Lempel-Ziv Data Compression on Parallel and Distributed Systems

We present a survey of results concerning Lempel-Ziv data compression on parallel and distributed systems, starting from the theoretical approach to parallel time complexity to conclude with the practical goal of designing distributed algorithms with low communication cost. An extension by Storer to image compression is also discussed

Lossless Image Compression Using Super-Spatial Structure Prediction

Digital Object Identifier 10.1109/LSP.2010.2040925We recognize that the key challenge in image compression is to efficiently represent and encode high-frequency image structure components, such as edges, patterns, and textures. In this work, we develop an efficient lossless image compression scheme called super-spatial structure prediction. This super-spatial prediction is motivated by motion prediction in video coding, attempting to find an optimal prediction of structure components within previously encoded image regions. We find that this super-spatial prediction is very efficient for image regions with significant structure components. Our extensive experimental results demonstrate that the proposed scheme is very competitive and even outperforms the state-of-the-art lossless image compression methods

Analysis and study on text representation to improve the accuracy of the Normalized Compression Distance

The huge amount of information stored in text form makes methods that deal with texts really interesting. This thesis focuses on dealing with texts using compression distances. More specifically, the thesis takes a small step towards understanding both the nature of texts and the nature of compression distances. Broadly speaking, the way in which this is done is exploring the effects that several distortion techniques have on one of the most successful distances in the family of compression distances, the Normalized Compression Distance -NCD-.Comment: PhD Thesis; 202 page

Tunability and performance enhancement for planar microwave filters

Radio-frequency (RF) spectrum is exploited as a valuable resource for wireless applications such as mobile and satellite communications. As a result, communication systems including satellite communication and emerging 5G are trending to have frequency-agility to adapt to highly complex RF environments. However, due to the nature of materials and components, electrically-tunable planar filters, which play essential roles in frequency agile RF systems, have their disadvantages of low-order, high-loss, and poor selectivity. This has limited the overall performances of the frequency agile RF systems. In the light of this scenario, the objective of this thesis is to develop efficient performance-enhancement techniques, including the lossy technique, and the active technique, into the high-selective tunable planar filters to boost the performances of tunable RF systems. First of all, an electrically reconfigurable microstrip dual-mode filter is demonstrated with nonuniform-quality-factor lossy technique. The 4-pole bandpass filter exhibits a continuously bandwidth tuning and centre frequency tuning capability. By making use of the doubly tuned resonant property of the dual-mode microstrip open-loop resonator, passband flatness can be improved by simply loading resistors on the even-odd mode symmetrical plane of resonators. Moreover, two intrinsic transmission zeros are in upper and lower stopbands enhancing the filter selectivity. The coupling matrix synthesis is introduced to describe the nonuniform-quality-factor distribution in a filter network. The experiment of this type of four-pole tunable lossy filter has presented a good agreement with the simulation. Then, the thesis reports a novel 5-pole lossy bandpass filter with the bandwidth tunability. In order to improve the filter selectivity, we choose a hybrid filter structure consist of hairpin resonators and dual behaviour resonators to produce two adjustable transmission zeros for high selective responses. A novel lossy technique named centre-loaded resistive cross-coupling is developed to efficiently reduce the insertion-loss variation of the tuned passband. The fabricated filter demonstrates an insertion loss variation of less than 1 dB for all bandwidth states. To compensate the loss within the varactor-tuned narrowband filter, a tunable 2-pole active filter is presented with a constant absolute bandwidth. The negative resistance generated from active circuits successfully cancels the loss within the varactor-loaded resonators resulting in high quality-factor resonator filter responses. With the transistor small-signal model, the value of the negative resistance of the active circuit can be predicted by network analysis. Experiments were carried out to validate the design

Fourth NASA Goddard Conference on Mass Storage Systems and Technologies

This report contains copies of all those technical papers received in time for publication just prior to the Fourth Goddard Conference on Mass Storage and Technologies, held March 28-30, 1995, at the University of Maryland, University College Conference Center, in College Park, Maryland. This series of conferences continues to serve as a unique medium for the exchange of information on topics relating to the ingestion and management of substantial amounts of data and the attendant problems involved. This year's discussion topics include new storage technology, stability of recorded media, performance studies, storage system solutions, the National Information infrastructure (Infobahn), the future for storage technology, and lessons learned from various projects. There also will be an update on the IEEE Mass Storage System Reference Model Version 5, on which the final vote was taken in July 1994

Mutual information-based gradient-ascent control for distributed robotics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 167-179).This thesis presents the derivation, analysis, and implementation of a novel class of decentralized mutual information-based gradient-ascent controllers that continuously move robots equipped with sensors to better observe their environment. We begin with the fundamental problem of deploying a single ground robot equipped with a range sensor and tasked to build an occupancy grid map. The desired explorative behaviors of the robot for occupancy grid mapping highlight the correlation between the information content and the spatial realization of the robot's range measurements. We prove that any occupancy grid controller tasked to maximize a mutual information reward function is eventually attracted to unexplored space, i.e., areas of highest uncertainty. We show that mutual information encodes geometric relationships that are fundamental to robot control and yields geometrically relevant reward surfaces on which robots can navigate. Taking inspiration from geometric-based approaches to distributed robot coordination, we show that many multi-robot inference tasks can be cast in terms of an optimization problem. This optimization problem defines the task of minimizing the conditional entropy associated with the robots' inferred beliefs of the environment, which is equivalent to maximizing the mutual information between the environment state and the robots' next joint observation. Given simple robot dynamics and few probabilistic assumptions, none of which involve Gaussianity, we derive a gradientascent solution approach to these optimization problems that is convergent between sensor observations and locally optimal. More formally, we invoke LaSalle's Invariance Principle to prove that, given enough time between consecutive joint observations, robots following the gradient of mutual information will converge to goal positions that locally maximize the expected information gain resulting from the next observation. We show that the algorithmic implementation of the generalized gradient-ascent controller is not readily distributed among multiple robots, and thus sample-based methods are introduced to distributively approximate the likelihoods of the robots' joint observations. Not only are the involved non-parametric representations compatible with any type of Bayesian filter, but the computational complexities of the resulting decentralized controllers are independent with respect to the number of robots. Concerning the distributed approximations, we give two example consensus-based algorithms that run on an undirected network graph. The first consensus-based algorithm approximates discrete measurement probabilities, while the second approximates continuous likelihood distributions. We show that these anytime approximations provably converge to the correct values on a static and connected network graph without knowledge of the number of robots in the network or the corresponding graph's topology. Lastly, we incorporate the resulting consensus-based algorithms into both a hardware system and a simulation environment to allow for decentralized controller evaluation under non-ideal network settings. For the hardware experiments, the task is to infer the state of a bounded, planar environment by deploying five quadrotor flying robots with simulated sensors in both indoor and outdoor settings. For the numerical simulations, Monte Carlo-based analyses are performed for 100 robots, where each robot is simulated on an independent computer node within a computer cluster system. Simulations are also performed for 1000 robots using a single workstation computer equipped with a multicore GPU-enabled graphics card. The results from both the hardware experiments and numerical simulations validate our theoretical and computational claims throughout the thesis.by Brian John Julian.Ph.D