Air vehicle simulator: an application for a cable array robot

The development of autonomous air vehicles can be an expensive research pursuit. To alleviate some of the financial burden of this process, we have constructed a system consisting of four winches each attached to a central pod (the simulated air vehicle) via cables - a cable-array robot. The system is capable of precisely controlling the three dimensional position of the pod allowing effective testing of sensing and control strategies before experimentation on a free-flying vehicle. In this paper, we present a brief overview of the system and provide a practical control strategy for such a system. ©2005 IEEE

Maunakea Spectroscopic Explorer (MSE) - The Prime Focus Subsystems: Requirements and Interfaces

MSE will be a massively multiplexed survey telescope, including a segmented primary mirror which feeds fibers at the prime focus, including an array of approximately four thousand fibers, positioned precisely to feed banks of spectrographs several tens of meters away. We describe the process of mapping top-level requirements on MSE to technical specifications for subsystems located at the MSE prime focus. This includes the overall top-level requirements based on knowledge of similar systems at other telescopes and how those requirements were converted into specifications so that the subsystems could begin working on their Conceptual Design Phases. We then discuss the verification of the engineering specifications and the compiling of lower-level requirements and specifications into higher level performance budgets (e.g. Image Quality). We also briefly discuss the interface specifications, their effect on the performance of the system and the plan to manage them going forward. We also discuss the opto-mechanical design of the telescope top end assembly and refer readers to more details for instrumentation located at the top end.Comment: 14 pages; Proceedings of SPIE Astronomical Telescopes + Instrumentation 2018; Modeling, Systems Engineering, and Project Management for Astronomy VII

Force and Motion Generation of Molecular Motors: A Generic Description

We review the properties of biological motor proteins which move along linear filaments that are polar and periodic. The physics of the operation of such motors can be described by simple stochastic models which are coupled to a chemical reaction. We analyze the essential features of force and motion generation and discuss the general properties of single motors in the framework of two-state models. Systems which contain large numbers of motors such as muscles and flagella motivate the study of many interacting motors within the framework of simple models. In this case, collective effects can lead to new types of behaviors such as dynamic instabilities of the steady states and oscillatory motion.Comment: 29 pages, 9 figure

Modeling and inference of multisubject fMRI data

Functional magnetic resonance imaging (fMRI) is a rapidly growing technique for studying the brain in action. Since its creation [1], [2], cognitive scientists have been using fMRI to understand how we remember, manipulate, and act on information in our environment. Working with magnetic resonance physicists, statisticians, and engineers, these scientists are pushing the frontiers of knowledge of how the human brain works. The design and analysis of single-subject fMRI studies has been well described. For example, [3], chapters 10 and 11 of [4], and chapters 11 and 14 of [5] all give accessible overviews of fMRI methods for one subject. In contrast, while the appropriate manner to analyze a group of subjects has been the topic of several recent papers, we do not feel it has been covered well in introductory texts and review papers. Therefore, in this article, we bring together old and new work on so-called group modeling of fMRI data using a consistent notation to make the methods more accessible and comparable

Information driven self-organization of complex robotic behaviors

Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well.Comment: 29 pages, 12 figure

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area