Self-organizing overlay networks have emerged as a powerful paradigm for providing network services. While most approaches assume that overlay networks are built over a single substrate network, generally, the Internet, this thesis addresses the construction of overlay networks over multiple substrate networks. We present the design, implementation and evaluation of Cross-Substrate Advertisement (CSA) mechanisms for overlay networks over multiple heterogeneous substrate networks. A key difficulty arises from the more complex address bindings, since a single logical identifier is bound to multiple substrate addresses. We present mechanisms for exchanging information on address bindings and evaluate their effectiveness. The CSA mechanisms have been implemented in the HyperCast overlay protocol architecture, and have been evaluated in measurement experiments on an Emulab testbed. The experiments show that our CSA methods are effective in disseminating address information in large networks and are robust in the presence of network disruptions.

With the growth of multimedia services, it is essential to find new transmission schemes to support higher data rates in wireless networks. In this thesis, we study networks in which the Channel State Information (CSI) is only available at the destination. We focus on the analysis of three different network setups. For each case, we propose a transmission scheme which maximizes the average performance of the network. The first scenario, which is studied in Chapter 2, is a multi-hop network in which the channel gain of each hop changes quasi-statically from one transmission block to the other. Our main motivation to study this network is the recent advances in deployment of relay nodes in wireless networks (e.g., LTE-A and IEEE 802.16j). In this setup, we assume that all nodes are equipped with a single antenna and the relay nodes are not capable of data buffering over multiple transmission blocks. The proposed transmission scheme is based on infinite-layer coding at all nodes (the source and all relays) in conjunction with the Decode-and-Forward DF relaying. The objective is to maximize the statistical average of the received rate per channel use at the destination. To find the optimal parameters of this code, we first formulate the problem for a two-hop scenario and describe the code design algorithm for this two-hop setting. The optimality of infinite-layer DF coding is also discussed for the case of two-hop networks. The result is then generalized to multi-hop scenarios. To show the superiority of the proposed scheme, we also evaluate the achievable average received rate of infinite-layer DF coding and compare it with the performance of previously known schemes. The second scenario, studied in Chapter 3, is a single-hop network in which both nodes are equipped with multiple antennas, while the channel gain changes quasi-statically and the CSI is not available at the source. The main reason for selecting this network setup is to study the transmission of video signals (compressed using a scalable video coding technique, e.g., SVC H.264/AVC) over a Multiple-Input Multiple-Output (MIMO) link. In this setup, although scalable video coding techniques compress the video signal into layers with different importance (for video reconstruction), the source cannot adapt the number of transmitted layers to the capacity of the channel (since it does not have the CSI in each time slot). An alternative approach is to always transmit all layers of the compressed video signal, but use unequal error protection for different layers. With this motivation, we focus on the design of multilayer codes for a MIMO link in which the destination is only able to perform successive decoding (not joint-decoding). In this chapter, we introduce a design rule for construction of multilayer codes for MIMO systems. We also propose a algorithm that uses this design rule to determine the parameters of the multilayer code. The performance analysis of the proposed scheme is also discussed in this chapter. In the two previous scenarios, the ambiguity of the source regarding the channel state comes from the fact that the channel gains randomly change in each transmission block and there is no feedback to notify the source about the current state of the channel. Apart from these, there are some scenarios in which the channel state is unknown at the source, even though the channel gain is fixed and the source knows its value. The third scenario of this thesis presents an example of such network setups. More precisely, in Chapter 4, we study a multiple access network with K users and one Access Point (AP), where all nodes are equipped with multiple antennas. To access the network, each user independently decides whether to transmit in a time slot or not (no coordination between users). Considering a two-user random access network, we first derive the optimal value of network average Degrees of Freedom (DoF) (introduced in Section 4.1). Generalizing the result to multiuser networks, we propose an upper-bound for the network average DoF of a K-user random access network. This upper-bound is then analyzed for different network configurations to identify the network classes in which the proposed upper-bound is tight. It is also shown that simple single-stream data transmission achieves the upper-bound in most network settings. However, for some network configurations, we need to apply multi-stream data transmission in conjunction with interference alignment to reach the upper-bound. Some illustrative examples are also presented in this chapter.

Current research has pointed out that a subsidiary ́s external network, constituted by its suppliers and customers at the local market, is very important for the corporation as a whole. In this study we seek to explore if this is the case for a certain Multinational corporation through exploring the usage of its networks. More specifically how the knowledge within its networks sharing contribute to the company’s innovation development process. A number of interviews have been conducted with personnel at a Multinational corporation, General Electric Healthcare Life Sciences in Uppsala. The results have been analyzed using a theoretical frame of reference covering network theory and a subsidiary’s part in it. Our findings show that even though the external network may play a necessary part, the internal network of a large Multinational corporation is an important resource and should not be ignored.

<p>Many biological processes are effectively modeled as networks, but a frequent assumption is that these networks do not change during data collection. However, that assumption does not hold for many phenomena, such as neural growth during learning or changes in genetic regulation during cell differentiation. Approaches are needed that explicitly model networks as they change in time and that characterize the nature of those changes.</p><p>In this work, we develop a new class of graphical models in which the conditional dependence structure of the underlying data-generation process is permitted to change over time. We first present the model, explain how to derive it from Bayesian networks, and develop an efficient MCMC sampling algorithm that easily generalizes under varying levels of uncertainty about the data generation process. We then characterize the nature of evolving networks in several biological datasets.</p><p>We initially focus on learning how neural information flow networks change in songbirds with implanted electrodes. We characterize how they change in response to different sound stimuli and during the process of habituation. We continue to explore the neurobiology of songbirds by identifying changes in neural information flow in another habituation experiment using fMRI data. Finally, we briefly examine evolving genetic regulatory networks involved in Drosophila muscle differentiation during development.</p><p>We conclude by suggesting new experimental directions and statistical extensions to the model for predicting novel neural flow results.</p> / Dissertation

Advances in micro-sensor and wireless technology enable small but smart sensors to be developed for wide range environment-monitor applications. Since Sensor nodes only have limited power capacity and are difficult to recharge, how to prolong network lifetime is an important issue in wireless sensor networks design. Several topology control algorithms have been proposed to maintain the connectivity of wireless sensor network and reduce the energy consumption. Multi-hop Infrastructure Network Architecture (MINA) is a kind of Multi-layer Architecture for WSN topology, which utilizes hundred of sensors to transmit data to a sink. This architecture partitions sensor nodes into layers based on their distances (calculated by hop count) to BS. In this way, the node connected to more nodes will relay more data for other nodes. This make the node exhaust its battery power quickly and thus reduces the network lifetime. This study proposes an Efficient Energy Time-Slot Allocation (EETA) scheme which distributes time slots in accordance with the energy of neighbor nodes and the number of neighbor nodes. In addition, this work also devises an adaptive time slot size to reduce data packet drop in case when the node buffer is full. The simulation results show that the EETA performs better than the MINA in terms of network lifetime.

Power quality has attracted considerable attentions from utilities and customers due to the popular uses of the sensitive electronic equipment. Harmonics, voltage swell, voltage sag, and power interruption could downgrade the service quality. Harmonic currents injected by non-linear loads throughout the network could degrade the quality of services to sensitive high-tech customers such as the science park of Xin-Zhu and Tai-Nan in Taiwan. In recent years, massive rapid transit system (MRT) and high speed railway (HSR) have been rapidly developed, with the applications of wide-spread semi-conductor technologies in the auto-traction system. Swell and sag could occur from thundering, capacitor switching, motor starting, nearby circuit faults, or artificial calamity, and could also attribute to the power interruption. To ensure the power quality, harmonic and voltage disturbances detection becomes important. Fourier transformation is used to analyze distorted waves in the frequency domain, with low-pass filter used to eliminate the fundamental component, and then characteristic harmonics can be detected. The complicated process is difficult to operate in real-time. The method-based processing model with physical harmonic data is needed to simplify the processing architecture. The thesis proposes to use wavelet transformation (WT) and probabilistic neural network (PNN) for power quality and characteristic harmonics detection. Wavelet-probabilistic network (WPN) is first used to extract distorted waves. PNN based processing model will then analyze the harmonic components. Computer simulation shows a simplified model to shorten the processing time in this study.

The paper proposes a model of detection for voltages and harmonics using wavelet-probabilistic network (WPN). WPN is a two-layer structure, containing the wavelet layer and probabilistic network. It uses the wavelet transformation (WT) and probabilistic neural network (PNN) to analyze distorted waves and classify tasks. In this thesis, the field programmable gate array (FPGA) is employed for the hardware realization of WPN. In the implementation process, by the use of the hardware description language, the WPN algorithm has been embedded into the FPGA chip. Firstly, we divide the mathematical formula of basic WPN algorithm into several parts in order to set up each module individually, then we integrate all modules to complete the design of basic WPN algorithm with digital circuits by the bottom-up process.

The technology of power electronics is used increasingly during recent years, and the electronic power facilities are used more and more in the power system. The non-linear electronic loads produce heavy harmonic currents and could significantly degrade the power quality. Nonlinear loads, including the un-interruptible power supply, motor control and converter, etc, are important equipment in a modern factory, however, these nonlinear loads could lead to power facility malfunction and capacitor damage. The harmonics would eventually cause severe unexpected capital loss. Identification of harmonic sources location becomes an important study for power quality. An effective tool is thus helpful for the harmonic source locating. This paper proposes a method to deal with the harmonic sources and location detection in the power system by using the artificial neural network (ANN). The non-linear loading characteristics are studied by the power flow analysis, and then the proposed methodology uses the Probabilistic Neural Networks¡]PNN¡^and wavelet-probabilistic network (WPN) for harmonic source locating. An IEEE 14-bus power system is used for study to show the effectiveness of the proposed approach.

Thesis (Ph. D.)--OGI School of Science & Engineering at OHSU, 2002. / Includes bibliographical references.

For decades, power engineers have used simulations to predict grid stability and voltage phase angles. Only recently have equipment been available to actually measure phase angle at points hundreds of miles away. A few of these systems are presently operating in the US by electric grids including the Electric Reliability Council of Texas (ERCOT) and California Independent System Operator (ISO). However, the systems are in their infancy and are far from being used to improve grid reliability. This thesis describes the only independent synchronized phasor network that exists in the US. Thanks to Schweitzer Engineering Laboratories (SEL), we are streaming in points from three locations plus the University of Texas at Austin (UT Austin) as of January 2009. This thesis will describe this network and grid analysis done this far. / text