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This work models shear heat-related temperature rise due to inter-book dip-slip normal faults by a bookshelf mechanism. Such faults are characterized by temporally changing the dip of the inter-book fault planes. Considerable temperature can be produced by such faulting, although the effects of high temperature have not yet been reported around such fault planes. This may mean that fluids flowing through such planes do not allow shear heating to accumulate.

Sleep related disorder causes diminished quality of lives in human beings. Sleep scoring or sleep staging is the process of classifying various sleep stages which helps to detect the quality of sleep. The identification of sleep stages using electroencephalogram (EEG) signals is an arduous task. Just by looking at an EEG signal, one cannot determine the sleep stages precisely. Sleep specialists may make errors in identifying sleep stages by visual inspection. To mitigate the erroneous identification and to reduce the burden on doctors, a computer-aided EEG based system can be deployed in the hospitals, which can help identify the sleep stages, correctly. Several automated systems based on the analysis of polysomnographic (PSG) signals have been proposed. A few sleep stage scoring systems using EEG signals have also been proposed. But, still there is a need for a robust and accurate portable system developed using huge dataset. In this study, we have developed a new single-channel EEG based sleep-stages identification system using a novel set of wavelet-based features extracted from a large EEG dataset. We employed a novel three-band time-frequency localized (TBTFL) wavelet filter bank (FB). The EEG signals are decomposed using three-level wavelet decomposition, yielding seven sub-bands (SBs). This is followed by the computation of discriminating features namely, log-energy (LE), signal-fractal-dimensions (SFD), and signal-sample-entropy (SSE) from all seven SBs. The extracted features are ranked and fed to the support vector machine (SVM) and other supervised learning classifiers. In this study, we have considered five different classification problems (CPs), (two-class (CP-1), three-class (CP-2), four-class (CP-3), five-class (CP-4) and six-class (CP-5)). The proposed system yielded accuracies of 98.3%, 93.9%, 92.1%, 91.7%, and 91.5% for CP-1 to CP-5, respectively, using 10-fold cross validation (CV) technique.

The Linear Quadratic Gaussian (LQG) and Optimal Static Output Feedback controls (OSOF) are used to control wind excited benchmark building by using semiactive variable stiffness-tuned mass damper (SAVS-TMD/ST) at its top. The control force is realized by varying the device stiffness around nominal stiffness which corresponds to the structure's fundamental frequency, by changing device configuration within its specified linear range and by using a simple control law. The time invariance of the system is maintained. The control is applied to nominal structure and structure with +/- 15% variation in stiffness, by using output feedback as one acceleration in STLQG (ST with LQG) control and three drifts and one velocity in STOSOF (ST with OSOF) control. Results show that, in response reduction the present STLQG controller is equally effective as ALC (active TMD with LQG) controller and mostly better than STSC (ST with Short Time Fourier Transform) controller, and STLC (ST with LQR) controller. The STOSOF also perform well as ALC and is mostly better than STSC in response reduction, for the 0% and -15% stiffness variation cases. For the +15% stiffness variation case, although STOSOF is not as good as ALC in all responses and as STSC in peak responses, it is still comparable with STLC in all responses and with STSC in RMS responses. With substantially less power and control force as compared to ALC, and with limited sensors, both controllers perform well in controlling the structural response.

Design of time-frequency localized filters and functions is a classical subject in the field of signal processing. Gabor's uncertainty principle states that a function cannot be localized in time and frequency domain simultaneously and there exists a nonzero lower bound of 0.25 on the product of its time variance and frequency variance called time-frequency product (TFP). Using arithmetic mean (AM) geometric mean (GM) inequality, product of variances and sum of variances can be related and it can be shown that sum of variances has lower bound of one. In this paper, we compute the frequency variance of the filter from its discrete Fourier transform (DFT) and propose an equivalent summation based discrete-time uncertainty principle which has the lower bound of one. We evaluate the performance of the proposed discrete-time time-frequency uncertainty measure in multiresolution setting and show that the proposed DFT based concentration measure generate sequences which are even more localized in time and frequency domain than that obtained from the Slepian, Ishii and Furukawa's concentration measures. The proposed design approach provides the flexibility in which the TFP can be made arbitrarily close to the lowest possible lower bound of 0.25 by increasing the length of the filter. In the other proposed approach, the sum of the time variance and frequency variance is used to formulate a positive definite matrix to measure the time-frequency joint localization of a bandlimited function from its samples. We design the time-frequency localized bandlimited low pass scaling and band pass wavelet functions using the eigenvectors of the formulated positive definite matrix. The samples of the time frequency localized bandlimited function are obtained from the eigenvector of the positive definite matrix corresponding to its minimum eigenvalue. The TFP of the designed bandlimited scaling and wavelet functions are close to the lowest possible lower bound of 0.25 and 2.25 respectively. We propose a design method for time-frequency localized three-band biorthogonal linear phase (BOLP) wavelet perfect reconstruction filter bank (PRFB) wherein the free parameters can be optimized for time-frequency localization of the synthesis basis functions for the specified frequency variance of the analysis scaling function. The performance of the designed filter bank is evaluated in classification of seizure and seizure free electroencephalogram (EEG) signals. It is found that the proposed filter bank outperforms other existing methods for the classification of seizure and seizure-free EEG signals. (C) 2017 Elsevier Inc. All rights reserved.

In recent years, several image fusion techniques have been proposed to cater to various objectives. An appropriate visualization of the data is one of the key objectives of image fusion, particularly in case of hyperspectral images where the number of bands are far more than those can be displayed on standard tristimulus display. While a few techniques that address the issue of visualization of hyperspectral data can be seen in the literature, the evaluation of performances of these different techniques is still an open problem. In this paper, we first introduce a notion called fusion consistency and we suggest that the fusion techniques should satisfy the consistency criterion under appropriate measures that evaluate the fusion performance. We also propose several modifications for a number of existing measures that can quantify the progression of fusion operation efficiently for the fusion of a large number of image bands. We use these observations to validate suitability of any given technique for fusion of hyperspectral images. (c) 2011 Elsevier B.V. All rights reserved.

Camouflage is an attempt to conceal a target by making it look similar to the background to make the detection and recognition of the target difficult for the observer (a man or a machine or both). Detecting a camouflaged target in a video, captured in visible band is a big challenge. One can use the infrared (IR) video where the visibility of the same target is much better, but the problem with IR band is that most of the contrast, color, and edge information about the background are lost and thus localizing the target becomes difficult. The objective of this work is to fuse registered videos, captured in visible and IR band such that the target is no longer camouflaged and hence clearly visible to the human monitor. More importantly, this should be done without losing the background details. We have proposed four different video fusion methods. All the proposed methods are intensity invariant so that camouflaged targets are detected independent of the illumination conditions. The performance of these methods has been compared using Wang–Bovik and Petrovic–Xydeas fusion metric along with other information theoretic indices. Experimental video results show that the proposed methods improve the perception and thus facilitates detection and localization of a camouflaged target. Moreover, the fused video has minimum artefacts as indicated by the highest peak signal-to-noise ratio, which is one of the most desired quality of a good fusion method.

The modal parameters of a scale-modeled discus-shaped data buoy in heave and pitch are estimated from the experimentally measured impulse response time histories. The use of phase-time, time-frequency domains for derivation of natural frequencies and damping are demonstrated in this paper. The phase-time method is based on the Hilbert transformation, whereas, the time-frequency method is based on the continuous wavelet transformation (CWT) of the measured time histories. In addition, the conventional time and frequency domain method of modal parameter estimation is also adopted for the comparison of results. The details of the model, test procedure, analysis and results are presented in this paper. The modal parameters obtained through CWT are found to be accurate compared to that obtained from the time and frequency domain analysis. � 2008 Elsevier Ltd. All rights reserved.