Bulent Cavusoglu

Application of a Useful Uncertainty Analysis as a Metric Tool for Assessing the Performance of Electromagnetic Properties Retrieval Methods of Bianisotropic Metamaterials

We applied a useful uncertainty model, ignored in most metamaterials retrieval studies, to monitor the accuracy of retrieved electromagnetic properties of bianisotropic metamaterial (MM) slabs composed of split-ring resonators and cut wires. Two difierent MM slab structures are considered to make the analysis complete. As uncertainty-making factors, we took into consideration of uncertainties in scattering (S-) parameters of bianisotropic MM slabs as well as the length of these slabs. The applied uncertainty model is based upon considering the efiect of minute change (difierential) in uncertainty factors on the retrieved electromagnetic properties of bianisotropic MM slabs. The signiflcant results concluded from the analysis are: 1) any abrupt changes in the phase of S-parameters of bianisotropic MM slabs remarkably in∞uence the retrieved electromagnetic properties; 2) any small-scale loss (i.e., the loss of the substrate) in the bianisotropic MM slabs improves the accuracy of the retrieved electromagnetic properties of these slabs; and 3) precise knowledge of bianisotropic MM slab lengths are required for correct analysis of exotic properties

Characterization of Porous Silicon Fabry–Pérot Optical Sensors for Reflectivity and Transmittivity Measurements

We investigate the effect of fabrication parameters (nonequal surface current densities, impurities inside the structure, etc.) and loss factor on reflectivity and transmittivity measurements from porous silicon Fabry-Pérot cavities with finite-size substrate thicknesses. We apply the formalism based on wave cascade matrix method for obtaining dependencies of reflectivity and transmittivity. From our analysis, we note the following results. First, resonance behavior of reflectivity and transmittivity changes only when optical/physical properties of middle layers of the cavity alter. Second, for lossless cavities, while reflectivity and transmittivity considerably change with surface characteristics (optical/physical properties of first layers), transmittivity is significantly modified by a change of optical/physical properties of middle layers (microcavity region). Third, loss inside a FP cavity makes the transmittivity more immune to variations in optical/physical properties of middle layers. Finally and most importantly, transmittivity values at resonance wavelength as well as the resonance wavelength shift can be utilized for the identification of unknown chemical/biological molecules by lossless FP cavities. For validation of these results, we carried out reflectivity and transmittivity measurements from some arbitrarily chosen positions but around the center of two fabricated FP cavities resonating at 1456 nm and at 542 nm.

Multiscale texture retrieval based on low-dimensional and rotation-invariant features of curvelet transform

Multiscale-based texture retrieval algorithms use low-dimensional feature sets in general. However, they do not have as good retrieval performances as those of the state-of-the-art techniques in the literature. The main motivation of this study is to use low-dimensional multiscale features to provide comparable retrieval performances with the state-of-the-art techniques. The proposed features of this study are low-dimensional, robust against rotation, and have better performance than the earlier multiresolution-based algorithms and the state-of-the-art techniques with low-dimensional feature sets. They are obtained through curvelet transformation and have considerably small dimensions. The rotation invariance is provided by applying a novel principal orientation alignment based on cross energies of adjacent curvelet blocks. The curvelet block pair with the highest cross energy is marked as the principle orientation, and the rest of the blocks are cycle-shifted around the principle orientation. Two separate rotation-invariant feature vectors are proposed and evaluated in this study. The first feature vector has 84 elements and contains the mean and standard deviation of curvelet blocks at each angle together with a weighting factor based on the spatial support of the curvelet coefficients. The second feature vector has 840 elements and contains the kernel density estimation (KDE) of curvelet blocks at each angle. The first and the second feature vectors are used in the classification of textures based on nearest neighbor algorithm with Euclidian and Kullback-Leibler distance measures, respectively. The proposed method is evaluated on well-known databases such as, Brodatz, TC10, TC12-t184, and TC12-horizon of Outex, UIUCTex, and KTH-TIPS. The best performance is obtained for kernel density feature vector. Mean and standard deviation feature vector also provides similar performance and has less complexity due to its smaller feature dimension. The results are reported as both precision-recall curves and classification rates and compared with the existing state-of-the-art texture retrieval techniques. It is shown through several experiments that the proposed rotation-invariant feature vectors outperform earlier multiresolution-based ones and provide comparable performances with the rest of the literature even though they have considerably small dimensions.

Identification of Gases by Porous Optical Sensors Using Reflectivity Difference and Wavelength Shift

We propose a novel method for identification of various gases using combined measurements of wavelength shift (Δλ₀) and reflectivity difference (ΔR). The method relies on plotting the measurement data on the ΔR-Δλ₀ space and exploits in the identification process the effect of significant loss behavior of bulk silicon near or within the visible range (for lossless gas vapors). It has been validated by reflectivity measurements on a Fabry-Perot cavity resonating around 745 nm for four different gas vapors whose reflective indices are close to each other and by two metrics for separability/identification analysis by the scattering matrix method. It is noted that the combined measurements of Δλ₀ and ΔR result in better identification than that obtained by the measurement of Δλ₀ (or ΔR) itself.

Estimation of available bandwidth share by tracking unknown cross-traffic with adaptive extended Kalman filter

Abstract We propose a nonlinear network model to estimate the available bandwidth share of a source by tracking the unknown cross-traffic. Especially sudden changes in cross-traffic behavior are challenging to adapt since measurement or process models of the existing algorithms generally do not include the cross-traffic in the model. As a novel approach, combined cross-traffic behavior, generally considered as additive noise, is modeled as an unknown source enabling tracking of both the cross-traffic and network behavior. Adaptive Extended Kalman Filter with Unknown Inputs (EKF-UI) is used for the estimation of available bandwidth share. This approach works recursively and is suitable for real-time applications. Moreover, the measurements are based on passive monitoring. Hence, no probe traffic is induced to the network. It is also shown with multiple simulations that this model is robust against variable network conditions.

Rotation invariant curvelet based image retrieval & classification via Gaussian mixture model and co-occurrence features

Demand for better retrieval methods continue to outstrip the capabilities of available technologies despite the rapid growth of new feature extraction techniques. Extracting discriminatory features that contain texture specific information are of crucial importance in image indexing. This paper presents a novel rotation invariant texture representation model based on the multi-resolution curvelet transform via co-occurrence and Gaussian mixture features for image retrieval and classification. To extract these features, curvelet transform is applied and the coefficients are obtained at each scale and orientation. The Gaussian mixture model (GMM) features are computed from each of the sub bands and co-occurrence features are computed for only specific sub band. Rotation invariance is provided by applying cycle-shift around the GMM features. The proposed method is evaluated on well-known databases such as Brodatz, Outex_TC_00010, Outex_TC_00012, Outex_TC_00012horizon, Outex_TC_00012tl84, Vistex and KTH-TIPS. When the feature vector is analyzed in terms of its size, it is observed that its dimension is smaller than that of the existing rotation-invariant variants and it has a very good performance. Simulation results show a good performance achieved by combining different techniques with the curvelet transform. Proposed method results in high degree of success rate in classification and in precision-recall value for retrieval.

Improving the limit of detection (LOD) of microsensor used in detection of brain diseases via wavelet filter

Limit of detection (LOD) gives the concentration amount that a microsensor can detect. It is desirable to have a LOD value of 1μM for microsensors used in brain diseases. The ones that cannot reach this sensitivity value are disposed and cannot be used in the experiments. The goal of this study is to increase the sensitivity of the produced microsensors by decreasing their LOD values. LOD increases linearly by baseline noise. The sensor data is used generally without any baseline filtering in the literature. In this study, LOD values are enhanced 3 times as much by using wavelet filtering, compared with the ones where no filtering is used.

Microelectrod fabrication for diagnosis and treatment of brain disorders

In this study, it is aimed to produce microelectrodes which can be used in the detection of neurotransmitters that are related with brain disorders such as Parkinson, Epilepsy, and Schizophrenia and that exist in the central nervous system (CNS). A 4-channel, ceramic-based fabrication is performed towards this goal by using photolithographic methods. The time-current graphic response against the addition of H2O2 the produced microelectrode is analyzed in the calibration test. It is observed that the response is in stepwise form. In addition, limit of detection (LOD) of the produced microelectrodes and linearity values are shown to be within the desired ranges.

Curvelet transform based image denoising via Gaussian mixture model

This paper presents a novel image denoising method based on curvelet transform and gaussian mixture model. After decomposing noisy images into curvelet domain, gaussian mixture model (GMM) is applied and obtained statistical parameters are used for calculating adaptive level depended thresholds. Noise removal is performed using hard threshold method in the curvelet coefficients of each sub-band. Due to the adaptive thresholding for each level the restored images are visually satisfactory.

Rotation invariant features of wavelet transform for texture retrieval

Wavelet transform is both sensitive to translation and rotation. This feature of the transform diminishes the discriminative power of wavelet coefficients among different classes where rotated versions of textures are present. We analyze statistical behavior of wavelet coefficients and show that some features of wavelets are more robust than others against translation and rotation. We also show that using higher order moments increase the overall performance. A new parameter is also proposed to determine the discriminative features out of a possible feature set. This new parameter is derived based on statistics of wavelet transform and called as effective discriminative power (EP). Based on EP, reduced subband feature set is proposed and applied on the modified Brodatz database for texture retrieval and shown that the reduced feature set have superior performance compared to the one which just includes mean and standard deviations of all the subbands. The reduced feature set is also computationally less expensive since it eliminates rotation-variant features and results in less number of features with better performance.