A sweep-plane algorithm by Lawrence for convex polytope computation is adapted to generate random tuples on simple polytopes. In our method an affine hyperplane is swept through the given polytope until a random fraction (sampled from a proper univariate distribution) of the volume of the polytope is covered. Then the intersection of the plane with the polytope is a simple polytope with smaller dimension. In the second part we apply this method to construct a black-box algorithm for log-concave and T-concave multivariate distributions by means of transformed density rejection. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

Different universal (also called automatic or black-box) methods have been suggested to sample from univariate log-concave distributions. The description of a universal generator for bivariate distributions has not been published up to now. The new algorithm for bivariate log-concave distributions is based on the method of transformed density rejection. In order to construct a hat function for a rejection algorithm the bivariate density is transformed by the logarithm into a concave function. Then it is possible to construct a dominating function by taking the minimum of several tangent planes which are by exponentiation transformed back into the original scale. The choice of the points of contact is automated using adaptive rejection sampling. This means that a point that is rejected by the rejection algorithm is used as additional point of contact until the maximal number of points of contact is reached. The paper describes the details how this main idea can be used to construct Algorithm ULC2D that can generate random pairs from bivariate log-concave distribution with a computable density. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

We give an algorithm that can be used to sample from any discrete log-concave distribution (e.g. the binomial and hypergeometric distributions). It is based on rejection from a discrete dominating distribution that consists of parts of the geometric distribution. The algorithm is uniformly fast for all discrete log-concave distributions and not much slower than algorithms designed for a single distribution. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

This thesis focuses on video watermarking robust against geometric attacks and video compressions. In addition to the requirements for an image watermarking algorithm, a digital video watermarking algorithm has to be robust against advanced video compressions, frame loss, frame swapping, aspect ratio change, frame rate change, intra- and inter-frame filtering, etc. Video compression, especially, the most efficient compression standard, H.264, and geometric attacks, such as rotation and cropping, frame aspect ratio change, and translation, are considered the most challenging attacks for video watermarking algorithms. In this thesis, we first review typical watermarking algorithms robust against geometric attacks and video compressions, and point out their advantages and disadvantages. Then, we propose our robust video watermarking algorithms against Rotation, Scaling and Translation (RST) attacks and MPEG-2 compression based on the logpolar mapping and the phase-only filtering method. Rotation or scaling transformation in the spatial domain results in vertical or horizontal shift in the log-polar mapping (LPM) of the magnitude of the Fourier spectrum of the target frame. Translation has no effect in this domain. This method is very robust to RST attacks and MPEG-2 compression. We also demonstrate that this method can be used as a RST parameters detector to work with other watermarking algorithms to improve their robustness to RST attacks. Furthermore, we propose a new video watermarking algorithm based on the 1D DFT (one-dimensional Discrete Fourier Transform) and 1D projection. This algorithm enhances the robustness to video compression and is able to resist the most advanced video compression, H.264. The 1D DFT for a video sequence along the temporal domain generates an ideal domain, in which the spatial information is still kept and the temporal information is obtained. With detailed analysis and calculation, we choose the frames with highest temporal frequencies to embed the fence-shaped watermark pattern in the Radon transform domain of the selected frames. The performance of the proposed algorithm is evaluated by video compression standards MPEG-2 and H.264; geometric attacks such as rotation, translation, and aspect-ratio changes; and other video processing. The most important advantages of this video watermarking algorithm are its simplicity, practicality and robustness.

Root Cause Analysis for software systems is a challenging diagnostic task due to complexity emanating from the interactions between system components. Furthermore, the sheer size of the logged data makes it often difficult for human operators and administrators to perform problem diagnosis and root cause analysis. The diagnostic task is further complicated by the lack of models that could be used to support the diagnostic process. Traditionally, this diagnostic task is conducted by human experts who create mental models of systems, in order to generate hypotheses and conduct the analysis even in the presence of incomplete logged data. A challenge in this area is to provide the necessary concepts, tools, and techniques for the operators to focus their attention to specific parts of the logged data and ultimately to automate the diagnostic process. The work described in this thesis aims at proposing a framework that includes techniques, formalisms, and algorithms aimed at automating the process of root cause analysis. In particular, this work uses annotated requirement goal models to represent the monitored systems' requirements and runtime behavior. The goal models are used in combination with log data to generate a ranked set of diagnostics that represent the combination of tasks that failed leading to the observed failure. In addition, the framework uses a combination of word-based and topic-based information retrieval techniques to reduce the size of log data by filtering out a subset of log data to facilitate the diagnostic process. The process of log data filtering and reduction is based on goal model annotations and generates a sequence of logical literals that represent the possible systems' observations. A second level of investigation consists of looking for evidence for any malicious (i.e., intentionally caused by a third party) activity leading to task failures. This analysis uses annotated anti-goal models that denote possible actions that can be taken by an external user to threaten a given system task. The framework uses a novel probabilistic approach based on Markov Logic Networks. Our experiments show that our approach improves over existing proposals by handling uncertainty in observations, using natively generated log data, and by providing ranked diagnoses. The proposed framework has been evaluated using a test environment based on commercial off-the-shelf software components, publicly available Java Based ATM machine, and the large publicly available dataset (DARPA 2000).

The objective of this thesis was to determine how aspen (Populus tremuloides Michx.) root systems and suckering are affected by decking area (site of log processing and storage) disturbances and seasonal timing of these disturbances. In a field study, summer-built log decks reduced regeneration by half compared to fall-built decks, and if decks were built in the fall, 11 month and 1.5-3 month storage were similar in their impact. A growth-chamber study examined the timing of traffic-induced wounding of the root system and simulated log storage on aspen root systems and suckering. For both summer and winter treatments the combination of root wounding and log storage killed nearly the entire root system and prevented suckering. Root wounding and log storage alone caused a 35-40% reduction in living root mass, carbohydrate reserves, and sucker growth. Sucker numbers were reduced by one half for the winter but were unaffected for the summer.

No description available.

The cochlea is an organ which extracts frequency information from the input sound wave. It also produces nerve signals, which are further analysed by the brain and ultimately lead to perception of the sound. An existing model of the cochlea by Fragni`ere is first analysed by simulation. This passive model is found to have the properties that the living cochlea does in terms of the frequency response. An analog VLSI circuit implementation of this cochlear model in CMOS weak inversion is proposed, using log-domain filters in current domain. It is fabricated on a chip and a measurement of a basilar membrane section is performed. The measurement shows a reasonable agreement to the model. However, the circuit is found to have a problem related to transistor mismatch, causing different behaviour in identical circuit blocks. An active cochlear model is proposed to overcome this problem. The model incorporates the effect of the outer hair cells in the living cochlea, which controls the quality factor of the basilar membrane filters. The outer hair cells are incorporated as an extra voltage source in series with the basilar membrane resonator. Its value saturates as the input signal becomes larger, making the behaviour rather closer to that of a passive model. The simulation results show this nonlinear phenomenon, which is also seen in the living cochlea. The contribution of this thesis is summarised as follows: a) the first CMOS weak inversion current domain basilar membrane resonator is designed and fabricated, and b) the first active two-dimensional cochlear model for analog VLSI implementation is developed.

Universiẗat, Diss., 2006--Freiburg (Breisg.).

Thesis (Ph. D. in Sociology)--Vanderbilt University, Dec. 2008. / Title from title screen. Includes bibliographical references.