Manifold Preserving Features and Regression for Semantic Labelling in High Dimensional Images

Wireless Personal Communications - This paper proposes an combined method for manifold preservation and Subspace Eigenvectors(SE) based regression in high dimensional (HD) images. We studied...     

Thermodynamic properties and phase equilibria for Pt-Rh alloys

The activity of rhodium in solid Pt-Rh alloys is measured in the temperature range from 900 to 1300 K using the solid-state cell

Integrated Data Management for a Fleet of Search‐and‐rescue Robots

Search‐and‐rescue operations have recently been confronted with the introduction of robotic tools that assist the human search‐and‐rescue workers in their dangerous but life‐saving job of searching for human survivors after major catastrophes. However, the world of search and rescue is highly reliant on strict procedures for the transfer of messages, alarms, data, and command and control over the deployed assets. The introduction of robotic tools into this world causes an important structural change in this procedural toolchain. Moreover, the introduction of search‐and‐rescue robots acting as data gatherers could potentially lead to an information overload toward the human search‐and‐rescue workers, if the data acquired by these robotic tools are not managed in an intelligent way. With that in mind, we present in this paper an integrated data combination and data management architecture that is able to accommodate real‐time data gathered by a fleet of robotic vehicles on a crisis site, and we present and publish these data in a way that is easy to understand by end‐users. In the scope of this paper, a fleet of unmanned ground and aerial search‐and‐rescue vehicles is considered, developed within the scope of the European ICARUS project. As a first step toward the integrated data‐management methodology, the different robotic systems require an interoperable framework in order to pass data from one to another and toward the unified command and control station. As a second step, a data fusion methodology will be presented, combining the data acquired by the different heterogenic robotic systems. The computation needed for this process is done in a novel mobile data center and then (as a third step) published in a software as a service (SaaS) model. The SaaS model helps in providing access to robotic data over ubiquitous Ethernet connections. As a final step, we show how the presented data‐management architecture allows for reusing recorded exercises with real robots and rescue teams for training purposes and teaching search‐and‐rescue personnel how to handle the different robotic tools. The system was validated in two experiments. First, in the controlled environment of a military testing base, a fleet of unmanned ground and aerial vehicles was deployed in an earthquake‐response scenario. The data gathered by the different interoperable robotic systems were combined by a novel mobile data center and presented to the end‐user public. Second, an unmanned aerial system was deployed on an actual mission with an international relief team to help with the relief operations after major flooding in Bosnia in the spring of 2014. Due to the nature of the event (floods), no ground vehicles were deployed here, but all data acquired by the aerial system (mainly three‐dimensional maps) were stored in the ICARUS data center, where they were securely published for authorized personnel all over the world. This mission (which is, to our knowledge, the first recorded deployment of an unmanned aerial system by an official governmental international search‐and‐rescue team in another country) proved also the concept of the procedural integration of the ICARUS data management system into the existing procedural toolchain of the search and rescue workers, and this in an international context (deployment from Belgium to Bosnia). The feedback received from the search‐and‐rescue personnel on both validation exercises was highly positive, proving that the ICARUS data management system can efficiently increase the situational awareness of the search‐and‐rescue personnel.     

Tool integration for flexible simulation of distributed algorithms

Over the last two decades, considerable research has been done in distributed operating systems, which can be attributed to faster processors and better communication technologies. A distributed operating system requires distributed algorithms to provide basic operating system functionality like mutual exclusion, deadlock detection, etc. A number of such algorithms have been proposed in the literature. Traditionally, these distributed algorithms have been presented in a theoretical way, with limited attempts to simulate actual working models. This paper discusses our experience in simulating distributed algorithms with the aid of some existing tools, including OPNET and Xplot. We discuss our efforts to define a basic model‐based framework for rapid simulation and visualization, and illustrate how we used this framework to evaluate some classic algorithms. We have also shown how the performance of different algorithms can be compared based on some collected statistics. To keep the focus of this paper on the approach itself, and our experience with tool integration, we only discuss some relatively simple models. Yet, the approach can be applied to more complex algorithm specifications. Copyright © 2001 John Wiley & Sons, Ltd.     

Influence of microstructural inhomogeneities on the fracture toughness of modified 9Cr–1Mo steel at 298–823 K

Quasistatic fracture behaviour of two heats of modified 9Cr–1Mo steel for steam generator applications have been assessed at 298, 653 and 823 K. J–R curves were established and the elastic–plastic fracture toughness values at 0.2 mm crack extension (J_0.2) were determined. The fracture mechanisms were entirely different for the two steels at 298 K, with brittle fracture controlled by cleavage crack initiation in one and ductile fracture in the other by void nucleation and growth. At 653 and 823 K, fracture in both materials was by ductile crack growth. The difference in behaviour between the two steels at 298 K was attributed to the differences in microstructure, distribution and density of inclusions as well as phosphorous contents.     

Direct three dimensional tomography of flames using maximization of entropy technique

Presently, there are numerous applications for non-destructive techniques like emission tomography, laser based methods and particle image velocimetry that are used to study flame characteristics. Reconstruction of the flame intensity field using emission tomography has the advantage over other technologies that it gives accurate results but at the same time requires relatively inexpensive equipment, and therefore, has numerous industrial applications. In the present paper, a new algorithm performing Direct-3D reconstruction using the maximization of entropy (MENT) methodology has been introduced. Through detailed studies using a mathematical object, it has been shown that the Direct-3D algorithm shows significantly reduced errors as compared to 2D slice-by-slice reconstruction algorithms. Secondly, the major features of the proposed algorithm, for e.g., effect of orientation, effect of number of views, and robustness have been discussed. Finally, a few qualitative results from actual flames have been presented using a candle and a gas fired burner, and the results match well with the actual flame geometry and intensity distribution.     

A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks

Real-time sensing plays an important role in ensuring the reliability of industrial wireless sensor networks (IWSNs). Sensor nodes in IWSNs have inherent limitations that give rise to different anomalies in the network. These anomalies can lead to disastrous and harmful situations or even serious system failures. This article presents a formulation to the design of an anomaly detection scheme for detecting the anomalous node along with the type of anomaly. The proposed scheme is divided into two major parts. First, spatiotemporal correlation within a cluster is obtained for the normal and anomalous behavior of sensor nodes. Second, the multilevel hybrid classifier is used by combining the sequential minimal optimization support vector machine (SMO-SVM) as a binary classifier with optimally pruned extreme learning machine (OP-ELM) as a multiclass classifier for detection of an anomalous node and type of anomalies, respectively. Mahalanobis distance-based lightweight K-Medoid clustering is used to build a new set of training datasets that represents the original training dataset, by significantly reducing the training time of a multilevel hybrid classifier. Results are analyzed using standard WSN datasets. The proposed model shows high accuracy, i.e., 94.79% and detection rate, i.e., 94.6% with a reduced false positive rate as compared to existing hybrid methods.     

A simulation as a service cloud middleware

Many seemingly simple questions that individual users face in their daily lives may actually require substantial number of computing resources to identify the right answers. For example, a user may want to determine the right thermostat settings for different rooms of a house based on a tolerance range such that the energy consumption and costs can be maximally reduced while still offering comfortable temperatures in the house. Such answers can be determined through simulations. However, some simulation models as in this example are stochastic, which require the execution of a large number of simulation tasks and aggregation of results to ascertain if the outcomes lie within specified confidence intervals. Some other simulation models, such as the study of traffic conditions using simulations may need multiple instances to be executed for a number of different parameters. Cloud computing has opened up new avenues for individuals and organizations with limited resources to obtain answers to problems that hitherto required expensive and computationally-intensive resources. This paper presents SIMaaS, which is a cloud-based Simulation-as-a-Service to address these challenges. We demonstrate how lightweight solutions using Linux containers (e.g., Docker) are better suited to support such services instead of heavyweight hypervisor-based solutions, which are shown to incur substantial overhead in provisioning virtual machines on-demand. Empirical results validating our claims are presented in the context of two case studies.     

Solubility and dissolution enhancement strategies: current understanding and recent trends

Identification of lead compounds with higher molecular weight and lower aqueous solubility has become increasingly prevalent with the advent of high throughput screening. Poor aqueous solubility of these lipophilic compounds can drastically affect the dissolution rate and subsequently the drug absorbed in the systemic circulation, imposing a significant burden of time and money during drug development process. Various pre-formulation and formulation strategies have been applied in the past that can improve the aqueous solubility of lipophilic compounds by manipulating either the crystal lattice properties or the activity coefficient of a solute in solution or both, if possible. However, despite various strategies available in the armor of formulation scientist, solubility issue still remains an overriding problem in the drug development process. It is perhaps due to the insufficient conceptual understanding of solubility and dissolution phenomenon that hinders the judgment in selecting suitable strategy for improving aqueous solubility and/or dissolution rate. This article, therefore, focuses on (i) revisiting the theoretical and mathematical concepts associated with solubility and dissolution, (ii) their application in making rationale decision for selecting suitable pre-formulation and formulation strategies and (iii) the relevant research performed in this field in past decade.