Exploring the main building blocks of SOA method: SOA maturity model perspective

The most successful enterprises plan its SOA adoption process as a series of maturity stages, where each stage acts as a foundation for the next. Various SOA maturity models (SOAMMs) are available to judge the current level of SOA adoption maturity of an enterprise. However, only a very few models review the formal use of SOA methods in the maturity assessment process. In this work, the SOAMMs are evaluated in terms of how they assess the methodological aspects of SOA adoption at each level of maturity. Based on methodical features, we introduce a set of building blocks for different maturity levels to assist the method engineers in SOA method definition. Welke’s SOAMM is selected as a base model to derive maturity guidelines for SOA methods. This work establishes the role of SOAMM in realizing the true potential of SOA and discusses why CMMI models are inapplicable to assess SOA adoption maturity. The main intention of this work is to develop a conceptual framework that describes the building block of SOA methods at different levels of maturity, including their processes, tools and technologies, and sourcing mechanisms. We adopt a real case study to determine the maturity level of SOA methodology dimension and to demonstrate how SOA adoption maturity can be improved by focusing on the methodical building blocks identified.     

Characterization of the Soft X-Ray Emission from the APF Plasma Focus Device Operated in Neon

Experimental results related to soft X-ray (SXR) properties of Neon plasma on the APF plasma focus device is presented. The experiments were carried on over wide range of neon pressure and at voltages 11, 12 and 13 kV six filtered photo PIN diodes and pin-hole camera. For the charging voltages of 11–13 kV with 2.17–3.04 kJ stored energy, the optimum operating pressure in neon is found to be in the range of 3.5–5 torr. The behavior of SXR intensities was registered by different filters and found out that Al-Mylar 6 μm and Cu 10 μm has the highest and lowest amount of X-ray transmission, respectively.     

Impact of polarization and incidence of the ASAR sensor on coastline mapping: example of Gabon

Coastline mapping is of great interest in many different applications. In this Letter, we analyse the effect of the polarization and the incidence angle of the new Advanced Synthetic Aperture Radar (ASAR) sensor on coastline detection. Results indicate a marked dependence of the incidence angle and polarization on the delimitation between land and sea. At low incidence angles (<30°), delimitation is much clearer with HV polarization than with HH and VV polarizations. Delimitation is high whatever the polarization at high incidence angles (>30°). The multi‐polarization aspect of ASAR, in addition to its multi‐incidence aspect, therefore, allows coastline evolution to be monitored both more efficiently and more frequently. Consequently, the ASAR sensor is better adapted to coastline monitoring than the older ERS‐1/2 and Radarsat‐1 sensors.     

Towards SoC Validation Through Prototyping: A Systematic Approach Based on Reconfigurable Platform

Hardware/software covalidation is becoming one of the most critical issues in current System-on-Chip (SoC) design. Nowadays, covalidation is usually performed by cosimulation which is slow and lacks accuracy. The other alternative is to build a hardware prototype specific to the application. However, this alternative is expensive in terms of time, man-power, and cost. As SoCs increase in complexity, validation becomes more and more difficult, time consuming and error prone. Thus, a new approach for covalidation is inescapable. In this paper, we present a novel efficient prototyping approach for complex SoC covalidation. The proposed approach enables systematic prototyping of embedded applications on a reconfigurable platform. The process starts from the RT level model of the application. The application and the reconfigurable platform have to be adapted to obtain the prototype. We decompose the prototyping process into four steps, in order to match the application and the platform. Besides, we propose adapted solutions to deal with constraints typically encountered in existing reconfigurable platforms. The main advantages of this method are: fast and accurate validation, systematic prototyping flow, and large application field. Prototyping of a subset of VDSL using the ARM Integrator platform illustrates the effectiveness of our approach.     

Airborne C-band SAR measurements of wet snow-covered areas

Using polarimetric synthetic aperture radar (SAR) data at C-band, the optimum polarization and range incidence angles for the classification of land covers found in the James Bay area, P.Q., Canada-open areas, lakes ice, and forests, all covered with wet snow-have been examined. The separability between classes shows that they can be classified by a single polarization. For &thetas;<30°, the overall classification accuracies were 97.1, 98, and 90.8% from HH-, cross, and VV-polarizations alone, respectively. They were greater than 99% for all polarizations at &thetas;>30°. However, the polarimetric parameters were not suitable for classification     

Survey of computer communications loop networks: Part 1

Loop networks for computer communications are of interest due to inherent advantages of performance, modularity, simplicity and cost. Several demand-shared loops are surveyed and compared, and some nondemand-shared loops described. The significant features of loops are identified and discussed, with emphasis on communications aspects, rather than operating systems or software. Topology (single and multiloops), multiplexing techniques, distribution of control, synchronization, reliability and performance are discussed. In a section on performance different loops are compared with each other and with other topologies, particularly stars, random-access highways and polled highways. It is shown that loops can have performance advantages over other configurations. In Part 1, loop networks are introduced and individual loops described. Configuration and topology and loop-capacity sharing are discussed. Timing and synchronization, control distribution, reliability and performance will be covered in subsequent issues of the journal.     

Silicon ribbon growth via the ribbon-to-ribbon (RTR) technique: Process update and material characterization

The current state of development of the RTR process for silicon ribbon growth is reviewed. Present growth capabilities, material characterization, and device performance are summarized. Specific process problems, such as poor laser coupling efficiencies, are discussed and approaches to their solution are described. Problems related to thermal stresses are discussed in some detail. Stress induced birefringence measurements are used to evaluate residual stresses. A linear temperature profile postheater is effective for reduction of residual stresses. A correlation may be obtained between minority carrier diffusion lengths, dislocation densities, and thermal stress levels.     

A machine learning approach to detect changes in gait parameters following a fatiguing occupational task

The purpose of this study is to provide a method for classifying non-fatigued vs. fatigued states following manual material handling. A method of template matching pattern recognition for feature extraction ($1 Recognizer) along with the support vector machine model for classification were applied on the kinematics of gait cycles segmented by our stepwise search-based segmentation algorithm. A single inertial measurement unit on the ankle was used, providing a minimally intrusive and inexpensive tool for monitoring. The classifier distinguished between states using distance-based scores from the recogniser and the step duration. The results of fatigue detection showed an accuracy of 90% across data from 20 recruited subjects. This method utilises the minimum amount of data and features from only one low-cost sensor to reliably classify the state of fatigue induced by a realistic manufacturing task using a simple machine learning algorithm that can be extended to real-time fatigue monitoring as a future technology to be employed in the manufacturing facilities.

Practitioner Summary: We examined the use of a wearable sensor for the detection of fatigue-related changes in gait based on a simulated manual material handling task. Classification based on foot acceleration and position trajectories resulted in 90% accuracy. This method provides a practical framework for predicting realistic levels of fatigue.     

Study of macromolecular interactions in low‐fat brined cheese modified with Zedu gum

The functionality of Zedu gum as a fat mimetic in low‐fat brined cheese was studied. The physicochemical, textural, rheological, microstructural and sensory properties of cheese samples modified with 0.1% and 0.25% of Zedu gum were compared to those of control cheeses (low‐fat and full‐fat cheeses with no fat mimetic) during ripening. To obtain further information about the cheeses' structure and interactions between macromolecules (casein protein and Zedu gum), other parameters were analysed by differential scanning calorimetry and Fourier transform infrared (FTIR) spectroscopy. Incorporation of Zedu gum into low‐fat cheese caused an open microstructure and softer texture in comparison with the control low‐fat cheese. The thermal properties and FTIR spectra of the cheeses were influenced by both fat mimetic and ripening time. On days 1 and 60 of ripening time, the lower value of enthalpy of the low‐fat cheese with 0.25 g of Zedu gum/kg of milk (AS 0.25) in comparison with control low‐fat cheese could have been due to the electrostatic nature of the interactions between Zedu gum and casein protein. On both days, the FTIR spectrum of AS 0.25 showed a well separated absorption at 1746 cm^?1 possibly due to the formation of ester groups as a result of the interaction of the carbonyl groups in Zedu gum with the hydroxyl groups of some amino acids in casein.     

Applying FCM-ANFIS algorithm as a novel computational method for prediction of viscosity of bitumen and heavy alkane mixture

Recently the studies expressed that the noticeable number of oil reservoirs in all over the world are heavy oil and bitumen reservoirs. So the importance of enhancement of oil recovery (EOR) processes for heavy oil and bitumen reservoirs is highlighted. The Dilution of the reservoir fluid by solvents such as tetradecane is one of well-known methods for these types of reservoirs which effects oil recovery by decreasing viscosity. In the present study, Fuzzy c-means (FCM) algorithm was coupled with Adaptive neuro-fuzzy inference system (ANFIS) to predict viscosity of bitumen and tetradecane in terms of temperature, pressure and weight percent of tetradecane. The coefficients of determination for training and testing steps were calculated such as 0.9914 and 0.9613. The comparison of results and experimental data expressed that FCM-ANFIS algorithm has great potential for estimation of viscosity of bitumen and tetradecane.