Statistical Rate Event Analysis with Elite Sample Selection Scheme

Accurately estimating the failure region of rare events for memory-cell and analog circuit blocks under process variations is a challenging task. As the first part of the thesis, author propose a new statistical method, called EliteScope to estimate the circuit failure rates in rare event regions and to provide conditions of parameters to achieve targeted per- formance. The new method is based on the iterative blockade framework to reduce the number of samples. But it consists of two new techniques to improve existing methods. First, the new approach employs an elite learning sample selection scheme, which can con- sider the effectiveness of samples and well-coverage for the parameter space. As a result, it can reduce additional simulation costs by pruning less effective samples while keeping the accuracy of failure estimation. Second, the EliteScope identifies the failure regions in terms of parameter spaces to provide a good design guidance to accomplish the performance target. It applies variance based feature selection to find the dominant parameters and then determine the in-spec boundaries of those parameters. We demonstrate the advantage of our proposed method using several memory and analog circuits with different number of process parameters. Experiments on four circuit examples show that EliteScope achieves a significant improvement on failure region estimation in terms of accuracy and simulation cost over traditional approaches. The 16-bit 6T-SRAM column example also demonstrate that the new method is scalable for handling large problems with large number of process variables

Statistical Classification Based Modelling and Estimation of Analog Circuits Failure Probability

At nanoscales, variations in transistor parameters cause variations and unpredictability in the circuit output, and may ultimately cause a violation of the desired specifications, leading to circuit failure. The parametric variations in transistors occur due to limitations in the manufacturing process and are commonly known as process variations. Circuit simulation is a Computer-Aided Design (CAD) technique for verifying the behavior of analog circuits but exhibits incompleteness under the effects of process variations. Hence, statistical circuit simulation is showing increasing importance for circuit design to address this incompleteness problem. However, existing statistical circuit simulation approaches either fail to analyze the rare failure events accurately and efficiently or are impractical to use. Moreover, none of the existing approaches is able to successfully analyze analog circuits in the presence of multiple performance specifications in timely and accurate manner. Therefore, we propose a new statistical circuit simulation based methodology for modelling and estimation of failure probability of analog circuits in the presence of multiple performance metrics. Our methodology is based on an iterative way of estimating failure probability, employing a statistical classifier to reduce the number of simulations while still maintaining high estimation accuracy. Furthermore, a more practical classifier model is proposed for analog circuit failure probability estimation. Our methodology estimates an accurate failure probability even when the failures resulting from each performance metric occur simultaneously. The proposed methodology can deliver many orders of speedup compared to traditional Monte Carlo methods. Moreover, experimental results show that the methodology generates accurate results for problems with multiple specifications, while other approaches fail totally

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Processes of hybrid knowledge creation in pastoralist development

This thesis addresses an under-researched disjunction surrounding knowledge creation between, and within, development and pastoralist groups. Many academics increasingly recognise pastoralist populations as creative and adaptable, yet these populations often lack the resources to develop innovations beyond the local context. Despite often being better resourced than pastoralist communities, development interventions in the Horn of Africa have achieved limited successes; an observation often linked in academic literature with a failure to rethink inappropriate established practices drawn from settled agriculture. The need to explore new ways of understanding hybrid knowledge creation in pastoralist settings emerged from the international community’s limited understanding of informal innovation processes and unique contexts of pastoralist regions, due in part to the unsuitability of current frameworks and research tools for conceptualising informal innovation in marginal settings. This study makes an original research contribution by exploring the factors that shape processes of knowledge creation between development and pastoralist groups to answer the question what factors influence innovation in pastoralist areas? An interconnected, mixed-methods research strategy was developed and applied to study the role of knowledge networks and framings in processes of knowledge creation amongst pastoralist and development actors innovating in North Horr, Kenya. The empirical data gathered throughout the research informed the development of an internally-valid analytical framework with which to explore innovation in this setting. The key findings of this study highlight the importance of the contextual and often asymmetric nature of relationships in processes of emergent knowledge creation within pastoralist development. The observations collected throughout the research process provide an empirical basis from which to discuss networks, framings, and knowledge creation in pastoralist settings; contributing to wider debates surrounding informal innovation processes and narratives of pastoralist development

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

An overview of population-based algorithms for multi-objective optimisation

In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided

Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm

A novel approach to incorporating Machine Learning into optimization routines is presented. An approach which combines the benefits of ML, optimization, and meta-model searching is developed and tested on a multi-modal test problem; a modified Rastragin\u27s function. An enhanced Particle Swarm Optimization method was derived from the initial testing. Optimization of a diesel engine was carried out using the modified algorithm demonstrating an improvement of 83% compared with the unmodified PSO algorithm. Additionally, an approach to enhancing the training of ML models by leveraging Virtual Sensing as an alternative to standard multi-layer neural networks is presented. Substantial gains were made in the prediction of Particulate matter, reducing the MMSE by 50% and improving the correlation R^2 from 0.84 to 0.98. Improvements were made in models of PM, NOx, HC, CO, and Fuel Consumption using the method, while training times and convergence reliability were simultaneously improved over the traditional approach

Assessing The Impact Of University Technology Incubator Practices On Client Performance

This research is designed to distinguish and describe or explain incubator practices that affect the performance of incubator clients of university technology incubator programs. The research focuses on understanding which practices significantly contribute to increasing job creation for the firms located in university based technology incubators. An increasing number of communities are embracing economic development strategies that target the high tech sector with high wage, high value jobs as a way to diversify their economies and boost local and regional economies. New economic development strategies include the notion of a creation strategy or growing your own instead of relying on recruiting of existing companies from other regions. In 1999-2000 (according to the most recent data), small businesses created three-quarters of U.S. net new jobs (2.5 million of the 3.4 million total). The small business percentage varies from year to year and reflects economic trends. Over the decade of the 1990s, small business net job creation fluctuated between 60 and 80 percent. Moreover, according to a Bureau of the Census working paper, start-ups in the first two years of operation accounted for virtually all of the net new jobs in the economy. The study is broken into three parts: (1) a review of the literature on incubation, focusing on its history, best practices, technology incubation, networking theory, and previous empirical studies (2) a review of previous data collected in a recent national survey and (3) case studies of the top performing incubators in the country based on employment growth of client firms contracted with case studies from non-top ten programs. The literature suggests that the study of incubation must be considered in the context of a larger enterprise development system of which the incubator will fill gaps in the larger regional enterprise development system. This notion is explored. In general, there is a great need for more empirical research into best practice of incubation. It is a non trivial task however as the nature of the industry limits the ability to obtain traditional, statistically defendable, measures