Quality aspects for component‐based systems: A metrics based approach

In component‐based development, software systems are built by assembling components already developed and prepared for integration. To estimate the quality of components, complexity, reusability, dependability, and maintainability are the key aspects. The quality of an individual component influences the quality of the overall system. Therefore, there is a strong need to select the best quality component, both from functional and nonfunctional aspects. The present paper produces a critical analysis of metrics for various quality aspects for components and component‐based systems. These aspects include four main quality factors: complexity, dependency, reusability, and maintainability. A systematic study is applied to find as much literature as possible. A total of 49 papers were found suitable after a defined search criteria. The analysis provided in this paper has a different objective as we focused on efficiency and practical ability of the proposed approach in the selected papers. The various key attributes from these two are defined. Each paper is evaluated based on the various key parameters viz. metrics definition, implementation technique, validation, usability, data source, comparative analysis, practicability, and extendibility. The paper critically examines various quality aspects and their metrics for component‐based systems. In some papers, authors have also compared the results with other techniques. For characteristics like complexity and dependency, most of the proposed metrics are analytical. Soft computing and evolutionary approaches are either not being used or much less explored so far for these aspects, which may be the future concern for the researchers. In addition, hybrid approaches like neuro‐fuzzy, neuro‐genetic, etc., may also be examined for evaluation of these aspects. However, to conclude that one particular technique is better than others may not be appropriate. It may be true for one characteristic by considering different set of inputs and dataset but may not be true for the same with different inputs. The intension in the proposed work is to give a score for each metric proposed by the researchers based on the selected parameters, but certainly not to criticize any research contribution by authors. Copyright © 2012 John Wiley & Sons, Ltd.     

Time and Space Optimal Data Parallel Volume Rendering Using Permutation Warping

In this paper we present a data parallel volume rendering algorithm that possesses numerous advantages over prior published solutions. Volume rendering is a three-dimensional graphics rendering algorithm that computes views of sampled medical and simulation data, but has been much slower than other graphics algorithms because of the data set sizes and the computational complexity. Our algorithm usespermutation warpingto achieve linear speedup (run time is O(S/P) forPprocessors whenP\= O(S/logS) forS\=n³samples), linear storage (O(S)) for large data sets, arbitrary view directions, and high-quality filters. We derived a new processor permutation assignment of five passes (our prior known solution was eight passes), and a new parallel compositing technique that is essential for scaling linearly on machines that have more processors than view rays to process (P>n²). We show a speedup of 15.7 for a 16k processor over a 1k processor MasPar MP-1 (16 is linear) and two frames/second with a 128³volume and trilinear view reconstruction. In addition, we demonstrate volume sizes of 256³, constant run time over angles 5 to 75°, filter quality comparisons, and communication congestion of just 19 to 29\%.     

On fuzzy sobriety

This paper mainly shows that sober fuzzy topological spaces form the epireflective hull of the fuzzy Sierpinski space in the category of T₀-fuzzy topological spaces.     

Distributed diagnosis in dynamic fault environments

The problem of distributed diagnosis in the presence of dynamic failures and repairs is considered. To address this problem, the notion of bounded correctness is defined. Bounded correctness is made up of three properties: bounded diagnostic latency, which ensures that information about state changes of nodes in the system reaches working nodes with a bounded delay, bounded start-up time, which guarantees that working nodes determine valid states for every other node in the system within bounded time after their recovery, and accuracy, which ensures that no spurious events are recorded by working nodes. It is shown that, in order to achieve bounded correctness, the rate at which nodes fail and are repaired must be limited. This requirement is quantified by defining a minimum state holding time in the system. Algorithm heartbeatcomplete is presented and it is proven that this algorithm achieves bounded correctness in fully-connected systems while simultaneously minimizing diagnostic latency, start-up time, and state holding time. A diagnosis algorithm for arbitrary topologies, known as algorithm forwardheartbeat, is also presented. Forwardheartbeat is shown to produce significantly shorter latency and state holding time than prior algorithms, which focused primarily on minimizing the number of tests at the expense of latency.     

Generalized notions of mind change complexity

Gold introduced the notion of learning in the limit where a class S is learnable iff there is a recursive machine M which reads the course of values of a function f and converges to a program for f whenever f is in S. An important measure for the speed of convergence in this model is the quantity of mind changes before the onset of convergence. The oldest model is to consider a constant bound on the number of mind changes M makes on any input function; such a bound is referred here as type 1. Later this was generalized to a bound of type 2 where a counter ranges over constructive ordinals and is counted down at every mind change. Although ordinal bounds permit the inference of richer concept classes than constant bounds, they still are a severe restriction. Therefore the present work introduces two more general approaches to bounding mind changes. These are based on counting by going down in a linearly ordered set (type 3) and on counting by going down in a partially ordered set (type 4). In both cases the set must not contain infinite descending recursive sequences. These four types of mind changes yield a hierarchy and there are identifiable classes that cannot be learned with the most general mind change bound of type 4. It is shown that existence of type 2 bound is equivalent to the existence of a learning algorithm which converges on every (also nonrecursive) input function and the existence of type 4 is shown to be equivalent to the existence of a learning algorithm which converges on every recursive function. A partial characterization of type 3 yields a result of independent interest in recursion theory. The interplay between mind change complexity and choice of hypothesis space is investigated. It is established that for certain concept classes, a more expressive hypothesis space can sometimes reduce mind change complexity of learning these classes. The notion of mind change bound for behaviourally correct learning is indirectly addressed by employing the above four types to restrict the number of predictive errors of commission in finite error next value learning (NV′′)—a model equivalent to behaviourally correct learning. Again, natural characterizations for type 2 and type 4 bounds are derived. Their naturalness is further illustrated by characterizing them in terms of branches of uniformly recursive families of binary trees.     

Sustained release microspheres of diclofenac sodium using PEGylated rosin derivatives

The PEGylated derivatives of rosin-PD-1 and PD-2 synthesized and characterized earlier (Nande et al., 2006) were investigated as potential materials for sustained release microsphere prepared by emulsion solvent evaporation method using diclofenac sodium (DCS) as model drug. All the microspheres exhibited smooth surfaces intercepted by pores; their sizes (d(90)) ranged between 11-24 microm. The entrapment efficiency (< 80%) of the microspheres increased proportionally with derivative concentration. Presence of solvent like isopropyl alcohol or dichloromethane rendered the microspheres with large sizes but with reduced drug entrapment. Microspheres with small size were obtained at an optimum viscosity of liquid paraffin; any change lead to increase in the particle size. Magnesium stearate was found to be most suitable detackifier in the present system. The drug release was directly related to the particle size--small sized microspheres released drug at a faster rate. The dissolution data complied with Higuchi equation while the mechanism of drug release was Fickian diffusion (n approximately 0.5). Controlled inhibition of edema, as tested by hind paw edema method, was observed for 10 h when the microspheres were administered intraperitoneally. The present study found the derivatives as promising materials for preparing microspheres for sustained delivery of DCS.     

Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed‐based region growing algorithm

Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed‐based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 ‐ w, T2 – w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C – Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO‐based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO‐based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 33–45, 2017     

Two-dimensional MX Dirac materials and quantum spin Hall insulators with tunable electronic and topological properties

We propose a novel class of two-dimensional(2D)Dirac materials in the MX family(M=Be,Mg,Zn and Cd,X=Cl,Br and I),which exhibit graphene-like band structures with linearly-dispersing Dirac-cone states over large energy scales(0.8–1.8 eV)and ultra-high Fermi velocities comparable to graphene.Spin-orbit coupling opens sizable topological band gaps so that these compounds can be effectively classified as quantum spin Hall insulators.The electronic and topological properties are found to be highly tunable and amenable to modulation via anion-layer substitution and vertical electric field.Electronic structures of several members of the family are shown to host a Van-Hove singularity(VHS)close to the energy of the Dirac node.The enhanced density-of-states associated with these VHSs could provide a mechanism for inducing topological superconductivity.The presence of sizable band gaps,ultra-high carrier mobilities,and small effective masses makes the MX family promising for electronics and spintronics applications.     

From template to image: reconstructing fingerprints from minutiae points

Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line integral convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent fingerprint