Radiation pattern and properties of double‐flared diagonal horn antenna

This article is concerned with analytical model for radiation pattern of a new double‐flared diagonal horn antenna using “plane wave spectra technique for 3D fields”. The double‐flared diagonal horn antenna is accomplished by adding one more set of flares in E‐ and H‐planes with equal flare angles into conventional diagonal horn. The copolarized and cross‐polarized radiation patterns in E‐, H‐, and D‐planes have been computed utilizing the analytical model and reported in the article. The parametric studies on peak cross‐polarization level have carried out. The radiation performance of double‐flared and conventional diagonal horns for same length and aperture size are also compared. It is investigated that double‐flared diagonal horn antenna posses better peak cross‐polarization level (≈?16.5 dB) in D‐plane (±45° plane) in comparison of conventional diagonal horn (≈?15.5 dB) at design frequency and retains almost circularly symmetric radiating beam at lower values of elevation angles. Also, double‐flared diagonal horn has better matches with free‐space and has slightly lower gain (≤0.5 dB) in comparison of conventional diagonal horn. The work presented here can provide useful design guidelines for development of prototypes of double‐flared diagonal horn, which may find potential application in satellite communication and imaging applications etc. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

VM3: Measuring,modeling and managing VM shared resources

With cloud and utility computing models gaining significant momentum, data centers are increasingly employing virtualization and consolidation as a means to support a large number of disparate applications running simultaneously on a chip-multiprocessor (CMP) server. In such environments, contention for shared platform resources (CPU cores, shared cache space, shared memory bandwidth, etc.) can have a significant effect on each virtual machine’s performance. In this paper, we investigate the shared resource contention problem for virtual machines by: (a) measuring the effects of shared platform resources on virtual machine performance, (b) proposing a model for estimating shared resource contention effects, and (c) proposing a transition from a virtual machine (VM) to a virtual platform architecture (VPA) that enables transparent shared resource management through architectural mechanisms for monitoring and enforcement. Our measurement and modeling experiments are based on a consolidation benchmark (vConsolidate) running on a state-of-the-art CMP server. Our virtual platform architecture experiments are based on detailed simulations of consolidation scenarios. Through detailed measurements and simulations, we show that shared resource contention affects virtual machine performance significantly and emphasize that virtual platform architectures is a must for future virtualized datacenters.     

Real-time supply chain control via multi-agent adjustable autonomy

Real-time supply chain management in a rapidly changing environment requires reactive and dynamic collaboration among participating entities. In this work, we model supply chain as a multi-agent system where agents are subject to an adjustable autonomy. The autonomy of an agent refers to its capability to make and influence decisions within a multi-agent system. Adjustable autonomy means changing the autonomy of the agents during runtime as a response to changes in the environment. In the context of a supply chain, different entities will have different autonomy levels and objective functions as the environment changes, and the goal is to design a real-time control technique to maintain global consistency and optimality. We propose a centralized fuzzy framework for sensing and translating environmental changes to the changes in autonomy levels and objectives of the agents. In response to the changes, a coalition-formation algorithm will be executed to allow agents to negotiate and re-establish global consistency and optimality. We apply our proposed framework to two supply chain control problems with drastic changes in the environment: one in controlling a military hazardous material storage facility under peace-to-war transition, and the other in supply management during a crisis (such as bird-flu or terrorist attacks). Experimental results show that by adjusting autonomy in response to environmental changes, the behavior of the supply chain system can be controlled accordingly.     

Agile Requirements Engineering Practices: An Empirical Study

An analysis of data from 16 software development organizations reveals seven agile RE practices, along with their benefits and challenges. The rapidly changing business environment in which most organizations operate is challenging traditional requirements-engineering (RE) approaches. Software development organizations often must deal with requirements that tend to evolve quickly and become obsolete even before project completion. Rapid changes in competitive threats, stakeholder preferences, development technology, and time-to-market pressures make prespecified requirements inappropriate.     

Binary BAT algorithm and RBFN based hybrid credit scoring model

Credit scoring is a process of calculating the risk associated with an applicant on the basis of applicant’s credentials such as social status, financial status, etc. and it plays a vital role to improve cash flow for financial industry. However, the credit scoring dataset may have a large number of irrelevant or redundant features which leads to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with huge number of features. This work emphasized on the role of feature selection and proposed a hybrid model by combining feature selection by utilizing Binary BAT optimization technique with a novel fitness function and aggregated with for Radial Basis Function Neural Network (RBFN) for credit score classification. Further, proposed feature selection approach is aggregated with Support Vector Machine (SVM) & Random Forest (RF), and other optimization approaches namely: Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Genetic Algorithm (PSOGA), Improved Krill Herd (IKH), Improved Cuckoo Search (ICS), Firefly Algorithm (FF) and Differential Evolution (DE) are also applied for comparative analysis.

     

An Efficient Approach for Enhancing Contrast Level and Segmenting Satellite Images: HNN and FCM Approach

Satellite image segmentation has gotten bunches of consideration of late because of the accessibility of commented on high-goals image informational indexes caught by the last age of satellites. The issue of fragmenting a satellite image can be characterized as ordering (or marking) every pixel of the image as indicated by various classes, for example, structures, streets, water, etc. In this paper centered to build up a satellite image segmenting process by utilizing distinctive optimization methods. The work is prepared dependent on three stages that are RGB change, preprocessing, and division. At first the database images are assembled from the database at that point select the blue band images by performing RGB change. To improve the differentiation and furthermore decreasing the commotion of these chose blue band images, Hopfield neural network (HNN) is utilized. After image upgrade, the images are fragmented dependent on fuzzy C means (FCM) clustering method. The images are clustered and segmented in the way of optimizing the centroid in FCM utilizing oppositional crow search algorithm. The exhibition of the proposed framework is investigated dependent on the presentation measurements, for example, affectability, particularity and accuracy. From the outcomes, the proposed strategy diminished the computational time by expanding the accuracy of 98.3% with HNN system.

     

Synthesis of zeolite Beta from dense system containing a minimum of template

Zeolite Beta has been synthesized, in 24 h at 170°C, from an extremely dense system in which the weight ratio of solid (sodium aluminate and SiO₂) to liquid (tetraethylammonium hydroxide and H₂O) mixtures is 1 1.8; the product has comparable catalytic properties to those samples prepared by previous methods.     

Powdered activated carbon coated hollow fiber membrane: Preliminary studies on its ability to limit membrane fouling and to remove organic materials

A new approach to a membrane hybrid system by pre-coating the hollow fiber membrane with powdered activated carbons (PAC) was evaluated for its ability to minimize the fouling of the membrane and to remove organic material from wastewater. This preliminary study evaluates the performance of a microfiltration membrane coated with three kinds of PACs: wood based (WB), charcoal based (CB) and coconut based (HA). Broadly, two scenarios were evaluated: one with low amounts of PAC coated on the membrane and another at higher amounts of PAC coating. The results indicate that the pre-coated membrane can effectively arrest the fouling agents in the wastewater in reaching the membrane pores and thereby limit membrane fouling. Interestingly, it was also found that, without any pre-treatment or addition of PAC in the tank, the pre-coated membrane also had the ability to retain organic materials. For the hollow fiber microfilter membrane used in the study having surface area of 2.58×10^-03 m² surface area, pre-coating the membrane individually with 458 mg of HA-PAC, 497 mg of WB-PAC and 906 mg CB-PAC, the reduction in permeate flux was as little as 14–20% after 8 hours of each operation and the maximum organic removals was about 76%, for all the three kinds of PAC coatings. The type of PAC coated on the membrane and the amount coated could be the key factors in deciding the performance of the system. Although further studies are required, it is evident that the PAC pre-coated membrane system has great potential in successfully reducing membrane fouling, which could improve membrane life, enhance process performance and reduce membrane cleaning time.     

Low-frequency ferroelectric switching studies in PVDF thin films across Cu or (Ag/Cu)/PVDF/Cu capacitor structures

Ferroelectric switching dynamics of polyvinylidene fluoride (PVDF) thin films in Cu or (Ag/Cu)/PVDF/Cu capacitors are explored by varying PVDF film thickness, applied electric field amplitude (4.35–87.5 MV/m) and frequency (100 mHz–200 Hz). Comprehending spontaneous polarization and its dependence upon interfaces, an electric field is critical for organic ferroelectric memory devices. In this article, quasi-static current–voltage, and polarization–electric field measurements are used to explain the relationship between the coercive field, signal amplitude, and frequency. The observed coercivity enhancement at lower PVDF film thicknesses and with rising frequencies of the applied signal is discussed with Kolmogorov-Avrami-Ishibashi domain nucleation and growth model. The relation between domain growth and the top electrode layer is further discussed from the exponent parameters.