Universal features of dimensional reduction schemes from general covariance breaking

Many features of dimensional reduction schemes are determined by the breaking of higher dimensional general covariance associated with the selection of a particular subset of coordinates. By investigating residual covariance we introduce lower dimensional tensors, that successfully generalize to one side Kaluza–Klein gauge fields and to the other side extrinsic curvature and torsion of embedded spaces, thus fully characterizing the geometry of dimensional reduction. We obtain general formulas for the reduction of the main tensors and operators of Riemannian geometry. In particular, we provide what is probably the maximal possible generalization of Gauss, Codazzi and Ricci equations and various other standard formulas in Kaluza–Klein and embedded spacetimes theories. After general covariance breaking, part of the residual covariance is perceived by effective lower dimensional observers as an infinite dimensional gauge group. This reduces to finite dimensions in Kaluza–Klein and other few remarkable backgrounds, all characterized by the vanishing of appropriate lower dimensional tensors

Quantum Fields and Extended Objects in Space-Times with Constant Curvature Spatial Section

The heat-kernel expansion and $\zeta$-regularization techniques for quantum field theory and extended objects on curved space-times are reviewed. In particular, ultrastatic space-times with spatial section consisting in manifold with constant curvature are discussed in detail. Several mathematical results, relevant to physical applications are presented, including exact solutions of the heat-kernel equation, a simple exposition of hyperbolic geometry and an elementary derivation of the Selberg trace formula. With regards to the physical applications, the vacuum energy for scalar fields, the one-loop renormalization of a self-interacting scalar field theory on a hyperbolic space-time, with a discussion on the topological symmetry breaking, the finite temperature effects and the Bose-Einstein condensation, are considered. Some attempts to generalize the results to extended objects are also presented, including some remarks on path integral quantization, asymptotic properties of extended objects and a novel representation for the one-loop (super)string free energy.Comment: Latex file, 122 page

A dynamic neural field approach to natural and efficient human-robot collaboration

A major challenge in modern robotics is the design of autonomous robots that are able to cooperate with people in their daily tasks in a human-like way. We address the challenge of natural human-robot interactions by using the theoretical framework of dynamic neural fields (DNFs) to develop processing architectures that are based on neuro-cognitive mechanisms supporting human joint action. By explaining the emergence of self-stabilized activity in neuronal populations, dynamic field theory provides a systematic way to endow a robot with crucial cognitive functions such as working memory, prediction and decision making . The DNF architecture for joint action is organized as a large scale network of reciprocally connected neuronal populations that encode in their firing patterns specific motor behaviors, action goals, contextual cues and shared task knowledge. Ultimately, it implements a context-dependent mapping from observed actions of the human onto adequate complementary behaviors that takes into account the inferred goal of the co-actor. We present results of flexible and fluent human-robot cooperation in a task in which the team has to assemble a toy object from its components.The present research was conducted in the context of the fp6-IST2 EU-IP Project JAST (proj. nr. 003747) and partly financed by the FCT grants POCI/V.5/A0119/2005 and CONC-REEQ/17/2001. We would like to thank Luis Louro, Emanuel Sousa, Flora Ferreira, Eliana Costa e Silva, Rui Silva and Toni Machado for their assistance during the robotic experiment

STATE OF CHARGE ESTIMATION FOR AN ELECTRIC VEHICLE BATTERY

The usage of electric vehicles has been increasing over the past decade globally. This is driven by the effort to reduce dependency of internal combustion engine (ICE) vehicles due to the global warming alerts by the environmental experts. As people began to explore the options of using electric vehicle (EV), the concern of battery or rather ‘battery anxiety’ has become a popular issue especially for new users transitioning from ICE vehicles to EV. State of charge (SOC) is the level of charge of a battery relative to the rated capacity and measured in percentage unit. It is the equivalent to the fuel level gauge for ICE vehicles. SOC cannot be measured directly because of the complexity of materials for different types of battery, thus it can only be estimated from measurement variables. Therefore, in this project which is State of Charge Estimation for Electric Vehicle Battery designed to estimate the SOC of a battery. The estimation of the SOC is crucial in the aspects of driving range estimation, battery health protection and to provide better charging routines. This project aims to simulate the battery SOC estimation with the Matrix Laboratory (MATLAB) Simulink and to compare the estimation methods and analyse the results from the simulation. Different methods will have different analysis on the SOC estimation due to external factors such as the battery model, the accuracy level, battery voltage, current, temperature and cell capacity