SIP-Based QoS Management Architecture for IP Multimedia Subsystems over IP Access Networks

True integration of multimedia services over wired or wireless networks increase the productivity and effectiveness in today-s networks. IP Multimedia Subsystems are Next Generation Network architecture to provide the multimedia services over fixed or mobile networks. This paper proposes an extended SIP-based QoS Management architecture for IMS services over underlying IP access networks. To guarantee the end-to-end QoS for IMS services in interconnection backbone, SIP based proxy Modules are introduced to support the QoS provisioning and to reduce the handoff disruption time over IP access networks. In our approach these SIP Modules implement the combination of Diffserv and MPLS QoS mechanisms to assure the guaranteed QoS for real-time multimedia services. To guarantee QoS over access networks, SIP Modules make QoS resource reservations in advance to provide best QoS to IMS users over heterogeneous networks. To obtain more reliable multimedia services, our approach allows the use of SCTP protocol over SIP instead of UDP due to its multi-streaming feature. This architecture enables QoS provisioning for IMS roaming users to differentiate IMS network from other common IP networks for transmission of realtime multimedia services. To validate our approach simulation models are developed on short scale basis. The results show that our approach yields comparable performance for efficient delivery of IMS services over heterogeneous IP access networks.

Detecting the Nonlinearity in Time Series from Continuous Dynamic Systems Based on Delay Vector Variance Method

Much time series data is generally from continuous dynamic system. Firstly, this paper studies the detection of the nonlinearity of time series from continuous dynamics systems by applying the Phase-randomized surrogate algorithm. Then, the Delay Vector Variance (DVV) method is introduced into nonlinearity test. The results show that under the different sampling conditions, the opposite detection of nonlinearity is obtained via using traditional test statistics methods, which include the third-order autocovariance and the asymmetry due to time reversal. Whereas the DVV method can perform well on determining nonlinear of Lorenz signal. It indicates that the proposed method can describe the continuous dynamics signal effectively.

Evaluation of Aerodynamic Noise Generation by a Generic Side Mirror

The aerodynamic noise radiation from a side view mirror (SVM) in the high-speed airflow is calculated by the combination of unsteady incompressible fluid flow analysis and acoustic analysis. The transient flow past the generic SVM is simulated with variable turbulence model, namely DES Detached Eddy Simulation and LES (Large Eddy Simulation). Detailed velocity vectors and contour plots of the time-varying velocity and pressure fields are presented along cut planes in the flow-field. Mean and transient pressure are also monitored at several points in the flow field and compared to corresponding experimentally data published in literature. The acoustic predictions made using the Ffowcs-Williams-Hawkins acoustic analogy (FW-H) and the boundary element (BEM).

The Effects of Whole-Body Vibration Training on Jump Performance in Handball Athletes

This study examined the effects of eight weeks of whole-body vibration training (WBVT) on vertical and decuple jump performance in handball athletes. Sixteen collegiate Level I handball athletes volunteered for this study. They were divided equally as control group and experimental group (EG). During the period of the study, all athletes underwent the same handball specific training, but the EG received additional WBVT (amplitude: 2 mm, frequency: 20 - 40 Hz) three time per week for eight consecutive weeks. The vertical jump performance was evaluated according to the maximum height of squat jump (SJ) and countermovement jump (CMJ). Single factor ANCOVA was used to examine the differences in each parameter between the groups after training with the pretest values as a covariate. The statistic significance was set at p < .05. After 8 weeks WBVT, the EG had significantly improved the maximal height of SJ (40.92 ± 2.96 cm vs. 48.40 ± 4.70 cm, F = 5.14, p < .05) and the maximal height CMJ (47.25 ± 7.48 cm vs. 52.20 ± 6.25 cm, F = 5.31, p < .05). 8 weeks of additional WBVT could improve the vertical and decuple jump performance in handball athletes. Enhanced motor unit synchronization and firing rates, facilitated muscular contraction stretch-shortening cycle, and improved lower extremity neuromuscular coordination could account for these enhancements.

Using Artificial Neural Network to Forecast Groundwater Depth in Union County Well

A concern that researchers usually face in different applications of Artificial Neural Network (ANN) is determination of the size of effective domain in time series. In this paper, trial and error method was used on groundwater depth time series to determine the size of effective domain in the series in an observation well in Union County, New Jersey, U.S. different domains of 20, 40, 60, 80, 100, and 120 preceding day were examined and the 80 days was considered as effective length of the domain. Data sets in different domains were fed to a Feed Forward Back Propagation ANN with one hidden layer and the groundwater depths were forecasted. Root Mean Square Error (RMSE) and the correlation factor (R2) of estimated and observed groundwater depths for all domains were determined. In general, groundwater depth forecast improved, as evidenced by lower RMSEs and higher R2s, when the domain length increased from 20 to 120. However, 80 days was selected as the effective domain because the improvement was less than 1% beyond that. Forecasted ground water depths utilizing measured daily data (set #1) and data averaged over the effective domain (set #2) were compared. It was postulated that more accurate nature of measured daily data was the reason for a better forecast with lower RMSE (0.1027 m compared to 0.255 m) in set #1. However, the size of input data in this set was 80 times the size of input data in set #2; a factor that may increase the computational effort unpredictably. It was concluded that 80 daily data may be successfully utilized to lower the size of input data sets considerably, while maintaining the effective information in the data set.

A Novel Multiple Valued Logic OHRNS Modulo rn Adder Circuit

Residue Number System (RNS) is a modular representation and is proved to be an instrumental tool in many digital signal processing (DSP) applications which require high-speed computations. RNS is an integer and non weighted number system; it can support parallel, carry-free, high-speed and low power arithmetic. A very interesting correspondence exists between the concepts of Multiple Valued Logic (MVL) and Residue Number Arithmetic. If the number of levels used to represent MVL signals is chosen to be consistent with the moduli which create the finite rings in the RNS, MVL becomes a very natural representation for the RNS. There are two concerns related to the application of this Number System: reaching the most possible speed and the largest dynamic range. There is a conflict when one wants to resolve both these problem. That is augmenting the dynamic range results in reducing the speed in the same time. For achieving the most performance a method is considere named “One-Hot Residue Number System" in this implementation the propagation is only equal to one transistor delay. The problem with this method is the huge increase in the number of transistors they are increased in order m2 . In real application this is practically impossible. In this paper combining the Multiple Valued Logic and One-Hot Residue Number System we represent a new method to resolve both of these two problems. In this paper we represent a novel design of an OHRNS-based adder circuit. This circuit is useable for Multiple Valued Logic moduli, in comparison to other RNS design; this circuit has considerably improved the number of transistors and power consumption.

Signal Generator Circuit Carrying Information as Embedded Features from Multi-Transducer Signals

A novel circuit for generating a signal embedded with features about data from three sensors is presented. This suggested circuit is making use of a resistance-to-time converter employing a bridge amplifier, an integrator and a comparator. The second resistive sensor (Rz) is transformed into duty cycle. Another bridge with varying resistor, (Ry) in the feedback of an OP AMP is added in series to change the amplitude of the resulting signal in a proportional relationship while keeping the same frequency and duty cycle representing proportional changes in resistors Rx and Rz already mentioned. The resultant output signal carries three types of information embedded as variations of its frequency, duty cycle and amplitude.

Adaptive Kernel Filtering Used in Video Processing

In this paper we present a noise reduction filter for video processing. It is based on the recently proposed two dimensional steering kernel, extended to three dimensions and further augmented to suit the spatial-temporal domain of video processing. Two alternative filters are proposed - the time symmetric kernel and the time asymmetric kernel. The first reduces the noise on single sequences, but to handle the problems at scene shift the asymmetric kernel is introduced. The performance of both are tested on simulated data and on a real video sequence together with the existing steering kernel. The proposed kernels improves the Rooted Mean Squared Error (RMSE) compared to the original steering kernel method on video material.

Removal of Hydrogen Sulphide from Air by Means of Fibrous Ion Exchangers

The removal of hydrogen sulphide is required for reasons of health, odour problems, safety and corrosivity problems. The means of removing hydrogen sulphide mainly depend on its concentration and kind of medium to be purified. The paper deals with a method of hydrogen sulphide removal from the air by its catalytic oxidation to elemental sulphur with the use of Fe-EDTA complex. The possibility of obtaining fibrous filtering materials able to remove small concentrations of H2S from the air were described. The base of these materials is fibrous ion exchanger with Fe(III)- EDTA complex immobilized on their functional groups. The complex of trivalent iron converts hydrogen sulphide to elemental sulphur. Bivalent iron formed in the reaction is oxidized by the atmospheric oxygen, so complex of trivalent iron is continuously regenerated and the overall process can be accounted as pseudocatalytic. In the present paper properties of several fibrous catalysts based on ion exchangers with different chemical nature (weak acid,weak base and strong base) were described. It was shown that the main parameters affecting the process of catalytic oxidation are:concentration of hydrogen sulphide in the air, relative humidity of the purified air, the process time and the content of Fe-EDTA complex in the fibres. The data presented show that the filtering layers with anion exchange package are much more active in the catalytic processes of hydrogen sulphide removal than cation exchanger and inert materials. In the addition to the nature of the fibres relative air humidity is a critical factor determining efficiency of the material in the air purification from H2S. It was proved that the most promising carrier of the Fe-EDTA catalyst for hydrogen sulphide oxidation are Fiban A-6 and Fiban AK-22 fibres.

Evaluation of State of the Art IDS Message Exchange Protocols

During the last couple of years, the degree of dependence on IT systems has reached a dimension nobody imagined to be possible 10 years ago. The increased usage of mobile devices (e.g., smart phones), wireless sensor networks and embedded devices (Internet of Things) are only some examples of the dependency of modern societies on cyber space. At the same time, the complexity of IT applications, e.g., because of the increasing use of cloud computing, is rising continuously. Along with this, the threats to IT security have increased both quantitatively and qualitatively, as recent examples like STUXNET or the supposed cyber attack on Illinois water system are proofing impressively. Once isolated control systems are nowadays often publicly available - a fact that has never been intended by the developers. Threats to IT systems don’t care about areas of responsibility. Especially with regard to Cyber Warfare, IT threats are no longer limited to company or industry boundaries, administrative jurisdictions or state boundaries. One of the important countermeasures is increased cooperation among the participants especially in the field of Cyber Defence. Besides political and legal challenges, there are technical ones as well. A better, at least partially automated exchange of information is essential to (i) enable sophisticated situational awareness and to (ii) counter the attacker in a coordinated way. Therefore, this publication performs an evaluation of state of the art Intrusion Detection Message Exchange protocols in order to guarantee a secure information exchange between different entities.

Software Maintenance Severity Prediction for Object Oriented Systems

As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done in time especially for the critical applications. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this present work, various Neural Network Based techniques are explored and comparative analysis is performed for the prediction of level of need of maintenance by predicting level severity of faults present in NASA-s public domain defect dataset. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that Generalized Regression Networks is the best algorithm for classification of the software components into different level of severity of impact of the faults. The algorithm can be used to develop model that can be used for identifying modules that are heavily affected by the faults.

Digital Redesign of Interval Systems via Particle Swarm Optimization

In this paper, a PSO-based approach is proposed to derive a digital controller for redesigned digital systems having an interval plant based on resemblance of the extremal gain/phase margins. By combining the interval plant and a controller as an interval system, extremal GM/PM associated with the loop transfer function can be obtained. The design problem is then formulated as an optimization problem of an aggregated error function revealing the deviation on the extremal GM/PM between the redesigned digital system and its continuous counterpart, and subsequently optimized by a proposed PSO to obtain an optimal set of parameters for the digital controller. Computer simulations have shown that frequency responses of the redesigned digital system having an interval plant bare a better resemblance to its continuous-time counter part by the incorporation of a PSO-derived digital controller in comparison to those obtained using existing open-loop discretization methods.

A Fuzzy Logic Based Navigation of a Mobile Robot

One of the long standing challenging aspect in mobile robotics is the ability to navigate autonomously, avoiding modeled and unmodeled obstacles especially in crowded and unpredictably changing environment. A successful way of structuring the navigation task in order to deal with the problem is within behavior based navigation approaches. In this study, Issues of individual behavior design and action coordination of the behaviors will be addressed using fuzzy logic. A layered approach is employed in this work in which a supervision layer based on the context makes a decision as to which behavior(s) to process (activate) rather than processing all behavior(s) and then blending the appropriate ones, as a result time and computational resources are saved.

Interactive Model Based On an Extended CPN

The UML modeling of complex distributed systems often is a great challenge due to the large amount of parallel real-time operating components. In this paper the problems of verification of such systems are discussed. ECPN, an Extended Colored Petri Net is defined to formally describe state transitions of components and interactions among components. The relationship between sequence diagrams and Free Choice Petri Nets is investigated. Free Choice Petri Net theory helps verifying the liveness of sequence diagrams. By converting sequence diagrams to ECPNs and then comparing behaviors of sequence diagram ECPNs and statecharts, the consistency among models is analyzed. Finally, a verification process for an example model is demonstrated.

A Consistency Protocol Multi-Layer for Replicas Management in Large Scale Systems

Large scale systems such as computational Grid is a distributed computing infrastructure that can provide globally available network resources. The evolution of information processing systems in Data Grid is characterized by a strong decentralization of data in several fields whose objective is to ensure the availability and the reliability of the data in the reason to provide a fault tolerance and scalability, which cannot be possible only with the use of the techniques of replication. Unfortunately the use of these techniques has a height cost, because it is necessary to maintain consistency between the distributed data. Nevertheless, to agree to live with certain imperfections can improve the performance of the system by improving competition. In this paper, we propose a multi-layer protocol combining the pessimistic and optimistic approaches conceived for the data consistency maintenance in large scale systems. Our approach is based on a hierarchical representation model with tree layers, whose objective is with double vocation, because it initially makes it possible to reduce response times compared to completely pessimistic approach and it the second time to improve the quality of service compared to an optimistic approach.

Efficient Spectral Analysis of Quasi Stationary Time Series

Power Spectral Density (PSD) of quasi-stationary processes can be efficiently estimated using the short time Fourier series (STFT). In this paper, an algorithm has been proposed that computes the PSD of quasi-stationary process efficiently using offline autoregressive model order estimation algorithm, recursive parameter estimation technique and modified sliding window discrete Fourier Transform algorithm. The main difference in this algorithm and STFT is that the sliding window (SW) and window for spectral estimation (WSA) are separately defined. WSA is updated and its PSD is computed only when change in statistics is detected in the SW. The computational complexity of the proposed algorithm is found to be lesser than that for standard STFT technique.

Incorporation Mechanism of Stabilizing Simulated Lead-Laden Sludge in Aluminum-Rich Ceramics

This study investigated a strategy of blending lead-laden sludge and Al-rich precursors to reduce the release of metals from the stabilized products. Using PbO as the simulated lead-laden sludge to sinter with γ-Al2O3 by Pb:Al molar ratios of 1:2 and 1:12, PbAl2O4 and PbAl12O19 were formed as final products during the sintering process, respectively. By firing the PbO + γ-Al2O3 mixtures with different Pb/Al molar ratios at 600 to 1000 °C, the lead transformation was determined through X-ray diffraction (XRD) data. In Pb/Al molar ratio of 1/2 system, the formation of PbAl2O4 is initiated at 700 °C, but an effective formation was observed above 750 °C. An intermediate phase, Pb9Al8O21, was detected in the temperature range of 800-900 °C. However, different incorporation behavior for sintering PbO with Al-rich precursors at a Pb/Al molar ratio of 1/12 was observed during the formation of PbAl12O19 in this system. In the sintering process, both temperature and time effect on the formation of PbAl2O4 and PbAl12O19 phases were estimated. Finally, a prolonged leaching test modified from the U.S. Environmental Protection Agency-s toxicity characteristic leaching procedure (TCLP) was used to evaluate the durability of PbO, Pb9Al8O21, PbAl2O4 and PbAl12O19 phases. Comparison for the leaching results of the four phases demonstrated the higher intrinsic resistance of PbAl12O19 against acid attack.

Defluoridation of Water by Schwertmannite

In the present study Schwertmannite (an iron oxide hydroxide) is selected as an adsorbent for defluoridation of water. The adsorbent was prepared by wet chemical process and was characterized by SEM, XRD and BET. The fluoride adsorption efficiency of the prepared adsorbent was determined with respect to contact time, initial fluoride concentration, adsorbent dose and pH of the solution. The batch adsorption data revealed that the fluoride adsorption efficiency was highly influenced by the studied factors. Equilibrium was attained within one hour of contact time indicating fast kinetics and the adsorption data followed pseudo second order kinetic model. Equilibrium isotherm data fitted to both Langmuir and Freundlich isotherm models for a concentration range of 5-30 mg/L. The adsorption system followed Langmuir isotherm model with maximum adsorption capacity of 11.3 mg/g. The high adsorption capacity of Schwertmannite points towards the potential of this adsorbent for fluoride removal from aqueous medium.

Effect of Catalyst Preparation on the Performance of CaO-ZnO Catalysts for Transesterification

In this research, CaO-ZnO catalysts (with various Ca:Zn atomic ratios of 1:5, 1:3, 1:1, and 3:1) prepared by incipientwetness impregnation (IWI) and co-precipitation (CP) methods were used as a catalyst in the transesterification of palm oil with methanol for biodiesel production. The catalysts were characterized by several techniques, including BET method, CO2-TPD, and Hemmett Indicator. The effects of precursor concentration, and calcination temperature on the catalytic performance were studied under reaction conditions of a 15:1 methanol to oil molar ratio, 6 wt% catalyst, reaction temperature of 60°C, and reaction time of 8 h. At Ca:Zn atomic ratio of 1:3 gave the highest FAME value owing to a basic properties and surface area of the prepared catalyst.

Effect of Curing Conditions on Strength of Fly ash-based Self-Compacting Geopolymer Concrete

This paper reports the results of an experimental work conducted to investigate the effect of curing conditions on the compressive strength of self-compacting geopolymer concrete prepared by using fly ash as base material and combination of sodium hydroxide and sodium silicate as alkaline activator. The experiments were conducted by varying the curing time and curing temperature in the range of 24-96 hours and 60-90°C respectively. The essential workability properties of freshly prepared Self-compacting Geopolymer concrete such as filling ability, passing ability and segregation resistance were evaluated by using Slump flow, V-funnel, L-box and J-ring test methods. The fundamental requirements of high flowability and resistance to segregation as specified by guidelines on Self-compacting Concrete by EFNARC were satisfied. Test results indicate that longer curing time and curing the concrete specimens at higher temperatures result in higher compressive strength. There was increase in compressive strength with the increase in curing time; however increase in compressive strength after 48 hours was not significant. Concrete specimens cured at 70°C produced the highest compressive strength as compared to specimens cured at 60°C, 80°C and 90°C.