this paper introduces a low complexity frame-based object detection algorithm for H.264/AVC video streams. the method solely parses and evaluates H.264/AVC macroblock types extracted from the video stream, which requi...
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ISBN:
(纸本)9789898565471
this paper introduces a low complexity frame-based object detection algorithm for H.264/AVC video streams. the method solely parses and evaluates H.264/AVC macroblock types extracted from the video stream, which requires only partial decoding. Different macroblock types indicate different properties of the video content. this fact is used to segment a scene in fore- and background or, more precisely, to detect moving objects within the scene. the main advantage of this algorithm is that it is most suitable for massively parallelprocessing, because it is very fast and combinable with several other pre- and post-processingalgorithms, without decreasing their performance. the actual algorithm is able to process about 3600 frames per second of video streams in CIF resolution, measured on an Intel® Core™ i5-2520M CPU @ 2.5 GHz with 4 GB RAM.
In this work, we present our implementation of a three-dimensional 5th order finite-difference weighted essentially non-oscillatory (WENO) scheme in double precision on CPU/GPU clusters, which targets on large-scale c...
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GPU architectures tend to be increasingly important in multi-core era nowadays due to their formidable computational horsepower. Withthe assistant of effective programming paradigms as CUDA, GPUs are widely adopted t...
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Melody extraction from polyphonic music is a valuable but difficult problem in music information retrieval. the extraction incurs a large computational cost that limits its application. Growing processing cores and in...
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ISBN:
(纸本)9783642408199;9783642408205
Melody extraction from polyphonic music is a valuable but difficult problem in music information retrieval. the extraction incurs a large computational cost that limits its application. Growing processing cores and increased bandwidth have made GPU an ideal candidate for the development of fine-grained parallelalgorithms. In this paper, we present a parallel approach for salience-based melody extraction from polyphonic music using CUDA. For 21 seconds of polyphonic clip, the extraction time is cut from 3 seconds to 33 milliseconds using NVIDIA GeForce GTX 480 which is up to 100 times faster. the increased performance allows the melody extraction to be carried out for real-time applications. Furthermore, the evaluation of the extraction on huge datasets is also possible. We give insight into how such significant speed gains are made and encourage the development and adoption of GPU in music information retrieval field.
the Internetware" paradigm is fundamentally changing the traditional way of software development. More and more software projects are developed, maintained and shared on the Internet. However, a large quantity of...
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ISBN:
(纸本)9781450323697
the Internetware" paradigm is fundamentally changing the traditional way of software development. More and more software projects are developed, maintained and shared on the Internet. However, a large quantity of heterogeneous software resources have not been organized in a reasonable and efficient way. Software feature is an ideal material to characterize software resources. the effectiveness of feature- related tasks will be greatly improved, if a multi-grained feature repository is available. In this paper, we propose a novel approach for organizing, analyzing and recommend- ing software features. Firstly, we construct a Hierarchical rEpository of Software feAture (HESA). then, we mine the hidden affnities among the features and recommend relevant and high-quality features to stakeholders based on HESA. Finally, we conduct a user study to evaluate our approach quantitatively. the results show that HESA can organize software features in a more reasonable way compared to the traditional and the state-of-the-art approaches. the result of feature recommendation is effective and interesting. Categories and Subject Descriptors D.2.9 [Software Engineering]: Mining Software Reposi- tory;H.3.3 [Information Storage and retrieval]: Fea- ture Model, Clustering, Query formulation General Terms algorithms, Human Factors.
the paper addresses the problem of pose-invariant recognition of faces via an MRF matching model. Unlike previous costly matching approaches, the proposed algorithm employs effective techniques to reduce the MRF infer...
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ISBN:
(纸本)9781479905270
the paper addresses the problem of pose-invariant recognition of faces via an MRF matching model. Unlike previous costly matching approaches, the proposed algorithm employs effective techniques to reduce the MRF inference time. To this end, processing is done in a parallel fashion on a GPU employing a dual decomposition framework. the optimisation is further accelerated taking a multi-resolution approach based on the Renormalisation Group theory (RGT) along with efficient methods for message passing and the incremental sub gradient approach. For the graph construction, Daisy features are used as node attributes exhibiting high cross-pose invariance, while high discriminatory capability in the classification stage is obtained via multi-scale LBP histograms. the experimental evaluation of the method is performed via extensive tests on the databases of XM2VTS, FERET and LFW in verification, identification and the unseen pair-matching paradigms. the proposed approach achieves state-of-the-art performance in pose-invariant recognition of faces and performs as well or better than the existing methods in the unconstrained settings of the challenging LFW database using a single feature for classification.
Mobile ad hoc networks are infrastructure less communication networks that are spontaneously created by a number of mobile devices. Due to the highly fluctuating topology of such networks, finding the optimal configur...
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A typical software product is developed so that it can fulfill the specific needs (problem that needs to be solved) within a given business domain, based on a proper product design context. Although, assuring an align...
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ISBN:
(纸本)9783642357022;9783642357015
A typical software product is developed so that it can fulfill the specific needs (problem that needs to be solved) within a given business domain, based on a proper product design context. Although, assuring an alignment between the technological developments withthe business domain is a demanding task. Withthe purpose of clarifying the relations between the models that support the business and the software representations, we present in this paper a V-Model based approach to align the business domain requirements withthe context for product design. this V-Model encompasses the models that support the initial definition of the project goals, expressed through organizational configurations, and the analysis and design of models that result in a process-level perspective of the system's logical architecture. Our approach adopts a process-level perspective withthe intent to create context for product-level requirement elicitation. We present a case study as a demonstration and assessment of the applicability of our approach. Since the case study is extremely complex, we illustrate how to use the ARID method to evaluate the obtained process-level architecture.
Recently, we have witnessed that cloud providers start to offer heterogeneous computing environments. there have been wide interests in both clusters and cloud of adopting graphics processors (GPUs) as accelerators fo...
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Recently, we have witnessed that cloud providers start to offer heterogeneous computing environments. there have been wide interests in both clusters and cloud of adopting graphics processors (GPUs) as accelerators for various applications. On the other hand, large-scale graph processing is important for many data-intensive applications in the cloud. In this paper, we propose to leverage GPUs to accelerate large-scale graph processing in the cloud. Specifically, we develop an in-memory graph processing engine G2 withthree non-trivial GPU-specific optimizations. Firstly, we adopt fine-grained APIs to take advantage of the massive thread parallelism of the GPU. Secondly, G2 embraces a graph partition based approach for load balancing on heterogeneous CPU/GPU architectures. thirdly, a runtime system is developed to perform transparent memory management on the GPU, and to perform scheduling for an improved throughput of concurrent kernel executions from graph tasks. We have conducted experiments on an Amazon EC2 virtual cluster of eight nodes. Our preliminary results demonstrate that 1) GPU is a viable accelerator for cloud-based graph processing, and 2) the proposed optimizations improve the performance of GPU-based graph processing engine. We further present the lessons learnt and open problems towards large-scale graph processing with GPU accelerations.
Multiprocessing modular exponentiation has a variety of uses, including cryptography, prime testing and computational number theory. It is also a very costly operation to compute. GPU parallelism can be used to accele...
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