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Table 4: PDFS Seek Times

in PODOS - The Design and Implementation of a Performance Oriented Linux Cluster
by Sudharshan Vazhkudai, Jeelani Syed, Tobin Maginnis 2002
"... In PAGE 14: ... When clustering activities tend to be intense these are considerable performance gains, which would improve overall system throughput. Listed below ( Table4 ) are the average times, in milliseconds, for the system calls: open, read and close. These were computed by... ..."
Cited by 2

Table 3: Procedure Store PDFs

in Exact Grading of Multiple Path Delay Faults
by Saravanan Padmanaban, Spyros Tragoudas

Table 4: Synthesis results for Discrete Cosine Transform example.

in
by unknown authors

Table 4: Possible pdfs of execution time for addition

in Probabilistic Retiming: A Circuit Optimization Technique
by Sissades Tongsima, Chantana Chantrapornchai, Nelson Luiz Passos, Edwin H. -m. Sha 1996
Cited by 1

Table 5: Possible pdfs of execution time for multiplication

in Probabilistic Retiming: A Circuit Optimization Technique
by Sissades Tongsima, Chantana Chantrapornchai, Nelson Luiz Passos, Edwin H. -m. Sha 1996
Cited by 1

Table 1 DiVTerent PDFs

in
by unknown authors 2001

Table 2: Procedure to Extract PDFs tested by VNR tests

in Non-Enumerative Path Delay Fault Diagnosis
by Saravanan Padmanaban Spyros, Spyros Tragoudas 2003
"... In PAGE 5: ... The set of PDFs with VNR tests are identified using the Procedure Extract VNRPDF. The Table2 illustrates the procedure to extract PDFs tested by VNR tests. Test CC BD does not test any PDF non-robustly.... ..."
Cited by 2

Table 3.2: Set of keypoints kernels summary table. m is the maximum cardinality of the two sets to be compared. D is the diameter of the feature space. Remarks: i) Match kernel: the geometry constraints used by Wallraven et al. [54] are absolute positions, destroying desired invariances of local image features, ii) Intermediate match kernel: p is the number of virtual features in V , the co-occurrence properties depend on the choice of V , iii) Bags-of- keypoints: p is the number of clusters used, unequal cardinalities are not well handled because the histogram vector norm depends on the set cardinality, iv) GMM and PDFs, complexity would be O(1) but this does not include MAP-EM fitting for each sample and only works for GMMs with one component, Kullback-Leibler divergence is not necessarily positive-definite, but the second tested Bhattacharyya kernel is.

in Technische Universität Berlin Object Classification using Local Image Features
by Sebastian Nowozin, Supervisor Marc Jäger 2006

Table 1. Impulsive noise models; envelope PDFs and LO nonlinear filters.

in APPROVED FOR PUBLIC RELEASE Sperm Whale Impulse Noise and Incoherent
by Derek Bertilone, Damien Killeen 2002
"... In PAGE 13: ...onparametric filters. These require no explicit knowledge of the noise PDF. An example is the hardlimiter narrowband correlator (HNC) filter ([8, 10]) which is widely used in impulsive environments; y y g 1 ) ( = (4) The parametric version of the processor requires a choice of noise model, and estimation of the model parameters from the received data. The LO filters for several impulsive noise models are given in Table1 . To apply the processor, we read-in a segment of time-series data, estimate the model parameters from that segment of data,9 and then input these parameter estimates into the nonlinear filter to tune the processor.... ..."

Table 4.1: Results: Experiments performed on industrial designs Circuit #gates #M-pdfs #UNT M-pdfs Time (seconds)

in Static Learning for Problems in VLSI Test and Verification
by Manan Syal, Manan Syal 2005
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