High resolution imaging devices have made a digital medical archiving system feasible. The large volumes of information generated must be stored and retrieved at high data rates in order to insure the timely diagnosis of patients. This creates some unique technological challenges that must be resolved, including the problem dealing with multiple vendor products interacting in one environment. The high speed interface card design presented in this thesis is able to deal with different computer host busses as well as different interprocessor communication protocols. The ACR-NEMA standard has been implemented in the design as one possible network protocol that provides a solution that can be easily adapted to different vendors. The design has been analyzed using the Network II.5 simulation language. The simulation was performed to insure that the original objectives are met and to determine the impact on the protocols rated throughput.

published_or_final_version / Computer Science and Information Systems / Doctoral / Doctor of Philosophy

No description available.

The following paper discusses the application of two subdivision algorithms for the purpose of finding an optimal way of rendering smooth spherical surfaces. Subdivision algorithms are used on three dimensional models. These algorithms typically manipulate the original object to produce one that is more visually pleasing and more realistic to the object we are attempting to recreate. We applied two popular subdivision algorithms to some simple meshes to compare their outcomes. In this project we implemented some of these algorithms in order to gain some insight into how these algorithms differ in the way that they are transforming the input mesh. Our desired goal was to see if there is any basis for which we can say that one algorithm outperforms the other. Our comparison runs through several iterations of subdivision and compares their theses meshes visually. In comparing these meshes our desired visual outcome is a mesh that is more smooth or more spherical. Another metric we looked at was the number of faces being produced for each mesh. In addition, we compared the algorithms in terms of the time they took to perform subdivision. These metrics form the basis for our comparison of performance and we discuss the details of these further in this paper.In our results we found that the two algorithms we are comparing perform quite similarly on certain meshes with respect to the visual output and the time they take to perform subdivision. On meshes of different types however the algorithms might output visually distinguishable meshes upon repeated subdivisions. Finding what factors influence whether the algorithms perform similarly provides an avenue for future work.

Electroencephalogram (EEG) Recording has been through a lot of changes and modification since it was first introduced in 1929 due to rising technologies and signal processing advancements. The EEG Data acquisition stage is the first and most valuable component in any EEG recording System, it has the role of gathering and conditioning its input and outputting reliable data to be effectively analyzed and studied by digital signal processors using sophisticated and advanced algorithms which help in numerous medical and consumer applications. We have designed a low noise low power EEG data acquisition system that can be set to act as a standalone mobile EEG data processing unit providing data preprocessing functions; it can also be a very reliable high speed data acquisition interface to an EEG processing unit. / by Sherif S. Fathalla. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

by Wan Lai Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 76-80). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Objectives --- p.3 / Chapter 1.3 --- Contributions --- p.4 / Chapter 1.4 --- Overview --- p.4 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Message Passing Interface --- p.6 / Chapter 2.1.1 --- Point-to-Point Communication --- p.7 / Chapter 2.1.2 --- Persistent Communication Request --- p.8 / Chapter 2.1.3 --- Collective Communication --- p.8 / Chapter 2.1.4 --- Derived Datatype --- p.9 / Chapter 2.2 --- Communications in Java --- p.10 / Chapter 2.2.1 --- Object Serialization --- p.10 / Chapter 2.2.2 --- Remote Method Invocation --- p.11 / Chapter 2.3 --- Performances Issues in Java --- p.11 / Chapter 2.3.1 --- Byte-code Interpreter --- p.11 / Chapter 2.3.2 --- Just-in-time Compiler --- p.12 / Chapter 2.3.3 --- HotSpot --- p.13 / Chapter 2.4 --- Parallel Computing in Java --- p.14 / Chapter 2.4.1 --- JavaMPI --- p.15 / Chapter 2.4.2 --- Bayanihan --- p.15 / Chapter 2.4.3 --- JPVM --- p.15 / Chapter 3 --- Infrastructure --- p.17 / Chapter 3.1 --- Layered Model --- p.17 / Chapter 3.2 --- Java Parallel Environment --- p.19 / Chapter 3.2.1 --- Job Coordinator --- p.20 / Chapter 3.2.2 --- HostApplet --- p.20 / Chapter 3.2.3 --- Formation of Java Parallel Environment --- p.21 / Chapter 3.2.4 --- Spawning Processes --- p.24 / Chapter 3.2.5 --- Message-passing Mechanism --- p.28 / Chapter 3.3 --- Application Programming Interface --- p.28 / Chapter 3.3.1 --- Message Routing --- p.29 / Chapter 3.3.2 --- Language Binding for MPI in Java --- p.31 / Chapter 4 --- Programming in JMPI --- p.35 / Chapter 4.1 --- JMPI Package --- p.35 / Chapter 4.2 --- Application Startup Procedure --- p.37 / Chapter 4.2.1 --- MPI --- p.38 / Chapter 4.2.2 --- JMPI --- p.38 / Chapter 4.3 --- Example --- p.39 / Chapter 5 --- Processes Management --- p.42 / Chapter 5.1 --- Background --- p.42 / Chapter 5.2 --- Scheduler Model --- p.43 / Chapter 5.3 --- Load Estimation --- p.45 / Chapter 5.3.1 --- Cost Ratios --- p.47 / Chapter 5.4 --- Task Distribution --- p.49 / Chapter 6 --- Performance Evaluation --- p.51 / Chapter 6.1 --- Testing Environment --- p.51 / Chapter 6.2 --- Latency from Java --- p.52 / Chapter 6.2.1 --- Benchmarking --- p.52 / Chapter 6.2.2 --- Experimental Results in Computation Costs --- p.52 / Chapter 6.2.3 --- Experimental Results in Communication Costs --- p.55 / Chapter 6.3 --- Latency from JMPI --- p.56 / Chapter 6.3.1 --- Benchmarking --- p.56 / Chapter 6.3.2 --- Experimental Results --- p.58 / Chapter 6.4 --- Application Granularity --- p.62 / Chapter 6.5 --- Scheduling Enable --- p.64 / Chapter 7 --- Conclusion --- p.66 / Chapter 7.1 --- Summary of the thesis --- p.66 / Chapter 7.2 --- Future work --- p.67 / Chapter A --- Performance Metrics and Benchmark --- p.69 / Chapter A.1 --- Model and Metrics --- p.69 / Chapter A.1.1 --- Measurement Model --- p.69 / Chapter A.1.2 --- Performance Metrics --- p.70 / Chapter A.1.3 --- Communication Parameters --- p.72 / Chapter A.2 --- Benchmarking --- p.73 / Chapter A.2.1 --- Ping --- p.73 / Chapter A.2.2 --- PingPong --- p.74 / Chapter A.2.3 --- Collective --- p.74 / Bibliography --- p.76

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript, etc. / Digitized by Kansas Correctional Industries