This thesis present a new receiver framework for the cyclic-prefix free (CP-free) MIMO-OFDM system, equipped with the space-time block coded (ST-BC) uplink transmission over (slowly) time varying multipath channels. Usually, without CP in the OFDM system the inter-carrier interference (ICI) could not be removed, effectively, at the receiver, when the inter-symbol-interference (ISI) has to be taken into account. In this thesis, by exploiting the spatial and frequency resources, we propose a novel frequency-domain decision-feedback equalizer, associated with the oblique projection (OB), to combat the effects of ISI and ICI, simultaneously. The OB is a non-orthogonal projection and is very useful to deal with the structure noise (e.g., the ISI term). From computer simulations, we observe that the performance of propose scheme can perform very close to the conventional CP-based MMO-OFDM with the ST-BC.

In this paper we estimate space-time impacts of the embeddedness in R&D networks on regional knowledge production by means of a dynamic spatial panel data model with non-linear effects for a set of 229 European NUTS-2 regions in the period 1999-2009. Embeddedness refers to the positioning in networks where nodes represent regions that are linked by joint R&D endeavours in European Framework Programmes. We observe positive immediate impacts on regional knowledge production arising from increased embeddedness in EU funded R&D networks, in particular for regions with lower own knowledge endowments. However, long-term impacts of R&D network embeddedness are comparatively small.(authors' abstract) / Series: Department of Economics Working Paper Series

M. Tech. Electrical Engineering. / Discusses MIMO systems have been hailed for the benefits of enhancing the reliability of the wireless communication link and increasing of the channel capacity, however the complexity of MIMO encoding and decoding algorithms increases considerably with the number of antennas. This research aims to suggest an optimisation approach to a reduced complexity implementation of the Alamouti 4×2 STBC. This is achieved by considering the FPGA parallelisation of the conditionally optimised ML decoding algorithm. The above problem can be divided into two subproblems. 1. The ML decoding of the Double Alamouti 4×2 STBC has a high computational cost when an exhaustive search is performed on the signal constellation for M-ary QAM. 2. Though the conditionally optimised ML decoding leads to less computational complexity compared to the full generic ML detection algorithm, the practical implementation remains unattractive for wireless systems.

M. Tech. Electrical Engineering. / Applies conditional optimisation to ML decoding of perfect STBCs, it is hypothesised that the obtained algorithms have reduced complexity and exhibit high DLP and TLP that can be exploited to map them on low-power multi-core SIMD processors, and possibly to reduce their runtimes and allow their real-time execution in a 4G wireless system.

No description available.

Under a number of practical operating scenarios, traditional moving target indicator (MTI) systems inadequately suppress ground clutter in airborne radar systems. Due to the moving platform, the clutter gains a nonzero relative velocity and spreads the power across Doppler frequencies. This obfuscates slow-moving targets of interest near the "direct current" component of the spectrum. In response, space-time adaptive processing (STAP) techniques have been developed that simultaneously operate in the space and time dimensions for effective clutter cancellation. STAP algorithms commonly operate under the assumption of homogeneous clutter, where the returns are described by complex, white Gaussian distributions. Empirical evidence shows that this assumption is invalid for many radar systems of interest, including high-resolution radar and radars operating at low grazing angles. We are interested in these heterogeneous cases, i.e., cases when the Gaussian model no longer suffices. Hence, the development of reliable STAP algorithms for real systems depends on the accuracy of the heterogeneous clutter models. The clutter of interest in this work includes heterogeneous texture clutter and point clutter. We have developed a cell-based clutter model (CCM) that provides simple, yet faithful means to simulate clutter scenarios for algorithm testing. The scene generated by the CMM can be tuned with two parameters, essentially describing the spikiness of the clutter scene. In one extreme, the texture resembles point clutter, generating strong returns from localized range-azimuth bins. On the other hand, our model can also simulate a flat, homogeneous environment. We prove the importance of model-based STAP techniques, namely knowledge-aided parametric covariance estimation (KAPE), in filtering a gamut of heterogeneous texture scenes. We demonstrate that the efficacy of KAPE does not diminish in the presence of typical spiky clutter. Computational complexities and susceptibility to modeling errors prohibit the use of KAPE in real systems. The computational complexity is a major concern, as the standard KAPE algorithm requires the inversion of an MNxMN matrix for each range bin, where M and N are the number of array elements and the number of pulses of the radar system, respectively. We developed a Gram Schmidt (GS) KAPE method that circumvents the need of a direct inversion and reduces the number of required power estimates. Another unavoidable concern is the performance degradations arising from uncalibrated array errors. This problem is exacerbated in KAPE, as it is a model-based technique; mismatched element amplitudes and phase errors amount to a modeling mismatch. We have developed the power-ridge aligning (PRA) calibration technique, a novel iterative gradient descent algorithm that outperforms current methods. We demonstrate the vast improvements attained using a combination of GS KAPE and PRA over the standard KAPE algorithm under various clutter scenarios in the presence of array errors.

Soft iterative detection and decoding techniques have been shown to be able to achieve near-capacity performance in multiple-antenna systems. To obtain the optimal soft information by marginalization over the entire observation space is intractable; and the current literature is unable to guide us towards the best way to obtain the suboptimal soft information. In this thesis, several existing soft-input soft-output (SISO) detectors, including minimum mean-square error-successive interference cancellation (MMSE-SIC), list sphere decoding (LSD), and Fincke-Pohst maximum-a-posteriori (FPMAP), are examined. Prior research has demonstrated that LSD and FPMAP outperform soft-equalization methods (i.e., MMSE-SIC); however, it is unclear which of the two scheme is superior in terms of performance-complexity trade-off. A comparison is conducted to resolve the matter. In addition, an improved scheme is proposed to modify LSD and FPMAP, providing error performance improvement and a reduction in computational complexity simultaneously. Although list-type detectors such as LSD and FPMAP provide outstanding error performance, issues such as the optimal initial sphere radius, optimal radius update strategy, and their highly variable computational complexity are still unresolved. A new detection scheme is proposed to address the above issues with fixed detection complexity, making the scheme suitable for practical implementation. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-07-08 19:29:17.66

The consequences of land use choices on soil water and gas transport properties are significant for gas and water flux in agricultural environments. Spatial and temporal patterns and associations of soil water and soil gas characteristics and processes in different land uses are not well understood. The objectives of this study were to 1) characterize soil structure under crop and grass systems, 2) quantify spatial patterns and associations of soil physical characteristics in crop and grass systems, and 3) quantify spatial and temporal patterns and associations of CO2 and N2O fluxes. The research was conducted in a 60 by 80 m field divided into grass and crop systems. Sixty sampling points were distributed in four transects with 5- and 1-m spatial intervals between measurement points. Gas fluxes were measured, at two-week time intervals, 22 times during a year. Pore size distribution was more homogeneous and more continuous pores were found in the grass than in the crop system. The spatial variability of most selected soil physical characteristics was more structured in the crop than in the grass system, which reflected the impact of land use and soil structure on their spatial patterns. CO2 flux was dependent for a longer distance in the grass than in the crop system, however, the two land-use systems exhibited similar spatial ranges of N2O flux. Gas fluxes were temporally dependent for a longer period in the grass than in the crop system. The spatial associations between CO2 and N2O fluxes and selected biochemical and physical factors depended on the flux sampling season and land use. Soil temperature was the dominant controlling factor on the temporal variability of CO2 and N2O fluxes but not on the spatial behavior. Considering the spatial and temporal ranges and dependency strength of soil variables helps identify efficient sampling designs that can result in better time and resource management. Spatial and temporal relationships between the selected soil variables also improve understanding soil management and sampling soil variables. This study provides the baseline and recommendations for future investigations specifically for sampling designs, soil management, and predictions of different soil processes related to gas fluxes.

Networks are models that help us understanding and thinking about relationships between entities in the real world. Many of these networks are dynamic, i.e. connectivity changes over time. Understanding changes in connectivity means to understand interactions between elements of complex systems; how people create and break up friendship relations, how signals get passed in the brain, how business collaborations evolve, or how food-webs restructure after environmental changes. However, understanding static networks is already difficult, due to size, density, attributes and particular motifs; changes over time very much increase this complexity. Quantification of change is often insufficient, but beyond an analysis that is driven by technology and algorithms, humans dispose a unique capability of understanding and interpreting information in data, based on vision and cognition. This dissertation explores ways to interactively explore dynamic networks by means of visualization. I develop and evaluate techniques to unfold the complexity of dynamic networks, making them understandable by looking at them from different angles, decomposing them into their parts and relating the parts in novel ways. While most techniques for dynamic network visualization rely on one particular type of view on the data, complementary visualizations allow for higher-level exploration and analysis. Covering three aspects Tasks, Visualization Design and Evaluation, I develop and evaluate the following unfolding techniques: (i) temporal navigation between individual time steps of a network and improved animated transitions to better understand changes, (ii) designs for the comparison of weighted graphs, (iii) the Matrix Cube, a space-time cube based on adjacency matrices, allowing to visualize dense dynamic networks by, as well as GraphCuisine, a system to (iv) generate synthetic networks with the primary focus on evaluating visualizations in user studies. In order to inform the design and evaluation of visualizations, we (v) provide a task taxonomy capturing users' tasks when exploring dynamic networks. Finally, (vi) the idea of unfolding networks with Matrix Cubes is generalized to other data sets that can be represented in space-time cubes (videos, geographical data, etc.). Visualizations in these domains can inspire visualizations for dynamic networks, and vice-versa. We propose a taxonomy of operations, describing how 3D space-time cubes are decomposed into a large variety of 2D visualizations. These operations help us exploring the design space for visualizing and interactively unfolding dynamic networks and other spatio-temporal data, as well as may serve users as a mental model of the data.

Networks are models that help us understanding and thinking about relationships between entities in the real world. Many of these networks are dynamic, i.e. connectivity changes over time. Understanding changes in connectivity means to understand interactions between elements of complex systems; how people create and break up friendship relations, how signals get passed in the brain, how business collaborations evolve, or how food-webs restructure after environmental changes. However, understanding static networks is already difficult, due to size, density, attributes and particular motifs; changes over time very much increase this complexity. Quantification of change is often insufficient, but beyond an analysis that is driven by technology and algorithms, humans dispose a unique capability of understanding and interpreting information in data, based on vision and cognition. This dissertation explores ways to interactively explore dynamic networks by means of visualization. I develop and evaluate techniques to unfold the complexity of dynamic networks, making them understandable by looking at them from different angles, decomposing them into their parts and relating the parts in novel ways. While most techniques for dynamic network visualization rely on one particular type of view on the data, complementary visualizations allow for higher-level exploration and analysis. Covering three aspects Tasks, Visualization Design and Evaluation, I develop and evaluate the following unfolding techniques: (i) temporal navigation between individual time steps of a network and improved animated transitions to better understand changes, (ii) designs for the comparison of weighted graphs, (iii) the Matrix Cube, a space-time cube based on adjacency matrices, allowing to visualize dense dynamic networks by, as well as GraphCuisine, a system to (iv) generate synthetic networks with the primary focus on evaluating visualizations in user studies. In order to inform the design and evaluation of visualizations, we (v) provide a task taxonomy capturing users' tasks when exploring dynamic networks. Finally, (vi) the idea of unfolding networks with Matrix Cubes is generalized to other data sets that can be represented in space-time cubes (videos, geographical data, etc.). Visualizations in these domains can inspire visualizations for dynamic networks, and vice-versa. We propose a taxonomy of operations, describing how 3D space-time cubes are decomposed into a large variety of 2D visualizations. These operations help us exploring the design space for visualizing and interactively unfolding dynamic networks and other spatio-temporal data, as well as may serve users as a mental model of the data.