In VLSI circuits, signal delays play an important role in design, timing verification and signal integrity checks. These delays are attributed to the presence of parasitic resistance, capacitance and inductance. With increasing clock speed and reducing feature sizes, these delays will be dominated by parasitic inductance. In the next generation VLSI circuits, with more than millions of components and interconnect segments, fast and accurate inductance estimation becomes a crucial step. A generalized approach for inductance extraction requires the solution of a large, dense, complex linear system that models mutual inductive effects among circuit elements. Iterative methods are used to solve the system without explicit computation of the system matrix itself. Fast hierarchical techniques are used to compute approximate matrix-vector products with the dense system matrix in a matrix-free way. Due to unavailability of system matrix, constructing a preconditioner to accelerate the convergence of the iterative method becomes a challenging task. This work presents a class of parallel algorithms for fast and accurate inductance extraction of VLSI circuits. We use the solenoidal basis approach that converts the linear system into a reduced system. The reduced system of equations is solved by a preconditioned iterative solver that uses fast hierarchical methods to compute products with the dense coefficient matrix. A Green’s function based preconditioner is proposed that achieves near-optimal convergence rates in several cases. By formulating the preconditioner as a dense matrix similar to the coefficient matrix, we are able to use fast hierarchical methods for the preconditioning step as well. Experiments on a number of benchmark problems highlight the efficient preconditioning scheme and its advantages over FastHenry. To further reduce the solution time of the software, we have developed a parallel implementation. The parallel software package is capable of analyzing interconnects con- figurations involving several conductors within reasonable time. A two-tier parallelization scheme enables mixed mode parallelization, which uses both OpenMP and MPI directives. The parallel performance of the software is demonstrated through experiments on the IBM p690 and AMD Linux clusters. These experiments highlight the portability and efficiency of the software on multiprocessors with shared, distributed, and distributed-shared memory architectures.

Thesis (Ph.D.), Memorial University of Newfoundland, 2000. / Includes index. Restricted until June 2001. Bibliography: leaves 82-84.

ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / We investigate pulse position modulation (PPM) and multipulse PPM (MPPM) for free space op- tical channels using bit interleaved coded modulation with iterative decoding. Data bits are first encoded by using a non recursive convolutional code and the coded bits after an interleaver are modulated before transmission. Iterative decoding is performed at the receiver. Optimized mapping is designed for MPPM. A genetic algorithm is used to find the optimized mapping for MPPM. Our simulation results show that a significant improvement in the error performance can be achieved by using optimized mapping and iterative decoding at the receiver.

In this paper, we investigate an adaptive decode-and-forward (DF) cooperative diversity scheme based on bit interleaved coded modulation with iterative decoding (BICM-ID). Data bits are first encoded by using a convolutional code and the coded bits after an interleaver are modulated before transmission. Iterative decoding is used at the receiver. Optimized constellation mapping is designed jointly for the source and the relay using a genetic algorithm. A novel error performance analysis for the adaptive DF scheme using BICM-ID is proposed. The simulation results agree well with the analytical results at high signal-to-noise ratio (SNR). More than 5.8 dB gain in terms of SNR over the existing mappings is achieved with proposed mappings.

Issues around Quality of Service (QoS) and security for Voice over IP (VoIP) have been extensively investigated separately, due to the great attention this technology currently attracts. The specific problem this work addresses centres upon the selection of optimal parameters for QoS and security for VoIP streams integrating both network impairments and user perception metrics into a novel empirically-driven approach. Specifically, the simulation model seeks the optimal parameters in terms of variable VoIP payloads, iterative block ciphers, codecs and authentication mechanisms to be used, so that optimum tradeoff between a set of conflicting factors is achieved. The model employs the widely used Transmission Rating Factor, R, as the methodology to predict and measure the perceived QoS based on current transmission and network impairments. The R factor is then used to map perceived QoS to the corresponding Mean Opinion Score value, which gives the average estimation of perceived voice quality (Quality of Experience). Furthermore, a genetic algorithm (GA) has been developed that uses the output from the simulation model as an input into an offline optimisation routine that simultaneously maximises the VoIP call volumes and the Level of Encryption (LoE) per call basis, without degrading the perceived quality of service under a specific threshold as dictated by the R factor. The solutions reflect the optimum combination of parameters for each codec used and due to the small size of the search space the actual speed of GA has been validated against an exhaustive search algorithm. The results extracted from this study demonstrate that under strict and pre-defined parameters the default payload size supported by the codecs is not the optimal selection in terms of call volume maximisation and perceived QoS when encryption is applied.

published_or_final_version / Mathematics / Master / Master of Philosophy

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We propose an efficient iterative data interpolation method using continuity along reflectors in seismic images via curvelet and discrete cosine transforms. The curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). The advantage of using curvelets is that these frames are sparse for high-frequency caustic-free solutions of the wave-equation. Since we are dealing with less than ideal data (e.g. bandwidth-limited), we compliment the curvelet frames with the discrete cosine transform. The latter is motivated by the successful data continuation with the discrete Fourier transform. By choosing generic basis functions we circumvent the necessity to make parametric assumptions (e.g. through linear/parabolic Radon or demigration) regarding the shape of events in seismic data. Synthetic and real data examples demonstrate that our algorithm provides interpolated traces that accurately reproduce the wavelet shape as well as the AVO behavior along events in shot gathers.

This thesis looks to techno music, and subsequently to the DJ, as models for a way of understanding urban spatial ecology. In its compositional use of rhythm to create a sense of musical space punctuated by time, the DJ’s act of mixing audio in performance is akin to the design process: architects similarly do well to observe and acknowledge the rhythm of city life, history, climate, geology, and to mould their dynamic into an appropriate spatial interface. By aligning aspects of documentation and process with DJ methods, the thesis translates the city’s various rhythms to produce responsive, intuitive architecture. A proposal for a built intervention on a former rail yard in central Halifax, Canada, the architectural response seeks to mix aspects of the city into the site, creating a multivalent graft between military facilities and civilian neighborhoods.

No description available.