Parallel Robot

Parallel robot (PR) is a mechanical system that utilized multiple computer-controlled limbs to support one common platform or end effector. Comparing to a serial robot, a PR generally has higher precision and dynamic performance and, therefore, can be applied to many applications. The PR research has attracted a lot of attention in the last three decades, but there are still many challenging issues to be solved before achieving PRs’ full potential. This chapter introduces the state-of-the-art PRs in the aspects of synthesis, design, analysis, and control. The future directions will also be discussed at the end.

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.

Electron-Impact Excitation of Fe-peak Ions for Astrophysical Applications

This paper discusses one of the major outstanding problems in atomic collision physics, namely the accurate theoretical treatment of electron scattering from open d-shell systems, and explores how this issue has been addressed over recent years with the development of the new parallel R-matrix suite of codes. It focuses on one code in particular - the new parallel R-matrix package PRMAT, which has recently been extended to account for relativistic fine-structure effects. This program facilitates the determination of accurate electron-impact excitation rates for complex open 3d-shell systems including the astrophysically important Fe-peak ions such as Ni II, Fe II and Fe III. Results are presented for collision strengths and Maxwellian averaged effective collision strengths for the optically forbidden fine-structure transitions of Ni II. To our knowledge this is the most extensive calculation completed to date for this ion.

Ni II: A comprehensive atomic data study with emphasis on astrophysical applications

In this work we report both the calculation of atomic collision data for the electron-impact excitation of Ni II using parallel R-matrix codes and the computation of atomic transition data using the general atomic structure package CIV3.

A parallel pattern for iterative stencil + reduce

We advocate the Loop-of-stencil-reduce pattern as a means of simplifying the implementation of data-parallel programs on heterogeneous multi-core platforms. Loop-of-stencil-reduce is general enough to subsume map, reduce, map-reduce, stencil, stencil-reduce, and, crucially, their usage in a loop in both data-parallel and streaming applications, or a combination of both. The pattern makes it possible to deploy a single stencil computation kernel on different GPUs. We discuss the implementation of Loop-of-stencil-reduce in FastFlow, a framework for the implementation of applications based on the parallel patterns. Experiments are presented to illustrate the use of Loop-of-stencil-reduce in developing data-parallel kernels running on heterogeneous systems.