An Adversarial Construction of Instability Bounds in LIS Networks

In this work, we study the impact of dynamically changing link slowdowns on the stability properties of packetswitched networks under the Adversarial Queueing Theory framework. Especially, we consider the Adversarial, Quasi-Static Slowdown Queueing Theory model, where each link slowdown may take on values in the two-valued set of integers {1, D} with D > 1 which remain fixed for a long time, under a (w, ¤ü)-adversary. In this framework, we present an innovative systematic construction for the estimation of adversarial injection rate lower bounds, which, if exceeded, cause instability in networks that use the LIS (Longest-in- System) protocol for contention-resolution. In addition, we show that a network that uses the LIS protocol for contention-resolution may result in dropping its instability bound at injection rates ¤ü > 0 when the network size and the high slowdown D take large values. This is the best ever known instability lower bound for LIS networks.

A Metric Framework for Analysis of Quality of Object Oriented Design

The impact of OO design on software quality characteristics such as defect density and rework by mean of experimental validation. Encapsulation, inheritance, polymorphism, reusability, Data hiding and message-passing are the major attribute of an Object Oriented system. In order to evaluate the quality of an Object oriented system the above said attributes can act as indicators. The metrics are the well known quantifiable approach to express any attribute. Hence, in this paper we tried to formulate a framework of metrics representing the attributes of object oriented system. Empirical Data is collected from three different projects based on object oriented paradigms to calculate the metrics.

Maximum Common Substructure Extraction in RNA Secondary Structures Using Clique Detection Approach

The similarity comparison of RNA secondary structures is important in studying the functions of RNAs. In recent years, most existing tools represent the secondary structures by tree-based presentation and calculate the similarity by tree alignment distance. Different to previous approaches, we propose a new method based on maximum clique detection algorithm to extract the maximum common structural elements in compared RNA secondary structures. A new graph-based similarity measurement and maximum common subgraph detection procedures for comparing purely RNA secondary structures is introduced. Given two RNA secondary structures, the proposed algorithm consists of a process to determine the score of the structural similarity, followed by comparing vertices labelling, the labelled edges and the exact degree of each vertex. The proposed algorithm also consists of a process to extract the common structural elements between compared secondary structures based on a proposed maximum clique detection of the problem. This graph-based model also can work with NC-IUB code to perform the pattern-based searching. Therefore, it can be used to identify functional RNA motifs from database or to extract common substructures between complex RNA secondary structures. We have proved the performance of this proposed algorithm by experimental results. It provides a new idea of comparing RNA secondary structures. This tool is helpful to those who are interested in structural bioinformatics.

Topographical Image Transference Compatibility Generated Through Moiré Technique Applying Parametrical Softwares of Computer Assisted Design

Computer aided design accounts with the support of parametric software in the design of machine components as well as of any other pieces of interest. The complexities of the element under study sometimes offer certain difficulties to computer design, or ever might generate mistakes in the final body conception. Reverse engineering techniques are based on the transformation of already conceived body images into a matrix of points which can be visualized by the design software. The literature exhibits several techniques to obtain machine components dimensional fields, as contact instrument (MMC), calipers and optical methods as laser scanner, holograms as well as moiré methods. The objective of this research work was to analyze the moiré technique as instrument of reverse engineering, applied to bodies of nom complex geometry as simple solid figures, creating matrices of points. These matrices were forwarded to a parametric software named SolidWorks to generate the virtual object. Volume data obtained by mechanical means, i.e., by caliper, the volume obtained through the moiré method and the volume generated by the SolidWorks software were compared and found to be in close agreement. This research work suggests the application of phase shifting moiré methods as instrument of reverse engineering, serving also to support farm machinery element designs.

Study of Unsteady Swirling Flow in a Hydrodynamic Vortex Chamber

The paper reports on the results of experimental and numerical study of nonstationary swirling flow in an isothermal model of vortex burner. It has been identified that main source of the instability is related to a precessing vortex core (PVC) phenomenon. The PVC induced flow pulsation characteristics such as precession frequency and its variation as a function of flowrate and swirl number have been explored making use of acoustic probes. Additionally pressure transducers were used to measure the pressure drops on the working chamber and across the vortex flow. The experiments have been included also the mean velocity measurements making use of a laser-Doppler anemometry. The features of instantaneous flowfield generated by the PVC were analyzed employing a commercial CFD code (Star-CCM+) based on Detached Eddy Simulation (DES) approach. Validity of the numerical code has been checked by comparison calculated flowfield data with the obtained experimental results. It has been confirmed particularly that the CFD code applied correctly reproduces the flow features.

Seismic Rehabilitation of R/C Buildings Designed with Earlier Technical Standards

The seismic rehabilitation designs of two reinforced concrete school buildings, representative of a wide stock of similar edifices designed under earlier editions of the Italian Technical Standards, are presented in this paper. The mutual retrofit solution elaborated for the two buildings consists in the incorporation of a dissipative bracing system including pressurized fluid viscous springdampers as passive protective devices. The mechanical parameters, layouts and locations selected for the constituting elements of the system; the architectural renovation projects developed to properly incorporate the structural interventions and improve the appearance of the buildings; highlights of the installation works already completed in one of the two structures; and a synthesis of the performance assessment analyses carried out in original and rehabilitated conditions, are illustrated. The results of the analyses show a remarkable enhancement of the seismic response capacities of both structures. This allows reaching the high performance objectives postulated in the retrofit designs with much lower costs and architectural intrusion as compared to traditional rehabilitation interventions designed for the same objectives.

Intelligent Network-Based Stepping Stone Detection Approach

This research intends to introduce a new usage of Artificial Intelligent (AI) approaches in Stepping Stone Detection (SSD) fields of research. By using Self-Organizing Map (SOM) approaches as the engine, through the experiment, it is shown that SOM has the capability to detect the number of connection chains that involved in a stepping stones. Realizing that by counting the number of connection chain is one of the important steps of stepping stone detection and it become the research focus currently, this research has chosen SOM as the AI techniques because of its capabilities. Through the experiment, it is shown that SOM can detect the number of involved connection chains in Network-based Stepping Stone Detection (NSSD).

A Comparative Study of SVM Classifiers and Artificial Neural Networks Application for Rolling Element Bearing Fault Diagnosis using Wavelet Transform Preprocessing

Effectiveness of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) classifiers for fault diagnosis of rolling element bearings are presented in this paper. The characteristic features of vibration signals of rotating driveline that was run in its normal condition and with faults introduced were used as input to ANN and SVM classifiers. Simple statistical features such as standard deviation, skewness, kurtosis etc. of the time-domain vibration signal segments along with peaks of the signal and peak of power spectral density (PSD) are used as features to input the ANN and SVM classifier. The effect of preprocessing of the vibration signal by Discreet Wavelet Transform (DWT) prior to feature extraction is also studied. It is shown from the experimental results that the performance of SVM classifier in identification of bearing condition is better then ANN and pre-processing of vibration signal by DWT enhances the effectiveness of both ANN and SVM classifier

In Cognitive Radio the Analysis of Bit-Error- Rate (BER) by using PSO Algorithm

The electromagnetic spectrum is a natural resource and hence well-organized usage of the limited natural resources is the necessities for better communication. The present static frequency allocation schemes cannot accommodate demands of the rapidly increasing number of higher data rate services. Therefore, dynamic usage of the spectrum must be distinguished from the static usage to increase the availability of frequency spectrum. Cognitive radio is not a single piece of apparatus but it is a technology that can incorporate components spread across a network. It offers great promise for improving system efficiency, spectrum utilization, more effective applications, reduction in interference and reduced complexity of usage for users. Cognitive radio is aware of its environmental, internal state, and location, and autonomously adjusts its operations to achieve designed objectives. It first senses its spectral environment over a wide frequency band, and then adapts the parameters to maximize spectrum efficiency with high performance. This paper only focuses on the analysis of Bit-Error-Rate in cognitive radio by using Particle Swarm Optimization Algorithm. It is theoretically as well as practically analyzed and interpreted in the sense of advantages and drawbacks and how BER affects the efficiency and performance of the communication system.

The Path to Wellbeing: The Role of Work-Family Conflict, Family-Work Conflict and Psychological Strain

Although considerable amount of research has attested to the link between work-to-family conflict (WFC) and family-to-work conflict (FWC) and psychological strain and wellbeing, there is a paucity of research investigating the phenomenon in the context of social workers. Moreover, very little is known about the impact of WFC and FWC in developing countries. The present study investigated the mediating effect of psychological strain on the relationship between WFC and FWC with wellbeing of social workers in India. Our findings show that WFC and FWC are influential antecedents of wellbeing; their influence is both direct on psychological strain, and indirect on wellbeing transmitted through psychological strain. Implications of the findings are discussed.

The Possibility to Resolve the Security Problems through the LTE in Vehicular Ad-hoc Networks

Vehicular Ad-Hoc Networks (VANET) can provide communications between vehicles or infrastructures. It provides the convenience of driving and the secure driving to reduce accidents. In VANET, the security is more important because it is closely related to accidents. Additionally, VANET raises a privacy issue because it can track the location of vehicles and users- identity when a security mechanism is provided. In this paper, we analyze the problem of an existing solution for security requirements required in VANET, and resolve the problem of the existing method when a key management mechanism is provided for the security operation in VANET. Therefore, we show suitability of the Long Term Evolution (LTE) in VANET for the solution of this problem.

Distributed 2-Vertex Connectivity Test of Graphs Using Local Knowledge

The vertex connectivity of a graph is the smallest number of vertices whose deletion separates the graph or makes it trivial. This work is devoted to the problem of vertex connectivity test of graphs in a distributed environment based on a general and a constructive approach. The contribution of this paper is threefold. First, using a preconstructed spanning tree of the considered graph, we present a protocol to test whether a given graph is 2-connected using only local knowledge. Second, we present an encoding of this protocol using graph relabeling systems. The last contribution is the implementation of this protocol in the message passing model. For a given graph G, where M is the number of its edges, N the number of its nodes and Δ is its degree, our algorithms need the following requirements: The first one uses O(Δ×N2) steps and O(Δ×logΔ) bits per node. The second one uses O(Δ×N2) messages, O(N2) time and O(Δ × logΔ) bits per node. Furthermore, the studied network is semi-anonymous: Only the root of the pre-constructed spanning tree needs to be identified.

Mechanical Design and Theoretical Analysis of a Four Fingered Prosthetic Hand Incorporating Embedded SMA Bundle Actuators

The psychological and physical trauma associated with the loss of a human limb can severely impact on the quality of life of an amputee rendering even the most basic of tasks very difficult. A prosthetic device can be of great benefit to the amputee in the performance of everyday human tasks. This paper outlines a proposed mechanical design of a 12 degree-of-freedom SMA actuated artificial hand. It is proposed that the SMA wires be embedded intrinsically within the hand structure which will allow for significant flexibility for use either as a prosthetic hand solution, or as part of a complete lower arm prosthetic solution. A modular approach is taken in the design facilitating ease of manufacture and assembly, and more importantly, also allows the end user to easily replace SMA wires in the event of failure. A biomimetric approach has been taken during the design process meaning that the artificial hand should replicate that of a human hand as far as is possible with due regard to functional requirements. The proposed design has been exposed to appropriate loading through the use of finite element analysis (FEA) to ensure that it is structurally sound. Theoretical analysis of the mechanical framework was also carried out to establish the limits of the angular displacement and velocity of the finger tip as well finger tip force generation. A combination of various polymers and Titanium, which are suitably lightweight, are proposed for the manufacture of the design.

Time Series Forecasting Using a Hybrid RBF Neural Network and AR Model Based On Binomial Smoothing

ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression (AR) model based on binomial smoothing (BS) technique which is efficient in data processing, which is called BSRBFAR. This method is examined by using the data of Canadian Lynx data. Empirical results indicate that the over-fitting problem can be eased using RBF neural network based on binomial smoothing which is called BS-RBF, and the hybrid model–BS-RBFAR can be an effective way to improve forecasting accuracy achieved by BSRBF used separately.

Discovering Complex Regularities: from Tree to Semi-Lattice Classifications

Data mining uses a variety of techniques each of which is useful for some particular task. It is important to have a deep understanding of each technique and be able to perform sophisticated analysis. In this article we describe a tool built to simulate a variation of the Kohonen network to perform unsupervised clustering and support the entire data mining process up to results visualization. A graphical representation helps the user to find out a strategy to optimize classification by adding, moving or delete a neuron in order to change the number of classes. The tool is able to automatically suggest a strategy to optimize the number of classes optimization, but also support both tree classifications and semi-lattice organizations of the classes to give to the users the possibility of passing from one class to the ones with which it has some aspects in common. Examples of using tree and semi-lattice classifications are given to illustrate advantages and problems. The tool is applied to classify macroeconomic data that report the most developed countries- import and export. It is possible to classify the countries based on their economic behaviour and use the tool to characterize the commercial behaviour of a country in a selected class from the analysis of positive and negative features that contribute to classes formation. Possible interrelationships between the classes and their meaning are also discussed.

Wireless Sensor Network: Characteristics and Architectures

An information procuring and processing emerging technology wireless sensor network (WSN) Consists of autonomous nodes with versatile devices underpinned by applications. Nodes are equipped with different capabilities such as sensing, computing, actuation and wireless communications etc. based on application requirements. A WSN application ranges from military implementation in the battlefield, environmental monitoring, health sector as well as emergency response of surveillance. The nodes are deployed independently to cooperatively monitor the physical and environmental conditions. The architecture of WSN differs based on the application requirements and focus on low cost, flexibility, fault tolerance capability, deployment process as well as conserve energy. In this paper we have present the characteristics, architecture design objective and architecture of WSN

Positive Definite Quadratic Forms, Elliptic Curves and Cubic Congruences

Let F(x, y) = ax2 + bxy + cy2 be a positive definite binary quadratic form with discriminant Δ whose base points lie on the line x = -1/m for an integer m ≥ 2, let p be a prime number and let Fp be a finite field. Let EF : y2 = ax3 + bx2 + cx be an elliptic curve over Fp and let CF : ax3 + bx2 + cx ≡ 0(mod p) be the cubic congruence corresponding to F. In this work we consider some properties of positive definite quadratic forms, elliptic curves and cubic congruences.

Impact of the Real Effective Exchange Rate (Reer) on Turkish Agricultural Trade

In this work, the autoregressive vectors are used to know dynamics of the Agricultural export and import, and the real effective exchange rate (REER). In order to analyze the interactions, the impulse- response function is used in decomposition of variance, causality of Granger as well as the methodology of Johansen to know the relations co integration. The REER causes agricultural export and import in the sense of Granger. The influence displays the innovations of the REER on the agricultural export and import is not very great and the duration of the effects is short. It displays that REER has an immediate positive effect, after the tenth year it displays smooth results on the agricultural export. Evidence of a vector exists co integration, In short run, REER has smaller effects on export and import, compared to the long-run effects.

Testing the Accuracy of ML-ANN for Harmonic Estimation in Balanced Industrial Distribution Power System

In this paper, we analyze and test a scheme for the estimation of electrical fundamental frequency signals from the harmonic load current and voltage signals. The scheme was based on using two different Multi Layer Artificial Neural Networks (ML-ANN) one for the current and the other for the voltage. This study also analyzes and tests the effect of choosing the optimum artificial neural networks- sizes which determine the quality and accuracy of the estimation of electrical fundamental frequency signals. The simulink tool box of the Matlab program for the simulation of the test system and the test of the neural networks has been used.