Forthcoming papers

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Preliminary course programme international centre for mechanical Sciences     

Mechanical Activation of Tetracalcium Phosphate

It was found that prolonged high-energy ball-milling of Hilgenstokite (tetracalcium phosphate, TTCP) resulted in a decrease in both particle and crystallite size, leading to a mechanical activation of the compound. This mechanically activated material demonstrated a high reactivity such that, in contrast to highly crystalline TTCP, a setting reaction with water to nanocrystalline hydroxyapatite (HA) and Ca(OH)₂ could be achieved at 37°C. However, crystalline TTCP is practically unreactive at physiologic temperatures because of the formation of a thin HA layer on the particle surface preventing further reaction.     

Dynamic light scattering measurements on the polystyrene/ethyl acetate system at semi-dilute concentrations as a function of temperature

Summary Measurements of autocorrelation functions extending over a broad time range are reported for a sample of polystyrene in ethyl acetate as a function of temperature between –44°C (-temperature) and 70°C. The corresponding spectra of decay times are obtained by two mathematical methods. The existence of three dynamic processes is shown and their temperature and angular behaviour is studied.     

Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.     

A Hierarchical and Contextual Model for Aerial Image Parsing

In this paper we present a hierarchical and contextual model for aerial image understanding. Our model organizes objects (cars, roofs, roads, trees, parking lots) in aerial scenes into hierarchical groups whose appearances and configurations are determined by statistical constraints (e.g. relative position, relative scale, etc.). Our hierarchy is a non-recursive grammar for objects in aerial images comprised of layers of nodes that can each decompose into a number of different configurations. This allows us to generate and recognize a vast number of scenes with relatively few rules. We present a minimax entropy framework for learning the statistical constraints between objects and show that this learned context allows us to rule out unlikely scene configurations and hallucinate undetected objects during inference. A similar algorithm was proposed for texture synthesis (Zhu et al. in Int. J. Comput. Vis. 2:107–126, 1998) but didn’t incorporate hierarchical information. We use a range of different bottom-up detectors (AdaBoost, TextonBoost, Compositional Boosting (Freund and Schapire in J. Comput. Syst. Sci. 55, 1997; Shotton et al. in Proceedings of the European Conference on Computer Vision, pp. 1–15, 2006; Wu et al. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, 2007)) to propose locations of objects in new aerial images and employ a cluster sampling algorithm (C4 (Porway and Zhu, 2009)) to choose the subset of detections that best explains the image according to our learned prior model. The C4 algorithm can quickly and efficiently switch between alternate competing sub-solutions, for example whether an image patch is better explained by a parking lot with cars or by a building with vents. We also show that our model can predict the locations of objects our detectors missed. We conclude by presenting parsed aerial images and experimental results showing that our cluster sampling and top-down prediction algorithms use the learned contextual cues from our model to improve detection results over traditional bottom-up detectors alone.     

A stochastic graph grammar for compositional object representation and recognition

This paper illustrates a hierarchical generative model for representing and recognizing compositional object categories with large intra-category variance. In this model, objects are broken into their constituent parts and the variability of configurations and relationships between these parts are modeled by stochastic attribute graph grammars, which are embedded in an And-Or graph for each compositional object category. It combines the power of a stochastic context free grammar (SCFG) to express the variability of part configurations, and a Markov random field (MRF) to represent the pictorial spatial relationships between these parts. As a generative model, different object instances of a category can be realized as a traversal through the And-Or graph to arrive at a valid configuration (like a valid sentence in language, by analogy). The inference/recognition procedure is intimately tied to the structure of the model and follows a probabilistic formulation consisting of bottom-up detection steps for the parts, which in turn recursively activate the grammar rules for top-down verification and searches for missing parts. We present experiments comparing our results to state of art methods and demonstrate the potential of our proposed framework on compositional objects with cluttered backgrounds using training and testing data from the public Lotus Hill and Caltech datasets.     

Temporal shifts in the biodiversity of nearshore small fishes in Lake Simcoe

Nearshore small fish species represent a large proportion of fish biodiversity in Lake Simcoe, a large inland lake in southern Ontario, Canada. Over the past 30 years, Lake Simcoe has experienced several changes to its aquatic habitat, benthic invertebrate communities and predatory fish populations. This study compared samples of the nearshore small fish community in three geographic areas of Lake Simcoe. Fish community data were grouped into two time periods: a contemporary period (2007–2009) and a historical period (1982–1995). The fish community was compared across time periods for each area to assess if observed ecological changes had an impact on the small fish community. Species richness significantly declined between time periods in two areas (Cook's Bay and the southeast shoreline), the number of individuals captured declined between time periods in one area of the lake (Kempenfelt Bay) and Simpson's diversity index declined between time periods in one area of the lake (southeast shoreline). There were no significant differences in the Shannon–Weiner evenness index between time periods in any of the study areas. Additional analyses of intra- and inter-annual variation in fish sampling results generally supported the findings that shifts in the fish community occurred between time periods. Overall, this study suggests that the nearshore small fish biodiversity of Lake Simcoe has shifted over time but these shifts are not clearly related to recent increases in water clarity, macrophyte growth and nearshore benthic invertebrate densities.     

Micro-engineered first wall tungsten armor for high average power laser fusion energy systems

The high average power laser program is developing an inertial fusion energy demonstration power reactor with a solid first wall chamber. The first wall (FW) will be subject to high energy density radiation and high doses of high energy helium implantation. Tungsten has been identified as the candidate material for a FW armor. The fundamental concern is long term thermo-mechanical survivability of the armor against the effects of high temperature pulsed operation and exfoliation due to the retention of implanted helium. Even if a solid tungsten armor coating would survive the high temperature cyclic operation with minimal failure, the high helium implantation and retention would result in unacceptable material loss rates. Micro-engineered materials, such as castellated structures, plasma sprayed nano-porous coatings and refractory foams are suggested as a first wall armor material to address these fundamental concerns. A micro-engineered FW armor would have to be designed with specific geometric features that tolerate high cyclic heating loads and recycle most of the implanted helium without any significant failure. Micro-engineered materials are briefly reviewed. In particular, plasma-sprayed nano-porous tungsten and tungsten foams are assessed for their potential to accommodate inertial fusion specific loads. Tests show that nano-porous plasma spray coatings can be manufactured with high permeability to helium gas, while retaining relatively high thermal conductivities. Tungsten foams where shown to be able to overcome thermo-mechanical loads by cell rotation and deformation. Helium implantation tests have shown, that pulsed implantation and heating releases significant levels of implanted helium. Helium implantation and release from tungsten was modeled using an expanded kinetic rate theory, to include the effects of pulsed implantations and thermal cycles. Although, significant challenges remain micro-engineered materials are shown to constitute potential candidate FW armor materials.