Downloads & Free Reading Options - Results

A high performance, versatile, and reliable underwater communications capability is of strategic importance to the U.S. Navy. Operational scenarios involving the use, monitoring, and coordination of multiple undersea assets, both manned and unmanned and mobile and fixed, are significantly enhanced by the ability to communicate quickly, reliably, in a wide range of environmental conditions, and with minimal constraints on the actions of the platforms involved. The long-term goal of the effort in underwater acoustic communications is to integrate research in the areas of physical oceanography, ocean acoustics, signal processing, and communications theory to 1. develop a underwater communications capability that can be deployed on and used by a wide range of platforms with minimal required external infrastructure and which achieves reliable and high performance under a wide range of environmental and operational conditions, and 2. develop an communications performance prediction capability that enables commanders to reliably predict the performance of deployed communications systems as a function of environmental conditions and the location, velocity, and capabilities of the deployed assets.

A high performance, versatile, and reliable underwater communications capability is of strategic importance to the U.S. Navy. Operational scenarios involving the use, monitoring, and coordination of multiple undersea assets, both manned and unmanned and mobile and fixed, are significantly enhanced by the ability to communicate quickly, reliably, in a wide range of environmental conditions, and with minimal constraints on the actions of the platforms involved. The long-term goal of the effort in underwater acoustic communications is to integrate research in the areas of physical oceanography, ocean acoustics, signal processing, and communications theory to 1. develop a underwater communications capability that can be deployed on and used by a wide range of platforms with minimal required external infrastructure and which achieves reliable and high performance under a wide range of environmental and operational conditions, and 2. develop an communications performance prediction capability that enables commanders to reliably predict the performance of deployed communications systems as a function of environmental conditions and the location, velocity, and capabilities of the deployed assets.

A high performance, versatile, and reliable underwater communications capability is of strategic importance to the U.S. Navy. Operational scenarios involving the use, monitoring, and coordination of multiple undersea assets, both manned and unmanned and mobile and fixed, are significantly enhanced by the ability to communicate quickly, reliably, in a wide range of environmental conditions, and with minimal constraints on the actions of the platforms involved. The long-term goal of the effort in underwater acoustic communications is to integrate research in the areas of physical oceanography, ocean acoustics, signal processing, and communications theory to 1. develop a underwater communications capability that can be deployed on and used by a wide range of platforms with minimal required external infrastructure and which achieves reliable and high performance under a wide range of environmental and operational conditions, and 2. develop an communications performance prediction capability that enables commanders to reliably predict the performance of deployed communications systems as a function of environmental conditions and the location, velocity, and capabilities of the deployed assets.

This thesis explores various theoretical aspects of machine learning with particular emphasis on techniques for designing and analyzing computationally efficient learning algorithms. Many of the results in this thesis are concerned with a model of concept learning proposed by Valiant. The thesis begins in Chapter 2 with a proof that any 'weak' learning algorithm in this model that performs slightly better than random guessing can be converted into one whose error can be made arbitrarily small. Several interesting consequences of this result are also described. Chapter 3 next explores in detail a simple but powerful technique for discovering the structure of an unknown read-once formula from random examples. An especially nice feature of this technique is its powerful resistance to noise. Chapter 4 considers a realistic extension of the PAC model to concepts that may exhibit uncertain or probabilistic behavior. A range of techniques are explored for designing efficient algorithms for learning such probabilistic concepts. In the last chapter, we present new algorithms for inferring an unknown finite-state automation from its input-output behavior. This problem is motivated by that faced by a robot in unfamiliar surroundings who must, through experimentation, discover the structure of its environment.

The characteristic of an asynchronous multiprocessor is that it is composed of several processors capable of carrying out the execution of their own programs in a completely independent fashion. As a consequence, parallel algorithms for asynchronous multiprocessors present some unique aspects in both their design and their analysis. This thesis explores the issues raised by the design and the analysis of parallel algorithms for asynchronous multiprocessors and illustrates the various notions and concepts involved with these algorithms by considering problems in diverse areas. The thesis demonstrates that asynchronous multiprocessors can be used efficiently in different problem domains, provided that appropriate algorithms are used. It also illustrates various techniques useful in the analysis of such algorithms. As evidenced by a series of experimental results, the computation time required by a process to execute several instances of the same task on an asynchronous multiprocessor cannot be regarded as constant and is actually subject to important fluctuations. These fluctuations in computation times have a negative effect on the performance of parallel algorithms when several processes cooperating in the solution of a problem communicate extensively among themselves. In this case, when synchronization is used, it tends to introduce a prohibitive overhead which decreases the parallelism. On the other hand, an algorithm is presented to illustrate that the fluctuations are not always a negative factor but can also be utilized advantageously.

The characteristic of an asynchronous multiprocessor is that it is composed of several processors capable of carrying out the execution of their own programs in a completely independent fashion. As a consequence, parallel algorithms for asynchronous multiprocessors present some unique aspects in both their design and their analysis. This thesis explores the issues raised by the design and the analysis of parallel algorithms for asynchronous multiprocessors and illustrates the various notions and concepts involved with these algorithms by considering problems in diverse areas. The thesis demonstrates that asynchronous multiprocessors can be used efficiently in different problem domains, provided that appropriate algorithms are used. It also illustrates various techniques useful in the analysis of such algorithms. As evidenced by a series of experimental results, the computation time required by a process to execute several instances of the same task on an asynchronous multiprocessor cannot be regarded as constant and is actually subject to important fluctuations. These fluctuations in computation times have a negative effect on the performance of parallel algorithms when several processes cooperating in the solution of a problem communicate extensively among themselves. In this case, when synchronization is used, it tends to introduce a prohibitive overhead which decreases the parallelism. On the other hand, an algorithm is presented to illustrate that the fluctuations are not always a negative factor but can also be utilized advantageously.

The characteristic of an asynchronous multiprocessor is that it is composed of several processors capable of carrying out the execution of their own programs in a completely independent fashion. As a consequence, parallel algorithms for asynchronous multiprocessors present some unique aspects in both their design and their analysis. This thesis explores the issues raised by the design and the analysis of parallel algorithms for asynchronous multiprocessors and illustrates the various notions and concepts involved with these algorithms by considering problems in diverse areas. The thesis demonstrates that asynchronous multiprocessors can be used efficiently in different problem domains, provided that appropriate algorithms are used. It also illustrates various techniques useful in the analysis of such algorithms. As evidenced by a series of experimental results, the computation time required by a process to execute several instances of the same task on an asynchronous multiprocessor cannot be regarded as constant and is actually subject to important fluctuations. These fluctuations in computation times have a negative effect on the performance of parallel algorithms when several processes cooperating in the solution of a problem communicate extensively among themselves. In this case, when synchronization is used, it tends to introduce a prohibitive overhead which decreases the parallelism. On the other hand, an algorithm is presented to illustrate that the fluctuations are not always a negative factor but can also be utilized advantageously.

Many applications -- from planning and scheduling to problems in molecular biology -- rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen's influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algorithm and a backtracking algorithm. For the path consistency algorithm, we develop techniques that can result in up to a ten-fold speedup over an already highly optimized implementation. For the backtracking algorithm, we develop variable and value ordering heuristics that are shown empirically to dramatically improve the performance of the algorithm. As well, we show that a previously suggested reformulation of the backtracking search problem can reduce the time and space requirements of the backtracking search. Taken together, the techniques we develop allow a temporal reasoning component to solve problems that are of practical size.