Energy efficiency for edge multimedia elastic applications

With the rise of edge computing paradigms, multimedia applications will have to tackle unprecedented management issues, pursuing an optimal balance between performance, Quality of Service (QoS), and power consumption. In this paper, we investigate a novel paradigm to deploy multimedia elastic applications at the edge in a very energy-efficient manner. Our approach is based on pre-provisioning virtual resources that remain “frozen” until the application scales out. Frozen resources are treated in a special way by the infrastructure, leveraging aggressive power-saving mechanisms that keep negligible their impact on energy consumption and performance. We report extensive measurements on QoS and power consumption that we carried out in a real testbed, which is the first working implementation of the proposed paradigm. Our work shows how resource utilization and performance can be increased by leveraging SDN technologies and conscious setting of cloud parameters. We investigate the trade-off between performance and power consumption (i.e., energy efficiency), in relation to different consolidation strategies. Finally, we measure power consumption and estimate energy saving for an elastic video transcoding application deployed at the network edge.

     

Spectroscopic Indications of Tunnel Barrier Charging as the Switching Mechanism in Memristive Devices

Resistive random access memory is a promising, energy‐efficient, low‐power “storage class memory” technology that has the potential to replace both flash storage and on‐chip dynamic memory. While the most widely employed systems exhibit filamentary resistive switching, interface‐type switching systems based on a tunable tunnel barrier are of increasing interest. They suffer less from the variability induced by the stochastic filament formation process and the choice of the tunnel barrier thickness offers the possibility to adapt the memory device current to the given circuit requirements. Heterostructures consisting of a yttria‐stabilized zirconia (YSZ) tunnel barrier and a praseodymium calcium manganite (PCMO) layer are employed. Instead of spatially localized filaments, the resistive switching process occurs underneath the whole electrode. By employing a combination of electrical measurements, in operando hard X‐ray photoelectron spectroscopy and electron energy loss spectroscopy, it is revealed that an exchange of oxygen ions between PCMO and YSZ causes an electrostatic modulation of the effective height of the YSZ tunnel barrier and is thereby the underlying mechanism for resistive switching in these devices.     

Toward a robust optimal point selection: a multiple-criteria decision-making process applied to multi-objective optimization using response surface methodology

During the multi-objective optimization process, numerous efficient solutions may be generated to form the Pareto frontier. Due to the complexity of formulating and solving mathematical problems, choosing the best point to be implemented becomes a non-trivial task. Thus, this paper introduces a weighting strategy named robust optimal point selection, based on ratio diversification/error, to choose the most preferred Pareto optimal point in multi-objective optimization problems using response surface methodology. Furthermore, this paper proposes to explore a theoretical gap—the prediction variance behavior related to the weighting. The ratios Shannon’s entropy/error and diversity/error and the unscaled prediction variance are experimentally modeled using mixture design and the optimal weights for the multi-objective optimization process are defined by the maximization of the proposed measures. The study could demonstrate that the weights used in the multi-objective optimization process influence the prediction variance. Furthermore, the use of diversification measures, such as entropy and diversity, associated with measures of error, such as mean absolute percent error, was determined to be useful in mapping regions of minimum variance within the Pareto optimal responses obtained in the optimization process.

     

Morphology,crystallization and melting behaviour of films of isotactic polypropylene blended with ethylene-propylene copolymers and polyisobutylene

The crystallization, the morphology and the thermal behaviour of thin films of isotactic polypropylene (iPP) blended with elastomers such as random ethylene-propylene copolymers (EPM) with different ethylene content and polyisobutylene (PiB) were investigated by means of optical microscopy, differential scanning calorimetry and wide angle X-ray diffractometry. During crystallization EPM copolymers are ejected on the surface of the film forming droplet-like domains. A different morphology is observed in iPP/PiB blends. For these mixtures the elastomers separate from the iPP phase forming spherical domains that are incorporated in the iPP intraspherulitic regions. Both EPM and PiB elastomers act as nucleant agents for iPP spherulites. This nucleation efficiency is strongly dependent on the chemical structure and molecular mass of the elastomers. The addition of EPM causes an elevation of the observed and equilibrium melting temperature of iPP. This unusual effect may be accounted for by assuming that the elastomers are able to extract selectively the more defective molecules of iPP. The depression of the growth rate of spherulites and the observed and equilibrium melting temperature of iPP, noted in iPP/PiB blends, suggests that these two polymers have a certain degree of compatibility in the melt.     

Plug-and-play model predictive control based on robust control invariant sets

We consider the problem of designing decentralized controllers for large-scale linear constrained systems composed by a number of interacting subsystems. As in Riverso et al. (2013b), (i) the design of local controllers requires limited transmission of information from other subsystems and (ii) the addition/removal of a subsystem triggers the design of local controllers for child subsystems only. These properties enable Plug-and-Play (PnP) operations, and we show how to perform them while preserving global stability of the origin and constraint satisfaction. We improve several aspects of the PnP design procedure proposed in Riverso et al. (2013b) and, using recent results in the computation of Robust Control Invariant (RCI) sets, we show that all critical steps in the design of a local controller can be solved through Linear Programming (LP). Finally, an application of the proposed design procedure to a large-scale mechanical system is presented.     

GPU-accelerated simulations of mass-action kinetics models with cupSODA

In the last years, graphics processing units (GPUs) witnessed ever growing applications for a wide range of computational analyses in the field of life sciences. Despite its large potentiality, GPU computing risks remaining a niche for specialists, due to the programming and optimization skills it requires. In this work we present cupSODA, a simulator of biological systems that exploits the remarkable memory bandwidth and computational capability of GPUs. cupSODA allows to efficiently execute in parallel large numbers of simulations, which are usually required to investigate the emergent dynamics of a given biological system under different conditions. cupSODA works by automatically deriving the system of ordinary differential equations from a reaction-based mechanistic model, defined according to the mass-action kinetics, and then exploiting the numerical integration algorithm, LSODA. We show that cupSODA can achieve a \(86 \times \) speedup on GPUs with respect to equivalent executions of LSODA on the CPU.     

Synthesis and characterization of thermoresponsive N-isopropylacrylamide/methacrylated pullulan hydrogels

Thermosensitive hydrogels were prepared by free radical polymerization starting from a methacrylated pullulan derivative (acting as the cross-linker) and using N-isopropylacrylamide (NIPAAM) as the monomer. Several hydrogels were obtained by changing the monomer to cross-linker ratio. A significant thermosensitivity was observed only when the molar amount of NIPAAM incorporated in the network was at least eight times higher that of methacrylate groups on pullulan. The hydrogel with high amount of NIPAAM deswells more than 80% after the T-jump. The lower critical solution temperature of thermosensitive hydrogels decreases with increasing amount of NIPAAM. The mechanical properties of the hydrogels are strongly affected by the percentage of incorporated NIPAAM and by the temperature.     

SPINE: a domain‐specific framework for rapid prototyping of WBSN applications

Wireless body sensor networks (WBSNs) enable a broad range of applications for continuous and real‐time health monitoring and medical assistance. Programming WBSN applications is a complex task especially due to the limitation of resources of typical hardware platforms and to the lack of suitable software abstractions. In this paper, SPINE (signal processing in‐node environment), a domain‐specific framework for rapid prototyping of WBSN applications, which is lightweight and flexible enough to be easily customized to fit particular application‐specific needs, is presented. The architecture of SPINE has two main components: one implemented on the node coordinating the WBSN and one on the nodes with sensors. The former is based on a Java application, which allows to configure and manage the network and implements the classification functions that are too heavy to be implemented on the sensor nodes. The latter supports sensing, computing and data transmission operations through a set of libraries, protocols and utility functions that are currently implemented for TinyOS platforms. SPINE allows evaluating different architectural choices and deciding how to distribute signal processing and classification functions over the nodes of the network. Finally, this paper describes an activity monitoring application and presents the benefits of using the SPINE framework. Copyright © 2010 John Wiley & Sons, Ltd.     

Accurate multi-robot targeting for keyhole neurosurgery based on external sensor monitoring

Robotics has recently been introduced in surgery to improve intervention accuracy, to reduce invasiveness and to allow new surgical procedures. In this framework, the ROBOCAST system is an optically surveyed multi-robot chain aimed at enhancing the accuracy of surgical probe insertion during keyhole neurosurgery procedures. The system encompasses three robots, connected as a multiple kinematic chain (serial and parallel), totalling 13 degrees of freedom, and it is used to automatically align the probe onto a desired planned trajectory. The probe is then inserted in the brain, towards the planned target, by means of a haptic interface. This paper presents a new iterative targeting approach to be used in surgical robotic navigation, where the multi-robot chain is used to align the surgical probe to the planned pose, and an external sensor is used to decrease the alignment errors. The iterative targeting was tested in an operating room environment using a skull phantom, and the targets were selected on magnetic resonance images. The proposed targeting procedure allows about 0.3 mm to be obtained as the residual median Euclidean distance between the planned and the desired targets, thus satisfying the surgical accuracy requirements (1 mm), due to the resolution of the diffused medical images. The performances proved to be independent of the robot optical sensor calibration accuracy.