Academic literature on the topic 'Autonomic computing models'

Human mobility is the key element of everyday life, its reduction or loss deeply affects daily activities. In assisted rehabilitation, robotic devices have focuses on the biomechanics of motor control. However, biomechanics does not study the neurological and physiological processes related to normal gait. Biomimetics combined with biomechanics, can generate a more efficient stimulation of the motor cortex and the locomotor system. The highest efficiency obtained through torque generation models, based on the physiological response of muscles and bones to reaction forces, together with control techniques based on autonomic computation. An autonomic control algorithm has a self-adjusting behaviour, ensuring patient safety and robot operation without the continuous monitoring of the physiotherapist. Thus, this work will identify the elements that characterize the physiological stimuli related to normal human gait, focusing on the ankle joint, aiming the development of biomimetic algorithms for robots for rehabilitation of the lower limbs.

AbstractThe notion of an enactive system requires thinking about the brain in a way that is different from the standard computational-representational models. In evolutionary terms, the brain does what it does and is the way that it is, across some scale of variations, because it is part of a living body with hands that can reach and grasp in certain limited ways, eyes structured to focus, an autonomic system, an upright posture, etc. coping with specific kinds of environments, and with other people. Changes to any of the bodily, environmental, or intersubjective conditions elicit responses from the system as a whole. On this view, rather than representing or computing information, the brain is better conceived as participating in the action.

We focus on Erol Gelenbe's scientific and technical contributions to probability models in the computer and information sciences, but limit our survey to the last fifteen years. We start with a brief overview of his work as a single author, as well as his work in collaboration with over 200 co-authors. We discuss some of his recent and innovative work regarding a new probability model that represents Intermittent Energy Sources for Computing and Communications, introducingEnergy Packet Networkswhich are a probabilistic representation of the flow, storage and consumption of electrical energy at the microscopic level (in electronic chips), and at the macroscopic level (e.g. in buildings or data centers) and for its routing and dynamic usage by consuming units (such as computer elements, chips or machines). We next discuss his work on designing computer and communication systems that parsimoniously use energy in order to achieve a satisfactory level of quality of service (QoS). Trade-offs between system QoS and energy consumption are also considered. Then we turn to Prof. Gelenbe's pioneering work on Autonomic Communications and the design and implementation of CPN, the Cognitive Packet Network, and we also briefly review his spiking random neural network that was used in CPN. This is followed by a brief review of work that he conducted since 1999 on human evacuation from dangerous or catastrophic environments, and the design of technology driven Emergency Management Systems. His research since the late 2000s on Gene Regulatory Networks is then covered together with its application to the detecting possible disease from microarray data. Finally, we briefly discuss some novel analytical models that he developed in this period with publications appearing in journals of physics and applied mathematics.