Single trial learning of external dynamics: What can the brain teach us about learning mechanisms?

Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has developed learning mechanisms to deal with and compensate for the constantly changing dynamics of the world. If this mechanism or mechanisms can be understood from a computational point of view, then they can also be used to drive the adaptability and learning of robots. In this paper, we will present a new technique for examining changes in the feedforward motor command due to adaptation. This technique can then be utilized for examining motor adaptation in humans and determining a computational algorithm which explains motor learning. © 2007.

Integrated billing mechanisms in the Internet of Things to support information sharing and enable new business opportunities

The Internet of Things (IOT) concept and enabling technologies such as RFID offer the prospect of linking the real world of physical objects with the virtual world of information technology to improve visibility and traceability information within supply chains and across the entire lifecycles of products, as well as enabling more intuitive interactions and greater automation possibilities. There is a huge potential for savings through process optimization and profit generation within the IOT, but the sharing of financial benefits across companies remains an unsolved issue. Existing approaches towards sharing of costs and benefits have failed to scale so far. The integration of payment solutions into the IOT architecture could solve this problem. We have reviewed different possible levels of integration. Multiple payment solutions have been researched. Finally we have developed a model that meets the requirements of the IOT in relation to openness and scalability. It supports both hardware-centric and software-centric approaches to integration of payment solutions with the IOT. Different requirements concerning payment solutions within the IOT have been defined and considered in the proposed model. Possible solution providers include telcos, e-payment service providers and new players such as banks and standardization bodies. The proposed model of integrating the Internet of Things with payment solutions will lower the barrier to invoicing for the more granular visibility information generated using the IOT. Thus, it has the potential to enable recovery of the necessary investments in IOT infrastructure and accelerate adoption of the IOT, especially for projects that are only viable when multiple benefits throughout the supply chain need to be accumulated in order to achieve a Return on Investment (ROI). In a long-term perspective, it may enable IT-departments to become profit centres instead of cost centres. © 2010 - IOS Press and the authors. All rights reserved.

Computational mechanisms of sensorimotor control.

In order to generate skilled and efficient actions, the motor system must find solutions to several problems inherent in sensorimotor control, including nonlinearity, nonstationarity, delays, redundancy, uncertainty, and noise. We review these problems and five computational mechanisms that the brain may use to limit their deleterious effects: optimal feedback control, impedance control, predictive control, Bayesian decision theory, and sensorimotor learning. Together, these computational mechanisms allow skilled and fluent sensorimotor behavior.

Metastability mechanisms in thin film transistors quantitatively resolved using post-stress relaxation of threshold voltage

A new approach is presented to resolve bias-induced metastability mechanisms in hydrogenated amorphous silicon (a-Si:H) thin film transistors (TFTs). The post stress relaxation of threshold voltage (V(T)) was employed to quantitatively distinguish between the charge trapping process in gate dielectric and defect state creation in active layer of transistor. The kinetics of the charge de-trapping from the SiN traps is analytically modeled and a Gaussian distribution of gap states is extracted for the SiN. Indeed, the relaxation in V(T) is in good agreement with the theory underlying the kinetics of charge de-trapping from gate dielectric. For the TFTs used in this work, the charge trapping in the SiN gate dielectric is shown to be the dominant metastability mechanism even at bias stress levels as low as 10 V.