Modes of information processing were examined for 91 subjects aged between 5 years 7 months and 6 years 3 months, using A.R. Luria's model of brain function as the theoretical basis of the study. A factor analysis of the results of six psychometric tests administered to all subjects indicated the presence of two distinct factors. These were hypothesised to represent the separate contributions of simultaneous and successive synthesis. Further separate factor analyses, of the six psychometric tests and tests of M-Space (derived from the work of R. Case) and tests of standard school assessment tasks (that were also administered to the subjects), were performed. The results indicated that although both modes of synthesis are available to children of this age, simultaneous synthesis is not a potent factor in school learning. A further exploratory study was carried out using the same 91 subjects. Subjects were given a series of verbal subtraction problems requiring understanding of mathematical relationships, and randomly assigned to two presentation groups. One group received pictorial information in addition to the verbal presentation. The other group received concrete materials. A multiple regression analysis was performed on the whole group using factor scores for simultaneous and successive syntheses (derived from the factor analysis of the six psychometric tests) as independent variables and criterion test scores for the verbal subtraction problems as the dependent variable. The analysis indicated that although neither aptitude for successive synthesis nor aptitude for simultaneous synthesis had predictive value for this kind of probelm solving, simultaneous synthesis was possibly the predominant mode of information processing. Further multiple regression analyses performed on each of the presentation groups indicated an interaction between successive synthesis and the modes of presentation of information. Due to the small numbers of subjects in each presentation group this result was inconclusive.

This study addresses the criticism leveled at A.C.T. Mathematics teachers with regard to their failure to use any other method of teaching than chalk-and-talk. By considering the changed needs of society for mathematics and the changed perceptions by society of education, the criticism is placed in context. The importance of spatial ability for mathematics is examined in the context of theories of cognitive abilities and its current under utilization within the classroom. On the basis of the increased need to utilize more talent the study was designed to operationalise in the classroom the constructs of simultaneous and successive synthesis, derived from Luria's model of brain functioning. The question of gender differences in mathematics achievment and spatial ability is addressed. The possible role of the maturation of language in determining differences in the acquistion of ability to form simultaneous synthesis is briefly discussed. The study was designed to utilize and enhance simultaneous synthesis. By demonstrating an Aptitude-Treatment Interaction it was intended to confirm that students, who function at a high level in simultaneous synthesis but at a low level in successive synthesis, would achieve more with experience with spatial activates than in a more traditional chalk-and-talk classroom. Gender differences in achievement were not found. Gender differences in successive/simultaneous profiles were found in accordance with theory predictions. The need for the duration of longer treatment periods is briefly discussed in the context of funding and the appearance of greater efficiency of traditional teaching methods when the students are functioning at the highest level of symbolic thought.

No description available.

Uronate isomerase catalyzes the isomerization of D-glucuronate and Dfructuronate. This enzyme has been classified as a member of the amidohydrolase superfamily. The reaction catalyzed by uronate isomerase is analogous to the isomerization of aldose/ketose sugars. These interconversions can occur via two mechanisms, a hydride or proton transfer. The solvent exchange experiments and the elimination of fluoride from 3-deoxy-3-fluoro-D-glucuronate catalyzed by the enzyme support a proton transfer. Assignment of the transferred proton as the proR proton further supports a proton transfer mechanism via a cis-enediol intermediate for uronate isomerase from E. coli. Cobyrinic acid a,c-diamide synthetase and cobyric acid synthetase from S. typhimurium catalyze ATP dependent amidations of carboxylate groups on the periphery of cobyrinic acid utilizing glutamine or ammonia as a nitrogen donor. The role of ATP in the reaction has been probed by positional isotope exchange (PIX). The results confirm the presence of phosphorylated intermediate species in the reactions catalyzed by cobyrinic acid a,c-diamide synthetase and cobyric acid synthetase from S. typhimurium. Cobyric acid synthetase catalyzes the amidation of carboxylate groups b, d, e, and g of adenosyl-cobyrinic acid a,c-diamide in the biosynthetic pathway for coenzyme B12. Analysis of the reaction time courses demonstrate the appearance of three unique intermediate species which are released from the active site after each amidation reaction. The identification of the intermediate species was accomplished by 1H, 15N HSQC NMR spectroscopy. The NMR spectrum of a sample quenched at the beginning of the reaction shows a single intermediate species corresponding to carboxamide e. Subsequent spectra establish the amidation order as e, d, b, and g. The structural basis for the dissociative and sequential reaction mechanism coupled with the rigid regiochemistry is unknown. However, mutations to aspartate 146 perturb the order of amidation. A NMR spectrum quenched early in the reaction with the D146N mutant shows two intermediate species corresponding to carboxamides e and d. Spectra of samples later in the reaction confirm the presence of multiple e, d, and g amide species. The reaction is completed with the amidation of carboxylate b.

Continuous and discrete-time Successive Backward Sweep (SBS) methods for solving nonlinear optimal control problems involving terminal and control constraints are proposed in this dissertation. They closely resemble the Neighboring Extremals and Differential Dynamic Programming algorithms, which are based on the successive solutions to a series of linear control problems with quadratic performance indices. The SBS methods are relatively insensitive to the initial guesses of the state and control histories, which are not required to satisfy the system dynamics. Hessian modifications are utilized, especially for non-convex problems, to avoid singularities during the backward integration of the gain equations. The SBS method requires the satisfaction of the Jacobi no-conjugate point condition and hence, produces optimal solutions. The standard implementation of the SBS method for continuous-time systems incurs terminal boundary condition errors due to an algorithmic singularity as well as numerical inaccuracies in the computation of the gain matrices. Alternatives for boundary error reduction are proposed, notably the aiming point and the switching between two forms of the sweep expansion formulae. Modification of the sweep formula expands the domain of convergence of the SBS method and allows for a rigorous testing for the existence of conjugate points. Numerical accuracy of the continuous-time formulation of the optimal control problem can be improved with the use of symplectic integrators, which generally are implicit schemes in time. A time-explicit group preserving method based on the Magnus series representation of the state transition is implemented in the SBS setting and is shown to outperform a non-symplectic integrator of the same order. Discrete-time formulations of the optimal control problem, directly accounting for a specific time-stepping method, lead to consistent systems of equations, whose solutions satisfy the boundary conditions of the discretized problem accurately. In this regard, the second-order, implicit mid-point averaging scheme, a symplectic integrator, is adapted for use with the SBS method. The performance of the mid-point averaging scheme is compared with other methods of equal and higher-order non-symplectic schemes to show its advantages. The SBS method is augmented with a homotopy- continuation procedure to isolate and regulate certain nonlinear effects for difficult problems, in order to extend its domain of convergence. The discrete-time SBS method is also extended to solve problems where the controls are approximated to be impulsive and to handle waypoint constraints as well. A variety of highly nonlinear optimal control problems involving orbit transfer, atmospheric reentry, and the restricted three-body problem are treated to demonstrate the performance of the methods developed in this dissertation.

Polar codes, recently invented by Arikan, are the first class of codes known to achieve the symmetric capacity for a large class of channels. The symmetric capacity is the highest achievable rate subject to using the binary input letters of the channel with equal probability. Polar code construction is based on a phenomenon called channel polarization. The encoding as well as the decoding operation of polar codes can be implemented with O(N logN) complexity, where N is the blocklength of the code. In this work, we study the factor graph representation of finite-length polar codes and their effect on the belief propagation (BP) decoding process over Binary Erasure Channel (BEC). Particularly, we study the parity-check-based (H-Based) as well as the generator based (G-based) factor graphs of polar codes. As these factor graphs are not unique for a code, we study and compare the performance of Belief Propagation (BP) decoders on number of well-known graphs. Error rates and complexities are reported for a number of cases. Comparisons are also made with the Successive Cancellation (SC) decoder. High errors are related to the so-called stopping sets of the underlying graphs. we discuss the pros and cons of BP decoder over SC decoder for various code lengths. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2012-01-31 17:10:59.955

This research is an effort to improve the performance of successive approximation algorithm with a prime aim of solving finite states and actions, infinite horizon, stationary, discrete and discounted Markov Decision Processes (MDPs). Successive approximation is a simple and commonly used method to solve MDPs. Successive approximation often appears to be intractable for solving large scale MDPs due to its computational complexity. Action elimination, one of the techniques used to accelerate solving MDPs, reduces the problem size through identifying and eliminating sub-optimal actions. In some cases successive approximation is terminated when all actions but one per state are eliminated. The bounds on value functions are the key element in action elimination. New terms (action gain, action relative gain and action cumulative relative gain) were introduced to construct tighter bounds on the value functions and to propose an improved action elimination algorithm. When span semi-norm is used, we show numerically that the actual convergence of successive approximation is faster than the known theoretical rate. The absence of easy-to-compute bounds on the actual convergence rate motivated the current research to try a heuristic action elimination algorithm. The heuristic utilizes an estimated convergence rate in the span semi-norm to speed up action elimination. The algorithm demonstrated exceptional performance in terms of solution optimality and savings in computational time. Certain types of structured Markov processes are known to have monotone optimal policy. Two special action elimination algorithms are proposed in this research to accelerate successive approximation for these types of MDPs. The first algorithm uses the state space partitioning and prioritize iterate values updating in a way that maximizes temporary elimination of sub-optimal actions based on the policy monotonicity. The second algorithm is an improved version that includes permanent action elimination to improve the performance of the algorithm. The performance of the proposed algorithms are assessed and compared to that of other algorithms. The proposed algorithms demonstrated outstanding performance in terms of number of iterations and omputational time to converge.

No description available.

In this work, we investigate circuit techniques to reduce the power consumption of Successive Approximation Register Analog-to-Digital Converter (SAR-ADC). We developed four low-power SAR-ADC design techniques, which are: 1) Low-power SAR-ADC design with split voltage reference, 2) Charge recycling techniques for low-power SAR-ADC design, 3) Low-power SAR-ADC design using two-capacitor arrays, 4) Power reduction techniques by dynamically minimizing SAR-ADC conversion cycles. Matlab simulations are performed to investigate the power saving by the proposed techniques. Simulation results show that significant power reduction can be achieved by using the developed techniques. In addition, design issues such as area overhead, design complexity associated with the proposed low-power techniques are also discussed in the thesis.

京都大学 / 新制・課程博士 / 博士(経済学) / 甲第23401号 / 経博第643号 / 新制||経||298(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)准教授 菊谷 達弥, 教授 関口 格, 教授 文 世一 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM