The Experts below are selected from a list of 88146 Experts worldwide ranked by ideXlab platform
Wassim M. Haddad - One of the best experts on this subject based on the ideXlab platform.
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adaptive sliding mode control for a general nonlinear multicompartment lung model with input pressure and rate saturation constraints
International Journal of Robust and Nonlinear Control, 2016Co-Authors: Saing Paul Hou, Nader Meskin, Wassim M. HaddadAbstract:Summary In this article, we develop an output feedback adaptive sliding mode controller for a general multicompartment lung mechanics model with nonlinear resistance and compliance respiratory parameters. Specifically, for a given clinically plausible Reference volume Pattern, we develop an adaptive sliding mode controller that accounts for input pressure and rate saturation constraints to automatically adjust the applied input pressure by the mechanical ventilator so that the total lung volume tracks the given Reference Pattern. The pressure due to lung muscle activity is also considered in the controller design. A Lyapunov-based approach is presented to show convergence of the closed-loop system, and the proposed control framework is applied to a two-compartment lung model to show the efficacy of the proposed control method. Copyright © 2015 John Wiley & Sons, Ltd.
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adaptive sliding mode control for a general nonlinear multicompartment lung model with input pressure and rate saturation constraints
Conference on Decision and Control, 2014Co-Authors: Saing Paul Hou, Nader Meskin, Wassim M. HaddadAbstract:In this article, we develop an output feedback adaptive sliding mode controller for a general multicompartment lung mechanics model with nonlinear resistance and compliance respiratory parameters. Specifically, for a given clinically plausible Reference volume Pattern, we develop an adaptive sliding mode controller that accounts for input pressure and rate saturation constraints to automatically adjust the applied input so that the total lung volume tracks the given Reference Pattern. The proposed tracking control framework is applied to a two-compartment lung mechanics model with nonlinear lung compliance and resistance parameters.
Miroslav Goljan - One of the best experts on this subject based on the ideXlab platform.
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digital camera identification from sensor Pattern noise
IEEE Transactions on Information Forensics and Security, 2006Co-Authors: Jan Lukas, Jessica Fridrich, Miroslav GoljanAbstract:In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's Pattern noise. For each camera under investigation, we first determine its Reference Pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the Reference Pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction.
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determining digital image origin using sensor imperfections
Conference on Image and video communications and processing, 2005Co-Authors: Jan Lukas, Jessica Fridrich, Miroslav GoljanAbstract:In this paper, we demonstrate that it is possible to use the sensor’s Pattern noise for digital camera identification from images. The Pattern noise is extracted from the images using a wavelet-based denoising filter. For each camera under investigation, we first determine its Reference noise, which serves as a unique identification fingerprint. This could be done using the process of flat-fielding, if we have the camera in possession, or by averaging the noise obtained from multiple images, which is the option taken in this paper. To identify the camera from a given image, we consider the Reference Pattern noise as a high-frequency spread spectrum watermark, whose presence in the image is established using a correlation detector. Using this approach, we were able to identify the correct camera out of 9 cameras without a single misclassification for several hundred images. Furthermore, it is possible to perform reliable identification even from images that underwent subsequent JPEG compression and/or resizing. These claims are supported by experiments on 9 different cameras including two cameras of exactly same model (Olympus C765).
A.j. Wilkinson - One of the best experts on this subject based on the ideXlab platform.
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Pattern matching analysis of electron backscatter diffraction Patterns for Pattern centre crystal orientation and absolute elastic strain determination accuracy and precision assessment
arXiv: Materials Science, 2019Co-Authors: Tomohito Tanaka, A.j. WilkinsonAbstract:Pattern matching between target electron backscatter Patterns (EBSPs) and dynamically simulated EBSPs was used to determine the Pattern centre (PC) and crystal orientation, using a global optimisation algorithm. Systematic analysis of error and precision with this approach was carried out using dynamically simulated target EBSPs with known PC positions and orientations. Results showed that the error in determining the PC and orientation was < 10$^{-5}$ of Pattern width and < 0.01{\deg} respectively for the undistorted full resolution images (956x956 pixels). The introduction of noise, optical distortion and image binning was shown to have some influence on the error although better angular resolution was achieved with the Pattern matching than using conventional Hough transform-based analysis. The accuracy of PC determination for the experimental case was explored using the High Resolution (HR-) EBSD method but using dynamically simulated EBSP as the Reference Pattern. This was demonstrated through a sample rotation experiment and strain analysis around an indent in interstitial free steel.
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high resolution electron backscatter diffraction measurements of elastic strain variations in the presence of larger lattice rotations
Ultramicroscopy, 2012Co-Authors: T B Britton, A.j. WilkinsonAbstract:Abstract In this paper we explore methods of measuring elastic strain variations in the presence of larger lattice rotations (up to ∼11°) using high resolution electron backscatter diffraction. We have examined the fundamental equations which relate Pattern shifts to the elastic strain tensor and modified them to a finite deformation framework from the original infinitesimal deformation one. We incorporate the traction free boundary condition into the minimisation problem for the finite deformation case (i.e. large rotations and small elastic strains). Numerical experiments show that this finite deformation kinematic analysis continues to work well, while the infinitesimal analysis fails, when the misorientation between test and Reference Pattern is made increasingly high. However, measurements on Patterns simulated using dynamical diffraction theory indicated that this formulation is not sufficient to recover elastic strains accurately because the Pattern shifts are not determined accurately when large rotations are present. To overcome this issue we remap the test Pattern to an orientation that is close to that of Reference Pattern. This remapping was defined by a finite rotation matrix, which was estimated from the infinitesimal rotation matrix measured using cross-correlation. A second cross-correlation analysis between the Reference Pattern and the remapped test Pattern allows the elastic strains to be recovered using the much simpler infinitesimal deformation theory. We have also demonstrated that accurate recovery of elastic strains requires accurate knowledge of the Pattern centre if this remapping algorithm is used.
Saing Paul Hou - One of the best experts on this subject based on the ideXlab platform.
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adaptive sliding mode control for a general nonlinear multicompartment lung model with input pressure and rate saturation constraints
International Journal of Robust and Nonlinear Control, 2016Co-Authors: Saing Paul Hou, Nader Meskin, Wassim M. HaddadAbstract:Summary In this article, we develop an output feedback adaptive sliding mode controller for a general multicompartment lung mechanics model with nonlinear resistance and compliance respiratory parameters. Specifically, for a given clinically plausible Reference volume Pattern, we develop an adaptive sliding mode controller that accounts for input pressure and rate saturation constraints to automatically adjust the applied input pressure by the mechanical ventilator so that the total lung volume tracks the given Reference Pattern. The pressure due to lung muscle activity is also considered in the controller design. A Lyapunov-based approach is presented to show convergence of the closed-loop system, and the proposed control framework is applied to a two-compartment lung model to show the efficacy of the proposed control method. Copyright © 2015 John Wiley & Sons, Ltd.
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adaptive sliding mode control for a general nonlinear multicompartment lung model with input pressure and rate saturation constraints
Conference on Decision and Control, 2014Co-Authors: Saing Paul Hou, Nader Meskin, Wassim M. HaddadAbstract:In this article, we develop an output feedback adaptive sliding mode controller for a general multicompartment lung mechanics model with nonlinear resistance and compliance respiratory parameters. Specifically, for a given clinically plausible Reference volume Pattern, we develop an adaptive sliding mode controller that accounts for input pressure and rate saturation constraints to automatically adjust the applied input so that the total lung volume tracks the given Reference Pattern. The proposed tracking control framework is applied to a two-compartment lung mechanics model with nonlinear lung compliance and resistance parameters.
Alessandro Piva - One of the best experts on this subject based on the ideXlab platform.
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Hybrid Reference-based Video Source Identification
MDPI AG, 2019Co-Authors: Massimo Iuliani, Marco Fontani, Dasara Shullani, Alessandro PivaAbstract:Millions of users share images and videos generated by mobile devices with different profiles on social media platforms. When publishing illegal content, they prefer to use anonymous profiles. Multimedia Forensics allows us to determine whether videos or images have been captured with the same device, and thus, possibly, by the same person. Currently, the most promising technology to achieve this task exploits unique traces left by the camera sensor into the visual content. However, image and video source identification are still treated separately from one another. This approach is limited and anachronistic, if we consider that most of the visual media are today acquired using smartphones that capture both images and videos. In this paper we overcome this limitation by exploring a new approach that synergistically exploits images and videos to study the device from which they both come. Indeed, we prove it is possible to identify the source of a digital video by exploiting a Reference sensor Pattern noise generated from still images taken by the same device. The proposed method provides performance comparable with or even better than the state-of-the-art, where a Reference Pattern is estimated from video frames. Finally, we show that this strategy is effective even in the case of in-camera digitally stabilized videos, where a non-stabilized Reference is not available, thus solving the limitations of the current state-of-the-art. We also show how this approach allows us to link social media profiles containing images and videos captured by the same sensor
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a new dataset for source identification of high dynamic range images
Sensors, 2018Co-Authors: Omar Al Shaya, Pengpeng Yang, Yao Zhao, Alessandro PivaAbstract:Digital source identification is one of the most important problems in the field of multimedia forensics. While Standard Dynamic Range (SDR) images are commonly analyzed, High Dynamic Range (HDR) images are a less common research subject, which leaves space for further analysis. In this paper, we present a novel database of HDR and SDR images captured in different conditions, including various capturing motions, scenes and devices. As a possible application of this dataset, the performance of the well-known Reference Pattern noise-based source identification algorithm was tested on both kinds of images. Results have shown difficulties in source identification conducted on HDR images, due to their complexity and wider dynamic range. It is concluded that capturing conditions and devices themselves can have an impact on source identification, thus leaving space for more research in this field.