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To introduce a new IQA method called SC-QI, this research work adapts a structural contrast index (SCI) that characterizes the perceptions of local and also the global visual features on behalf of different image characters through different varieties of structural distortion. This research work also attempts for the development of SC-QI visual dependability involvement and expand the refitted picture quality optimization (SC-QI) called SC-DM. For local image characteristics & different forms of distortion, several appearances recycled in computational IQA can almost not describe visual quality conditions [18]. The complexity of the texture is a quality that increases as the visibility of distortions, which increases due to the contrast effect in the texture of the background image. Selecting user-friendly methods for optimization depends on FR-IQA. Here, image quality is considered as an important aspect of image processing, so these approaches can certainly contribute towards enhancing the visual quality by reducing the image structural distortion. FR-IQA (SC-QI) methods (SC-DM) have directly or indirectly affected the role of contrast/structural data in image signals that have observed the pictorial feature. Conclusion operative geographies that are used to describe such contrast/structural datacould therefore remain as a frequent problem in displaying. [5]

Solving a problem can have multiple methods, each having its own merits and demerits. The ultimate level of complexity of solution models is highly subjective in nature because a method easy for one person may be much difficult for others. Moreover, methods friendly for humans need not be suitable for automated systems like computers. Similar is the case among various automated systems too. All depend on the nature of the problem and the solver, availability of resources, optimization requirements and the like. For example, loops in source code reduce the complexity while programming but give additional overhead during execution. So, optimization of programs by reducing the number of loops can make considerable improvement in performance at runtime. There are several techniques for optimizing the loops. This study is based on the popular loop optimization technique known as Loop unrolling which is having own advantages and disadvantages, but able to open up the additional scope for enhanced parallelism and automated code generation.

Time series analysis has a wide range of applications in many research fields and tools using linear methods to the artificial neural network has been employed to model the same. In the recent past, increased attention has been gained by network theory to study the time series arise from various systems. There are different methods available to construct the network from time series. Even though many naturally occurring time series are distorted by noise, its effects on the associated networks are not been investigated yet. In this work, we analyse the effect of time series measurement noise on the parameters of the associated network. The research demonstrates that upto a certain level of noise networks are stable and it has no effect on the structural properties. As the noise level is increased the structure started deteriorating towards a completely disconnected network.

Magneto-optic Kerr effect (MOKE) is a widely used tool in surface physics to characterize magnetic thin films and single crystals. In most of its applications, the magneto-optic (MO) coupling is assumed to be linear on magnetization by neglecting the quadratic and higher dependences. However, recent observations have shown that the quadratic dependence cannot be always ignored, particularly in systems like ferromagnetic metal thin films, Heusler alloys, and even in half-metallic ferrimagnets. We have used a rotating field method to extract the QMOKE signal from Fe(100) film grown on (100) surface of a single crystal iridium. Our results show that the QMOKE signal from this system is comparable to the linear MOKE signal. We report the parameters L, b, and c which relate the linear (K) and quadratic (G and $\Delta \text{G}$ ) MO coupling coefficients. The real part of L, b, and c obtained from Fe(100)/Ir(100) are −9.75 ± 0.03 mdeg, 7.28 ± 0.09 mdeg, and −5.0 ± 0.1 mdeg, respectively.

Losses during Transmission is a major challenge while distributing electricity through large distribution networks especially in countries like India where population is unequally distributed in a large geographical area with a lot of diversities. Distance between transmission points is a major factor in determining the technical loss of electrical energy due to conversion as heat while using physical metallic media. Software models of Travelling Salesman Problem can simulate the process of finding optimized paths while designing distribution networks connecting remote places. Limitations in scalability and flexibility restrict conventional algorithmic models of TSP in complex practical situations of real world projects. Soft computing models can help to achieve more scalability, flexibility and even a better control over desired level of quality in output with respect to situational constraints. So they are more suitable in modeling real project environments which are subjected to dynamic changes as in electricity distribution networks. This paper illustrates the scope of soft computing technique known as Simulated Annealing, in such projects, for finding optimized paths within a restricted environment with constraints, through an iterative process model.

Computing techniques like Cloud computing demand for very high level of parallelism, which has been achieved at hardware level through advancements in Computer Architecture. However to exploit the maximum benefits of the hardware parallelism, the software solutions implemented in such systems also shall exhibit parallel processing capabilities. Most of the popular problem solving methods and algorithms need not be capable for imparting parallelism in software solutions. This opens up the scope for the exploration aiming alternative solution models with capabilities to achieve parallel, scalable and distributable nature in processes. This study illustrates the scope of seeking alternative process models to achieve software parallelism at component level, using simple meta-models of mathematical problems.

Information is becoming the most precious resource. Mining out implied information from unstructured data has become essential in several project situations, and this itself is a complex process. Anonymity in data makes the data mining process much more difficult. Data Anonymization techniques, as being used in areas such as Cryptography and Cloud computing, aim to limit the possibilities of unnecessary data mining activities. It is being done by concealing, suppressing or generalizing the parts of information which is irrelevant to each of the individual stakeholders. However, collaboration of such stakeholders who are provided with different versions of anonymized data can reveal the anonymity up to a certain extent. This is an analytical study on such a possibility illustrated with the example of solving an anonymization puzzle involving two different persons who are trying to mine out the suppressed information and concealed knowledge through a collaborative approach in knowledge mining. This study points out the scope for a model driven approach in problem solving, and also the necessity of taking precautionary measures by the practitioners in both Data Anonnymization and Data Mining to resist the mutual competition, based on the ultimate project requirements.

Intrusion detection systems are intelligent systems designed to identify and prevent the misuse of computer networks and systems. This research work aims at developing hybrid algorithms using data mining techniques for the effective enhancement of anomaly intrusion detection performance. Many proposed algorithms have not addressed their reliability with varying amount of malicious activity or their adaptability for real time use. The study incorporates a theoretical basis for improvement in performance of IDS using K- Means Algorithm, Fuzzy Rule System and Neural Network techniques. Also statistical significance of estimates has been looked into for finalizing the best one using DARPA network traffic datasets.

We present the evolution of the state-of-the-art multiuser, open access, Nanofab facility at the Indian Institute of Science Bangalore. The process behind the design and execution of the cleanroom will be described. The challenges faced in moving the equipments from the old facility and the issues in coordinating installation of new equipments and utility hookup will be presented. The methodology used to manage the facility, challenges in staffing to run the facility and balancing the time sharing between internal and external users will be discussed.

Relational sub graph analysis, e.g. finding labeled sub graphs in a network, which are isomorphic to a template, is a key problem in many graph related applications. It is computationally challenging for large networks and complex templates. In this paper, we develop SAHAD, an algorithm for relational sub graph analysis using Hadoop, in which the sub graph is in the form of a tree. SAHAD is able to solve a variety of problems closely related with sub graph isomorphism, including counting labeled/unlabeled sub graphs, finding supervised motifs, and computing graph let frequency distribution. We prove that the worst case work complexity for SAHAD is asymptotically very close to that of the best sequential algorithm. On a mid-size cluster with about 40 compute nodes, SAHAD scales to networks with up to 9 million nodes and a quarter billion edges, and templates with up to 12 nodes. To the best of our knowledge, SAHAD is the first such Hadoop based subgraph/subtree analysis algorithm, and performs significantly better than prior approaches for very large graphs and templates. Another unique aspect is that SAHAD is also amenable to running quite easily on Amazon EC2, without needs for any system level optimization.

SrRuO3 is a well-known itinerant ferromagnet with many intriguing characteristics. The Ru deficiency in this system is believed to play a pivotal role in influencing many of its magnetic and transport properties. The present study involves the magnetic and transport properties of the Ru-deficient SrRu0.93O3 sample to gain more insight into the unusual low-temperature behavior. The ac susceptibility study reveals a sharp ferromagnetic transition at 150 K followed by a hump at T-h similar to 50 K, which has anomalous frequency dependence. Besides, the T-h shifts to lower temperatures with an increase in the superposed dc-biasing field and adheres to H-2 dependence, in accordance with the Gabay and Toulouse line for the Heisenberg spin glass systems. We also observe a pronounced memory effect toward the low-temperature side, signifying the characteristic of glassy behavior. The temperature-dependent magnetoresistance indicates the signature of an additional ordering toward the low-temperature side. All of the interesting findings combined unveil the existence of low-temperature cryptic magnetic phase in SrRu0.93O3. (C) 2012 American Institute of Physics. doi:10.1063/1.3673427]

This research examines the impact of demand bids which account for geographic complementarity in spectrum demand, on the allocation and pricing of wireless spectrum licenses. Using an individual based simulation environment and a model of spectrum demand for the region of Portland, OR, we study a primary market to allocate spectrum licenses to wireless service providers. A truthful and efficient market clearing mechanism is used to sell the available licenses. A demand estimation model creates spatial and temporal demand estimates for each of the service providers. A valuation system determines the marginal value of each license which is further used in the bidding process. Three different scenarios are considered. First, the entire city of Portland is considered as one region and the estimated demand for this region is used to construct bids. The auction determines the clearing price for each license and the winner of the licenses based on the marginal valuations. After the market clearing is done and license allocations are made, we measure the total cost of licenses to the providers, the amount of unused capacity, and the number of unserved calls. In the second scenario, the city is divided into 2 regions in such a way that the number of call pairs are minimized across regions. Each region is auctioned separately. The providers can now decide their valuations sequentially for each region, so that they can use information on the allocations of the first region to optimally bid in the second region. The same set of measurements are taken again to understand the social impact of this scenario in comparison to the fist one. Finally a third scenario is run which is just like the second scenario but the city is now split into 2 regions in such a way that the call density and population is split evenly between regions. Results from the three scenarios are compared and analyzed to determine the impact of geographically complementary demand bids on the social cost and capacity used.

Large scale realistic simulations of malware on mobile wireless networks have recently become an increasingly important application of high-performance computing. We propose EpiCure - an individual-based, scalable high performance computing oriented modeling environment to study malware propagation over realistic mobile networks. It is designed specifically to work on commodity cluster architectures. EpiCure runs extremely fast for realistic instances that involve: (i) large time-varying networks consisting of millions of heterogeneous individuals with time varying interaction neighborhoods, (ii) dynamic interactions between disease propagation, device behavior, and the exogenous interventions, and (iii) large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution. We find that EpiCure runs several orders of magnitude faster than another comparable simulation tool while delivering similar results. Beyond simple compute speed, EpiCure has been designed so that analysts can easily represent a range of interventions leading to improved human productivity and ease of use. This is an increasingly important metric in high performance computing. We illustrate EpiCure using three case studies that bring out the novel features of EpiCure.

Identifying motifs (or commonly occurring subgraphs/templates) has been found to be useful in a number of applications, such as biological and social networks; they have been used to identify building blocks and functional properties, as well as to characterize the underlying networks. Enumerating subgraphs is a challenging computational problem, and all prior results have considered networks with a few thousand nodes. In this paper, we develop a parallel subgraph enumeration algorithm, ParSE, that scales to networks with millions of nodes. Our algorithm is a randomized approximation scheme, that estimates the subgraph frequency to any desired level of accuracy, and allows enumeration of a class of motifs that extends those considered in prior work. Our approach is based on parallelization of an approach called color coding, combined with a stream based partitioning. We also show that ParSE scales well with the number of processors, over a large range.

This research conducts a systematic study of human-initiated cascading failures in critical inter-dependent societal infrastructures. The focus is on three closely coupled systems: (i) cellular and mesh networks, (ii) transportation networks and (iii) social phone call networks. We analyze cascades that occur in inter-dependent infrastructures due to behavioral adaptations in response to a crisis. During crises, changes in individual behavioral lead to altered calling patterns and activities, which influence the urban transport network. This, in turn, affects the loads on wireless networks. The interaction between these systems and their co-evolution poses significant technical challenges for representing and reasoning about these systems. We develop interaction-based models in which individuals and infrastructure elements are placed in a common geographic coordinate system. The goal is to study the impact of a chemical plume in a densely populated urban region. Authorities order evacuation of the affected area which leads to change in people's activity patterns as they are forced to drive home or to evacuation shelters. They also use the wireless networks for coordination among family members and information sharing. These two behavioral adaptations, cause flash-congestion in the urban transport network and the wireless network. We analyze how extended periods of unanticipated road congestion can result in failure of infrastructures, starting with the servicing base stations in the congested area. Finally, we study the criticality and robustness of the various base stations and measure how congestion in the transportation network impacts communication infrastructure.

Modeling and analysis of Primary User (PU) spectrum requirement is key to effective Dynamic Spectrum Access (DSA). Allocation of long term licenses, as well as opportunistic spectrum usage by secondary users (SU) cannot be done without accurate modeling of PU behavior. This is especially important in the case of cellular network traffic, which exhibits a significant spatio-temporal variation. Recently, there has been a lot of interest in modeling PU behavior in cellular networks by means of detailed analysis of proprietary data from wireless providers (e.g., Willcomm et al., IEEE DySpan 2008). While such analysis gives useful insights, major shortcomings of such an approach include (i) unavailability of data for open scientific study, and (ii) hard to predict future trends, and changes resulting from behavioral modifications. In this paper, we develop a methodology to generate synthetic network traffic data to model primary usage, by combining a number of different data sets for mobility, device ownership and call generation in a large synthetic urban population. Unlike simple random graph techniques, these methods use real world data sources and combine them with behavioral and social theories to synthesize spatial and dynamic relational networks. We use our tool to model the network traffic in the region of Portland, Oregon, calibrated by using published aggregate measurements of Wilcomm et al. As an illustration of our approach, we study the variation in demand as a result of changes in calling patterns based on user activities, and the impact of increased user demand on hotspots and their cascades within the region.

We study the effect of routing topologies on maximizing the aggregated data collection rate and minimizing the maximum packet delay in TDMA-based sensor networks. We propose a bi-criteria formulation to the optimal routing tree construction problem, and show that a tree with bounded node degree and minimum radius can achieve the best trade-off between the data collection rate and packet delays.

Fast and periodic collection of aggregated data is of considerable interest for mission-critical and continuous monitoring applications in sensor networks. In the many-to-one communication paradigm known as convergecast, we consider scenarios where data packets are aggregated at each hop en route to a sink node along a tree-based routing topology and focus on maximizing the data collection rate at the sink by employing TDMA scheduling and multiple frequency channels. Our key result in the paper lies in proving that minimizing the schedule length for an arbitrary network in the presence of multiple frequencies is NP-hard, and in designing approximation algorithms with worst-case provable performance guarantees for geometric networks. In particular, we design a constant factor approximation for networks modeled as unit disk graphs (UDG) where every node has a uniform transmission range, and a O(Δ(T)log n) approximation for general disk graphs where nodes have different transmission ranges; n is the number of nodes in the network and Δ(T) is the maximum node degree on a given routing tree T. We also prove that a constant factor approximation is achievable on UDG even for unknown routing topologies so long as the maximum node degree in the tree is bounded by a constant. We also show that finding the minimum number of frequencies required to remove all the interfering links in an arbitrary network in NP-hard. We give an upper bound on the maximum number of such frequencies required and propose a polynomial time algorithm that minimizes the schedule length under this scenario. Finally, we evaluate our algorithms through simulations and show various trends in performance for different network parameters.

Peer-to-peer (P2P) technology provides a scalable solution in multimedia streaming. Many streaming applications, such as IPTV and video conferencing, have rigorous constraints on end-to-end delays. Obtaining assurances on meeting those delay constraints in dynamic and heterogenous network environments is a challenge. In this paper, we devise a streaming scheme which minimizes the maximum end-to-end streaming delay for a mesh-based overlay network paradigm. We first formulate the minimum-delay P2P streaming problem, called the MDPS problem, and prove its NP-completeness. We then present a polynomial-time approximation algorithm to this problem, and show that the performance of our algorithm is bounded by a ratio of O. Our simulation study reveals the effectiveness of our algorithm, and shows a reasonable message overhead.

Particle shape and size determine the physicochemical and optoelectronic properties of nanoscale materials, including optical absorption, fluorescence, and electric and magnetic moments. It is thus desirable to be able to synthesize and separate various particle shapes and sizes. Biosynthesis using microorganisms has emerged as a more ecologically friendly, simpler, and more reproducible alternative to chemical synthesis of metal and semiconductor nanoparticles, allowing the generation of rare forms such as triangles. Here we show that the plant pathogenic fungus Helminthosporum solani, when incubated with an aqueous solution of chloroaurate ions, produces a diverse mixture of extracellular gold nanocrystals in the size range from 2–70 nm. A plurality are polydisperse spheres, but a significant number are homogeneously-sized rods, triangles, pentagons, pyramids, and stars. The particles can be separated according to their size and shape by using a sucrose density gradient using the centrifuge, a novel and facile approach to nanocrystal purification. The smallest separated gold nanoparticles were conjugated to the anti-cancer drug doxorubicin and efficient uptake by and toxicity to HEK293 cells was demonstrated.

There has recently been renewed interest among various research communities in understanding the structure of social and infrastructure networks. Motivated by this line of research, we conduct an in-depth structural analysis of large ad hoc networks derived by placing nodes randomly as well as by placing them in realistic urban environments, a scenario that is rapidly gaining interest (K. Jain, et al, 2003). We use structural analysis in two illustrative settings. First, we use it to study the performance of network protocols. Our results indicate that structural analysis of interference graphs that model ad hoc networks can yield a good first order prediction of the overall protocol performance. Second, we study the robustness of a network to random node and edge failures. This study is important in the context of ad hoc networks wherein one expects nodes/edges to fail due various natural or system dependent reasons. The experimental results presented in this paper show the following: (i) structural properties of ad hoc networks depend crucially on the spatial distribution of the nodes. (ii) Structural properties of the network significantly affect the performance of protocols. (iii) Graph theoretic measures can provide good first order insights into the network protocol performance. (iv) The measures are also useful in characterizing the robustness of such networks.

Active powders of Ni–Zn ferrite were synthesized by soft chemical approaches and were processed with varying concentrations of Bi2O3. The sintered products were characterized extensively using a vibrating sample magnetometer, scanning electron microscopy, and resistivity techniques. The loss factor and the permeability have been evaluated. At an optimum concentration of Bi2O3 doping the loss factor has been reduced by 20%. The rotational responses are found to be strongly dependent on particle size.

We present provably efficient parallel algorithms for sweep scheduling on unstructured meshes. Sweep scheduling is a commonly used technique in radiation transport problems, and involves inverting an operator by iteratively sweeping across a mesh. Each sweep involves solving the operator locally at each cell. However, each direction induces a partial order in which this computation can proceed. On a distributed computing system, the goal is to schedule the computation, so that the length of the schedule is minimized. Several heuristics have been proposed for this problem; but none of the heuristics have worst case performance guarantees. We present a simple, almost linear time randomized algorithm which (provably) gives a schedule of length at most O(log/sup 2/n) times the optimal schedule for instances with n cells, when the communication cost is not considered, and a slight variant, which coupled with a much more careful analysis, gives a schedule of (expected) length O(logmlogloglogm) times the optimal schedule for m processors. These are the first such provable guarantees for this problem. We also design a priority based list schedule using these ideas, with the same theoretical guarantee, but much better performance in practice. We complement our theoretical results with extensive empirical analysis. The results show that (i) our algorithm performs very well and has significantly better performance guarantee in practice and (ii) the algorithm compares favorably with other natural and efficient parallel algorithms proposed in the literature [S. Pautz, (2002), S. Plimpton et al., (2001)].