The Principle Component Analysis (PCA) and its variations are the most popular approach for features clustering, which is mostly implemented for face recognition. The optimum projection matrix of the PCA is typically ...
The Principle Component Analysis (PCA) and its variations are the most popular approach for features clustering, which is mostly implemented for face recognition. The optimum projection matrix of the PCA is typically obtained by eigenanalysis of global covariance matrix. However, the projection data using the PCA are lack of discriminatory power. This problem is caused by removing the null space of data scatter that contains much discriminant information. To solve this problem, we present alternative strategy to the PCA called alternative PCA, which obtains the optimum projection matrix from within class scatter instead of global covariance matrix. This algorithm not only provides better features clustering than that of common PCA (CPCA) but also can overcome the retraining problem of the CPCA. In this paper, this algorithm is applied for face recognition with the holistic features of face image, which has compact size and powerful energy compactness as dimensional reduction of the raw face image. From the experimental results, the proposed method provides better performance for both recognition rate and accuracy parameters than those of CPCA and its variations when the tests were carried out using data from several databases such as ITS-LAB., INDIA, ORL, and FERET.
Multi-layer structures of single-walled carbon nanotubes mixtures with epoxy resin at different weight percentages are studied for maximizing the absorption of incident waves over X-Band. A short survey is also perfor...
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This Letter presents a search for new resonances with mass larger than 250 GeV, decaying to a Z boson and a photon. The dataset consists of an integrated luminosity of 3.2 fb−1 of pp collisions collected at s=13 TeV w...
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Several problems in computational biology require the all-against-all pairwise comparisons of tens of thousands of individual biological sequences. Each such comparison can be performed with the well-known Needleman-W...
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ISBN:
(纸本)9781479904945
Several problems in computational biology require the all-against-all pairwise comparisons of tens of thousands of individual biological sequences. Each such comparison can be performed with the well-known Needleman-Wunsch alignment algorithm. However, with the rapid growth of biological databases, performing all possible comparisons with this algorithm in serial becomes extremely time-consuming. The massive computational power of graphics processing units (GPUs) makes them an appealing choice for accelerating these computations. As such, CPU-GPU clusters can enable all-against-all comparisons on large datasets. In this paper, we present a hybrid MPI-CUDA framework for computing multiple pairwise sequence alignments on CPU-GPU clusters. Our design targets both homogeneous and heterogeneous clusters with nodes characterized by different hardware and computing capabilities. Our framework consists of the following components: a cluster-level dispatcher, a set of node-level dispatchers, and a set of CPU- and GPU-workers. The cluster-level dispatcher progressively distributes work to the compute nodes and aggregates the results. The node-level dispatchers distribute alignment tasks to available CPUs and GPUs and perform dual-buffering to hide data transfers between CPU and GPU. CPU- and GPU-workers perform pairwise sequence alignments using the Needleman-Wunsch algorithm. We propose and evaluate three designs for these GPU workers, all of them outperforming the existing open-source implementation from the Rodinia Benchmark Suite.
The emergence of Multi-Agent systems as a software paradigm that most suitably fits all types of problems and architectures is already experiencing significant revisions. A more consistent approach on agent programmin...
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Byzantine music is typically monophonic and is characterized by (i) prolonged music phrases and (ii) Byzantine scales that often contain intervals smaller than the Western semitone. As happens with most religious musi...
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ISBN:
(纸本)9781629935270
Byzantine music is typically monophonic and is characterized by (i) prolonged music phrases and (ii) Byzantine scales that often contain intervals smaller than the Western semitone. As happens with most religious music genres, reverberation is a key element of Byzantine music. Byzantine churches/cathedrals are usually characterized by particularly diffuse fields and very long Reverberation Time (KT) values. In the first part of this work, the perceptual effect of long reverberation on Byzantine music excerpts is investigated. Then, a case where Byzantine music is recorded in non-ideal acoustic conditions is considered. In such scenarios, a sound engineer might require to add artificial reverb on the recordings. Here it is suggested that the step of adding extra reverberation can be preceded by a dereverberation processing to suppress the originally recorded non ideal reverberation. Therefore, in the second part of the paper a subjective test is presented that evaluates the above sound engineering scenario.
We design, implement, and evaluate GPU-based algorithms for the maximum cardinality matching problem in bipartite graphs. Such algorithms have a variety of applications in computer science, scientific computing, bioin...
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A low computational power method is proposed for detecting actuators/sensors faults. Typical model-based fault detection units for multiple sensor faults, require a bank of observers (these can be either conventional ...
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ISBN:
(纸本)9781479909964
A low computational power method is proposed for detecting actuators/sensors faults. Typical model-based fault detection units for multiple sensor faults, require a bank of observers (these can be either conventional observers of artificial intelligence based). The proposed control scheme uses an artificial intelligence approach for the development of the fault detection unit abbreviated as 'iFD'. In contrast with the bank-of-estimators approach, the proposed iFD unit employs a single estimator for multiple sensor fault detection. The efficacy of the scheme is illustrated on an Electromagnetic Suspension system example with a number of sensor fault scenaria.
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