An Electrically Programmable Split-Electrode Charge-Coupled Transversal Filter (EPSEF)

A CCD split-electrode transversal filter (EPSEF) with analog controlled tap weights is described. The programmable tap weighting utilizes a novel analog multiplier for sampled data, based on charge profiling underneath a resistive gate structure. The EPSEF device concept and the performance data of a prototype filter with eight programmable taps are presented. Applications of the EPSEF in several programmed filter functions and in an adaptive filter system are demonstrated

Radio Science Experiment Data Analysis in the framework of the ESA Missions “Venus Express" and “Rosetta"

Occultation measurements exploit an observational geometry in which the spacecraft to Earth communication link is interrupted by the planet itself. Coherent, high-rate (100 ksamples/s) sampling of the down-converted RF incoming signal enables the OL receiving system to safeguard the high dynamics (up to 2 kHz/s) of the weak signals (attenuation > 50dB) emerging from the deep layers of the Venus atmosphere. The purpose of the developed software package, the Open-Loop data processing software (OL SW), is to extract the information embedded in noise by means of an iterative strategy. Essential skill of the OL SW is the progressive reduction of the signal bandwidth while at the same time maintaining high time resolution of the data. This implies high spacial resolution of the sounded media (i.e., the Venus atmosphere) and the capability of resolving effects of multipath propagation

Coded spread spectrum digital transmission system design study

Results are presented of a comprehensive study of the performance of Viterbi-decoded convolutional codes in the presence of nonideal carrier tracking and bit synchronization. A constraint length 7, rate 1/3 convolutional code and parameters suitable for the space shuttle coded communications links are used. Mathematical models are developed and theoretical and simulation results are obtained to determine the tracking and acquisition performance of the system. Pseudorandom sequence spread spectrum techniques are also considered to minimize potential degradation caused by multipath

Automated Complexity-Sensitive Image Fusion

To construct a complete representation of a scene with environmental obstacles such as fog, smoke, darkness, or textural homogeneity, multisensor video streams captured in diferent modalities are considered. A computational method for automatically fusing multimodal image streams into a highly informative and unified stream is proposed. The method consists of the following steps: 1. Image registration is performed to align video frames in the visible band over time, adapting to the nonplanarity of the scene by automatically subdividing the image domain into regions approximating planar patches 2. Wavelet coefficients are computed for each of the input frames in each modality 3. Corresponding regions and points are compared using spatial and temporal information across various scales 4. Decision rules based on the results of multimodal image analysis are used to combine thewavelet coefficients from different modalities 5. The combined wavelet coefficients are inverted to produce an output frame containing useful information gathered from the available modalities Experiments show that the proposed system is capable of producing fused output containing the characteristics of color visible-spectrum imagery while adding information exclusive to infrared imagery, with attractive visual and informational properties

Discriminative Features via Generalized Eigenvectors

Representing examples in a way that is compatible with the underlying classifier can greatly enhance the performance of a learning system. In this paper we investigate scalable techniques for inducing discriminative features by taking advantage of simple second order structure in the data. We focus on multiclass classification and show that features extracted from the generalized eigenvectors of the class conditional second moments lead to classifiers with excellent empirical performance. Moreover, these features have attractive theoretical properties, such as inducing representations that are invariant to linear transformations of the input. We evaluate classifiers built from these features on three different tasks, obtaining state of the art results

Digital Filters

The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature

Nonlinear adaptive filter design for integrated vehicle handling dynamics state estimation

This thesis considers nonlinear filter design for integrated vehicle handling dynamics state estimation. Such a state estimator is needed as not all of the vehicle states can be measured directly by the existing sensors, mostly due to reliability and economical reasons. Accurate information about vehicle handling states is essential for vehicle chassis control and chassis design evaluation. This study considers mathematical model-based filtering methods. A nonlinear 6DoF vehicle model employing an intermediate tyre magic formula is developed for the filter basis. The main problem faced by such a model-based filter is model uncertainties, especially in tyre parameters. The main objective of this study is to design filters which are robust against model uncertainties. Two nonlinear filtering methods are investigated: extended Kalman filter (EKF) and nonlinear robust filter (NRF). The EKF relies on accurate nominal model and ideal white/time uncorrelated assumption about model error noises. In contrast, the NRF tolerates inaccuracy of the nominal model as it accounts for the time-correlated behaviour of the model errors more properly. [Continues.