Efficient structural outlooks for vertex product networks

In this thesis, a new classification for a large set of interconnection networks, referred to as "Vertex Product Networks" (VPN), is provided and a number of related issues are discussed including the design and evaluation of efficient structural outlooks for algorithm development on this class of networks. The importance of studying the VPN can be attributed to the following two main reasons: first an unlimited number of new networks can be defined under the umbrella of the VPN, and second some known networks can be studied and analysed more deeply. Examples of the VPN include the newly proposed arrangement-star and the existing Optical Transpose Interconnection Systems (OTIS-networks). Over the past two decades many interconnection networks have been proposed in the literature, including the star, hyperstar, hypercube, arrangement, and OTIS-networks. Most existing research on these networks has focused on analysing their topological properties. Consequently, there has been relatively little work devoted to designing efficient parallel algorithms for important parallel applications. In an attempt to fill this gap, this research aims to propose efficient structural outlooks for algorithm development. These structural outlooks are based on grid and pipeline views as popular structures that support a vast body of applications that are encountered in many areas of science and engineering, including matrix computation, divide-and- conquer type of algorithms, sorting, and Fourier transforms. The proposed structural outlooks are applied to the VPN, notably the arrangement-star and OTIS-networks. In this research, we argue that the proposed arrangement-star is a viable candidate as an underlying topology for future high-speed parallel computers. Not only does the arrangement-star bring a solution to the scalability limitations from which the Abstract existing star graph suffers, but it also enables the development of parallel algorithms based on the proposed structural outlooks, such as matrix computation, linear algebra, divide-and-conquer algorithms, sorting, and Fourier transforms. Results from a performance study conducted in this thesis reveal that the proposed arrangement-star supports efficiently applications based on the grid or pipeline structural outlooks. OTIS-networks are another example of the VPN. This type of networks has the important advantage of combining both optical and electronic interconnect technology. A number of studies have recently explored the topological properties of OTIS-networks. Although there has been some work on designing parallel algorithms for image processing and sorting, hardly any work has considered the suitability of these networks for an important class of scientific problems such as matrix computation, sorting, and Fourier transforms. In this study, we present and evaluate two structural outlooks for algorithm development on OTIS-networks. The proposed structural outlooks are general in the sense that no specific factor network or problem domain is assumed. Timing models for measuring the performance of the proposed structural outlooks are provided. Through these models, the performance of various algorithms on OTIS-networks are evaluated and compared with their counterparts on conventional electronic interconnection systems. The obtained results reveal that OTIS-networks are an attractive candidate for future parallel computers due to their superior performance characteristics over networks using traditional electronic interconnects

Array communications in wireless sensor networks

Imperial Users onl

Play Among Books

How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an “infinite flow” of real books

The Fifth NASA Symposium on VLSI Design

The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design

Journal of Telecommunications and Information Technology, 2014, nr 1

kwartalni

NASA/ASEE Summer Faculty Fellowship Program, 1990, Volume 1

The 1990 Johnson Space Center (JSC) NASA/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by the University of Houston-University Park and JSC. A compilation of the final reports on the research projects are presented. The topics covered include: the Space Station; the Space Shuttle; exobiology; cell biology; culture techniques; control systems design; laser induced fluorescence; spacecraft reliability analysis; reduced gravity; biotechnology; microgravity applications; regenerative life support systems; imaging techniques; cardiovascular system; physiological effects; extravehicular mobility units; mathematical models; bioreactors; computerized simulation; microgravity simulation; and dynamic structural analysis

Long-Short-Term Memory in Active Wavefield Geophysical Methods

The PhD thesis discusses the application of Long Short-Term Memory (LSTM) networks in active wavefield geophysical methods. In this work we emphasizes the advantages of Deep Learning (DL) techniques in geophysics, such as improved accuracy, handling complex datasets, and reducing subjectivity. The work explores the suitability of LSTM networks compared to Convolutional Neural Networks (CNNs) in some geophysical applications. The research aims to comprehensively investigate the strengths, limitations, and potential of recurrent neurons, particularly LSTM, in active wavefield geophysics. LSTM networks have the ability to capture temporal dependencies and are well-suited for analyzing geophysical data with non-stationary behavior. They can process both time and frequency domain information, making them valuable for analyzing Seismic and Ground Penetrating Radar (GPR) data. The PhD thesis consists of five main chapters covering methodological development, regression, classification, data fusion, and frequency domain signal processing.The PhD thesis discusses the application of Long Short-Term Memory (LSTM) networks in active wavefield geophysical methods. In this work we emphasizes the advantages of Deep Learning (DL) techniques in geophysics, such as improved accuracy, handling complex datasets, and reducing subjectivity. The work explores the suitability of LSTM networks compared to Convolutional Neural Networks (CNNs) in some geophysical applications. The research aims to comprehensively investigate the strengths, limitations, and potential of recurrent neurons, particularly LSTM, in active wavefield geophysics. LSTM networks have the ability to capture temporal dependencies and are well-suited for analyzing geophysical data with non-stationary behavior. They can process both time and frequency domain information, making them valuable for analyzing Seismic and Ground Penetrating Radar (GPR) data. The PhD thesis consists of five main chapters covering methodological development, regression, classification, data fusion, and frequency domain signal processing