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 novel classification approach based on Extreme Learning Machine and Wavelet Neural Networks

Multimedia Tools and Applications - In this paper, we present a novel classification approach based on Extreme Learning Machine (ELM) and Wavelet Neural Networks. We introduce two novel...     

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.     

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.