Optimization of abrasive water jet cutting of ductile materials

Full factorial design of experiments was developed in order to investigate the effects of jet pressure, abrasive mixing rate, cutting feed, and plate thickness upon three response variables, surface finish of cutting wear zone, percentage proportion of striation free area, and maximum width of cut. The set of sixteen experiments was performed on each of the following two ductile materials: AISI 4340 (high strength low alloy steel, hardened to 49HRc) and Aluminum 2219. Analysis of Variance (ANOVA) was performed on experimental data in order to determine the significance of effects of different parameters on the performance measures. It was found that cutting feed and thickness were highly influential parameters, while abrasive mixing rate is influential upon surface roughness only. Strong interaction was found between jet pressure and workpiece material. Multi-criteria numerical optimization was performed in order to simultaneously maximize/minimize different combinations of performance measures.     

Pivotal Role of Phytohormones and Their Responsive Genes in Plant Growth and Their Signaling and Transduction Pathway under Salt Stress in Cotton

The presence of phyto-hormones in plants at relatively low concentrations plays an indispensable role in regulating crop growth and yield. Salt stress is one of the major abiotic stresses limiting cotton production. It has been reported that exogenous phyto-hormones are involved in various plant defense systems against salt stress. Recently, different studies revealed the pivotal performance of hormones in regulating cotton growth and yield. However, a comprehensive understanding of these exogenous hormones, which regulate cotton growth and yield under salt stress, is lacking. In this review, we focused on new advances in elucidating the roles of exogenous hormones (gibberellin (GA) and salicylic acid (SA)) and their signaling and transduction pathways and the cross-talk between GA and SA in regulating crop growth and development under salt stress. In this review, we not only focused on the role of phyto-hormones but also identified the roles of GA and SA responsive genes to salt stress. Our aim is to provide a comprehensive review of the performance of GA and SA and their responsive genes under salt stress, assisting in the further elucidation of the mechanism that plant hormones use to regulate growth and yield under salt stress.     

Mammogram Enhancement Using Lifting Dyadic Wavelet Transform and Normalized Tsallis Entropy

In this paper, we present a new technique for mammogram enhancement using fast dyadic wavelet transform （FDyWT） based on lifted spline dyadic wavelets and normalized Tsallis entropy. First, a mammogram image is decom- posed into a multiscale hierarchy of low-subband and high-subband images using FDyWT. Then noise is suppressed using normalized Tsallis entropy of the local variance of the modulus of oriented high-subband images. After that, the wavelet coefficients of high-subbands are modified using a non-linear operator and finally the low-subband image at the first scale is modified with power law transformation to suppress background. Though FDyWT is shift-invariant and has better poten- tial for detecting singularities like edges, its performance depends on the choice of dyadic wavclcts. On the other hand, the nulnber of vanishing moments is an important characteristic of dyadic wavelets for singularity analysis because it provides an upper bound measurement for singularity characterization. Using lifting dyadic schemes, we construct lifted spline dyadic wavelets of different degrees with increased number of vanishing moments. We also examine the effect of these wavelets on mammogram enhancement. The method is tested on mammogram images, taken from MIAS （Mammographic Image Analysis Society） database, having various background tissue types and containing different abnormalities. The comparison with tile state-of-the-art contrast enhancement methods reveals that the proposed method performs better and the difference is statistically significant.     

On parallel push–relabel based algorithms for bipartite maximum matching

We study multithreaded push–relabel based algorithms for computing maximum cardinality matching in bipartite graphs. Matching is a fundamental combinatorial problem with applications in a wide variety of problems in science and engineering. We are motivated by its use in the context of sparse linear solvers for computing the maximum transversal of a matrix. Other applications can be found in many fields such as bioinformatics (Azad et al., 2010) [4], scheduling (Timmer and Jess, 1995) [27], and chemical structure analysis (John, 1995) [14]. We implement and test our algorithms on several multi-socket multicore systems and compare their performance to state-of-the-art augmenting path-based serial and parallel algorithms using a test set comprised of a wide range of real-world instances.     

Toward the modeling of data provenance in scientific publications

In this paper, we implement a provenance-aware system for documenting publications, called PADS. It employs a three-layered provenance hierarchy, which can output diverse types of provenance data related to the research life cycle. From this, we generate different profiles for research ventures, reviewers, and authors. PADS employs the standard Open Provenance Model (OPM) specification for capturing provenance data, and stores this data as ontological instances. We show that data is retrieved without any apparent delay in the execution time of the queries. We also demonstrate how this data can be used to make useful recommendations to the organizers, in order to manage upcoming research ventures.     

Control of a robotic orthosis for gait rehabilitation

Robot assisted gait training may help in producing rapid improvements in functional gait parameters. This paper presents new experimental results with an intrinsically compliant robotic gait training orthosis. The newly developed robotic orthosis has 6 degrees of freedom (DOFs). A trajectory tracking controller based on the boundary layer augmented sliding control (BASMC) law was implemented to guide the subject’s limbs on physiological gait trajectories. The compliance of the robotic orthosis sagittal plane hip and knee joints was also controlled, independently of the trajectory tracking control. The robotic orthosis and the control scheme were evaluated on three neurologically intact subjects walking on a treadmill. A maximum trajectory tracking error of 10° was recorded at the hip and knee sagittal plane joints. The results showed that subjects can walk in the robotic orthosis with comfort and the BASMC law was able to guide the subject’s limbs on reference physiological trajectories.     

A group theoretic approach to construct cryptographically strong substitution boxes

In this paper, we present a method to construct a substitution box used in encryption applications. The proposed algorithm for the construction of substitution box relies on the linear fractional transform method. The design methodology is simple, while the confusion-creating ability of the new substitution box is complex. The strength of the proposed substitution box is evaluated, and an insight is provided to quantify the confusion-creating ability. In addition, tests are performed to assess the vulnerability of the encrypted data to algebraic and statistical attacks. The substitution box is critically analyzed by strict avalanche criterion, bit independent criterion, differential approximation probability test, linear approximation probability test, non-linearity test, and majority logic criterion. The performance of the proposed substitution box is also compared with those of some of the well-known counterparts including AES, APA, Gray, S₈, Skipjack, Xyi, and prime of residue substitution boxes. It is apparent that the performance, in terms of confusion-creating ability, of the new substitution box is better than those of some of the existing non-linear components used in encryption systems. The majority logic criterion is applied to these substitution boxes to further evaluate the strength and usefulness.     

Pareto-optimal clustering scheme using data aggregation for wireless sensor networks

The presence of cluster heads (CHs) in a clustered wireless sensor network (WSN) leads to improved data aggregation and enhanced network lifetime. Thus, the selection of appropriate CHs in WSNs is a challenging task, which needs to be addressed. A multicriterion decision-making approach for the selection of CHs is presented using Pareto-optimal theory and technique for order preference by similarity to ideal solution (TOPSIS) methods. CHs are selected using three criteria including energy, cluster density and distance from the sink. The overall network lifetime in this method with 50% data aggregation after simulations is 81% higher than that of distributed hierarchical agglomerative clustering in similar environment and with same set of parameters. Optimum number of clusters is estimated using TOPSIS technique and found to be 9–11 for effective energy usage in WSNs.     

Design and Implementation of Optical Signal Reinstatement Technique for High DPSK RZ Transceiver Scheme

Wireless Personal Communications - Optical signal reinstatement (OSR) is a common technique used in high-speed communication systems. OSR can be achieved using various techniques such as;...     

A Novel Refined Track Initiation Algorithm for Group Targets Based on Group Model

Traditional refined track initiation methods for group targets have mistakes or loss of tracks when tracking irregular motions, for the reason that they rely on a stable relative position of group members. To solve the problem, a group dynamic model was introduced for proposing a new initiation algorithm and its whole framework. We made a self-adaptive improvement of the group separation on various group radii. After the pre-association of these groups, a state equation derived from the model was used for predictions of group members. Then a relational matrix was defined for refined data associations. Finally tracks were validated by logic-based method. Particular scenarios and Monte Carlo simulations showed that, compared with algorithms based on relative position, this algorithm has better performance on the adaptability to changes of a group structure and the correctness of initiation.