Compressive estimation for signal integration in rendering

In rendering applications, we are often faced with the problem of computing the integral of an unknown function. Typical approaches used to estimate these integrals are often based on Monte Carlo methods that slowly converge to the correct answer after many point samples have been taken. In this work, we study this problem under the framework of compressed sensing and reach the conclusion that if the signal is sparse in a transform domain, we can evaluate the integral accurately using a small set of point samples without requiring the lengthy iterations of Monte Carlo approaches. We demonstrate the usefulness of our framework by proposing novel algorithms to address two problems in computer graphics: image antialiasing and motion blur. We show that we can use our framework to generate good results with fewer samples than is possible with traditional approaches.     

Effect of different precooking methods on chemical composition and lipid damage of silver carp (Hypophthalmichthys molitrix) muscle

The influence of three precooking methods (steaming, oven‐baking and microwave‐cooking) on the chemical composition and lipid quality of silver carp fillets was evaluated. The changes in protein, fat and moisture were found to be significant for all the treatments (P ≤ 0.05). The iron content in the samples subjected to steam‐cooking increased; however, the other precooking methods did not change the mineral contents (P ≥ 0.05). The free fatty acid content of the fillets did not change by the different precooking methods, while thiobarbituric acid (TBA) values increased for oven‐ and microwave‐cooked fillets and remained constant in the steam‐cooked samples. Conjugated diene and browning colour formation levels significantly increased in the oven‐baked fillets. Oven‐baking and microwave‐cooking marginally affected the fatty acid composition of the silver carp. On comparing the raw and precooked fillets, steam‐cooking was found to be the best precooking method on retaining nutritional constituents.     

A confluence of contexts: Asymmetric versus global failures of selective attention to Stroop dimensions.

In 6 experiments probing selective attention through Stroop classification, 4 factors of context were manipulated: (a) psychophysical context, the distinctiveness of values along the color and word dimensions; (b) set size context, the number of stimulus values tested; (c) production context the mode used to respond; and (d) covariate context, the correlation between the dimensions. The psychophysical and production contexts mainly caused an asymmetry in selective attention failure between colors and words, whereas the set size and covariate contexts contributed primarily to the average or global magnitudes of attentional disruption across dimensions. The results suggest that (a) Stroop dimensions are perceptually separable, (b) J. R. Stroop's (1935) classic findings arose from his particular combination of contexts, and (c) stimulus uncertainty and dimensional imbalance are the primary sources of task and congruity effects in the Stroop paradigm. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Computational modeling and design of actively-cooled microvascular materials

The computational modeling and design of an actively-cooled microvascular fin specimen is presented. The design study is based on three objective functions: (i) minimizing the maximum temperature in the thermally loaded fin, (ii) optimizing the flow efficiency of the embedded microchannel, and (iii) minimizing the void volume fraction of the microvascular material. A recently introduced Interface-enriched Generalized Finite Element Method (IGFEM) is employed to evaluate the temperature field in a 2D model of the specimen, allowing for the accurate and efficient capturing of the gradient discontinuity along the fluid/solid interface without the need of meshes that conform to the geometry of the problem. Finding the optimal shape of the embedded microchannel is thus accomplished with a single non-conforming mesh for all configurations. Prior to the optimization study, the IGFEM solver is validated through comparison with infrared measurements of the thermal response of an epoxy fin with a sinusoidal microchannel.     

Numerical solution of stokes flow in a circular cavity using mesh-free local RBF-DQ

This work reports the results of a numerical investigation of Stokes flow problem in a circular cavity as an irregular geometry using mesh-free local radial basis function-based differential quadrature (RBF-DQ) method. This method is the combination of differential quadrature approximation of derivatives and function approximation of Radial Basis Function. As a result, the method can be used to directly approximate the derivatives of dependent variables on a scattered set of knots. In this study knots were distributed irregularly in the solution domain using the Halton sequences. The method is applied on a two-dimensional geometry. The obtained results from the numerical simulations are compared with those gained by previous works. Outcomes prove that the current technique is in very good agreement with previous investigations and this fact that RBF-DQ method is an accurate and flexible method in solution of partial differential equations (PDEs).     

Modelling Intelligent Driving Behaviour Using Machine Learning

In vehicular systems, driving is considered to be the most complex task, involving many aspects of external sensory skills as well as cognitive intelligence. External skills include the estimation of distance and speed, time perception, visual and auditory perception, attention, the capability to drive safely and action-reaction time. Cognitive intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent system.These cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic environments. The parameters for understanding intelligent behaviour are knowledge, reasoning, decision making, habit and cognitive skill. Modelling intelligent behaviour reveals that many of these parameters operate simultaneously to enable drivers to react to current situations. Environmental changes prompt the parameter values to change, a process which continues unless and until all processes are completed. This paper model intelligent behaviour by using a ‘driver behaviour model’ to obtain accurate intelligent driving behaviour patterns. This model works on layering patterns in which hierarchy and coherence are maintained to transfer the data with accuracy from one module to another. These patterns constitute the outcome of different modules that collaborate to generate appropriate values. In this case, accurate patterns were acquired using ANN static and dynamic non-linear autoregressive approach was used and for further accuracy validation, time-series dynamic backpropagation artificial neural network, multilayer perceptron and random sub-space on real-world data were also applied.     

Shape memory response of porous NiTi shape memory alloys fabricated by selective laser melting

Porous NiTi scaffolds display unique bone-like properties including low stiffness and superelastic behavior which makes them promising for biomedical applications. The present article focuses on the techniques to enhance superelasticity of porous NiTi structures. Selective Laser Melting (SLM) method was employed to fabricate the dense and porous (32–58%) NiTi parts. The fabricated samples were subsequently heat-treated (solution annealing?+?aging at 350?°C for 15?min) and their thermo-mechanical properties were determined as functions of temperature and stress. Additionally, the mechanical behaviors of the samples were simulated and compared to the experimental results. It is shown that SLM NiTi with up to 58% porosity can display shape memory effect with full recovery under 100?MPa nominal stress. Dense SLM NiTi could show almost perfect superelasticity with strain recovery of 5.65 after 6% deformation at body temperatures. The strain recoveries were 3.5, 3.6, and 2.7% for samples with porosity levels of 32%, 45%, and 58%, respectively. Furthermore, it was shown that Young’s modulus (i.e., stiffness) of NiTi parts can be tuned by adjusting the porosity levels to match the properties of the bones.

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Performance evaluation of EWMA and CUSUM control charts to detect anomalies in social networks using average and standard deviation of degree measures

Detection of communication outbreak among members of a network or a subgroup of a network has been a topic of interest in the literature of social network analysis. One approach to monitoring changes in a social network is to monitor graph measures related to the network representation in each period and detecting anomalies by applying a control chart. In this paper, we compare the performance of average degree and standard deviation of degree measures of a network for detecting outbreaks on a weighted undirected network using exponentially weighted moving average and cumulative sum control charts. Evaluation results indicate that average degree measure is better in detecting small changes than standard deviation of degree measure. Whereas for greater changes and outbreaks consisting of more members of the network, the opposite is true. In addition, exponentially weighted moving average control charts perform better than cumulative sum in detecting smaller changes and outbreaks consisting of less members of the network.