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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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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.

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.

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.

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.

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.

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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