Topology synthesis of three-legged spherical parallel manipulators employing Lie group theory

Driven by the requirements of the bionic joint or tracking equipment for the spherical parallel manipulators (SPMs) with three rotational degrees-of-freedom (DoFs), this paper carries out the topology synthesis of a class of three-legged SPMs employing Lie group theory. In order to achieve the intersection of the displacement subgroups, the subgroup characteristics and operation principles are defined in this paper. Mainly drawing on the Lie group theory, the topology synthesis procedure of three-legged SPMs including four stages and two functional blocks is proposed, in which the assembly principles of three legs are defined. By introducing the circular track, a novel class of three-legged SPMs is synthesized, which is the important complement to the existing SPMs. Finally, four typical examples are given to demonstrate the finite displacements of the synthesized three-legged SPMs.

Whole-mitochondrial genome sequencing in primary open-angle glaucoma using massively parallel sequencing identifies novel and known pathogenic variants

Purpose:The aim of this study was to determine whether mutations in mitochondrial DNA play a role in high-pressure primary open-angle glaucoma (OMIM 137760) by analyzing new data from massively parallel sequencing of mitochondrial DNA.
Methods:Glaucoma patients with high-tension primary open-angle glaucoma and ethnically matched and age-matched control subjects without glaucoma were recruited. The entire human mitochondrial genome was amplified in two overlapping fragments by long-range polymerase chain reaction and used as a template for massively parallel sequencing on an Ion Torrent Personal Genome Machine. All variants were confirmed by conventional Sanger sequencing.
Results:Whole-mitochondrial genome sequencing was performed in 32 patients with primary open-angle glaucoma from India (n = 16) and Ireland (n = 16). In 16 of the 32 patients with primary open-angle glaucoma (50% of cases), there were 22 mitochondrial DNA mutations consisting of 7 novel mutations and 8 previously reported disease-associated sequence variants. Eight of 22 (36.4%) of the mitochondrial DNA mutations were in complex I mitochondrial genes.
Conclusion:Massively parallel sequencing using the Ion Torrent Personal Genome Machine with confirmation by Sanger sequencing detected a pathogenic mitochondrial DNA mutation in 50% of the primary open-angle glaucoma cohort. Our findings support the emerging concept that mitochondrial dysfunction results in the development of glaucoma and, more specifically, that complex I defects play a significant role in primary open-angle glaucoma pathogenesis.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Design patterns percolating to parallel programming framework implementation

Structured parallel programming is recognised as a viable and effective means of tackling parallel programming problems. Recently, a set of simple and powerful parallel building blocks RISC pb²l) has been proposed to support modelling and implementation of parallel frameworks. In this work we demonstrate how that same parallel building block set may be used to model both general purpose parallel programming abstractions, not usually listed in classical skeleton sets, and more specialized domain specific parallel patterns. We show how an implementation of RISC pb² l can be realised via the FastFlow framework and present experimental evidence of the feasibility and efficiency of the approach.

Cost-Directed Refactoring for Parallel Erlang Programs

This paper presents a new programming methodology for introducing and tuning parallelism in Erlang programs, using source-level code refactoring from sequential source programs to parallel programs written using our skeleton library, Skel. High-level cost models allow us to predict with reasonable accuracy the parallel performance of the refactored program, enabling programmers to make informed decisions about which refactorings to apply. Using our approach, we demonstrate easily obtainable, significant and scalable speedups of up to 21 on a 24-core machine over the sequential code.

Autonomic scheduling of tasks from data parallel patterns to CPU/GPU core mixes

We propose a methodology for optimizing the execution of data parallel (sub-)tasks on CPU and GPU cores of the same heterogeneous architecture. The methodology is based on two main components: i) an analytical performance model for scheduling tasks among CPU and GPU cores, such that the global execution time of the overall data parallel pattern is optimized; and ii) an autonomic module which uses the analytical performance model to implement the data parallel computations in a completely autonomic way, requiring no programmer intervention to optimize the computation across CPU and GPU cores. The analytical performance model uses a small set of simple parameters to devise a partitioning-between CPU and GPU cores-of the tasks derived from structured data parallel patterns/algorithmic skeletons. The model takes into account both hardware related and application dependent parameters. It computes the percentage of tasks to be executed on CPU and GPU cores such that both kinds of cores are exploited and performance figures are optimized. The autonomic module, implemented in FastFlow, executes a generic map (reduce) data parallel pattern scheduling part of the tasks to the GPU and part to CPU cores so as to achieve optimal execution time. Experimental results on state-of-the-art CPU/GPU architectures are shown that assess both performance model properties and autonomic module effectiveness. © 2013 IEEE.

Influence of embedded length on strength of BFRP rods bonded parallel to the grain in low grade timber by pullout-bending tests

Bonded-in rod connections in timber possess many desirable attributes in terms of efficiency, manufacture, performance, aesthetics and cost. In recent years research has been conducted on such connections using fibre reinforced polymers (FRPs) as an alternative to steel. This research programme investigates the pull-out capacity of Basalt FRP rods bonded-in in low grade Irish Sitka Spruce. Embedded length is thought to be the most influential variable contributing to pull- out capacity of bonded-in rods after rod diameter. Previous work has established an optimum embedded length of 15 times the hole diameter. However, this work only considered the effects of axial stress on the bond using a pull-compression testing system which may have given an artificially high pull out capacity as bending effects were neglected. A hinge system was utilised that allows the effects of bending force to be taken in to consideration along with axial forces in a pull-out test. This paper describes an experimental programme where such pull-bending tests were carried out on samples constructed of 12mm diameter BFRP bars with a 2mm glueline thickness and embedded lengths between 80mm and 280mm bonded-in to low-grade timber with an epoxy resin. Nine repetitions of each were tested. A clear increase in pull-out strength was found with increasing embedded length.

Robust visual tracking using structurally random projection and weighted least squares

Sparse representation based visual tracking approaches have attracted increasing interests in the community in recent years. The main idea is to linearly represent each target candidate using a set of target and trivial templates while imposing a sparsity constraint onto the representation coefficients. After we obtain the coefficients using L1-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. In spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximised. In addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. In this paper, we propose a real-time visual tracking method based on structurally random projection and weighted least squares techniques. In particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. To handle appearance variations over time, we relax the sparsity constraint using a weighed least squares (WLS) method to obtain the representation coefficients. To further reduce the computational complexity, structurally random projection is used to reduce the dimensionality of the feature space while preserving the pairwise distances between the data points in the feature space. Experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.