Daniel A. Sierra

A NEW LINEAR MUSCLE FIBER MODEL FOR NEURAL CONTROL OF SACCADES

A comprehensive model for the control of horizontal saccades is presented using a new muscle fiber model for the lateral and medial rectus muscles. The importance of this model is that each muscle fiber has a separate neural input. This model is robust and accounts for the neural activity for both large and small saccades. The muscle fiber model consists of serial sequences of muscle fibers in parallel with other serial sequences of muscle fibers. Each muscle fiber is described by a parallel combination of a linear length tension element, viscous element and active state tension generator.

Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian decomposition and Bayesian neural networks

Neuro-oncologists must ultimately rely on their acquired knowledge and accumulated experience to undertake the sensitive task of brain tumour diagnosis. This task strongly depends on indirect, non-invasive measurements, which are the source of valuable data in the form of signals and images. Expert radiologists should benefit from their use as part of an at least partially automated computer-based medical decision support system. This paper focuses on Magnetic Resonance Spectroscopy signal analysis and illustrates a method that combines Gaussian Decomposition, dimensionality reduction by Moving Window with Variance Analysis and classification using adaptively regularized Artificial Neural Networks. The method yields encouraging results in the task of binary classification of human brain tumours, even for tumour types that have seldom been analyzed from this viewpoint.

A Lyapunov treatment of swarm coordination under conflict

We consider hostile conflicts between two multi-agent swarms, called pursuers and evaders. A Newtonian dynamics-based double integrator model is taken into account, as well as a control strategy using the relative positions and velocities of opposing swarm members. This control is introduced to achieve stability and the capture of the evaders by the pursuers. The present document considers only swarms with equal membership strengths and equal mass for simplicity. This effort begins with a set of suggested interaction force profiles, which are functions of local vectors. To formulate a robust control law, a Lyapunov-based stability analysis is used. The group pursuit is conceived in two phases: the approach phase, during which the two swarms act like two individual agents, and the assigned pursuit phase, where each pursuer has an assigned evader. We show that the uncontrolled dynamics, which are marginally stable, are stabilized by the new controller.

Brain tumour classification using Gaussian decomposition and neural networks

The development, implementation and use of computer-based medical decision support systems (MDSS) based on pattern recognition techniques holds the promise of substantially improving the quality of medical practice in diagnostic and prognostic tasks. In this study, the core of a decision support system for brain tumour classification from magnetic resonance spectroscopy (MRS) data is presented. It combines data pre-processing using Gaussian decomposition, dimensionality reduction using moving window with variance analysis, and classification using artificial neural networks (ANN). This combination of techniques is shown to yield high diagnostic classification accuracy in problems concerning diverse brain tumour pathologies, some of which have received little attention in the literature.

3D dynamic modeling of the head-neck complex for fast eye and head orientation movements research

A 3D dynamic computer model for the movement of the head-neck complex is presented. It incorporates anatomically correct information about the diverse elements forming the system. The skeleton is considered as a set of interconnected rigid 3D bodies following the Newton-Euler laws of movement. The muscles are modeled using Enderles linear model, which shows equivalent dynamic characteristics to Loebs virtual muscle model. The soft tissues, namely, the ligaments, intervertebral disks, and facet joints, are modeled considering their physiological roles and dynamics. In contrast with other head and neck models developed for safety research, the model is aimed to study the neural control of the complex during fast eye and head movements, such as saccades and gaze shifts. In particular, the time-optimal hypothesis and the feedback control ones are discussed.

Linear Homeomorphic Models for Muscles in the Head–Neck Region

The linear homeomorphic muscle model proposed by Enderle and coworkers for the rectus eye muscle is fitted to reflect the dynamics of muscles in the head–neck complex, specifically in muscles involved in gaze shifts. This parameterization of the model for different muscles in the neck region will serve to drive a 3D dynamic computer model for the movement of the head–neck complex, including bony structures and soft tissues, and aimed to study the neural control of the complex during fast eye and head movements such as saccades and gaze shifts. Parameter values for the different muscles in the neck region were obtained by optimization using simulated annealing. These linear homeomorphic muscle models provide non-linear force–velocity profiles and linear length tension profiles, which are in agreement with results from the more complex Virtual Muscle model, which is based on Zajac’s non-linear muscle model.

Swarm coordination under conflict

We consider the conflict dynamics between two multi-agent swarms. First, the complex nature of a single pursuer attempting to intercept a single evader (1P-1E) is investigated, and some rudimentary rules of engagement are established. We elaborate on the stability repercussions of these rules. Second, we extend the modeling and stability analysis to multi-agent swarms. The present document considers only swarms with equal membership strengths for simplicity. Due to the strong nonlinearities in the dynamics, Lyapunov-based stability analysis is used. Swarm interactions are taken in two phases: the approach phase during which the two swarms act like individuals in the 1P-1E interaction; and the individual pursuit phase where each pursuer is assigned to an evader.

3D Dynamic Computer Model of the Head-Neck Complex

A 3D dynamic computer model for the movement of the head is presented that incorporates anatomically correct information about the diverse elements forming the system. The skeleton is considered as a set of interconnected rigid 3D bodies following the Newton-Euler laws of movement. The muscles are modeled using Enderles linear model. Finally, the soft tissues, namely the ligaments, intervertebral disks, and zigapophysial joints, are modeled using the finite elements approach. The model is intended to study the neural network that controls movement and maintains the balance of the head- neck complex during eye movements

Swarm Coordination Under Conflict and Use of Enhanced Lyapunov Control

We consider hostile conflicts between two multi-agent swarms. First, we investigate the complex nature of a single pursuer attempting to intercept a single evader (1P-1E), and establish some rudimentary rules of engagement. The stability repercussions of these rules are investigated using a Lyapunov-based stability analysis. Second, we extend the modeling and stability analysis to interactions between multi-agent swarms of pursuers and evaders. The present document considers only swarms with equal membership strengths for simplicity. This effort is based on a set of suggested momenta deployed on individual agents. The control of group pursuit is divided into two phases: the approach phase during which the two swarms act like individuals in the 1P-1E interaction, and the assigned pursuit phase, where each pursuer follows an assigned evader. A simple, single-step dissipative control strategy, which results in undesirable control chatter, is considered first. A distributed control logic is then introduced, in order to ameliorate the chatter problems. In this new logic, the dissipative control action is spread out over a time window. A wide range of case studies is tested in order to quantify the parametric effects of the new strategy.

Infrared-based eye-tracker system for saccades

An infrared-based eye tracker system was designed and implemented for recording saccades from both eyes simultaneously, and stored on a computers hard disk. The system uses infrared emitters to generate a narrow band infrared signal. An array of ten photo-detectors record the reflectance patterns from each eye. Data acquisition is made using LabView and a multiple linear regression algorithm is used to calibrate the system.