We consider the problem of transmitting compressed still images over lossy channels. In particular, we examine the situation where the data stream is partitioned into two independent channels, as is often considered in the multiple descriptions approach to image compression. In the present work, we introduce a coder design called smoothed descriptions, that matches a data partitioning method utilized at the encoder to the error concealment technique employed at the decoder. This approach has the advantage of inserting minimal overhead into the transmitted data streams, so that system performance is undiminished when there are no packet losses over the channel. We show that, by using a combination of DC averaging and maximal smoothing to conceal errors, performance comparable to or better than multiple descriptions can be achieved for packet loss rates up to 5%. By adding a feedback loop that requests retransmission of important data, we also demonstrate the need to exploit latency when...

. We detail several methods used in the production of pseudorandom numbers for scalable systems. We will focus on methods based on parameterization, meaning that we will not consider splitting methods. We describe parameterized versions of the following pseudorandom number generation: 1. linear congruential generators 2. linear matrix generators 3. shift-register generators 4. lagged-Fibonacci generators 5. inversive congruential generators We briefly describe the methods, detail some advantages and disadvantages of each method and recount results from number theory that impact our understanding of their quality in parallel applications. Several of these methods are currently part of scalable library for pseudorandom number generation, called the SPRNG package available at the URL: www.ncsa.uiuc.edu/Apps/CMP/RNG. Key words. pseudorandom number generation, parallel computing, linear congruential, lagged-Fibonacci, inversive congruential, shift-register AMS(MOS) subject classifications....

PRNGlib provides several pseudo-random number generators through a common interface on any Shared or Distributed Memory Parallel architecture. Common routines are specified to initialize the generators with appropriate seeds on each processor and to generate uniform or (normal, Poisson, exponential) distributed random vectors. We concentrate on those generators which assure high quality (i.e., passing most of the empirical and theoretical tests), have a long period, and can be calculated quickly, also in parallel, i.e., it must be possible to generate the same random sequence independent of the number of processors. This splitting facility implies a method to skip over n pseudo-random numbers without calculating all intermediate values, i.e., an O(log n) algorithm is required. Taking into account these criteria Lagged Fibonacci, Generalized Shift Register, and Multiplicative Linear Congruential generators are implemented with (almost) arbitrary specifications for lags, multipliers, m...

Computer models are used in landscape ecology to simulate the effects of human land-use decisions on the environment. Many socioeconomic as well as ecological factors must be considered, requiring the integration of spatially explicit multidisciplinary data. The Land-Use Change Analysis System or LUCAS has been developed to study the effects of land-use on landscape structure in such areas as the Little Tennessee River Basin in western North Carolina and the Olympic Peninsula of Washington state. These effects include land-cover change and species habitat suitability. The map layers used by LUCAS are derived from remotely sensed images, census and ownership maps, topological maps, and output from econometric models. A public-domain geographic information system (GIS) is used to store, display and analyze these map layers. A parallel version of LUCAS (pLUCAS) was developed using the Parallel Virtual Machine (PVM) on a network of workstations giving a speedup factor of 10.77 with 20 node...

ce of my life. iv Preface The emergence of Java programming language together with the Internet creates a new exciting area in the field of simulation, web-based simulation, allows us to develop dynamic simulation models and makes simulation models widely available over the Internet. The main objective of this thesis is to develop a simulation library in Java to make construction, distribution and execution of simulation models easy. The thesis states the motivations and objectives of this project, introduces basic simulation concepts, provides an overview of the major features of the Java programming language and its importance in web-based simulation and presents JSIM, a Java-based simulation and animation foundation library. A bank simulation example developed in JSIM is also discussed. The JSIM library includes six packages: queue, statistic, variate, event,<F

Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent requirements on the quality of Gaussian random number generators (GRNG). This article describes the algorithms underlying various GRNGs, compares their computational requirements, and examines the quality of the random numbers with emphasis on the behaviour in the tail region of the Gaussian probability density function.

This paper develops a high-performance, Internet-wide service discovery tool, which we call IRLscanner, whose main design objectives have been to maximize politeness at remote networks, allow scanning rates that achieve coverage of the Internet in minutes/hours (rather than weeks/months), and significantly reduce administrator complaints. Using IRLscanner and 24-hour scans, we perform 21 Internet-wide experiments using 6 different protocols (i.e., DNS, HTTP, SMTP, EPMAP, ICMP and UDP ECHO), demonstrate the usefulness of ACK scans in detecting live hosts behind stateless firewalls, and undertake the first Internet-wide OS fingerprinting. In addition, we analyze the feedback generated (e.g., complaints, IDS alarms) and suggest novel approaches for reducing the amount of blowback during similar studies, which should enable researchers to collect valuable experimental data in the future with significantly fewer hurdles.

classes for internal use: ffl BlockProcess ffl ChiSquareStatistic ffl IncrementalBlock ffl Probability ffl TupleProcess 5.1 ChiSquareStatistic The ChiSquareStatistic, a subclass of IncrementalBlock, is an abstract class which implements methods for computing the Ø 2 -statistic [JK70]. For subclasses which can have instances, see Section 5.7 for IntegerChiSquare and Section 5.24 for UnitCubeChiSquare. Basically, ChiSquareStatistic maintains the bin-counters and computes the test statistic. The computation of bin indices and expected frequencies are left to its subclasses. 5.1.1 Instance variables ffl dimension The dimension of its master's domain; maintained by the master: method. ffl df The degrees of freedom or, equivalently, the number of bins minus one. Subclasses must set this variable in the master: method. ffl observedHits If df is k, then this is a field of k + 1 bin-counters. Subclasses must initialize this variable in the master: method. 5.1.2 Action protocol f...

In a set of stochastic simulations, which we collectively call the digit test, we compare the widely used linear congruential with the new inversive random number generators. The inversive generators are found to perform always at least as good as any of the linear congruential generators; in some simulation runs, they perform significantly better.

Introduction and Preliminaries For mobile robots, uncertainty is everywhere. Wheels slip. Sensors are a#ected by noise. Obstacles move unpredictably. Truly autonomous robots (and decision-makers or agents in general) must act in ways that are robust to these sorts of failures and unexpected events which we may think of in general as uncertainty. In this chapter, we attempt to meet uncertainty head-on by explicitly modeling it and reasoning about it. We use the term decision theoretic planning to refer to this broad class of planning methods characterized by explicit accounting for uncertainty. We will consider a number of formulations for the problem of planning under uncertainty and present algorithms for planning under these formulations. Uncertainty can take many forms, but for brevity and clarity we will restrict our attention to only two important types: . Prediction uncertainty occurs when the e#ects of actions are not fully predictable. This can be thought of as an uncertain