abstract: With robots being used extensively in various areas, a certain degree of robot autonomy has always been found desirable. In applications like planetary exploration, autonomous path planning and navigation are considered essential. But every now and then, a need to modify the robot's operation arises, a need for a human to provide it some supervisory parameters that modify the degree of autonomy or allocate extra tasks to the robot. In this regard, this thesis presents an approach to include a provision to accept and incorporate such human inputs and modify the navigation functions of the robot accordingly. Concepts such as applying kinematical constraints while planning paths, traversing of unknown areas with an intent of maximizing field of view, performing complex tasks on command etc. have been examined and implemented. The approaches have been tested in Robot Operating System (ROS), using robots such as the iRobot Create, Personal Robotics (PR2) etc. Simulations and experimental demonstrations have proved that this approach is feasible for solving some of the existing problems and that it certainly can pave way to further research for enhancing functionality. / Dissertation/Thesis / M.S. Electrical Engineering 2013

Falls and collisions are the most common types of events leading to sports-related concussions where impacts to the head play an important role on the onset of traumatic brain injury. Each event can be described by impact parameters that define the loading conditions on the head and brain and are necessary for accurate accident reconstruction employing physical impact tests, anthropometric headforms, and finite element (FE) modelling. It was the purpose of this research to describe the effects and interactions of impact velocity, compliance, mass and impact location on head acceleration and brain tissue strain measures associated with risk of concussions in sports. Impact parameters were varied to capture responses from no-injury up to concussive levels. Study one examined the effect of impact parameters on fall events simulated using a monorail drop tower. Impact mass was varied using three different headforms representing child, adolescent, and adult sizes measuring peak linear and angular acceleration and maximum principal strain. Regression analysis revealed that impact compliance was the most influential on peak linear and angular acceleration measures, meanwhile FE strain was most affected by changes in impact velocity. Smaller headforms tend to produce higher acceleration and strain values, supporting the need for age and size related mechanical definitions of risk. Study two examined the effect of impact parameters for collision events simulated using a multi-mass pendulum to represent common striking masses in sport measuring peak linear and angular acceleration and strain. Study three provided further insight into collision impacts by evaluating the distribution of peak strains in different brain lobes and the volume of the brain experiencing strains passed a critical level. Results show that compliance was similarly the most influential on peak head acceleration whereas peak strain and volume were most affected by impact velocity. Mass-velocity interactions had effects where a 9 kg mass had greater response than 15 kg, but similar to 21 kg. The temporal lobe consistently contained the highest strains with the rear boss non-centric impact location producing the largest values. Interacting impact parameters illustrate the challenges with predicting associated risk of concussion from head collisions in sport and supports the need to identify effective performance ranges of protective materials.

Many emerging applications call for wideband analog-to-digital converters and some require medium-to-high resolution. Incorporating such ADCs allows for shifting as much of the signal processing tasks as possible to the digital domain, where more flexible and programmable circuits are available. However, realizing such ADCs with the existing single stage architectures is very challenging. Therefore, parallel ADC architectures such as time-interleaved structures are used. Unfortunately, such architectures require high-speed high-precision sample-and-hold (S/H) stages that are challenging to implement. In this thesis, a parallel ADC architecture, namely, the frequency-translating hybrid ADC (FTH-ADC) is proposed to increase the conversion speed of the ADCs, which is also suitable for applications requiring medium-to-high resolution ADCs. This architecture addresses the sampling problem by sampling on narrowband baseband subchannels, i.e., sampling is accomplished after splitting the wideband input signals into narrower subbands and frequency-translating them into baseband where identical narrowband baseband S/Hs can be used. Therefore, lower-speed, lower-precision S/Hs are required and single-chip CMOS implementation of the entire ADC is possible. A proof of concept board-level implementation of the FTH-ADC is used to analyze the effects of major analog non-idealities and errors. Error measurement and compensation methods are presented. Using four 8-bit, 100 MHz subband ADCs, four 25 MHz Butterworth filters, two 64-tap FIR reconstruction filters, and four 10-tap FIR compensation filters, a total system with an effective sample rate of 200 MHz is implemented with an effective number of bits of at least 7 bits over the entire 100 MHz input bandwidth. In addition, one path of an 8-GHz, 4-bit, FTH-ADC system, including a highly-linear mixer and a 5th-order, 1 GHz, Butterworth Gm-C filter, is implemented in a 90 nm CMOS technology. Followed by a 4-bit, 4-GHz subband ADC, the blocks consume a total power of 52 mW from a 1.2 V supply, and occupy an area of 0.05 mm2. The mixer-filter has a THD ≤ 5% (26 dB) over its full 1 GHz bandwidth and provides a signal with a voltage swing of 350 mVpp for the subsequent ADC stage. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

In this paper, the benefits and advantages of combining advanced embedded processing capabilities with an FPGA based approach within a Data Acquisition Unit (DAU) are discussed. The paper begins with a discussion of some of the services and functionality that such a system enables. Basic features such as system discovery, verification, configuration and upgrade are discussed in addition to other value added services such as continuous built in test (CBIT) and embedded real-time parameter quick-look. Finally, the paper discusses some advanced services that could be deployed to these systems such as emerging communication protocols, multimedia connectivity and discovery, and advanced Machine Learning based systems diagnostics.

In order to increase results accuracy and to provide some time-dependency to CFD results, embedded RANS/LES simulations are getting more and more interesting. In order to run these simulations accurate LES boundary conditions are required, not to affect the downstream results with a poor quality synthetic turbulence generation. Considering the currently developped methodologies, it is not possible to generate a divergence free turbulent flow which satisfy a non isotropic state of turbulence. The author started from the Synthetic Eddy Method (SEM) defined by Jarrin (2009), and defined a new shape function with the ability to satisfy continuity. The new methodology, named Divergence Free SEM (DFSEM), is able to reproduce almost any kind of turbulence anisotropy by using a special shape function and adapting the eddies intensities in order to match the Reynolds stress tensor rather than using the Lund coefficients, as most of the precursor methodologies did. Results comparisons against SEM and some other very popular synthetic turbulence models in some academic cases, proved that a reduce influence on the downstream flow was achieved. In most of the cases the friction coefficient Cf , used as a performance parameter, benefit by reducing the downstream developping zone by almost 50% in most cases, when compared against SEM. Another issue that has been tackled regards the unphysical pressure fluctuations present because of the synthetic turbulence, due to non perfectly constant mass-flow rate imposed in stochastic methodologies. The new methodology also showed an increased flexibility as it has been tested in embedded DDES simulation, by using the blending function to activate/deactivate it, and again it showed improved performances when compared against standard SEM.

A hybrid dynamical system is a mathematical model for a part of the real world where discrete and continuous parts interact with each other. Typically such systems are complex, and it is difficult to know how they will behave for general parameters and initial conditions. However, the method of formal verification gives us the ability to prove automatically that certain behaviour does or does not happen for a range of parameters in a system. The challenge is then to define suitable methods for proving properties on hybrid systems.This thesis looks at using formal verification for proving liveness properties on hybrid systems: a liveness property says that something good eventually happens in the system. This work presents the theoretical background and practical application of various methods for proving and disproving inevitability properties (a type of liveness) in different classes of hybrid systems. The methods combine knowledge of dynamical behaviour of a system with the brute-force approach of model checking, in order to make the most of the benefits of both sides. The work on proving liveness properties is based on abstraction of dynamical systems to timed automata. This thesis explores the limits of a pre-defined abstraction method, adds some dynamical knowledge to the method, and shows that this improvement makes liveness properties provable in certain continuous dynamical systems. The limits are then pushed further to see how this method can be used for piecewise-continuous dynamical systems. The resulting algorithms are implemented for both classes of systems.In order to disprove liveness properties in hybrid systems a novel framework is proposed, using a new property called deadness. Deadness is a dynamically-aware property of the hybrid system which, if true, disproves the liveness property by means of a finite execution: we usually require an infinite execution to disprove a liveness property. An algorithm is proposed which uses dynamical properties of hybrid systems to derive deadness properties automatically, and the implementation of this algorithm is discussed and applied to a simplified model of an oilwell drillstring.

The beachfront hotel with hybrid geothermal heat pump system (HyGSHP), located in the hot-humid climate, is simulated by TRNSYS in the thesis, and the simulation results are validated by the measured data. The simulation of alternative HVAC systems, complete ground source heat pump and conventional air source heat pump, are included to conduct the comparative study with HyGSHP based on the energy consumption and life cycle analysis. The advantages and disadvantages of HyGSHP are discussed in the thesis. Two ground source heat exchanger parameters, U-tube size and grout materials, are investigated in order to study the effects on the ground heat exchanger thermal performance. The preliminary work and results are shown in the thesis.

This research aims at a study of the hybrid flow shop problem which has parallel batch-processing machines in one stage and discrete-processing machines in other stages to process jobs of arbitrary sizes. The objective is to minimize the makespan for a set of jobs. The problem is denoted as: FF|batch1, sj|Cmax. The problem is formulated as a mixed-integer linear program. The commercial solver, AMPL/CPLEX, is used to solve problem instances to their optimality. Experimental results show that AMPL/CPLEX requires considerable time to find the optimal solution for even a small size problem, i.e., a 6-job instance requires 2 hours in average. A bottleneck-first-decomposition heuristic (BFD) is proposed in this study to overcome the computational (time) problem encountered while using the commercial solver. The proposed BFD heuristic is inspired by the shifting bottleneck heuristic. It decomposes the entire problem into three sub-problems, and schedules the sub-problems one by one. The proposed BFD heuristic consists of four major steps: formulating sub-problems, prioritizing sub-problems, solving sub-problems and re-scheduling. For solving the sub-problems, two heuristic algorithms are proposed; one for scheduling a hybrid flow shop with discrete processing machines, and the other for scheduling parallel batching machines (single stage). Both consider job arrival and delivery times. An experiment design is conducted to evaluate the effectiveness of the proposed BFD, which is further evaluated against a set of common heuristics including a randomized greedy heuristic and five dispatching rules. The results show that the proposed BFD heuristic outperforms all these algorithms. To evaluate the quality of the heuristic solution, a procedure is developed to calculate a lower bound of makespan for the problem under study. The lower bound obtained is tighter than other bounds developed for related problems in literature. A meta-search approach based on the Genetic Algorithm concept is developed to evaluate the significance of further improving the solution obtained from the proposed BFD heuristic. The experiment indicates that it reduces the makespan by 1.93% in average within a negligible time when problem size is less than 50 jobs.

Porous materials have exhibited some remarkable performances in wide range of applications such as in the field of catalysis, gas adsorption, water treatment, bio- imaging, drugs delivery and energy applications. This is due to the pore characteristic of these materials. In fact, their properties depend mainly on the pore size, pore morphology and pore size distribution. The knowledge of understanding the effect of chemical nature of porous materials on the heterogeneous catalysis has significantly increased since last decades resulting in the increase in the development of innovative porous nano-hybrid materials. Scientists have integrated inorganic and organic materials to generate new structures with unique properties. A significant enhancement in their properties have been observed compared to their single components. This research work focuses on the design and tailoring of innovative hybrid materials with intrinsic porosity based on well studied single components for catalysis and energy applications. The first example is represented by the impregnation technique of gold nanoclusters (Au NCs) inside the pores of mesoporous silica nanoparticles (MSNs). The performance of Au NCs/ MSN as catalyst was evaluated by the epoxidation reaction of styrene. It shows a remarkable catalytic activity, high selectivity towards styrene epoxide (74%) and high conversion of styrene (88%). We have also investigated the self-assembly of polyoxomolybdates (P2Mo5O23) and cyclodextrins (CDs) as molecular building blocks (MBBs) through the bridging effect of counter cations (Na+, K+, and Cs+). This assembly has resulted in the formation of seven different crystals to give seven crystal structures of POM-CD MOFs. These novel porous hybrid frameworks with intrinsic porosity and tunable porosity have been well studied and characterized using different techniques. Interestingly, one of these structures (K-PMo-γ-CD) was obtained in good yield (70 % based on γ-CD), and was therefore selected to further study the catalytic performance of this type of the hybrid organic-inorganic structures (POM-CD MOFs). The ketalization process of cyclohexanone with glycol using K-PMo-γ-CD as catalysts, have been chosed as module reaction for this study. Our results showed that the material give the best catalytic performance, which reached its maximum conversion of 99.94 %, at 100oC. Finally, the scope of our research have been extended by combining another porous macrocycle, a trianglamine (TA), with the metal cluster complex system (polyoxometalate). This hybrid framework (POM-TA) have been well designed and synthesized based on molecular recognition. A detailed characterization shows that the POM-TA material has high surface area that suggests that it can be suitable as catalyst for some industrial processes. Our research on such organic-inorganic hybrid frameworks represents a promising enrichment in the field of heterogeneous catalysis. This is largely due to the possibility of combining different molecular building blocks to form a hybrid framework with improved properties such as intrinsic porosity, large surface area, and tunable structural properties.

Wireless sensor networks (WSN) have been largely used in various applications due to its ease of deployment and scalability. The throughput of such networks, however, suffers from high bit error rates mainly because of medium characteristics. Maximizing bandwidth utilization while maintaining low frame error rate has been an interesting problem. Frame fragmentation into small blocks with dedicated error detection codes per block can reduce the unnecessary retransmission of the correctly received blocks. The optimal block size, however, varies based on the wireless channel conditions. In addition, blocks within a frame can have different optimal sizes based on the variations on interference patterns. This thesis studies two dynamic partial packet recovery approaches experimentally over several interference intensities with various transmission-power levels. It also proposes a dynamic data link layer protocol: Hybrid Seda (H-Seda). H-Seda effectively addresses the challenges associated with dynamic partitioning of blocks while taking the observed error patterns into consideration. The design of H-Seda is discussed in details and compared to other previous approaches, namely Seda+ and Seda. The implementation of H-Seda shows substantial enhancements over fixed-size partial packet recovery protocols, achieving up to 2.5x improvement in throughput when the channel condition is noisy, while delay experienced decreases to only 14 % of the delay observed in Seda. On average, it shows 35% gain in goodput across all channel conditions used in our experiments. This significant improvement is due to the selective nature of H-Seda which minimizes retransmission overhead by selecting the appropriate number of blocks in each data frame. Additionally, H-Seda successfully reduces block overhead by 50% through removing block number field reaching to better performance when channel conditions are identical.