Hitherto, the low electrochemical stability of the catalyst is one of the big issues hindering the commercial application of proton exchange membrane fuel cells (PEMFCs) . In this work, more stable support materials b...
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Hitherto, the low electrochemical stability of the catalyst is one of the big issues hindering the commercial application of proton exchange membrane fuel cells (PEMFCs) . In this work, more stable support materials based on functionalized graphene nanosheets (GNS), porous GNS, heteroatom doped GNS, and alternative GNS composites including GNS/nano-carbon (or nano-ceramics) sandwiches, nanoceramic wedged GNS, and core-shell graphene and amorphous carbon composites are prepared and applied in catalysts towards oxygen reduction reaction (ORR). based on the idea of bifunction of GNS to Pt catalysts, highly active and stable Pt/reduced graphene oxide (RGO) catalysts are developed by tuning the O/C atom ratio of RGO supports where the optimized O/C atom ratio of 0.14 is determined. Meantime, both perfluorosulfonic acid (PFSA) functionalized GNS and sulfonic acid group-grafted RGO supported Pt catalysts show a higher catalytic activity and a lower loss rate of electrochemical active area (ECA) in comparison withthat of the plain Pt/GNS and conventional Pt/C catalysts. In addition, the N-doped RGO supported Pt catalyst (Pt/NRGO) is synthesized using a lyophilisation-assisted N-doping method, revealing a higher catalytic activity and a lower ECA loss of the Pt/NRGO catalyst to compare withthat of the Pt/GO and Pt/C catalysts. In addition, to tackle the stacking issues of GNS which leads to the low mass transport property, the porous GNS are synthesized. Besides, we also describe a new strategy to synthesize GNS hybrids including GNS/nano-carbon (nano-creamics) sandwiches and nano-ceramic wedged GNS architectures. these unique architectures with highly dispersed Pt NPs exhibit much high catalytic activities towards ORR and an excellent electrochemical stability. At last, a new graphene @ amorphous carbon core-shell material also shows an excellent electrochemical property. this work was supported financially by the National Natural Science Foundation of China (NSFC) (No. 5
Complex networks are a technique for the modeling and analysis of large data sets in many scientific and engineering disciplines. Due to their excessive size conventional algorithms and single core processors struggle...
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ISBN:
(纸本)9781479904945;9781479904938
Complex networks are a technique for the modeling and analysis of large data sets in many scientific and engineering disciplines. Due to their excessive size conventional algorithms and single core processors struggle withthe efficient processing of such networks. Employing multi-core graphic processing units (GPUs) could provide sufficient processing power for the analysis of such networks. However, commonly designed algorithms cannot exploit these massively parallelprocessing power for the analysis of such networks. In this paper, we present the Multi Layer Network Decomposition (MLND) approach which provides a general approach for parallel network analysis using multi-core processors via efficient partitioning and mapping of networks onto GPU architectures. Evaluation using a 336 core GPU graphic card demonstrated a 16x speed-up in complex network analysis relative to a CPU based approach.
this paper presents Fast Fourier Transform (FFT) benchmark results to measure and compare the performance of various DSP and Intel processors for underwater signal processing applications. this paper aims to show perf...
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ISBN:
(纸本)9781467344265;9781467344258
this paper presents Fast Fourier Transform (FFT) benchmark results to measure and compare the performance of various DSP and Intel processors for underwater signal processing applications. this paper aims to show performance enhancement in Intel processors as compared to DSP processors by using parallel programming for implementing signal processing functions in real time. this paper provides results that show a significant decrease in FFT execution time on an Intel based Multicore processor using parallel programming. therefore comparative analysis among different processor architectures presented in this paper will help the system designers in selecting an optimal processor for underwater signal processing applications.
As CPU technology trend is strongly moving towards multi-core architectures, HEVC tried to embrace the parallelprocessing trend to possible extent. Hence, HEVC exploits some of the parallelprocessing capabilities li...
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ISBN:
(纸本)9789897581298
As CPU technology trend is strongly moving towards multi-core architectures, HEVC tried to embrace the parallelprocessing trend to possible extent. Hence, HEVC exploits some of the parallelprocessing capabilities like tiles, slices and WPP at frame level (Sullivan et al., 2012). Although slices, tiles and WPP can be used to achieve parallelism, they might end-up degrading either visual quality or compression efficiency. To address this problem, this paper tries to summarize/exploit the possible parallelprocessing capabilities of HEVC at Coding Tree Block (CTB) level with insignificant compromise in video quality and compression.
Advanced SSDs employ a RAM-based write buffer to improve their write performance. the buffer intentionally delays write requests in order to reduce flash write traffic and reorders them to minimize the cost of garbage...
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ISBN:
(纸本)9781479961245
Advanced SSDs employ a RAM-based write buffer to improve their write performance. the buffer intentionally delays write requests in order to reduce flash write traffic and reorders them to minimize the cost of garbage collection. this work presents a novel buffer algorithm for page-mapping multichannel SSDs. We propose grouping temporally or spatially correlated buffer pages and writing these grouped buffer pages to the same flash block. this strategy dramatically increases the probability of bulk data invalidations in flash blocks. In multichannel architectures, channels are assigned to their own groups of buffer pages for writing, and so channel striping does not divide a group of correlated buffer pages into small pieces. We have conducted simulations and experiments using a SSD simulator and a real SSD platform, respectively. Our results show that our design greatly outperforms existing buffer algorithms.
Mixed signal SoC has always played an important role in long haul, high capacity fiber optic transmission systems. In early days of 10Gbit NRZ transmission, the functions were simple, but implementing decision device,...
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Mixed signal SoC has always played an important role in long haul, high capacity fiber optic transmission systems. In early days of 10Gbit NRZ transmission, the functions were simple, but implementing decision device, clock recovery and de-multiplexing functions at such high data rate was challenging at the time. In the span of 15 years, the single channel data rate and network capacities have increased 20x, owing to a fundamental change in the way data is modulated and demodulated over the fiber optic channel. Intensity modulation and direct detection has given way to coherent detection and advanced modulation formats such as Polarization-Multiplexed QPSK and higher order QAM. Today, commercial state-of-art single channel data rate have reached 100Gbit/s. 400Gbit/s data rates are being attempted in the laboratory. these astounding achievements are not possible without the parallel advancements in CMOS technology. High speed A/D and D/A converters implemented in CMOS side-by-side with massively parallel, highly dense digital circuits is a key enabler in delivering the network capacities demanded by network operators and consumers. In this tutorial, we first review the challenges of the fiber optic channel, and introduce the necessary digital signal processing functions that need to be implemented in the SOC. We provide some example implementations of clock recovery, carrier recovery and equalization algorithms. We will also review forward error correction methods adopted in the most state-of-art designs. We conclude by discussing the challenges in designing next generation transceiver ASICs.
this paper proposes a novel strategy using parallel optimization computations for nonlinear moving horizon state estimation, and parameter identification problems of dynamic systems. the parallelization is based on th...
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ISBN:
(纸本)9781467352000;9781467351980
this paper proposes a novel strategy using parallel optimization computations for nonlinear moving horizon state estimation, and parameter identification problems of dynamic systems. the parallelization is based on the multi-point derivative-based Gauss-Newton search, as one of the most efficient algorithms for the nonlinear least-square problems. A numerical experiment is performed to demonstrate the parallel computations withthe comparison to sequential computations.
Image processing and computer vision applications are used intensively in several domains in particular multimedia and medicine. the main challenge in developing such applications is how to guarantee both high accurac...
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Image processing and computer vision applications are used intensively in several domains in particular multimedia and medicine. the main challenge in developing such applications is how to guarantee both high accuracy and low execution time. Accordingly, we observe two research directions: the first focuses on improving the algorithms and the second focuses on designing fast hardware platforms. In this paper, we propose an efficient parallel implementation of an accurate extended Canny edge detection algorithm suitable for medical applications on embedded many-core platform. the proposed implementation is running at a frame rate of 10 frames/s for image size of 512×512 with high accurate and smooth line edges.
the desire to build a computer that operates in the same manner as our brains is as old as the computer itself. Although computer engineering has made great strides in hardware performance as a result of Dennard scali...
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ISBN:
(纸本)9781450323055
the desire to build a computer that operates in the same manner as our brains is as old as the computer itself. Although computer engineering has made great strides in hardware performance as a result of Dennard scaling, and even great advances in 'brain like' computation, the field still struggles to move beyond sequential, analytical computing architectures. Neuromorphic systems are being developed to transcend the barriers imposed by silicon power consumption, develop new algorithmsthat help machines achieve cognitive behaviors, and both exploit and enable further research in neuroscience. In this talk I will discuss a system im-plementing spiking neural networks. these systems hold the promise of an architecture that is event based, broad and shallow, and thus more power efficient than conventional computing solu-tions. this new approach to computation based on modeling the brain and its simple but highly connected units presents a host of new challenges. Hardware faces tradeoffs such as density or lower power at the cost of high interconnection overhead. Consequently, software systems must face choices about new language design. Highly distributed hardware systems require complex place and route algorithms to distribute the execution of the neural network across a large number of highly interconnected processing units. Finally, the overall design, simulation and testing process has to be entirely reimagined. We discuss these issues in the context of the Zeroth processor and how this approach compares to other neuromorphic systems that are becoming available.
the parallelization of numerical simulation algorithms, i.e., their adaptation to parallelprocessingarchitectures, is an aim to reach in order to hinder exorbitant execution times. the parallelism has been imposed a...
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the parallelization of numerical simulation algorithms, i.e., their adaptation to parallelprocessingarchitectures, is an aim to reach in order to hinder exorbitant execution times. the parallelism has been imposed at the level of processor architectures and graphics cards are now used for general-purpose calculation, also known as "General-Purpose computation on Graphics processing Unit (GPGPU)". the clear benefit is the excellent performance over price ratio. Besides hiding the low level programming, software engineering leads to a faster and more secure application development. this paper presents the real interest of using GPU processors to increase performance of larger problems which concern electrical machines simulation. Indeed, we show that our auto-generated code applied to several models allows achieving speedups of the order of 10 x.
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