Adaptive image denoising using cuckoo algorithm

This paper presents a novel denoising approach based on smoothing linear and nonlinear filters combined with an optimization algorithm. The optimization algorithm used was cuckoo search algorithm and is employed to determine the optimal sequence of filters for each kind of noise. Noises that would be eliminated form images using the proposed approach including Gaussian, speckle, and salt and pepper noise. The denoising behaviour of nonlinear filters and wavelet shrinkage threshold methods have also been analysed and compared with the proposed approach. Results show the robustness of the proposed filter when compared with the state-of-the-art methods in terms of peak signal-to-noise ratio and image quality index. Furthermore, a comparative analysis is provided between the said optimization algorithm and the genetic algorithm.     

Information Security Policy Development and Implementation

ABSTRACT

Development of the information security policy is a critical activity. Credibility of the entire information security program of an organization depends upon a well-drafted information security policy. Most of the stakeholders do not have time or inclination to wade through a lengthy policy document. This article tries to formulate an approach to the information security policy development that will make the policy document capture the essentials of information security as applicable to a business. The document will also convey the urgency and importance of implementing the policy, not only in letter but also in spirit.     

New Hybrid Access Method for Femtocell through Adjusting QoS

The increasing number of mobile users raises issues about coverage extension in some areas such as rural zones, indoor or underground locations： one of suggestion solution to accommodate this growing of mobile user is femtocell. Femtocell have been attracting considerable attention in mobile communications, it is a small base station that install to improve the indoor coverage of a given cellular and to enhance user＇s capacity. Call admission control and resource allocation infemtocell＇s hybrid mode are an essential performance promotion issue. Developing methods for femtocell utilization is very comparative nowadays. The two major limitations of wireless communication are capacity and range. The main contribution of our paper is developing resource allocation scheme that can manage the femocell resources between subscriber （femtocell user） and non-subscriber （macrocell user in order to maximizing the system utilizations, we provide a mechanism that leads to serve more users by admitting more subscribers basing on adjusting QoS of the connected users.     

Optisim: A System Simulation Methodology for Optically Interconnected HPC Systems

Although CAD tools have significantly assisted electronic system simulation, the system-level optoelectronics modeling field has lagged behind due to a lack of simulation methodologies and tools. Optisim, a system-level modeling and simulation methodology of optical interconnects for HPC systems, can provide computer architects, designers, and researchers with a highly optimized, efficient, and accurate discrete-event environment to test various HPC systems.     

Learning to Locate Informative Features for Visual Identification

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.     

Stochastic and Dynamic Shipper Carrier Network Design Problem

The focus of this work is to determine the optimal storage capacity to be installed on transhipment nodes by shippers in a dynamic shipper carrier network under stochastic demand. A two stage linear program with recourse formulation is developed where in the first stage, the shipper decides the optimal capacity to be installed on transhipment nodes. In the second stage, the shipper chooses a routing strategy based on the realized demand. The performance of the following solution methods: Stochastic L Shaped Method, Regularized Decomposition and L Shaped Method with preliminary cuts were compared for various network sizes and numerous demand scenarios. A novel capacity shifting heuristic was introduced to generate a feasible implementable solution which significantly improves the performance of Regularized Decomposition and provides the best performance in the cases tested. Various ways of generating analytical bounds on the objective function value was discussed. The new capacity shifting heuristic was found to be efficient in generating tight upper bounds. Even though the formulation considered in this paper is for a single commodity, the model can be easily extended to account for multiple commodities.     

Synthesis and characterisation of neem leaf extract, 2, 3‐dehydrosalanol and quercetin dihydrate mediated silver nano particles for therapeutic applications

The utility of green silver nanoparticles (AgNPs) in veterinary medicine is steadily increasing as they have many therapeutic applications against pathogens and arthropods of livestock. In this study, green AgNPs using neem (N‐AgNPs), 2,3‐dehydrosalanol (2,3‐DHS‐AgNPs) and quercetin dihydrate (QDH‐AgNPs) were synthesised and characterised. Synthesised compounds were characterised by UV‐Vis spectroscopy and the peak absorbance was recorded at 370 nm for neem extract. For N‐AgNPs, 2,3‐DHS‐AgNPs and QDH‐AgNPs, the maximum absorbance peaks were at 430, 230 and 220 nm, respectively. The FTIR analysis confirmed the synthesis of green AgNPs. The XRD pattern of N‐AgNPs showed the peaks corresponding to whole spectra of 2 θ values ranging from 10–80. The relatively higher intensity of (111, 222) planes in face centred cubic crystalline structure supports the formation of synthesised AgNPs. In DLS analysis, the hydrodynamic diameter of neem leaf extract was found to be 259.8 nm, followed by 5.3, 6.7 and 261.8 nm for 2,3‐DHS‐AgNPs, N‐AgNPs and QDH‐AgNPs, respectively. Based on the transmission electron microscopy and scanning electron microscopy image analyses, confirmed the formation of N‐AgNPs, 2,3‐DHS‐AgNPs and QDH‐AgNPs. These eco‐friendly phyto‐AgNPs may be of use as an effective alternative to chemical control methods against the arthropods of livestock.Inspec keywords: nanoparticles, silver, nanomedicine, biomedical materials, nanofabrication, Fourier transform infrared spectra, ultraviolet spectra, visible spectra, X‐ray diffraction, light scattering, transmission electron microscopy, scanning electron microscopy, aggregation, veterinary medicineOther keywords: 2,3‐dehydrosalanol mediated silver nanoparticles, quercetin dihydrate mediated silver nanoparticles, therapeutic applications, green silver nanoparticles, veterinary medicine, Azadirachta indica, UV‐visible spectroscopy, Fourier transformed infrared analysis, X‐ray diffraction, (111) planes, (222) planes, face centred cubic crystalline structure, dynamic light scattering, hydrodynamic diameter, aqueous neem leaf extract, transmission electron microscopy, hexagonal shape, pencil head shape, cuboid shape, scanning electron microscopy, aggregation, arthropod infesting livestock, Ag, in‐vivo antiectoparasitic activity, in‐vitro antiectoparasitic activity     

EEG‐based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.