8,172 research outputs found
Multiple bottlenecks sorting criterion at initial sequence in solving permutation flow shop scheduling problem
This paper proposes a heuristic that introduces the
application of bottleneck-based concept at the beginning of an initial sequence
determination with the objective of makespan minimization. Earlier studies
found that the scheduling activity become complicated when dealing with
machine, m greater than 2, known as non-deterministic polynomial-time
hardness (NP-hard). To date, the Nawaz-Enscore-Ham (NEH) algorithm is
still recognized as the best heuristic in solving makespan problem in
scheduling environment. Thus, this study treated the NEH heuristic as the
highest ranking and most suitable heuristic for evaluation purpose since it is
the best performing heuristic in makespan minimization. This study used the
bottleneck-based approach to identify the critical processing machine which
led to high completion time. In this study, an experiment involving machines
(m =4) and n-job (n = 6, 10, 15, 20) was simulated in Microsoft Excel Simple
Programming to solve the permutation flowshop scheduling problem. The
overall computational results demonstrated that the bottleneck machine M4
performed the best in minimizing the makespan for all data set of problems
Stack-run adaptive wavelet image compression
We report on the development of an adaptive wavelet image coder based on stack-run representation of the quantized coefficients. The coder works by selecting an optimal wavelet packet basis for the given image and encoding the quantization indices for significant coefficients and zero runs between coefficients using a 4-ary arithmetic coder. Due to the fact that our coder exploits the redundancies present within individual subbands, its addressing complexity is much lower than that of the wavelet zerotree coding algorithms. Experimental results show coding gains of up to 1:4dB over the benchmark wavelet coding algorithm
Synchrosqueezed Wave Packet Transforms and Diffeomorphism Based Spectral Analysis for 1D General Mode Decompositions
This paper develops new theory and algorithms for 1D general mode
decompositions. First, we introduce the 1D synchrosqueezed wave packet
transform and prove that it is able to estimate the instantaneous information
of well-separated modes from their superposition accurately. The
synchrosqueezed wave packet transform has a better resolution than the
synchrosqueezed wavelet transform in the time-frequency domain for separating
high frequency modes. Second, we present a new approach based on
diffeomorphisms for the spectral analysis of general shape functions. These two
methods lead to a framework for general mode decompositions under a weak
well-separation condition and a well different condition. Numerical examples of
synthetic and real data are provided to demonstrate the fruitful applications
of these methods.Comment: 39 page
The wave packet propagation using wavelets
It is demonstrated that the wavelets can be used to considerably speed up
simulations of the wave packet propagation in multiscale systems. Extremely
high efficiency is obtained in the representation of both bound and continuum
states. The new method is compared with the fast Fourier algorithm. Depending
on ratios of typical scales of a quantum system in question, the wavelet method
appears to be faster by a few orders of magnitude.Comment: Latex 7 pages, 3 colored figures (Fig1 postscript, Fig2,3 gif) in
files separate from the pape
A robust adaptive wavelet-based method for classification of meningioma histology images
Intra-class variability in the texture of samples is an important problem in the domain of histological image classification. This issue is inherent to the field due to the high complexity of histology image data. A technique that provides good results in one trial may fail in another when the test and training data are changed and therefore, the technique needs to be adapted for intra-class texture variation. In this paper, we present a novel wavelet based multiresolution analysis approach to meningioma subtype classification in response to the challenge of data variation.We analyze the stability of Adaptive Discriminant Wavelet Packet Transform (ADWPT) and present a solution to the issue of variation in the ADWPT decomposition when texture in data changes. A feature selection approach is proposed that provides high classification accuracy
- …