No. 4 (2013)
Published:
2013-12-30
ARTICLES FROM THIS ISSUE
-
Assessing Coastal Sustainability: A Bayesian Approach for Modeling and Estimating a Global Index for Measuring Risk
Abstract
Integrated Coastal Zone Management is an emerging research area. The aim is to provide a global view of different and heterogeneous aspects interacting in a geographical area. Decision Support Systems, integrating Computational Intelligence methods, can be successfully used to estimate useful anthropic and environmental indexes. Bayesian Networks have been widely used in the environmental science domain. In this paper a Bayesian model for estimating the Sustainable Coastal Index is presented. The designed Bayesian Network consists of 17 nodes, hierarchically organized in 4 layers. The first layer is initialized with the season and the physiographic region information. In the second layer, the first-order indexes, depending on raw data, of physiographic regions are computed. The third layer estimates the second-order indexes of the analyzed physiographic regions. In the fourth layer, the global Sustainable Coastal Index is inferred. Processed data refers to 13 physiographic regions in the Province of Trapani, western Sicily, Italy. Gathered data describes the environmental information, the agricultural, fisheries, and economical behaviors of the local population and land. The Bayesian Network was trained and tested using a real dataset acquired between 2000 and 2006. The developed system presents interesting results.
-
The Development of Kalman Filter Learning Technique for Artificial Neural Networks
Abstract
The paper presents an idea of using the Kalman Filtering (KF) for learning the Artificial Neural Networks (ANN). It is shown that KF can be fully competitive or more beneficial method with comparison standard Artificial Neural Networks learning techniques. The development of the method is presented respecting selective learning of chosen part of ANN. Another issue presented in this paper is the author’s concept of automatic selection of architecture of ANN learned by means of KF based on removing unnecessary connection inside the network. The effectiveness of presented ideas is illustrated on the examples of time series modeling and prediction. Considered data came from the experiments and situ measurements in the field of structural mechanics and materials.
-
Statistical Analysis and Modeling of SIP Traffic for Parameter Estimation of Server Hysteretic Overload Control
Abstract
The problem of overload control in Session Initiation Protocol (SIP) signaling networks gives rise to many questions which attract researchers from theoretical and practical point of view. Any mechanism that is claimed to settle this problem down demands estimation of local (control) parameters on which its performance is greatly dependent. In hysteretic mechanism these parameters are those which define hysteretic loops. In order to find appropriate values for parameters one needs adequate model of SIP traffic flow circulating in the network under consideration. In this paper the attempt is made to address this issue. Analysis of SIP traffic collected from telecommunication operator’s network is presented. Traffic profile is built. It is shown that fitting with Markov Modulated Poisson Process with more than 2 phases is accurate. Estimated values of its parameters are given.
-
Predictive Modeling in a VoIP System
Abstract
An important problem one needs to deal with in a Voice over IP system is server overload. One way for preventing such problems is to rely on prediction techniques for the incoming traffic, namely as to proactively scale the available resources. Anticipating the computational load induced on processors by incoming requests can be used to optimize load distribution and resource allocation. In this study, the authors look at how the user profiles, peak hours or call patterns are shaped for a real system and, in a second step, at constructing a model that is capable of predicting trends.
-
Telemaco: A Language Oriented Tool for Graph-based Models Layout Optimization
Abstract
Progress of ICT is shifting the paradigm of systems organization towards a distributed approach, in which physical deployment of components influences the evaluation of systems properties. This contribution can be considered as a problem of graph layout optimization, well-known in literature where several approaches have been exploited in different application fields with different solving techniques. Then again, complex systems can be only studied by means of different formalisms which codification is the aim of language engineering. Telemaco is a tool that supports a novel approach for the application of graph layout optimizations to heterogeneous models, based on the OsMoSys framework and on the language engineering principles. It can cope with different graph-based formalisms by exploiting either their core graph nature or their different specialized features by means of language hierarchies. In this paper Telemaco is introduced together with its foundations and an example of application to Wireless Sensor Networks (WSN) deployment.
-
Recent Developments in Mobile Cloud Scheduling: State-of-the-Art, Challenges and Perspectives
Abstract
Cloud computing became recently one of the most popular multi-layer distributed computational and data processing environments with various types of services, distributed data storages and resources. With rapid development of mobile technologies, computational clouds have been transformed into the systems with dynamically changing topology and flexible infrastructure through integration with the mobile devices and mobile users as the whole system nodes and actors. The aim of this paper is to provide a comprehensive study and critical comparative analysis of the recent developments in the Mobile Clouds with a new energy optimization criterion scheduling.
-
Adaptive Distributed Data Storage for Context-Aware Applications
Abstract
Context-aware computing is a paradigm that relies on the active use of information coming from a variety of sources, ranging from smartphones to sensors. The paradigm usually leads to storing large volumes of data that need to be processed to derive higher-level context information. The paper presents a cloud-based storage layer for managing sensitive context data. To handle the storage and aggregation of context data for context-aware applications, Clouds are perfect candidates. But a Cloud platform for context-aware computing needs to cope with several requirements: high concurrent access (all data needs to be available to potentially a large number of users), mobility support (such platform should actively use the caches on mobile devices whenever possible, but also cope with storage size limitations), real-time access guarantees – local caches should be located closer to the end-user whenever possible, and persistency (for traceability, a history of the context data should remain available). BlobSeer, a framework for Cloud data storage, is a perfect candidate for storing context data for large-scale applications. It offers capabilities such as persistency, concurrency and support for flexible storage schema requirement. On top of BlobSeer, Context Aware Framework is designed as an extension that offers context-aware data management to higherlevel applications, and enables scalable high-throughput under high-concurrency. On a logical level, the most important capabilities offered by Context Aware Framework are transparency, support for mobility, real-time guarantees and support for access based on meta-information. On the physical layer, the most important capability is persistent Cloud storage.
-
Recognition of the Numbers in the Polish Language
Abstract
Automatic Speech Recognition is one of the hottest research and application problems in today’s ICT technologies. Huge progress in the development of the intelligent mobile systems needs an implementation of the new services, where users can communicate with devices by sending audio commands. Those systems must be additionally integrated with the highly distributed infrastructures such as computational and mobile clouds, Wireless Sensor Networks (WSNs), and many others. This paper presents the recent research results for the recognition of the separate words and words in short contexts (limited to the numbers) articulated in the Polish language. Compressed Sensing Theory (CST) is applied for the first time as a methodology of speech recognition. The effectiveness of the proposed methodology is justified in numerical tests for both separate words and short sentences.
-
Evaluation of the Energy Harvestable from an Airless Tire Equipped with Piezoelectric Bimorphs on the Lamellar Spokes
Abstract
In this work, one evaluates the electrical power generated by an airless tire equipped with piezoelectric bimorphs on both lateral surfaces of the radially distributed lamellar spokes. Such sheet-like spokes are hinged both toward the wheel drum at the inner annular band, and toward the wheel tread at the outer annular band. Since the hinged spokes are able to transmit tension forces but unable to transmit compression forces, bending and buckling of the spokes occur in the region of contact between the tire and the road. Models for the rolling friction of the airless tire, for the bending and buckling deformation of the spokes, and for the electrical power generated by the airless tire are suggested. Variation of the curvature radii and bending deformations for the spokes in the region of contact with the road are illustrated for various values of the rolling friction coefficient and spoke length. Then, variation of the generated electrical power versus the length of contact is obtained for various travel speeds of the vehicle. One observes that the generated electrical power increases at augmentation of the rolling friction coefficient, spoke length and travel speed. Although the obtained electrical power for the proposed harvesting system is relatively modest, it is not depending on the road roughness, i.e. harvesting becomes possible even on smooth roads, such as highway surfaces.
-
Application of High-Performance Techniques for Solving Linear Systems of Algebraic Equations
Abstract
Solving many problems in mechanics, engineering, medicine and other (e.g., diffusion tensor magnetic resonance imaging or finite element modeling) requires the efficient solving of algebraic equations. In many cases, such systems are very complex with a large number of linear equations, which are symmetric positive-defined (SPD). This paper is focused on improving the computational efficiency of the solvers dedicated for the linear systems based on incomplete and noisy SPD matrices by using preconditioning technique – Incomplete Cholesky Factorization, and modern set of processor instructions – Advanced Vector Extension. Application of these techniques allows to fairly reduce the computational time, number of iterations of conventional algorithms and improve the speed of calculation.
-
An Incentives Model Based on Reputation for P2P Systems
Abstract
In this paper an incentive model to improve the collaboration in peer-to-peer networks is introduced. The proposed solution uses an incentives model associated with reputation issues as a way to improve the performance of a P2P system. The reputation of the all peers in the system is based on their donated resources and on their behavior. Supplying peers use these rules as a way to assign its outgoing bandwidth to the requesting peers during a content distribution. Each peer can build its best paths by using a best-neighbor policy within its neighborhood. A peer can use its best paths to obtain best services related to content search or download. The obtained results show that proposed scheme insulates the misbehaving peers and reduces the free-riding so that the systems performance is maximized.
-
Two Semantics of Trust Management Language with Negation
Abstract
The family of Role-based Trust management languages is used for representing security policies by defining a formalism, which uses credentials to handle trust in decentralized, distributed access control systems. A credential provides information about the privileges of users and the security policies issued by one or more trusted authorities. The main topic of this paper is RT⊖, a language which provides a carefully controlled form of non-monotonicity. The core part of the paper defines two different semantics of RT⊖ language – a relational, set-theoretic semantics for the language, and an inference system, which is a kind of operational semantics. The set-theoretic semantics maps roles to a set of entity names. In the operational semantics credentials can be derived from an initial set of credentials using a set of inference rules. The soundness and the completeness of the inference system with respect to the set-theoretic semantics of RT⊖ will be proven.
