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Abstract - SSAP1

SSAP1.1	Exploiting Structure in Positioning of Non-Symmetric Signals A. Ghazanfarian, X. Chen, T. Kailath, M. McCord, F. Pease (Stanford University, USA) One of the most crucial emerging challenges in lithography is achieving rapid and accurate alignment under a wide variety of conditions brought about by different processing steps. Current alignment algorithms assume symmetric alignment signals. In this paper, we propose a new algorithm based on subspace decomposition of alignment signals. We assume that the process-induced asymmetries are small enough so that only linear effects need to be considered. We first find the subspace of alignment signals using a set of signals with pre-known positions. The position of a new signal is calculated considering that, if shifted correctly, it will lie in the same subspace of previous signals. Since this method exploits the structure of the signals, it results in more accurate measurement of the position. Simulation results show that the alignment error is about an order of magnitude smaller than that achieved with conventional Maximum Likelihood or phase-fitting approaches.
SSAP1.2	On-line Detection/Estimation Scheme in Laser Doppler Anemometry F. Galtier, O. Besson (ENSICA, France) Laser anemometers have become a promising technique for estimating velocities in a flow. In this paper, we study their use for on-board aircraft speed of flight estimation. More specifically, this paper addresses the problem of simultaneous detection of the arrival of aerosol particles in a laser anemometer and estimation of their velocity. A joint detection-estimation scheme is proposed. A Likelihood Ratio Test is presented and considerations about the specificities of the problem are used to calculate the threshold. Computationally efficient algorithms for estimating the parameters of interest are derived and on-line implementation issues are addressed. Numerical examples attest for the performance of the method, on both simulated and real data recorded during a flight test.
SSAP1.3	Tripulse: An Accurate Orientation/Attitude Estimation System for Satellite Borne Phased Arrays S. Silverstein, J. Ashe, G. Kautz, F. Wheeler (GE Corporate Research and Development, USA) Tripulse is a novel orientation/attitude estimation system that is designed to accurately estimate the orientation of a satellite borne phased array relative to one or more earth stations. This system has an accuracy potential that is significantly better than conventional Earth-Sun-Moon attitude sensors. Tripulse has conceptual similarities to amplitude comparison onopulse systems used in tracking radars. Detailed Tripulse statistical performance analyses for noise, beamforming quantization errors, and hardware failures are presented.
SSAP1.4	Automatic Detection System of Venous Air Embolism Employing Signal Processing Methods R. Schlag (Universität Kaiserslautern, Germany); U. Korell (Klinikum Kaiserslautern, Germany); B. Siegmund, M. Pfeiffer, M. Pandit (Universität Kaiserslautern, Germany) Methods of signal processing which have been developed and tested for the detection of venous air embolism using ultrasound Doppler systems are presented. The detection scheme developed is based on a time-frequency characterization of the Doppler signals obtained with a suitable transducer placed on the cartoid vein. The developed scheme has been implemented and tested for the automatic signaling of an embolism which can occur in the course of a surgical operation.
SSAP1.5	Undersampling for Parameter Estimation with Application to Time of Arrival Estimation H. Messer (Tel Aviv University, Israel) This paper deals with the effect of sampling the continuous observations on parameter estimation errors. In particular, we study the problem of estimating the time of arrival (TOA) of a continuous, deterministic signal in noise. For this problem, the sampling procedure transforms the continuous parameter space into a discrete one, resulting in inherent estimation errors. We introduce a general tool for evaluating the achievable performance for any parameter estimation problem at a given sampling rate. For TOA estimation with a Gaussian-shaped signal, we show that one can undersample with a factor up to 3 times the Nyquist rate with average TOA estimation performance reduction of less than 3dB.
SSAP1.6	Nonlinear System Identification of Hydraulic Actuator Friction Dynamics Using a Hammerstein Model B. Kwak, A. Yagle, J. Levitt (University of Michigan, USA) We present two Hammerstein-type models for parametric system identification of the lip seal friction process in a hydraulic actuator. Adaptive algorithms with least-squares criteria are derived, and the performances of the two models are evaluated using experimental results.
SSAP1.7	Information-Theoretic Analysis of Neural Coding D. Johnson, C. Gruner (Rice University, USA) We describe a family of new techniques for analyzing single- and multi-unit discharge patterns. These techniques are ased on information theoretic distance measures and on empirical theories derived from work on universal signal processing They are capable of determining transneuron statistical dependencies even when time-varying responses occur. The response portion contributing most to information coding can be identified and the coding fidelity can be quantified regardless of the neural coding mechanisms---be it timing, rate or transneural correlations.
SSAP1.8	Multidimensional Independent Component Analysis J. Cardoso (CNRS & ENST, France) This discussion paper proposes to generalize the notion of Independent Component Analysis (ICA) to the notion of Multidimensional Independent Component Analysis (MICA). We start from the ICA or blind source separation (BSS) model and show that it can be uniquely identified provided it is properly parameterized in terms of one-dimensional subspaces. From this standpoint, the BSS/ICA model is generalized to multidimensional components. We discuss how ICA standard algorithms can be adapted to MICA decomposition. The relevance of these ideas is illustrated by a MICA decomposition of ECG signals.

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