Virtualization is a key enabling technology in Cloud computing that allows users to run multiple virtual machines (VMs) with their own application environment on top of physical hardware. It permits scaling up and down of applications by elastic on-demand provisioning of VMs in response to their variable load to achieve increased utilization efficiency at a lower operational cost, while guaranteeing the desired level of Quality of Service (QoS) to the end-users. Typically, VMs are created using provider-specific templates that are stored in proprietary repositories, leading to provider lock-in and hampering portability or simultaneous usage of multiple federated Clouds. In this context, optimization at the level of the virtual machine image is needed both by the applications and by the underlying Cloud providers for improved resource usage, operational costs, elasticity, storage use, and other desired QoS-related features. To overcome those issues, the ENTICE project researches and creates a novel VM repository and operational environment for federated Cloud infrastructures. There exists a large variety of industrial applications that can strongly benefit by the ENTICE environment. In this paper we present an interesting selection of complementary use cases that drive the definition of the essential requirements for the ENTICE environment, and more importantly, validate the introduced innovations.

Flexible-finger hands/grippers have some advantages over rigid ones when used in grasping tasks. For example, they absorb energy during the impact, which make them suitable in delicate manipulation or human interaction. In this work, we look at their performance during grasping tasks, analyzing the effect of some design parameters like precurvature and stiffness of the links, number/DOFs of fingers or scale of the hand. Our work is based on a grasp stability analysis together with a grasp closure analysis adapted to flexible hands. Simulation and experimental tests with a two flexible-finger gripper are provided as validation of our analysis.

During the past 20 years, scientists have focused their efforts in the quest of real solutions in which the neural signals produced inside the human brain could be connected with computers or artificial prostheses that in a near future could be used to restore the mobility and communication abilities of patients with some damage in the central nervous system. In this paper, the procedure to control an artificial device with the thought is explained; the techniques used to extract the neural activity from the brain are classified and compared to establish their advantages, drawbacks, and future development. In addition, the main breakthroughs so far in brain-machine interfaces are described.

Flexible manipulators have many advantages over rigid robots when used in grasping tasks, owing to the characteristic flexibility of the materials used in their construction, which allow them to absorb energy during impact and make them controllable in order to limit the force applied during contact. In this paper, a two-flexible-finger gripper with a simple sensory feedback system is proposed. A generalized proportional-integral controller is implemented to acquire high control accuracy, while eliminating the need for derivatives of system output. The performance of this force feedback control system is investigated when mounted on a 6-degree-of-freedom (6-DOF) manipulator and grasping different objects. The experimental results indicate that the system proposed is able to manipulate different types of objects (both rigid and flexible) in an industrial environment by perfectly controlling the applied contact torque.

In this paper, we propose a fast online closed-loop identification method combined with an output-feedback controller of the generalized proportional integral (GPI) type for the control of an uncertain flexible robotic arm with unknown mass at the tip, including a Coulomb friction term in the motor dynamics. A fast nonasymptotic algebraic identification method developed in continuous time is used to identify the unknown system parameter and update the designed certainty equivalence GPI controller. In order to verify this method, several informative simulations and experiments are shown.

In this article, we propose a fast on-line closed loop identification method of continuous-time combined with an output feedback controller of the generalized proportional integral type (GPI), for the control of an uncertain flexible robotic arm with unknown mass at the tip, including a Coulomb friction term in the motor dynamics. A fast, non-asymptotic, algebraic identification method is used to identify the unknown system parameter and update the designed certainty equivalence GPI controller. In order to verify this method several informative experiments are shown.