Two-stage iterations based on composite splittings for rectangular linear systems

In this paper, we introduce a two-stage method to solve rectangular linear systems that exhibits faster convergence than typical stationary iterative methods. Under suitable conditions, we prove convergence of the new method. The number of outer iterations can be reduced by using a few significant number of inner iterations for efficient computations. Further, we perform a comparison analysis, and establish that a higher number of inner iterations ensures a smaller spectral radius of the global iteration matrix. We also discuss the uniqueness of a proper splitting, and illustrate different comparison theorems for different subclasses of proper splittings.     

Bridging fault detection in cluster based FPGA by using Muller C element

In this work, we propose a testing technique for detecting single stuck-at and bridging faults in the interconnects of the cluster based FPGA. The presence of the feedback-bridging fault, race and glitch poses major challenges to the detection of the fault. The feedback bridging fault has a high ingredient of delay dependent properties due to the variation of the feedback path delay. So we have exploited the concept of asynchronous logic in order to detect the fault. We configure the block under test (BUT) with a pseudo delay independent asynchronous element known as Muller C element. The novelty of this scheme lies in the fact that, it can detect the stuck-at and bridging fault including the feedback bridging fault by a single test configuration. The Xilinx Jbits 3.0 API (Application Program Interface) is used to implement the BISTER (Built-in-self-tester) structure in the FPGA. In comparison to the traditional FPGA development tool (ISE), ‘Jbits’ gives more controllability for which the partial run time reconfiguration of the FPGA is easily achieved.     

Simulation and experimental investigation of a ballistic compression soft recovery system

Journal of Mechanical Science and Technology - A soft recovery system is used to arrest a supersonic object over a limited distance in a controlled manner. This may be achieved through ballistic...     

Efficient exponential compact higher order difference scheme for convection dominated problems

Present work is the development of a finite difference scheme based on Richardson extrapolation technique. It gives an exponential compact higher order scheme (ECHOS) for two-dimensional linear convection-diffusion equations (CDE). It uses a compact nine point stencil, over which the governing equations are discretized for both fine and coarse grids. The resulting algebraic systems are solved using a line iterative approach with alternate direction implicit (ADI) procedure. Combining the solutions over fine and coarse grids, initially a sixth order solution over coarse grid points is obtained. The resultant solution is then extended to finer grid by interpolation derived from the difference operator. The convergence of the iterative procedure is guaranteed as the coefficient matrix of the developed scheme satisfies the conditions required to be monotone. The higher order accuracy and better rate of convergence of the developed algorithm have been demonstrated by solving numerous model problems.     

A mathematical framework for design discovery from multi-threaded applications using neural sequence solvers

Comprehending existing multi-threaded applications effectively is a challenge without proper assistance. Research has been proposed to mine programs to extract aspects of high-level design but not much to reverse-engineer the concurrent design from multi-threaded applications. To address the same, we develop a generic mathematical model to interpret run-time non-deterministic events and encode functional as well as thread-specific behaviour in form of quantifiable features, which can be fitted into a standard solver for automated inference of design aspects from multi-threaded applications. We build a tool Dcube based on the mathematical model and use various classifiers of a machine learning framework to infer design aspects related to concurrency and resource management. We collect a dataset of 480 projects from Github, CodeProject and Stack Overflow and 3 benchmark suites—CDAC Pthreads, Open POSIX Test Suites and PARSEC 3.0 and achieve an accuracy score of around 93.71% for all the design choices.