Partitioning and mapping in embedded multiprocessor architectures in the presence of constraints

The paper focuses on the problem of partitioning and mapping parallel programs onto heterogeneous embedded multiprocessor architectures for real-time applications. Such applications present unique constraints and challenges. In addition to heterogeneity, the proposed partitioning and mapping algorithms satisfy memory, task throughput, task placement, intertask communication bandwidth, and co-location constraints. They do so for architectures that utilize circuit-switched (rather than packet-switched) interprocessor communication and optimize latency and throughput in addition to load-balancing. Finally, these mapping algorithms make use of knowledge of the local scheduling discipline to accommodate real-time scheduling constraints. Our focus is on unstructured parallel programs that fall into one of two classes: (i) the class of computations characteristic of control applications in a real-time environment where tasks execute concurrently, periodically exchanging information, and (ii) pipelined computation graphs found in sensor data processing applications. The algorithms are implemented in a set of tools that operate with commercial CASE tools at one end, and present an interface to multiprocessor simulators at the other end. Collectively, the algorithms form a significant component of an interactive design environment for the development and mapping of real-time embedded parallel programs. The paper describes the algorithms, the encapsulating toolset, and presents an example of their application to an existing embedded application—an Autonomous Underwater Vehicle application.     

Intramuscular EMG classifier for detecting myopathy and neuropathy

This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one-dimensional ($1$-D) signals. In this approach, the $1$-D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer-Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated $10$-fold cross-validation, CSLBP-Combined-Classifiers-BMMV (CSLBP-CC-BMMV) achieved an average classification accuracy of $92.80$%, $94.25$%, and $93.71$% for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP-CC-BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.     

Rheograms of filled polymer melts from melt-flow index

Polymers filled with extending fillers, such as calcium carbonate or talc, or with reinforcing fillers, such as glass fibers or mica, are increasingly being used in a number of applications. The addition of fillers to a polymer changes the melt rheological behavior of the polymer. A knowledge of the viscosity vs. shear-rate flow curves of the filled system at various temperatures and as a function of filler parameters (such as filler type, shape, and amount) is necessary for process design, optimization, and trouble shooting. The generation of such rheological data is ezpensive and cumbersome in view of the broad range of fillers and the large numbers of filler parameters. In the present article, a unifying approach is proposed that coalesces the flow curves of filled systems of a polymer at various temperatures into a master curve that is independent of the filler parameters. An effective methods is presented to estimate the rheograms of filled systems with the use of a master curve, characteristic of a genetic resin type, knowing the melt-flow index and glass-transition temperature of the system. Master curves have been reported for filled system of low-density polyethylene, high-density polyethylene, poly-propylene polystyrene, nylons, poly(ethylene terephthalate), and polycarbonate.     

Melt rheology of highly loaded ferrite-filled polymer composites

This paper presents new experimental data on the viscosity of ferrite-filled polymers at high levels of loading. The choice of matrix and the choice of filler and filler loading has direct relevance to commercially important systems of magnetic plastics and rubbers. The effect of shear rate, concentration, and temperature on the rheological behavior of such highly loaded systems has been discussed in detail. The differences in the relative viscosities between the filled and unfilled systems for various matrices have been explained on the basis of polymer filler affinity and filler-filler interaction.