tractable algorithms for geometric sensor data alignment that are robust to outliers and not susceptible to spurious local optima. This thesis makes several significant contributions to the geometric alignment literature, founded on new insights into robust alignment and the geometry of transformations. Firstly, a novel discriminative sensor data representation is proposed that has better viewpoint invariance than generative models and is time and memory efficient without sacrificing model fidelity. Secondly, a novel local optimisation algorithm is developed for nD-nD geometric alignment under a robust distance measure. It manifests a wider region of convergence and a greater robustness to outliers and sampling artefacts than other local optimisation algorithms. Thirdly, the first optimal solution for 3D-3D geometric alignment with an inherently robust objective function is proposed. It outperforms other geometric alignment algorithms on challenging datasets due to its guaranteed optimality and outlier robustness, and has an efficient parallel implementation. Fourthly, the first optimal solution for 2D-3D geometric alignment with an inherently robust objective function is proposed. It outperforms existing approaches on challenging datasets, reliably finding the global optimum, and has an efficient parallel implementation. Finally, another optimal solution is developed for 2D-3D geometric alignment, using a robust surface alignment measure. Ultimately, robust and optimal methods, such as those in this thesis, are necessary to reliably find accurate solutions to geometric sensor data alignment problems

Space programs summary no. 37-27, volume IV for the period April 1, 1964 to May 31, 1964. Supporting research and advanced development

Space exploration programs - systems analysis - spacecraft power and guidance systems - propellant engineering and communications system

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Estimation and detection techniques for doubly-selective channels in wireless communications

A fundamental problem in communications is the estimation of the channel. The signal transmitted through a communications channel undergoes distortions so that it is often received in an unrecognizable form at the receiver. The receiver must expend significant signal processing effort in order to be able to decode the transmit signal from this received signal. This signal processing requires knowledge of how the channel distorts the transmit signal, i.e. channel knowledge. To maintain a reliable link, the channel must be estimated and tracked by the receiver. The estimation of the channel at the receiver often proceeds by transmission of a signal called the 'pilot' which is known a priori to the receiver. The receiver forms its estimate of the transmitted signal based on how this known signal is distorted by the channel, i.e. it estimates the channel from the received signal and the pilot. This design of the pilot is a function of the modulation, the type of training and the channel. [Continues.

Blind source separation for interference cancellation in CDMA systems

Communication is the science of "reliable" transfer of information between two parties, in the sense that the information reaches the intended party with as few errors as possible. Modern wireless systems have many interfering sources that hinder reliable communication. The performance of receivers severely deteriorates in the presence of unknown or unaccounted interference. The goal of a receiver is then to combat these sources of interference in a robust manner while trying to optimize the trade-off between gain and computational complexity. Conventional methods mitigate these sources of interference by taking into account all available information and at times seeking additional information e.g., channel characteristics, direction of arrival, etc. This usually costs bandwidth. This thesis examines the issue of developing mitigating algorithms that utilize as little as possible or no prior information about the nature of the interference. These methods are either semi-blind, in the former case, or blind in the latter case. Blind source separation (BSS) involves solving a source separation problem with very little prior information. A popular framework for solving the BSS problem is independent component analysis (ICA). This thesis combines techniques of ICA with conventional signal detection to cancel out unaccounted sources of interference. Combining an ICA element to standard techniques enables a robust and computationally efficient structure. This thesis proposes switching techniques based on BSS/ICA effectively to combat interference. Additionally, a structure based on a generalized framework termed as denoising source separation (DSS) is presented. In cases where more information is known about the nature of interference, it is natural to incorporate this knowledge in the separation process, so finally this thesis looks at the issue of using some prior knowledge in these techniques. In the simple case, the advantage of using priors should at least lead to faster algorithms.reviewe