iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator

Simulation techniques have become a powerful tool for deciding the best starting conditions on pay-as-you-go scenarios. This is the case of public cloud infrastructures, where a given number and type of virtual machines (in short VMs) are instantiated during a specified time, being this reflected in the final budget. With this in mind, this paper introduces and validates iCanCloud, a novel simulator of cloud infrastructures with remarkable features such as flexibility, scalability, performance and usability. Furthermore, the iCanCloud simulator has been built on the following design principles: (1) it’s targeted to conduct large experiments, as opposed to others simulators from literature; (2) it provides a flexible and fully customizable global hypervisor for integrating any cloud brokering policy; (3) it reproduces the instance types provided by a given cloud infrastructure; and finally, (4) it contains a user-friendly GUI for configuring and launching simulations, that goes from a single VM to large cloud computing systems composed of thousands of machines.     

Design of aperiodic planar arrays for isoflux radiation in GEO satellites by applying evolutionary optimization

The design of an aperiodic planar array is presented in this research. This design of aperiodic arrays considers the reduction of the side lobe level and the isoflux radiation requirements for GEO (Geostationary Earth Orbit) satellite applications. In this way, it is considered four different optimization cases. The first two cases are optimizations of amplitude and phase excitations for the antenna elements in a uniform antenna array and the last two cases are optimizations of positions of the antenna elements and certain number of levels of amplitude excitation in an aperiodic array. In this case, it is proposed a simple new approach combining the main idea of both thinned theory and random arrays approaches. For this optimization problem, the well-known method of Genetic Algorithms (GA) is utilized. The obtained results show the proper performance of the array factor to provide the isoflux radiation and low side lobe level. Depending on the performance requirements, the design of the aperiodic array could lead the satellite hardware to be reduced significantly even more that results presented previously in the literature.     

Effect of Processing on the Detectability of Pecan Proteins Assessed by Immunological and Proteomic Tools

The present study evaluates the effect of food processing on the antigenicity of pecan proteins as measured by enzyme-linked immunosorbent assay (ELISA). In addition, proteomic tools were used to identify potential pecan markers suitable for confirming the presence of pecan proteins in food and validating new methods developed to detect traces of the commodity. To assess the effects of processing on protein stability and antigenicity, pecan nuts were submitted to heat treatments and extracts were analysed by ELISA, sodium dodecyl sulphate polyacrylamide gel electrophoresis and Western blot. The ELISA method was able to detect pecan traces even after submitting the commodity to rigorous treatments, though these treatments affected the detectability to varying degrees. Proteomic assessment showed that the majority of pecan proteins were matched by homology to walnut proteins, which are more abundantly populated in the protein sequence databases. However, there were a few important exceptions: 7S vicilin, 11S legumin and putative allergen I1, unambiguously identified as pecan in origin. Interestingly, putative allergen I1 offered unique analytical advantages to be used as a pecan marker for validation and confirmation purposes.     

Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill

In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, temperature estimation is performed by physical modeling; however, it is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques such as artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker entry temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.     

Residual behavior of steel rebars and R/C sections after a fire

The mechanical properties of various steel bars exposed to high temperature (“residual” properties) are experimentally investigated up to 850 °C, with reference to a number of steel and bar types (carbon and stainless steel; quenched and self-tempered bars; hot-rolled and cold-worked bars; smooth and deformed bars). The aim is to clarify to what extent the thermal sensitivity of the different bars affects the ultimate capacity of a typical R/C section subjected to an eccentric axial force, past a fire (“residual” capacity). As usual in the design of R/C sections under combined bending and axial loading, the ultimate behavior is represented through the “M–N envelopes”, where the materials strength decay due to high temperature is taken into account. The results show that quenched and self-tempered bars (QST), very popular in Europe, are more temperature-sensitive above 600 °C than the carbon-steel bars extensively used in the States and nowadays rarely used in Europe. Furthermore, the best response is exhibited by the stainless-steel bars, provided that they are hot rolled, as it is generally the case for medium- and large-diameter bars. Similar conclusions can be drawn for the sections reinforced with the different bar types.     

Process Capability Analysis for NonLinear Profiles Using Depth Functions

There are practical situations in which the quality of a process or product can be better characterized by a functional relationship between a response variable and one or more explanatory variables, which is called profile. Such profiles frequently can be represented adequately using linear or nonlinear models. While there are several studies in monitoring profiles, there are few studies to evaluate the capability of a process with profile quality characteristic; specifically, there is no method in the literature to analyze process capability characterized by nonlinear profiles. In this paper, we propose two methods to measure the capability of these processes, based on the concept of functional depth. These methods do not have distributional assumptions and extend to functional data the Process Capability Indexes proposed by Clements 1 to measure the capability of a process characterized by a random variable. Performance of the proposed methods is evaluated through simulation studies. An example illustrates the applicability of these methods. Copyright © 2013 John Wiley & Sons, Ltd.     

Parallel sphere detector algorithm providing optimal MIMO detection on massively parallel architectures

Multiple‐input multiple‐output (MIMO) systems have attracted considerable attention in wireless communications because they offer a significant increase in data throughput and link coverage without additional bandwidth requirement or increased transmit power. The price that has to be paid is the increased complexity of hardware components and algorithms. The sphere detector (SD) algorithm solves the problem of maximum likelihood (ML) detection for MIMO channels by significantly reducing the search space of possible solutions. The main drawback of the SD algorithm is in its sequential nature, consequently, running it on massively parallel architectures (MPAs) is very inefficient. In order to overcome the drawbacks of the SD algorithm, a new parallel sphere detector (PSD) algorithm is proposed. It implements a novel hybrid tree search method, where the algorithm parallelism is assured by the efficient combination of depth‐first search and breadth‐first search algorithms. A path metric‐based parallel sorting is employed at each intermediate stage. The PSD algorithm is able to adjust its memory requirements and extent of parallelism to fit a wide range of parallel architectures. Mapping details for MPAs are proposed by giving the details of thread dependent, highly parallel building blocks of the algorithm. Based on the building blocks proposed, a mapping to general‐purpose graphics processing unit is provided, and its performance is evaluated. In order to achieve high‐throughput, several levels of parallelism are introduced, and different scheduling strategies are considered. Copyright © 2015 John Wiley & Sons, Ltd.