2,391 research outputs found
The wavelet-NARMAX representation : a hybrid model structure combining polynomial models with multiresolution wavelet decompositions
A new hybrid model structure combing polynomial models with multiresolution wavelet decompositions is introduced for nonlinear system identification. Polynomial models play an important role in approximation theory, and have been extensively used in linear and nonlinear system identification. Wavelet decompositions, in which the basis functions have the property of localization in both time and frequency, outperform many other approximation schemes and offer a flexible solution for approximating arbitrary functions. Although wavelet representations can approximate even severe nonlinearities in a given signal very well, the advantage of these representations can be lost when wavelets are used to capture linear or low-order nonlinear behaviour in a signal. In order to sufficiently utilise the global property of polynomials and the local property of wavelet representations simultaneously, in this study polynomial models and wavelet decompositions are combined together in a parallel structure to represent nonlinear input-output systems. As a special form of the NARMAX model, this hybrid model structure will be referred to as the WAvelet-NARMAX model, or simply WANARMAX. Generally, such a WANARMAX representation for an input-output system might involve a large number of basis functions and therefore a great number of model terms. Experience reveals that only a small number of these model terms are significant to the system output. A new fast orthogonal least squares algorithm, called the matching pursuit orthogonal least squares (MPOLS) algorithm, is also introduced in this study to determine which terms should be included in the final model
A hybrid neuro--wavelet predictor for QoS control and stability
For distributed systems to properly react to peaks of requests, their
adaptation activities would benefit from the estimation of the amount of
requests. This paper proposes a solution to produce a short-term forecast based
on data characterising user behaviour of online services. We use \emph{wavelet
analysis}, providing compression and denoising on the observed time series of
the amount of past user requests; and a \emph{recurrent neural network} trained
with observed data and designed so as to provide well-timed estimations of
future requests. The said ensemble has the ability to predict the amount of
future user requests with a root mean squared error below 0.06\%. Thanks to
prediction, advance resource provision can be performed for the duration of a
request peak and for just the right amount of resources, hence avoiding
over-provisioning and associated costs. Moreover, reliable provision lets users
enjoy a level of availability of services unaffected by load variations
Hyperspectral colon tissue cell classification
A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy
Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images
Machine learning methods are increasingly used in various fields of medicine, contributing to early diagnosis and better quality of care. These outputs are particularly desirable in case of neuropsychiatric disorders, such as schizophrenia, due to the inherent potential for creating a new gold standard in the diagnosis and differentiation of particular disorders. This paper presents a scheme for automated classification from magnetic resonance images based on multiresolution representation in the wavelet domain. Implementation of the proposed algorithm, utilizing support vector machines classifier, is introduced and tested on a dataset containing 104 patients with first episode schizophrenia and healthy volunteers. Optimal parameters of different phases of the algorithm are sought and the quality of classification is estimated by robust cross validation techniques. Values of accuracy, sensitivity and specificity over 71% are achieved
Hubungan gaya pembelajaran dengan pencapaian akademik pelajar aliran vokasional
Analisis keputusan Sijil Pelajaran Malaysia (SPM) 2011 menunjukkan penurunan
pencapaian bagi Sekolah Menengah Vokasional. Oleh itu, kajian ini dilaksanakan
bertujuan untuk mengkaji hubungan di antara gaya pembelajaran dengan pencapaian
akademik pelajar. Kajian ini juga ingin mengenalpasti gaya pembelajaran paling
dominan yang diamalkan oleh pelajar serta melihat perbezaan gaya pembelajaran
dengan jantina pelajar. Seramai 131 orang Pelajar Tingkatan Empat Kursus
Vokasional Di Sekolah Menengah Vokasional Segamat di Johor telah terlibat dalam
kajian ini. Soal selidik Index of Learning Style (ILS) yang dibangunkan oleh Felder
dan Silverman (1991) yang mengandungi 44 soalan telah digunakan untukh
menjalankan kajian ini. Gaya pembelajaran pelajar dapat dilihat melalui empat
dimensi gaya pembelajaran yang terdiri dari dua sub-skala yang bertentangan iaitu
dimensi pelajar Aktif dan Reflektif, dimensi pelajar Konkrit dan Intuitif, dimensi
pelajar Verbal dan Visual, serta dimensi pelajar Tersusun dan Global. Data yang
diperolehi dianalisis dengan menggunakan perisian Statistical Package for Social
Science for WINDOW release 20.0 (SPSS.20.0). Ujian Korelasi Pearson digunakan
untuk menganalisis data dalam mengkaji hubungan gaya pembelajaran dengan
pencapaian akademik pelajar. Nilai pekali p yang diperolehi di antara gaya
pembelajaran dengan pencapaian pelajar adalah (p=0.1 hingga 0.4). Ini menunjukkan
tidak terdapat hubungan yang signifikan di antara dua pembolehubah tersebut. Kajian
ini juga mendapati bahawa gaya pembelajaran yang menjadi amalan pelajar ialah
gaya pembelajaran Tersusun. Hasil kajian juga mendapati bahawa tidak terdapat
perbezaan yang signifikan di antara gaya pembelajaran dengan jantina pelajar
Application of Fractal and Wavelets in Microcalcification Detection
Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection
Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images
A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform(DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images.The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction
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