Digital Clay represents a new type of 3-D human-computer interface device that enables tactile and haptic interactions. The Digital Clay kinematics structure is computer controlled and can be commanded to acquire a wide variety of desired shapes (shape display), or be deformed by the user in a manner similar to that of real clay (shape editing). The design of the structure went through various modifications where we finally settled on a crust matrix of spherical joint unit cells. After designing the kinematics structure, the next step is predicting the deformation of the crust matrix based upon a handful of inputs. One possible solution for predicting the shape outcome is considering minimizing the potential energy of the system. In this thesis two methods will be introduced. The first method will be an abstract model of the crust where the energy is calculated from a simplified model with one type of angular springs. The second method is the actual manufacturable crust model with two types of angular springs. From the implementation of these two methods, the output will be center-points of the unit cells. From the center-points, one can also calculate the joint angles within each unit cell.

In this paper we study through iterative methods the minimization problem min_{x∈C} £K(x) (P) where the set C of constraints is the set of fixed points of a nonexpansive mapping T in a real Hilbert space H, and the objective function £K:H¡÷R is supposed to be continuously Gateaux dierentiable. The gradient projection method for solving problem (P) involves with the projection P_{C}. When C = Fix(T), we provide a so-called hybrid iterative method for solving (P) and the method involves with the mapping T only. Two special cases are included: (1) £K(x)=(1/2)||x-u||^2 and (2) £K(x)=<Ax,x> - <x,b>. The first case corresponds to finding a fixed point of T which is closest to u from the fixed point set Fix(T). Both cases have received a lot of investigations recently.

In this paper, we report existing convergence results on monotone variational inequalities where the governing monotone operators are either strongly monotone or inverse strongly monotone. We reformulate the variational inequality problem as an equivalent fixed point problem and then use fixed point iteration method to solve the original variational inequality problem. In the case of strong monotonicity case we use the Banach¡¦s contraction principle to define out iteration sequence; while in the case of inverse strong monotonicity we use the technique of averaged mappings to define our iteration sequence. In both cases we prove strong convergence for our iteration methods. An application to a minimization problem is also included.

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

M. Tech. Construction Management / This dissertation is primarily concerned with the investigation of waste generation rates, the causes of waste and waste management strategies adopted in the construction industry in Tshwane, South Africa.

The power consumption of data centers and cloud systems has increased almost three times between 2007 and 2012. The traditional resource allocation methods are typically designed for high performance as the primary objective to support peak resource requirements. However, it is shown that server utilization is between 12% and 18%, while the power consumption is close to those at peak loads. Hence, there is a pressing need for devising sophisticated resource management approaches. State of the art dynamic resource management schemes typically rely on only a single resource such as core number, core speed, memory, disk, and network. There is a lack of fundamental research on methods addressing dynamic management of multiple resources and properties with the objective of allocating just enough resources for each workload to meet quality of service requirements while optimizing for power consumption. The main focus of this dissertation is to simultaneously manage power and performance for large cloud systems. The objective of this research is to develop a framework of performance and power management and investigate a general methodology for an integrated autonomic cloud management. In this dissertation, we developed an autonomic management framework based on a novel data structure, AppFlow, used for modeling current and near-term future cloud application behavior. We have developed the following capabilities for the performance and power management of the cloud computing systems: 1) online modeling and characterizing the cloud application behavior and resource requirements; 2) predicting the application behavior to proactively optimize its operations at runtime; 3) a holistic optimization methodology for performance and power using number of cores, CPU frequency, and memory amount; and 4) an autonomic cloud management to support the dynamic change in VM configurations at runtime to simultaneously optimize multiple objectives including performance, power, availability, etc. We validated our approach using RUBiS benchmark (emulating eBay), on an IBM HS22 blade server. Our experimental results showed that our approach can lead to a significant reduction in power consumption upto 87% when compared to the static resource allocation strategy, 72% when compared to adaptive frequency scaling strategy, and 66% when compared to a multi-resource management strategy.

Software is finding its way into an increasing range of devices (phones, medical equipment, cars...). A challenge is to design verification methods to ensure correctness of software. We focus on model checking, an approach in which an abstract model of the implementation and a specification of requirements are provided. The task is to answer automatically whether the system conforms with its specification.We concentrate on (i) timed systems, and (ii) parameterized systems. Timed systems can be modeled and verified using the classical model of timed automata. Correctness is translated to language inclusion between two timed automata representing the implementation and the specification. We consider variants of timed automata, and show that the problem is at best highly complex, at worst undecidable. A parameterized system contains a variable number of components. The problem is to verify correctness regardless of the number of components. Regular model checking is a prominent method which uses finite-state automata. We present a semi-symbolic minimization algorithm combining the partition refinement algorithm by Paige and Tarjan with decision diagrams. Finally, we consider systems which are both timed and parameterized: Timed Petri Nets (TPNs), and Timed Networks (TNs). We present a method for checking safety properties of TPNs based on forward reachability analysis with acceleration. We show that verifying safety properties of TNs is undecidable when each process has at least two clocks, and explore decidable variations of this problem.

In any power system, fault means abnormal flow of current. Insulation breakdown is the cause of fault generation. Different factors can cause the breakdown: Wires drifting together in the wind, Lightning ionizing air, wires with contacts of animals and plants, Salt spray or pollution on insulators. The common type of faults on a three phase system are single line-to-ground (SLG), Line-to-line faults (LL), double line-to-ground (DLG) faults, and balanced three phase faults. And these faults can be symmetrical (balanced) or Unsymmetrical (imbalanced).In this Study, a technique to predict the zero crossing point has been discussed and simulated. Zero crossing point prediction for reliable transmission and distribution plays a significant role. Electrical power control switching works in zero crossing point when a fault occurs. The precision of measuring zero crossing point for syncing power system control and instrumentation requires a thoughtful approach to minimize noise and external signals from the corrupted waveforms A faulted current waveform with estimated faulted phase/s, the technique is capable of identifying the time of zero crossing point. Proper Simulation has been organized on MATLAB R2012a.

Objective: To complete a cost-minimization analysis (CMA) of the cost of sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND) in the management of early-stage breast cancer (ESBC) in a hypothetical Ontario hospital setting. Methods: Decision-analysis modeling, with a decision-tree and Markov states, was used to estimate hospital costs for the two treatment options. The model was populated with data from the literature and costs from the Ontario Case Costing Initiative, a publicly accessible, government-sponsored, costing database. Model variability and parameter uncertainty were quantified by probabilistic sensitivity analysis (PSA). Results: The SLNB treatment algorithm was cost-minimizing compared to the ALND-only treatment option. The costs of treating postoperative complications did not contribute to the incremental average cost. Conclusion: A treatment algorithm that involves SLNB as the initial axillary-staging procedure in the setting of ESBC offers a cost-savings over the ALND-only option. This result is generalizable to all Ontario hospitals.

Objective: To complete a cost-minimization analysis (CMA) of the cost of sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND) in the management of early-stage breast cancer (ESBC) in a hypothetical Ontario hospital setting. Methods: Decision-analysis modeling, with a decision-tree and Markov states, was used to estimate hospital costs for the two treatment options. The model was populated with data from the literature and costs from the Ontario Case Costing Initiative, a publicly accessible, government-sponsored, costing database. Model variability and parameter uncertainty were quantified by probabilistic sensitivity analysis (PSA). Results: The SLNB treatment algorithm was cost-minimizing compared to the ALND-only treatment option. The costs of treating postoperative complications did not contribute to the incremental average cost. Conclusion: A treatment algorithm that involves SLNB as the initial axillary-staging procedure in the setting of ESBC offers a cost-savings over the ALND-only option. This result is generalizable to all Ontario hospitals.