Physical Education Centre Plans

Site Plan for the Physical Educaiton Centre and future athletic sites. Note the proposed ice rink, track and tennis courts. This site plan would have been made c. 1969.

Pool Plans

Herb de Bray standing at the edge of the soon to be completed pool of the Elanor Misner Aquatic Center.

What gets plans off the shelf? : a multi-site case study of the factors influencing municipal recreation plan implementation

Municipalities that engage in recreation planning have the potential to use their resources more effectively. However, successful planning means getting the plan off the shelf and implemented. This study investigated the factors that influenced municipal recreation plan implementation in three municipalities. Interviews were conducted with eleven key informants (recreation directors, planning consultants, a city councillor, and members of plan steering committees). The findings of this study suggested that because the implementation of recreation plans occurs in a highly political environment, recreation professionals will need effective strategies to get their plans implemented and that implementation can be facilitated by developing or expanding strategies that: (l) build the power of the recreation department within the municipal government structure; (2) build support for recreation within the local community; and (3) build the political and organizational capacity in the recreation department.

City of Niagara Falls Plans, 1846, 1877, 1884, 1886, 1889-1890, 1892-1894, 1903-1905, 1910, 1914, 1919, 1925, 1927-1928, n.d.

The collection consists of 37 plans, surveys or maps of the City of Niagara Falls beginning in 1846 until 1928. Some of the plans were created for the Falls Company, a group of land speculators that included Buchanan, Murray, Street, Allen, Robinson and others. Other plans relate to the building of the suspension bridge and the railway. Some plans and drawing may refer to estate documents in RG 167 Niagara South estate and legal documents collection. Item 22 has been scanned for preservation purposes.

Evolution of architectural floor plans

Layout planning is a process of sizing and placing rooms (e.g. in a house) while a t t empt ing to optimize various criteria. Often the r e are conflicting c r i t e r i a such as construction cost, minimizing the distance between r e l a t ed activities, and meeting the area requirements for these activities. The process of layout planning ha s mostly been done by hand, wi th a handful of a t t empt s to automa t e the process. Thi s thesis explores some of these pa s t a t t empt s and describes several new techniques for automa t ing the layout planning process using evolutionary computation. These techniques a r e inspired by the existing methods, while adding some of the i r own innovations. Additional experimenLs are done to t e s t the possibility of allowing polygonal exteriors wi th rectilinear interior walls. Several multi-objective approaches are used to evaluate and compare fitness. The evolutionary r epr e s ent a t ion and requirements specification used provide great flexibility in problem scope and depth and is worthy of considering in future layout and design a t t empt s . The system outlined in thi s thesis is capable of evolving a variety of floor plans conforming to functional and geometric specifications. Many of the resulting plans look reasonable even when compared to a professional floor plan. Additionally polygonal and multi-floor buildings were also generated.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Turbines Plans - General Arrangement of Shaft & Cross Section of Turbine, 1903

Two photographs of turbine drawings from the year 1903. The first drawing is titled "10000HP Turbines. Canadian Niagara Power Company. General Arrangement of Shaft." It was last revised 18 May, 1903. The second drawing is titled "10000HP Turbines Canadian Niagara Power Company. Cross Section of Turbine" This drawing revised 6 May, 1903 and 26 June, 1903.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Notice that the Port Dalhousie and Thorold Railway map and plans have been certified by commissioners

Notice that the Port Dalhousie and Thorold Railway map and plans have been certified by commissioners. This is signed “secretary”, n.d.

List which includes costs, plans and receipts for amounts received between S.D. Woodruff and Boyd and Schurr

List (8 pages, handwritten) which includes costs, plans and receipts for amounts received between S.D. Woodruff and Boyd and Schurr, Dec. 3, 1875.