Qualitative Modelling of Linear Networks in Engineering Applications

Lee M.H., Qualitative Modelling of Linear Networks in Engineering Applications, in Proc. ECAI?2000, 14th European Conf. on Artificial Intelligence, Berlin, August 19th - 25th 2000, pp161-5.

Qualitative Modelling of Linear Networks in ECAD Applications

Lee M.H., Qualitative Modelling of Linear Networks in ECAD Applications, Expert Update, Vol. 3, Num. 2, pp23-32, BCS SGES, Summer 2000. Qualitative modeling of linear networks in ecad applications (1999) by M Lee Venue: Pages 146?152 of: Proceedings 13th international workshop on qualitative reasoning, QR ?99

Qualitative Modelling of Linear Networks in ECAD Applications

Lee M.H., Qualitative Modelling of Linear Networks in ECAD Applications, Proc. 13th Int. Workshop on Qualitative Reasoning, (QR'99), Loch Awe, Scotland, 1999, pp146-52.

Linear structures in mammographic images: detection and classification

R. Zwiggelaar, S.M. Astley, C.J. Taylor and C.R.M. Boggis, 'Linear structures in mammographic images: detection and classification', IEEE Transaction on Medical Imaging 23 (9), 1077-1086 (2004)

Automatic point correspondence and registration based on linear structures

R. Marti, R. Zwiggelaar, C.M.E. Rubin, 'Automatic point correspondence and registration based on linear structures', International Journal of Pattern Recognition and Artificial Intelligence 16 (3), 331-340 (2002)

Piecewise Linear Models for the Quasiperiodic Transition to Chaos

We formulate and study analytically and computationally two families of piecewise linear degree one circle maps. These families offer the rare advantage of being non-trivial but essentially solvable models for the phenomenon of mode-locking and the quasi-periodic transition to chaos. For instance, for these families, we obtain complete solutions to several questions still largely unanswered for families of smooth circle maps. Our main results describe (1) the sets of maps in these families having some prescribed rotation interval; (2) the boundaries between zero and positive topological entropy and between zero length and non-zero length rotation interval; and (3) the structure and bifurcations of the attractors in one of these families. We discuss the interpretation of these maps as low-order spline approximations to the classic ``sine-circle'' map and examine more generally the implications of our results for the case of smooth circle maps. We also mention a possible connection to recent experiments on models of a driven Josephson junction.

EXPERIMENTAL INVESTIGATION OF LINEAR BUBBLE DYNAMICS IN A VISCOELASTIC XANTHAN GEL

Oceanic bubble plumes caused by ship wakes or breaking waves disrupt sonar communi- cation because of the dramatic change in sound speed and attenuation in the bubbly fluid. Experiments in bubbly fluids have suffered from the inability to quantitatively characterize the fluid because of continuous air bubble motion. Conversely, single bubble experiments, where the bubble is trapped by a pressure field or stabilizing object, are limited in usable frequency range, apparatus complexity, or the invasive nature of the stabilizing object (wire, plate, etc.). Suspension of a bubble in a viscoelastic Xanthan gel allows acoustically forced oscilla- tions with negligible translation over a broad frequency band. Assuming only linear, radial motion, laser scattering from a bubble oscillating below, through, and above its resonance is measured. As the bubble dissolves in the gel, different bubble sizes are measured in the range 240 – 470 μm radius, corresponding to the frequency range 6 – 14 kHz. Equalization of the cell response in the raw data isolates the frequency response of the bubble. Compari- son to theory for a bubble in water shows good agreement between the predicted resonance frequency and damping, such that the bubble behaves as if it were oscillating in water.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Statistical Properties of Single and Competing Non-Linear Fast-Slow Oscillators in Noise

Statistical properties offast-slow Ellias-Grossberg oscillators are studied in response to deterministic and noisy inputs. Oscillatory responses remain stable in noise due to the slow inhibitory variable, which establishes an adaptation level that centers the oscillatory responses of the fast excitatory variable to deterministic and noisy inputs. Competitive interactions between oscillators improve the stability in noise. Although individual oscillation amplitudes decrease with input amplitude, the average to'tal activity increases with input amplitude, thereby suggesting that oscillator output is evaluated by a slow process at downstream network sites.

Emergence of Tri-Phasic Muscle Activation from the Non-linear Interactions of Central and Spinal Neural Network Circuits

The origin of the tri-phasic burst pattern, observed in the EMGs of opponent muscles during rapid self-terminated movements, has been controversial. Here we show by computer simulation that the pattern emerges from interactions between a central neural trajectory controller (VITE circuit) and a peripheral neuromuscularforce controller (FLETE circuit). Both neural models have been derived from simple functional constraints that have led to principled explanations of a wide variety of behavioral and neurobiological data, including, as shown here, the generation of tri-phasic bursts.