Image composition schemes for sort-last polygon rendering on 2Dmesh multicomputers

In a sort-last polygon rendering system, the efficiency of image composition is very important for achieving fast rendering. In this paper, the implementation of a sort-last rendering system on a general purpose multicomputer system is described. A two-phase sort-last-full image composition scheme is described first, and then many variants of it are presented for 2D mesh message-passing multicomputers, such as the Intel Delta and Paragon. All the proposed schemes are analyzed and experimentally evaluated on Caltech's Intel Delta machine for our sort-last parallel polygon renderer. Experimental results show that sort-last-sparse strategies are better suited than sort-last-full schemes for software implementation on a general purpose multicomputer system. Further, interleaved composition regions perform better than coherent regions. In a large multicomputer system. Performance can be improved by carefully scheduling the tasks of rendering and communication. Using 512 processors to render our test scenes, the peak rendering rate achieved on a 282,144 triangle dataset is dose to 4.6 million triangles per second which is comparable to the speed of current state-of-the-art graphics workstations     

Formulation and in vitro evaluation of transdermal delivery of zidovudine—An anti‐HIV drug

This study was designed to develop matrix type of transdermal‐controlled delivery system for zidovudine using Eudragit L100 by varying the amounts of drug in addition to polyethylene glycol as a plasticizer and Tween 80® as a penetration enhancer. Transparent smooth and flexible films were characterized for weight, thickness uniformity, and drug content. Drug interactions with the polymer films were studied by differential scanning calorimetry, whereas X‐ray diffraction was used to understand the drug polymorphism in the films. The in vitro drug release experiments were performed in phosphate buffer using the Keshary‐Chien diffusion cell. Variations of drug release profiles were analyzed using the Ritger and Peppas empirical equation to describe the type of release mechanism. The exponent n values of the equation varied over the wide range of 0.75–2.23, suggesting non‐Fickian to super Case‐II type of diffusion transport. Statistical analyses of release data performed by the analysis of variance (ANOVA) method indicated significant differences within experimental measurements. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Communication and computation optimization of concurrent kernels using kernel coalesce on a GPU

General purpose computation on graphics processing unit (GPU) is rapidly entering into various scientific and engineering fields. Many applications are being ported onto GPUs for better performance. Various optimizations, frameworks, and tools are being developed for effective programming of GPU. As part of communication and computation optimizations for GPUs, this paper proposes and implements an optimization method called as kernel coalesce that further enhances GPU performance and also optimizes CPU to GPU communication time. With kernel coalesce methods, proposed in this paper, the kernel launch overheads are reduced by coalescing the concurrent kernels and data transfers are reduced incase of intermediate data generated and used among kernels. Computation optimization on a device (GPU) is performed by optimizing the number of blocks and threads launched by tuning it to the architecture. Block level kernel coalesce method resulted in prominent performance improvement on a device without the support for concurrent kernels. Thread level kernel coalesce method is better than block level kernel coalesce method when the design of a grid structure (i.e., number of blocks and threads) is not optimal to the device architecture that leads to underutilization of the device resources. Both the methods perform similar when the number of threads per block is approximately the same in different kernels, and the total number of threads across blocks fills the streaming multiprocessor (SM) capacity of the device. Thread multi‐clock cycle coalesce method can be chosen if the programmer wants to coalesce more than two concurrent kernels that together or individually exceed the thread capacity of the device. If the kernels have light weight thread computations, multi clock cycle kernel coalesce method gives better performance than thread and block level kernel coalesce methods. If the kernels to be coalesced are a combination of compute intensive and memory intensive kernels, warp interleaving gives higher device occupancy and improves the performance. Multi clock cycle kernel coalesce method for micro‐benchmark1 considered in this paper resulted in 10–40% and 80–92% improvement compared with separate kernel launch, without and with shared input and intermediate data among the kernels, respectively, on a Fermi architecture device, that is, GTX 470. A nearest neighbor (NN) kernel from Rodinia benchmark is coalesced to itself using thread level kernel coalesce method and warp interleaving giving 131.9% and 152.3% improvement compared with separate kernel launch and 39.5% and 36.8% improvement compared with block level kernel coalesce method, respectively.Copyright © 2013 John Wiley & Sons, Ltd.     

Quantitation of Multiple Sphingolipid Classes Using Normal and Reversed-Phase LC–ESI–MS/MS: Comparative Profiling of Two Cell Lines

Sphingolipids are an important class of compounds that regulate signal transduction and other vital cellular processes. Herein, we report sensitive normal and reversed phase LC–MS/MS methods for quantitation of multiple sphingolipid classes. In the normal-phase ESI/MS/MS method, a high content of organic solvents was utilized, which, although it included hexane, ethyl acetate, acetonitrile containing 2% methanol, 1–2% acetic acid, and 5 mM ammonium acetate, resulted in a very efficient electrospray ionization of the ceramides (Cers) and hexosylceramides (MHCers). Three normal-phase LC–MS/MS methods using segmented phases were developed to specifically target Cers, MHCers, or sphingomyelins (SMs). This segmentation scheme increases the number of data points acquired for a given analyte and enhances the sensitivity and specificity of the measurements. Nine separate reversed phase chromatography methods were developed for the three classes of compounds. These assays were used for comparing the levels of Cers, SMs, and MHCers from mouse embryonic fibroblast (pMEF) and human embryonic kidney (HEK293) cells. These findings were then compared with the reported data from RAW264.7 mouse macrophage cells, BHK21 hamster cells, and human plasma and serum samples. The analysis of cell lines, using both normal and reversed phase chromatography, revealed discrimination based on the type of chromatography chosen, while sphingolipid assays of samples containing different amounts of protein showed different results, even after normalizing for protein content. Also, LC/MS/MS profiles were provided for the classes and individual compounds so that they could be used as “molecular profiles” for class or individual sample analysis.     

A fault‐tolerant modular multilevel inverter topology

Multilevel inverter topologies have received an increasing importance for their modular structures with better integration of renewable energy sources and reduced filtering requirements. But reliability is compromised in the process of decreasing the number of switches to produce the maximum number of voltage levels. This paper elevates a single‐phase fault‐tolerant inverter topology, which is modular in structure. The proposed inverter is analyzed for open‐ or short‐circuit faults in sources and open‐circuit faults in switches. Sine Pulse Width Modulation with multicarriers technique is used to control the circuit. The carrier signals are reconfigured under fault conditions based on levels to be generated by bypassing the faulted switch or source. The circuit is simulated in MATLAB/SIMULINK, and experimental setup is developed to claim the fault tolerance of proposed inverter.     

Thermal bonding of polypropylene films and fibers

The thermal bonding behavior of different grades of polypropylene was studied in this research. Initial bonding studies were done with polypropylene films of different grades of polymer with varying morphology, and the studies were extended to polypropylene fibers. Polypropylene fibers manufactured with different cross‐sections, deniers, polymer melt‐flow rates, and different processing conditions were bonded under various conditions. The effect of film structure, properties, and bonding conditions on the bonding efficiency was studied by comparing the tensile strength of bonded films. Thermal bonding was carried for a range of temperatures covering poor, optimum, and over bonding. Changes taking place to the polymer in bond point and the original surface were analyzed using the SEM. Higher bond strength was observed in the vicinity of melting temperature and the strength reduced with a further increase in bonding temperature. The frequent failure point was observed at the bond edge crossover points where film undergoes maximum thickness transition. Lower pressure and shorter time were found to be appropriate for bonding. Films with lower orientation formed better bonds. The optimum bond strength and the optimum bonding temperature observed were different for different polymers. Fiber bond strength results were similar to the results observed in the case of films with respect to the bonding temperatures studied. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Arithmetizing Classes Around \textsf{NC} 1 and \textsf{L}

The parallel complexity class $\textsf{NC}$ ¹ has many equivalent models such as polynomial size formulae and bounded width branching programs. Caussinus et al. (J. Comput. Syst. Sci. 57:200–212, 1992) considered arithmetizations of two of these classes, $\textsf{\#NC}$ ¹ and $\textsf{\#BWBP}$ . We further this study to include arithmetization of other classes. In particular, we show that counting paths in branching programs over visibly pushdown automata is in $\textsf{FLogDCFL}$ , while counting proof-trees in logarithmic width formulae has the same power as $\textsf{\#NC}$ ¹. We also consider polynomial-degree restrictions of $\textsf{SC}$ ⁱ , denoted $\textsf{sSC}$ ⁱ , and show that the Boolean class $\textsf{sSC}$ ¹ is sandwiched between $\textsf{NC}$ ¹ and $\textsf{L}$ , whereas $\textsf{sSC}$ ⁰ equals $\textsf{NC}$ ¹. On the other hand, the arithmetic class $\textsf{\#sSC}$ ⁰ contains $\textsf{\#BWBP}$ and is contained in $\textsf{FL}$ , and $\textsf{\#sSC}$ ¹ contains $\textsf{\#NC}$ ¹ and is in $\textsf{SC}$ ². We also investigate some closure properties of the newly defined arithmetic classes.     

FFCAEs: An efficient feature fusion framework using cascaded autoencoders for the identification of gliomas

Intracranial tumors arise from constituents of the brain and its meninges. Glioblastoma (GBM) is the most common adult primary intracranial neoplasm and is categorized as high-grade astrocytoma according to the World Health Organization (WHO). The survival rate for 5 and 10 years after diagnosis is under 10%, contributing to its grave prognosis. Early detection of GBM enables early intervention, prognostication, and treatment monitoring. Computer-aided diagnostics (CAD) is a computerized process that helps to differentiate between GBM and low-grade gliomas (LGG), using the perceptible analysis of magnetic resonance (MR) of the brain. This study proposes a framework consisting of a feature fusion algorithm with cascaded autoencoders (CAEs), referred to as FFCAEs. Here we utilized two CAEs and extracted the relevant features from multiple CAEs. Inspired by the existing work on fusion algorithms, the obtained features are then fused by using a novel fusion algorithm. Finally, the resultant fused features are classified with the Softmax classifier to arrive at an average classification accuracy of 96.7%, which is 2.45% more than the previously best-performing model. The method is shown to be efficacious thus, it can be useful as a utility program for doctors.     

Hierarchical fuzzy-based Quality of Experience (QoE)-aware application placement in fog nodes

Fog computing or a fog network is a decentralized network placed in between data source and the cloud to minimize the network latency issues and thus support in-time service delivery, of Internet of Things (IoT) applications. However, placing computational tasks of IoT applications in fog infrastructure is a challenging task. State of the art focuses on quality of service and quality of experience (QoE) based application placement. In this article, we design hierarchical fuzzy based QoE-aware application placement strategy for mapping IoT applications with compatible instances in the fog network. The proposed method considers user application expectation parameters and metrics of available fog instances, and assigns the priority of applications using hierarchical fuzzy logic. The method later uses Hungarian maximization assignment algorithm to map applications with compatible instances. The simulation results of the proposed policy show better performance over the existing baseline algorithms in terms of resource gain (RG), processing time reduction ratio (PTRR), and similarly network relaxation ratio. When considering 10 applications in the fog network, our proposed method simulation results show 70.00%, 22.44%, 37.83% improvement in RG, and 28.46%, 37.5%, 23.07% improvement in PTRR, when compared with QoE-aware, randomized, FIFO algorithms, respectively.