Horst Haussecker

Scalable Information-Driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks

This paper describes two novel techniques, information-driven sensor querying (IDSQ) and constrained anisotropic diffusion routing (CADR), for energy-efficient data querying and routing in ad hoc sensor networks for a range of collaborative signal processing tasks. The key idea is to introduce an information utility measure to select which sensors to query and to dynamically guide data routing. This allows us to maximize information gain while minimizing detection latency and bandwidth consumption for tasks such as localization and tracking. Our simulation results have demonstrated that the information-driven querying and routing techniques are more energy efficient, have lower detection latency, and provide anytime algorithms to mitigate risks of link/node failures.

Monitoring and fault diagnosis of hybrid systems

Many networked embedded sensing and control systems can be modeled as hybrid systems with interacting continuous and discrete dynamics. These systems present significant challenges for monitoring and diagnosis. Many existing model-based approaches focus on diagnostic reasoning assuming appropriate fault signatures have been generated. However, an important missing piece is the integration of model-based techniques with the acquisition and processing of sensor signals and the modeling of faults to support diagnostic reasoning. This paper addresses key modeling and computational problems at the interface between model-based diagnosis techniques and signature analysis to enable the efficient detection and isolation of incipient and abrupt faults in hybrid systems. A hybrid automata model that parameterizes abrupt and incipient faults is introduced. Based on this model, an approach for diagnoser design is presented. The paper also develops a novel mode estimation algorithm that uses model-based prediction to focus distributed processing signal algorithms. Finally, the paper describes a diagnostic system architecture that integrates the modeling, prediction, and diagnosis components. The implemented architecture is applied to fault diagnosis of a complex electro-mechanical machine, the Xerox DC265 printer, and the experimental results presented validate the approach. A number of design trade-offs that were made to support implementation of the algorithms for online applications are also described.

Computing optical flow with physical models of brightness variation

This paper exploits physical models of time-varying brightness in image sequences to estimate optical flow and physical parameters of the scene. Previous approaches handled violations of brightness constancy with the use of robust statistics or with generalized brightness constancy constraints that allow generic types of contrast and illumination changes. We consider models of brightness variation that have time-dependent physical causes, namely, changing surface orientation with respect to a directional illuminant, motion of the illuminant, and physical models of heat transport in infrared images. We simultaneously estimate the optical flow and the relevant physical parameters. The estimation problem is formulated using total least squares (TLS), with confidence bounds on the parameters.

Study of Dynamical Processes with Tensor-Based Spatiotemporal Image Processing Techniques

Image sequence processing techniques are used to study exchange, growth, and transport processes and to tackle key questions in environmental physics and biology. These applications require high accuracy for the estimation of the motion field since the most interesting parameters of the dynamical processes studied are contained in first-order derivatives of the motion field or in dynamical changes of the moving objects. Therefore the performance and optimization of low-level motion estimators is discussed. A tensor method tuned with carefully optimized derivative filters yields reliable and dense displacement vector fields (DVF) with an accuracy of up to a few hundredth pixels/frame for real-world images. The accuracy of the tensor method is verified with computer-generated sequences and a calibrated image sequence. With the improvements in accuracy the motion estimation is now rather limited by imperfections in the CCD sensors, especially the spatial nonuniformity in the responsivity. With a simple two-point calibration, these effects can efficiently be suppressed. The application of the techniques to the analysis of plant growth, to ocean surface microturbulence in IR image sequences, and to sediment transport is demonstrated.

Fault modeling for monitoring and diagnosis of sensor-rich hybrid systems

This paper presents a framework for modeling faults in hybrid systems that leads to an efficient approach for monitoring and diagnosis of real-time embedded systems. We describe a fault parameterization based on hybrid automata models and consider both abrupt failures and gradual degradation of system components. Our approach also addresses the computational problem of coping with large amount of sensor data by using a discrete event model of the system so as to focus distributed signal analysis on when and where to look for signatures of interest. The approach has been demonstrated for the online diagnosis of a hybrid system, the Xerox DC265 printer.

Motion Determination in Actin Filament Fluorescence Images with a Spatio-Temporal Orientation Analysis Method

We present a novel approach of automatically measuring motion in series of microscopic fluorescence images. As a differential method, the three-dimensional structure tensor technique is used to calculate the displacement vector field for every image of the sequence, from which the velocities are subsequently derived. We have used this method for the analysis of the movement of single actin filaments in the in vitro motility assay, where fluorescently labeled actin filaments move over a myosin decorated surface. With its fast implementation and subpixel accuracy, this approach is, in general, very valuable for analyzing dynamic processes by image sequence analysis.

A Total Least Squares Framework for Low-Level Analysis of Dynamic Scenes and Processes

We present a new method to simultaneously estimate optical flow fields and parameters of dynamic processes, violating the standard brightness change constraint equation. This technique constitutes a straightforward generalization of the standard brightness constancy assumption. Using TLS estimation the spatiotemporal brightness structure is analyzed in an entirely symmetric way with respect to the spatial and temporal coordinates. We directly incorporate nonlinear brightness changes based upon differential equations of the underlying processes.

Simultaneous estimation of optical flow and heat transport in infrared image sequences

This paper incorporates physical models of heat transport into motion analysis in infrared image sequences. Physical transport processes, such as heat diffusion and decay, are causing time dependent brightness variations, violating the common brightness constancy assumption. Previous approaches to optical flow computation have accommodated violations of brightness constancy with the use of robust statistics or with generalized brightness constancy constraints that allow generic types of contrast and illumination changes. Here, we consider realistic models of brightness variation that have time-dependent physical causes. We simultaneously estimate the optical flow and the relevant physical parameters, such as the heat diffusion and decay constants. The estimation problem is formulated for a wide class of physical models using total least squares (TLS), with confidence bounds on the parameters.

Air-sea gas transfer and micro turbulence at the ocean surface using infrared image processing

To obtain an insight into the transfer process at the air-water interface new techniques for the quantitative investigation of the gas exchange have been developed. The controlled flux technique, CFT (Jahne et al. 1989) uses heat as a proxy tracer for gases to measure the air-sea gas transfer velocity with a high spatial and temporal resolution. The results of a field cruise and a laboratory study are discussed and compared with a model (Schimpf et al.) predicting the sea surface temperature distribution based on surface renewal (Danckwerts 1970). The sea surface temperature fluctuations associated with the interplay of diffusive and turbulent transport give direct insight into the mechanisms of gas transfer. Using infrared image processing the spatial structure of the micro turbulence at the ocean surface is analyzed.

Measurements of the air-sea gas transfer and its mechanisms by active and passive thermography

In order to reliably measure air-sea gas transfer velocities in the field with a high spatial and temporal resolution a new technique has been developed called the controlled flux technique, CFT. The current implementation splits up into two independent techniques using active and passive thermography, respectively. The CFT field instrument has been successfully used during two research cruises along the California Pacific coast (MBL/CoOP, 1995) and on the North Atlantic (CoOP, 1997). In addition to in-situ gas transfer rates, thermography of the ocean surface gives direct insight into the mechanisms of gas transfer. It has been shown that surface renewal dominates the transfer processes even at low wind speeds.