Engineering Commons^™

Accelerating Quantum Monte Carlo Simulations With Emerging Architectures, Akila Gothandaraman

Doctoral Dissertations

Scientific computing applications demand ever-increasing performance while traditional microprocessor architectures face limits. Recent technological advances have led to a number of emerging computing platforms that provide one or more of the following over their predecessors: increased energy efficiency, programmability/flexibility, different granularities of parallelism, and higher numerical precision support. This dissertation explores emerging platforms such as reconfigurable computing using fieldprogrammable gate arrays (FPGAs), and graphics processing units (GPUs) for quantum Monte Carlo (QMC), a simulation method widely used in physics and physical chemistry. This dissertation makes the following significant contributions to computational science. First, we develop an open-source userfriendly hardware-accelerated simulation …

Quality Of Service Provisioning Through Resource Allocation And Data Aggregation In Wireless Sensor Networks, Carl Larsen

Doctoral Dissertations

"In this dissertation, improvement of quality of service (QoS) provisioning in wireless sensor networks (WSN) is examined using four different methods. The first two focus on allocation of limited resources in the face of changing network and channel conditions, and thus improving performance in terms of throughput and delay. The second two methods focus on using data aggregation to minimize the size and number of packet transmissions reducing the energy consumption and improving network lifetime.

Therefore, in the first paper a novel adaptive and distributed fair scheduling (ADFS) protocol for WSN is presented that allocates the channel bandwidth in proportion …

Meta-Learning Computational Intelligence Architectures, Ryan J. Meuth

Doctoral Dissertations

"In computational intelligence, the term 'memetic algorithm' has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a 'meme' has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as 'memetic algorithm' is too specific, and ultimately a misnomer, as much as a 'meme' is defined too generally to be of scientific use. In this dissertation the notion of memes and meta-learning is extended from a computational viewpoint and the purpose, definitions, design guidelines …