An Advanced Hybrid P2p Botnet 2.0

Recently, malware attacks have become more serious over the Internet by e-mail, denial of service (DoS) or distributed denial of service (DDoS). The Botnets have become a significant part of the Internet malware attacks. The traditional botnets include three parts – botmaster, command and control (C&C) servers and bots. The C&C servers receive commands from botmaster and control the distributions of computers remotely. Bots use DNS to find the positions of C&C server. In this paper, we propose an advanced hybrid peer-to-peer (P2P) botnet 2.0 (AHP2P botnet 2.0) using web 2.0 technology to hide the instructions from botmaster into social sites, which are regarded as C&C servers. Servent bots are regarded as sub-C&C servers to get the instructions from social sites. The AHP2P botnet 2.0 can evaluate the performance of servent bots, reduce DNS traffics from bots to C&C servers, and achieve harder detection bots actions than IRC-based botnets over the Internet.

Effect of Natural Animal Fillers on Polymer Rheology Behaviour

This paper deals with the evaluation of flow properties of polymeric matrix with natural animal fillers. Technical university of Liberec cooperates on the long-term development of “green materials“ that should replace conventionally used materials (especially in automotive industry). Natural fibres (of animal and plant origin) from all over the world are collected and adapted (drying, cutting etc.) for extrusion processing. Inside the extruder these natural additives are blended with polymeric (synthetic and biodegradable - PLA) matrix and created compound is subsequently cut for pellets in the wet way. These green materials with unique recipes are then studied and their mechanical, physical and processing properties are determined. The main goal of this research is to develop new ecological materials very similar to unfilled polymers. In this article the rheological behaviour of chosen natural animal fibres is introduced considering their shape and surface that were observed with use of SEM microscopy.

Analysis of Residual Strain and Stress Distributions in High Speed Milled Specimens using an Indentation Method

Through a proper analysis of residual strain and stress distributions obtained at the surface of high speed milled specimens of AA 6082–T6 aluminium alloy, the performance of an improved indentation method is evaluated. This method integrates a special device of indentation to a universal measuring machine. The mentioned device allows introducing elongated indents allowing to diminish the absolute error of measurement. It must be noted that the present method offers the great advantage of avoiding both the specific equipment and highly qualified personnel, and their inherent high costs. In this work, the cutting tool geometry and high speed parameters are selected to introduce reduced plastic damage. Through the variation of the depth of cut, the stability of the shapes adopted by the residual strain and stress distributions is evaluated. The results show that the strain and stress distributions remain unchanged, compressive and small. Moreover, these distributions reveal a similar asymmetry when the gradients corresponding to conventional and climb cutting zones are compared.

Approximating Maximum Weighted Independent Set Using Vertex Support

The Maximum Weighted Independent Set (MWIS) problem is a classic graph optimization NP-hard problem. Given an undirected graph G = (V, E) and weighting function defined on the vertex set, the MWIS problem is to find a vertex set S V whose total weight is maximum subject to no two vertices in S are adjacent. This paper presents a novel approach to approximate the MWIS of a graph using minimum weighted vertex cover of the graph. Computational experiments are designed and conducted to study the performance of our proposed algorithm. Extensive simulation results show that the proposed algorithm can yield better solutions than other existing algorithms found in the literature for solving the MWIS.

Application of Extreme Learning Machine Method for Time Series Analysis

In this paper, we study the application of Extreme Learning Machine (ELM) algorithm for single layered feedforward neural networks to non-linear chaotic time series problems. In this algorithm the input weights and the hidden layer bias are randomly chosen. The ELM formulation leads to solving a system of linear equations in terms of the unknown weights connecting the hidden layer to the output layer. The solution of this general system of linear equations will be obtained using Moore-Penrose generalized pseudo inverse. For the study of the application of the method we consider the time series generated by the Mackey Glass delay differential equation with different time delays, Santa Fe A and UCR heart beat rate ECG time series. For the choice of sigmoid, sin and hardlim activation functions the optimal values for the memory order and the number of hidden neurons which give the best prediction performance in terms of root mean square error are determined. It is observed that the results obtained are in close agreement with the exact solution of the problems considered which clearly shows that ELM is a very promising alternative method for time series prediction.

An Improved QRS Complex Detection for Online Medical Diagnosis

This paper presents the work of signal discrimination specifically for Electrocardiogram (ECG) waveform. ECG signal is comprised of P, QRS, and T waves in each normal heart beat to describe the pattern of heart rhythms corresponds to a specific individual. Further medical diagnosis could be done to determine any heart related disease using ECG information. The emphasis on QRS Complex classification is further discussed to illustrate the importance of it. Pan-Tompkins Algorithm, a widely known technique has been adapted to realize the QRS Complex classification process. There are eight steps involved namely sampling, normalization, low pass filter, high pass filter (build a band pass filter), derivation, squaring, averaging and lastly is the QRS detection. The simulation results obtained is represented in a Graphical User Interface (GUI) developed using MATLAB.

Output Regulation of Perturbed Nonlinear Systems by Nested Sliding Mode Control

In this paper, we consider nested sliding mode control of SISO nonlinear systems, perturbed by bounded matched and unmatched uncertainties. The systems are assumed to be in strict-feedback form. A step wise procedure is introduced to obtain the controller. In each step, a continuous sliding mode controller is designed as virtual control law. Then the next step sliding surface is defined by using this virtual controller. These sliding surfaces are selected as nonlinear static functions of the system states. Finally in the last step, smooth static state feedback control law is determined such that the output reaches the desired set-point while the system is forced arbitrary close to the intersection of sliding surfaces and the states remain bounded.

Impregnation of Cupper into Kanuma Volcanic Ash Soil to Improve Mercury Sorption Capacity

The present study attempted to improve the Mercury (Hg) sorption capacity of kanuma volcanic ash soil (KVAS) by impregnating the cupper (Cu). Impregnation was executed by 1 and 5% Cu powder and sorption characterization of optimum Hg removing Cu impregnated KVAS was performed under different operational conditions, contact time, solution pH, sorbent dosage and Hg concentration using the batch operation studies. The 1% Cu impregnated KVAS pronounced optimum improvement (79%) in removing Hg from water compare to control. The present investigation determined the equilibrium state of maximum Hg adsorption at 6 h contact period. The adsorption revealed a pH dependent response and pH 3.5 showed maximum sorption capacity of Hg. Freundlich isotherm model is well fitted with the experimental data than that of Langmuir isotherm. It can be concluded that the Cu impregnation improves the Hg sorption capacity of KVAS and 1% Cu impregnated KVAS could be employed as cost-effective adsorbent media for treating Hg contaminated water.

Sliding-Mode Control of Synchronous Reluctance Motor

This paper presents a controller design technique for Synchronous Reluctance Motor to improve its dynamic performance with fast response and high accuracy. The sliding mode control is the most attractive and suitable method to use for this purpose, since it is simple in design and for its insensitivity to parameter variations or external disturbances. When this method implemented it yields fast dynamic response without overshoot and a zero steady-state error. The current loop control with decentralized sliding mode is presented in this paper. The mathematical model for the synchronous machine, the inverter and the controller is developed. The stability of the sliding mode controller is analyzed. Simulation of synchronous reluctance motor and the controller with PWM-inverter has been curried out, using the SIMULINK software package of MATLAB. Simulation results are presented to show the effectiveness of the approach.

Audio User Interface for Visually Impaired Computer Users: in a Two Dimensional Audio Environment

In this paper we discuss a set of guidelines which could be adapted when designing an audio user interface for the visually impaired. It is based on an audio environment that is focused on audio positioning. Unlike current applications which only interpret Graphical User Interface (GUI) for the visually impaired, this particular audio environment bypasses GUI to provide a direct auditory output. It presents the capability of two dimensional (2D) navigation on audio interfaces. This paper highlights the significance of a 2D audio environment with spatial information in the context of the visually impaired. A thorough usability study has been conducted to prove the applicability of proposed design guidelines for these auditory interfaces. While proving these guidelines, previously unearthed design aspects have been revealed in this study.

A Novel Low Power Very Low Voltage High Performance Current Mirror

In this paper a novel high output impedance, low input impedance, wide bandwidth, very simple current mirror with input and output voltage requirements less than that of a simple current mirror is presented. These features are achieved with very simple structure avoiding extra large node impedances to ensure high bandwidth operation. The circuit's principle of operation is discussed and compared to simple and low voltage cascode (LVC) current mirrors. Such outstanding features of this current mirror as high output impedance ~384K, low input impedance~6.4, wide bandwidth~178MHz, low input voltage ~ 362mV, low output voltage ~ 38mV and low current transfer error ~4% (all at 50μA) makes it an outstanding choice for high performance applications. Simulation results in BSIM 0.35μm CMOS technology with HSPICE are given in comparison with simple, and LVC current mirrors to verify and validate the performance of the proposed current mirror.

Effects of Geometry of Disk Openers on Seed Slot Properties

Offset Double-Disk Opener (DDO) is a popular furrow opener in conservation tillage. It has some limitations such as negative suction to penetrate in the soil, hair pinning and mixing seed and fertilizer in the slot. Because of importance of separation of seed and fertilizer in the slot, by adding two horizontal mini disks to DDO a modified opener was made (MDO) which placed the fertilizer between and under two rows of seed. To consider performance of novel opener an indoor comparison test between DDO and MDO was performed at soil bin. The experiment was conducted with three working speeds (3, 6 and 8 km h-1), two bulk densities of soil (1.1 and 1.4 Mg m-3) and two levels of residues (1 and 2 ton ha-1). The experimental design consisted in a (3×2×2) complete randomized factorial with three replicates for each test. Moisture of seed furrow, separation of seed and fertilizer, hair pinning and resultant forces acting on the openers were used as assessing indexes. There was no significant difference between soil moisture content in slots created by DDO and MDO at 0-4 cm depth, but at 4-8 cm the in the slot created by MDO moisture content was higher about 9%. Horizontal force for both openers increased with increasing speed and soil bulk density. Vertical force for DDO was negative so it needed additional weight for penetrating in the soil, but vertical force for MDO was positive and, which can solve the challenge of penetration in the soil in DDO. In soft soil with heavy residues some trash was pushed by DDO into seed furrow (hair pinning) but at MDO seed were placed at clean groove. Lateral and vertical separation of seed and fertilizer was performed effectively by MDO (4.5 and 5 cm, respectively) while DDO put seed and fertilizer close to each other. Overall, the Modified Offset Double-disks (MDO) had better performance. So by adapting this opener with no-tillage drillers it would possible to have higher yield in conservation tillage where the most appropriate opener is disk type.

Design of Domain-Specific Software Systems with Parametric Code Templates

Domain-specific languages describe specific solutions to problems in the application domain. Traditionally they form a solution composing black-box abstractions together. This, usually, involves non-deep transformations over the target model. In this paper we argue that it is potentially powerful to operate with grey-box abstractions to build a domain-specific software system. We present parametric code templates as grey-box abstractions and conceptual tools to encapsulate and manipulate these templates. Manipulations introduce template-s merging routines and can be defined in a generic way. This involves reasoning mechanisms at the code templates level. We introduce the concept of Neurath Modelling Language (NML) that operates with parametric code templates and specifies a visualisation mapping mechanism for target models. Finally we provide an example of calculating a domain-specific software system with predefined NML elements.

Optimal Design of Selective Excitation Pulses in Magnetic Resonance Imaging using Genetic Algorithms

The proper design of RF pulses in magnetic resonance imaging (MRI) has a direct impact on the quality of acquired images, and is needed for many applications. Several techniques have been proposed to obtain the RF pulse envelope given the desired slice profile. Unfortunately, these techniques do not take into account the limitations of practical implementation such as limited amplitude resolution. Moreover, implementing constraints for special RF pulses on most techniques is not possible. In this work, we propose to develop an approach for designing optimal RF pulses under theoretically any constraints. The new technique will pose the RF pulse design problem as a combinatorial optimization problem and uses efficient techniques from this area such as genetic algorithms (GA) to solve this problem. In particular, an objective function will be proposed as the norm of the difference between the desired profile and the one obtained from solving the Bloch equations for the current RF pulse design values. The proposed approach will be verified using analytical solution based RF simulations and compared to previous methods such as Shinnar-Le Roux (SLR) method, and analysis, selected, and tested the options and parameters that control the Genetic Algorithm (GA) can significantly affect its performance to get the best improved results and compared to previous works in this field. The results show a significant improvement over conventional design techniques, select the best options and parameters for GA to get most improvement over the previous works, and suggest the practicality of using of the new technique for most important applications as slice selection for large flip angles, in the area of unconventional spatial encoding, and another clinical use.

Starting Pitcher Rotation in the Chinese Professional Baseball League based on AHP and TOPSIS

The rotation of starting pitchers is a strategic issue which has a significant impact on the performance of a professional team. Choosing an optimal starting pitcher from among many alternatives is a multi-criteria decision-making (MCDM) problem. In this study, a model using the Analytic Hierarchy Process (AHP) and Technique for Order Performance by Similarity to the Ideal Solution (TOPSIS) is proposed with which to arrange the starting pitcher rotation for teams of the Chinese Professional Baseball League. The AHP is used to analyze the structure of the starting pitcher selection problem and to determine the weights of the criteria, while the TOPSIS method is used to make the final ranking. An empirical analysis is conducted to illustrate the utilization of the model for the starting pitcher rotation problem. The results demonstrate the effectiveness and feasibility of the proposed model.

A Novel Modified Adaptive Fuzzy Inference Engine and Its Application to Pattern Classification

The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a novel Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data sets. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher-s Iris and Wisconsin breast cancer data sets and shown to be very competitive.

The Effects of Extracorporeal Shockwave Therapy on Pain, Function, Range of Motion and Strength in Patients with Plantar Fasciitis

Ten percent of the population will develop plantar fasciitis (PF) during their lifetime. Two million people are treated yearly accounting for 11-15% of visits to medical professionals. Treatment ranges from conservative to surgical intervention. The purpose of this study was to assess the effects of extracorporeal shockwave therapy (ECSWT) on heel pain, function, range of motion (ROM), and strength in patients with PF. One hundred subjects were treated with ECSWT and measures were taken before and three months after treatment. There was significant differences in visual analog scale scores for pain at rest (p=0.0001); after activity (p= 0.0001) and; overall improvement (p=0.0001). There was also significant improvement in Lower Extremity Functional Scale scores (p=0.0001); ankle plantarflexion (p=0.0001), dorsiflexion (p=0.001), and eversion (p=0.017),and first metatarsophalangeal joint flexion (p=0.002) and extension (p=0.003) ROM. ECSWT is an effective treatment improving heel pain, function and ROM in patients with PF.

Addressing Scalability Issues of Named Entity Recognition Using Multi-Class Support Vector Machines

This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features. The performance results of a set of experiments conducted using binary and multi-class SVM with increasing training data sizes are examined. The NER domain chosen for these experiments is the biomedical publications domain, especially selected due to its importance and inherent challenges. A simple machine learning approach is used that eliminates prior language knowledge such as part-of-speech or noun phrase tagging thereby allowing for its applicability across languages. No domain-specific knowledge is included. The accuracy measures achieved are comparable to those obtained using more complex approaches, which constitutes a motivation to investigate ways to improve the scalability of multiclass SVM in order to make the solution more practical and useable. Improving training time of multi-class SVM would make support vector machines a more viable and practical machine learning solution for real-world problems with large datasets. An initial prototype results in great improvement of the training time at the expense of memory requirements.

Supply Chain Modeling and Improving Manufacturing Industry in Developing Countries: A Research Agenda

This paper presents a research agenda on the SCOR model adaptation. SCOR model is designated to measure supply chain performance and logistics impact across the boundaries of individual organizations. It is at its growing stage of its life cycle and is enjoying the leverage of becoming the industry standard. The SCOR model has been developed and used widely in developed countries context. This research focuses on the SCOR model adaptation for the manufacturing industry in developing countries. With a necessary understanding of the characteristics, difficulties and problems of the manufacturing industry in developing countries- supply chain; consequently, we will try to designs an adapted model with its building blocks: business process model, performance measures and best practices.

Ocean Wave Kinetic Energy Harvesting System for Automated Sub Sea Sensors

This paper presents an overview of the Ocean wave kinetic energy harvesting system. Energy harvesting is a concept by which energy is captured, stored, and utilized using various sources by employing interfaces, storage devices, and other units. Ocean wave energy harvesting in which the kinetic and potential energy contained in the natural oscillations of Ocean waves are converted into electric power. The kinetic energy harvesting system could be used for a number of areas. The main applications that we have discussed in this paper are to how generate the energy from Ocean wave energy (kinetic energy) to electric energy that is to eliminate the requirement for continual battery replacement.