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Watermarking and content protection for digital images and videoAgung, I. Wiseto P. January 2002 (has links)
No description available.
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A weighted grid for measuring program robustnessAbdallah, Mohammad Mahmoud Aref January 2012 (has links)
Robustness is a key issue for all the programs, especially safety critical ones. In the literature, Program Robustness is defined as “the degree to which a system or component can function correctly in the presence of invalid input or stressful environment” (IEEE 1990). Robustness measurement is the value that reflects the Robustness Degree of the program. In this thesis, a new Robustness measurement technique; the Robustness Grid, is introduced. The Robustness Grid measures the Robustness Degree for programs, C programs in this instance, using a relative scale. It allows programmers to find the program’s vulnerable points, repair them, and avoid similar mistakes in the future. The Robustness Grid is a table that contains Language rules, which is classified into categories with respect to the program’s function names, and calculates the robustness degree. The Motor Industry Software Reliability Association (MISRA) C language rules with the Clause Program Slicing technique will be the basis for the robustness measurement mechanism. In the Robustness Grid, for every MISRA rule, a score will be given to a function every time it satisfies or violates a rule. Furthermore, Clause program slicing will be used to weight every MISRA rule to illustrate its importance in the program. The Robustness Grid shows how much each part of the program is robust and effective, and assists developers to measure and evaluate the robustness degree for each part of a program. Overall, the Robustness Grid is a new technique that measures the robustness of C programs using MISRA C rules and Clause program slicing. The Robustness Grid shows the program robustness degree and the importance of each part of the program. An evaluation of the Robustness Grid is performed to show that it offers new measurements that were not provided before.
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A study of optimality in the H#infininty# loop-shaping design methodFeng, Jie January 1995 (has links)
No description available.
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Robustness and Vulnerability Design for Autonomic ManagementBigdeli, Alireza 20 August 2012 (has links)
This thesis presents network design and operations algorithms suitable for use in
an autonomic management system for communication networks with emphasis on
network robustness. We model a communication network as a weighted graph and
we use graph-theoretical metrics such as network criticality and algebraic connectivity
to quantify robustness. The management system under consideration is composed of
slow and fast control loops, where slow loops manage slow-changing issues of the
network and fast loops react to the events or demands that need quick response.
Both of control loops drive the process of network management towards the most
robust state.
We fist examine the topology design of networks. We compare designs obtained
using different graph metrics. We consider well-known topology classes including
structured and complex networks, and we provide guidelines on the design and simplification of network structures. We also compare robustness properties of several
data center topologies. Next, the Robust Survivable Routing (RSR) algorithm is presented to assign working and backup paths to online demands. RSR guarantees 100%
single-link-failure recovery as a path-based survivable routing method. RSR quanti es each path with a value that represents its sensitivity to incremental changes in
external traffic and topology by evaluating the variations in network criticality of the
network. The path with best robustness (path that causes minimum change in total
network criticality) is chosen as primary (secondary) path.
In the last part of this thesis, we consider the design of robust networks with
emphasis on minimizing vulnerability to single node and link failures. Our focus
in this part is to study the behavior of a communication network in the presence
of node/link failures, and to optimize the network to maximize performance in the
presence of failures. For this purpose, we propose new vulnerability metrics based on
the worst case or the expected value of network criticality or algebraic connectivity
when a single node/link failure happens. We show that these vulnerability metrics
are convex (or concave) functions of link weights and we propose convex optimization problems to optimize each vulnerability metric. In particular, we convert the
optimization problems to SDP formulation which leads to a faster implementation
for large networks.
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Robustness and Vulnerability Design for Autonomic ManagementBigdeli, Alireza 20 August 2012 (has links)
This thesis presents network design and operations algorithms suitable for use in
an autonomic management system for communication networks with emphasis on
network robustness. We model a communication network as a weighted graph and
we use graph-theoretical metrics such as network criticality and algebraic connectivity
to quantify robustness. The management system under consideration is composed of
slow and fast control loops, where slow loops manage slow-changing issues of the
network and fast loops react to the events or demands that need quick response.
Both of control loops drive the process of network management towards the most
robust state.
We fist examine the topology design of networks. We compare designs obtained
using different graph metrics. We consider well-known topology classes including
structured and complex networks, and we provide guidelines on the design and simplification of network structures. We also compare robustness properties of several
data center topologies. Next, the Robust Survivable Routing (RSR) algorithm is presented to assign working and backup paths to online demands. RSR guarantees 100%
single-link-failure recovery as a path-based survivable routing method. RSR quanti es each path with a value that represents its sensitivity to incremental changes in
external traffic and topology by evaluating the variations in network criticality of the
network. The path with best robustness (path that causes minimum change in total
network criticality) is chosen as primary (secondary) path.
In the last part of this thesis, we consider the design of robust networks with
emphasis on minimizing vulnerability to single node and link failures. Our focus
in this part is to study the behavior of a communication network in the presence
of node/link failures, and to optimize the network to maximize performance in the
presence of failures. For this purpose, we propose new vulnerability metrics based on
the worst case or the expected value of network criticality or algebraic connectivity
when a single node/link failure happens. We show that these vulnerability metrics
are convex (or concave) functions of link weights and we propose convex optimization problems to optimize each vulnerability metric. In particular, we convert the
optimization problems to SDP formulation which leads to a faster implementation
for large networks.
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Gaussian Mixture Model-based Feature Compensation with Application to Noise-robust Speech RecognitionYeh, Bing-Feng 28 August 2012 (has links)
In this paper, we propose a new method for noise robustness base on Gaussian Mixture
Model (GMM), and the method we proposed can estimate the noise feature effectively
and reduce noise effect by plain fashion, and we can retain the smoothing and continuity
from original feature in this way. Compared to the traditional feature transformation method
MMSE(Minimum Mean Square Error) which want to find a clean one, the different is that
the method we proposed only need to fine noise feature or the margin of noise effect and subtract
the noise to achieve more robustness effect than traditional methods. In the experiment
method, the test data pass through the trained noise classifier to judge the noise type and SNR,
and according to the result of classifier to choose the corresponding transformation model and
generate the noise feature by this model, and then we can use different weight linear combination
to generate noise feature, and finally apply simple subtraction to achieve noise reduction.
In the experiment, we use AURORA 2.0 corpus to estimate noise robustness performance,
and using traditional method can achieve 36:8% relative improvement than default, and the
our method can achieve 52:5% relative improvement, and compared to the traditional method
our method can attain 24:9% relative improvement.
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Color Image Watermarking Technique Robust to Geometric attacks and Signal ProcessingLin, Chih-hsueh 10 July 2006 (has links)
Developing a robust method of image watermarking that resists rotation, scaling and/or translation (RST) transformations is widely considered to be a more challenging task than developing a method resistant to other attacks. Altering the orientation or size of the image, even only slightly, reduces the receiver¡¦s ability to retrieve the watermark. Protecting against both geometric distortion and signal processing with blind detection is even more problematic. This investigation proposes a novel approach, based on the properties of histograms to measure the numerous global features of all pixels in a cover image and to construct the three-dimensional feature space. The feature space is dynamically partitioned to identify several blocks used to embed the watermark. One feature of the pixels is modified to form a specially distributed histogram for embedding the watermark in a blind digital watermarking method that can be applied to color images. Experimental results demonstrate the robustness of the proposed method against common geometric attacks and signal processing.
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Estimation of Parameters for Gaussian Random Variables using Robust Differential Geometric TechniquesYellapantula, Sudha 16 January 2010 (has links)
Most signal processing systems today need to estimate parameters of the underlying
probability distribution, however quantifying the robustness of this system has
always been difficult. This thesis attempts to quantify the performance and robustness
of the Maximum Likelihood Estimator (MLE), and a robust estimator, which
is a Huber-type censored form of the MLE. This is possible using diff erential geometric
concepts of slope. We compare the performance and robustness of the robust
estimator, and its behaviour as compared to the MLE. Various nominal values of
the parameters are assumed, and the performance and robustness plots are plotted.
The results showed that the robustness was high for high values of censoring and
was lower as the censoring value decreased. This choice of the censoring value was
simplifi ed since there was an optimum value found for every set of parameters. This
study helps in future studies which require quantifying robustness for di fferent kinds
of estimators.
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Design of a robust parameter estimator for nominally Laplacian noiseBhagawat, Pankaj 30 September 2004 (has links)
In this work we have made use of a geometric approach which quantifies robustness and performance and we finally combine them using a cost function. In particular, we calculate the robustness
of the estimate of standard deviation of nominally Laplacian distribution. As this distribution is imperfectly known,
we employ a more general family, the generalized Gaussian; Laplacian distribution, is one of the members of this family.
We compute parameter estimates and present a classical algorithm which is then analyzed for distribution from the generalized Gaussian family.
We calculate the mean squared error according to the censoring height k.
We measure performance as a function of (1/MSE) and combine it with robustness using a cost criterion and design
a robust estimator which optimizes a mix of performance and robustness specified by the user.
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Acceptance sampling : Robust alternatives for sampling by variablesMalik, M. B. January 1985 (has links)
No description available.
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