KEYWORDS: Terahertz radiation, Signal to noise ratio, Wavelets, Spectroscopy, Statistical modeling, System identification, Absorption, Signal processing, Signal attenuation, Classification systems
This work compares classification results of lactose, mandelic acid and dl-mandelic acid, obtained on the basis of their
respective THz transients. The performance of three different pre-processing algorithms applied to the time-domain
signatures obtained using a THz-transient spectrometer are contrasted by evaluating the classifier performance. A range
of amplitudes of zero-mean white Gaussian noise are used to artificially degrade the signal-to-noise ratio of the time-domain
signatures to generate the data sets that are presented to the classifier for both learning and validation purposes.
This gradual degradation of interferograms by increasing the noise level is equivalent to performing measurements
assuming a reduced integration time. Three signal processing algorithms were adopted for the evaluation of the complex
insertion loss function of the samples under study; a) standard evaluation by ratioing the sample with the background
spectra, b) a subspace identification algorithm and c) a novel wavelet-packet identification procedure. Within class and
between class dispersion metrics are adopted for the three data sets. A discrimination metric evaluates how well the three
classes can be distinguished within the frequency range 0.1 - 1.0 THz using the above algorithms.
We use the theory of two dimensional discrete wavelet transforms to derive inversion formulas for the Radon
transform of terahertz datasets. These inversion formulas with good localised properties are implemented for the
reconstruction of terahertz imaging in the area of interest, with a significant reduction in the required measurements.
As a form of optical coherent tomography, terahertz CT complements the current imaging techniques and
offers a promising approach for achieving non-invasive inspection of solid materials, with potentially numerous
applications in industrial manufacturing and biomedical engineering.
This study investigates binary and multiple classes of classification via support vector machines (SVMs). A couple of groups of two dimensional features are extracted via frequency orientation components, which result in the effective classification of Terahertz (T-ray) pulses for discrimination of RNA data and various powder samples. For each classification task, a pair of extracted feature vectors from the terahertz signals corresponding to each class is viewed as two coordinates and plotted in the same coordinate system. The current classification method extracts specific features from the Fourier spectrum, without applying an extra feature extractor. This method shows that SVMs can employ conventional feature extraction methods for a T-ray classification task. Moreover, we discuss the challenges faced by this method. A pairwise classification method is applied for the multi-class classification of powder samples. Plots of learning vectors assist in understanding the classification task, which exhibit improved clustering, clear learning margins, and least support vectors. This paper highlights the ability to use a small number of features (2D features) for classification via analyzing the frequency spectrum, which greatly reduces the computation complexity in achieving the preferred classification performance.
This study investigates the application of one dimensional discrete wavelet transforms in the classification of T-ray pulsed signals. The Fast Fourier Transform (FFT) is used as a feature extraction tool and a Mahalanobis distance classifier is employed for classification. In this work, soft threshold wavelet shrinkage de-noising plays an important part in de-noising and reconstructing T-ray pulsed signals. In addition, Mallat's pyramid algorithm and a local modulus maxima method to reconstruct T-ray signals are investigated. Particularly the local modulus maxima method is analyzed and comparisons are made before and after reconstruction of signals. The results demonstrate that these two methods are especially effective in analyzing and reconstructing T-ray pulsed responses. Moreover, to test wavelet de-noising effectiveness, the accuracy of the classiffication is calculated and results are displayed in the form of scatter-plots. Results show that soft threshold wavelet shrinkage de-noising improves the classification accuracy and successfully generates visually pleasing scatter plots at selected three frequency components.
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