Comparison of Two Drying Schedules for European Hophornbeam (Ostrya carpinifolia Scop.) Lumber

Two types of conventional kiln-drying schedules (mild and harsh) based on moisture content (MC) were compared with regard to time, drying quality, and energy cost. The results were evaluated according to the classification of the European Drying Group. Proper drying periods of mild and harsh schedules were found to be 550 and 514 h, respectively. Evaluations in terms of drying quality indicated that better results were achieved with the mild schedule, especially when comparing drying defects and final MC. From an energy efficiency point of view, the harsh schedule, by saving 36 h of drying time, reduced electricity by 594 KWh and was therefore found to be $65 more profitable in this trial.     

An experimental evaluation of visual similarity for HDR images

In this paper, we investigate visual similarity for high dynamic range (HDR) images. We collect crowdsourcing data through a web-based experimental interface, in which the participants are asked to choose one of the two candidate images as being more similar to the query image. Triplets forming the query-and-candidates sets are obtained by random sampling from existing HDR data sets. Experimental control factors include choice of tone mapping operator (TMO), choice of distance metric, and choice of image feature. The image features that we experiment with are chosen from the features that are commonly used in the usual low dynamic range setting including features learned via Convolutional Neural Networks. The set of image features also includes combined features where the combination coefficients are estimated using logistic regression. We compute correlations between human judgments and quantitative features to understand how much each feature contributes to visual similarity. Combined features yield nearly 84% agreement with human judgments when applied on tone mapped images. Though we observed that using common features directly on raw or linearly scaled HDR images yield subpar correlation estimates compared to using them on tone mapped HDR images, we did not observe significant effect due to the choice of TMO on the estimates. As an application, we propose an improvement to style-based tone mapping for more correctly imparting desired styles to HDR images with different characteristics.

     

Toward an efficient and scalable feature selection approach for internet traffic classification

There is significant interest in the network management and industrial security community about the need to identify the “best” and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps to identify the best features in order to improve the classification accuracy as well as to reduce the computational complexity related to the construction of the classifier. In practice, feature selection (FS) techniques can be used as a preprocessing step to eliminate irrelevant features and as a knowledge discovery tool to reveal the “best” features in many soft computing applications. In this paper, we investigate the advantages and disadvantages of such FS techniques with new proposed metrics (namely goodness, stability and similarity). We continue our efforts toward developing an integrated FS technique that is built on the key strengths of existing FS techniques. A novel way is proposed to identify efficiently and accurately the “best” features by first combining the results of some well-known FS techniques to find consistent features, and then use the proposed concept of support to select a smallest set of features and cover data optimality. The empirical study over ten high-dimensional network traffic data sets demonstrates significant gain in accuracy and improved run-time performance of a classifier compared to individual results produced by some well-known FS techniques.     

Parabolic curves of evolving surfaces

In this article we show how certain geometric structures which are also associated with a smooth surface evolve as the shape of the surface changes in a 1-parameter family. We concentrate on the parabolic set and its image under the Gauss map, but the same techniques also classify the changes in the dual of the surface. All these have significance for computer vision, for example through their connection with specularities and apparent contours. With the aid of our complete classification, which includes all the phenomena associated with multi-contact tangent planes as well as those associated with parabolic sets, we re-examine examples given by J. Koenderink in his book (1990) under the title of Morphological Scripts.We also explain some of the connections between parabolic sets and ridges of a surface, where principal curvatures achieve turning values along lines of curvature.The point of view taken is the analysis of the contact between surfaces and their tangent planes. A systematic investigation of this yields the results using singularity theory. The mathematical details are suppressed here and appear in Bruce et al. (1993).The third author was supported by the Esprit grant VIVA while this paper was in preparation.     

Dissimilarity between two skeletal trees in a context

Skeletal trees are commonly used in order to express geometric properties of the shape. Accordingly, tree-edit distance is used to compute a dissimilarity between two given shapes. We present a new tree-edit based shape matching method which uses a recent coarse skeleton representation. The coarse skeleton representation allows us to represent both shapes and shape categories in the form of depth-1 trees. Consequently, we can easily integrate the influence of the categories into shape dissimilarity measurements. The new dissimilarity measure gives a better within group versus between group separation, and it mimics the asymmetric nature of human similarity judgements.     

Mumford-Shah Regularizer with Contextual Feedback

We present a simple and robust feature preserving image regularization by letting local region measures modulate the diffusivity. The purpose of this modulation is to disambiguate low level cues in early vision. We interpret the Ambrosio-Tortorelli approximation of the Mumford-Shah model as a system with modulatory feedback and utilize this interpretation to integrate high level information into the regularization process. The method does not require any prior model or learning; the high level information is extracted from local regions and fed back to the regularization step. An important characteristic of the method is that both negative and positive feedback can be simultaneously used without creating oscillations. Experiments performed with both gray and color natural images demonstrate the potential of the method under difficult noise types, non-uniform contrast, existence of multi-scale patterns and textures.

Energy- and reliability-aware task scheduling onto heterogeneous MPSoC architectures

Scheduling periodic tasks onto a multiprocessor architecture under several constraints such as performance, cost, energy, and reliability is a major challenge in embedded systems. In this paper, we present an Integer Linear Programming (ILP) based framework that maps a given task set onto an Heterogeneous Multiprocessor System-on-Chip (HMPSoC) architecture. Our framework can be used with several objective functions; minimizing energy consumption, minimizing cost (i.e., the number of heterogeneous processors), and maximizing reliability of the system under performance constraints. We use Dynamic Voltage Scaling (DVS) for reducing energy consumption while we employ task duplication to maximize reliability. We illustrate the effectiveness of our approach through several experiments, each with a different number of tasks to be scheduled. We also propose two heuristics based on Earliest Deadline First (EDF) algorithm for minimizing energy under performance and cost constraints. Our experiments on generated task sets show that ILP-based method reduces the energy consumption up to 62% percent against a method that does not apply DVS. Heuristic methods obtain promising results when compared to optimal results generated by our ILP-based method.     

Dynamic soil–structure interaction analysis of layered unbounded media via a coupled finite element/boundary element/scaled boundary finite element model

In this paper, a coupled model based on finite element method (FEM), boundary element method (BEM) and scaled boundary FEM (SBFEM) (also referred to as the consistent infinitesimal finite element cell method) for dynamic response of 2D structures resting on layered soil media is presented. The SBFEM proposed by Wolf and Song (Finite‐element Modelling of Unbounded Media. Wiley: England, 1996) and BEM are used for modelling the dynamic response of the unbounded media (far‐field). The standard FEM is used for modelling the finite region (near‐field) and the structure. In SBFEM, which is a semi‐analytical technique, the radiation condition at infinity is satisfied exactly without requiring the fundamental solution. This method, also eliminates the need for the discretization of interfaces between different layers. In both SBFEM and BEM, the spatial dimension is decreased by one. The objective of the development of this coupled model is to combine advantages of above‐mentioned three numerical models to solve various soil–structure interaction (SSI) problems efficiently and effectively. These three methods are coupled (FE–BE–SBFEM) via substructuring method, and a computer programme is developed for the harmonic analyses of SSI systems. The coupled model is established in such a way that, depending upon the problem and far‐field properties, one can choose BEM and/or SBFEM in modelling related far‐field region(s). Thus, BEM and/or SBFEM can be used efficiently in modelling the far‐field. The proposed model is applied to investigate dynamic response of rigid and elastic structures resting on layered soil media. To assess the proposed SSI model, several problems existing in the literature are chosen and analysed. The results of the proposed model agree with the results presented in the literature for the chosen problems. The advantages of the model are demonstrated through these comparisons. Copyright © 2004 John Wiley & Sons, Ltd.     

Apoptotic induction in transformed follicular lymphoma cells by Bcl-2 downregulation

The roles of Bcl-2 protein and the protein ratio of Bcl-2/Bax in regulating cell growth in various lymphoma cell lines were examined. A dose-dependent decrease in Bcl-2 protein expression was observed in the different lymphomas incubated with lipid-incorporated bcl-2 antisense oligonucleotides (L-bcl-2). Growth inhibition was observed in a transformed follicular lymphoma (FL) cell line, which has the t(14;18) translocation and Bcl-2 protein overexpression. One of the mechanisms by which L-bcl-2 growth inhibition is mediated in these transformed FL cells might be through apoptotic induction, because the treated cells had an increased apoptotic index and showed the typical DNA fragmentation. These studies indicate that Bcl-2 protein is critical in the growth regulation of transformed FL cells. L-bcl-2 did not induce growth inhibition in lymphoma cells not expressing Bcl-2 or Bax protein. Thus, the protein ratio of Bcl-2/Bax may also be important in regulating the growth of these lymphomas.     

Computationally highly efficient mixture of adaptive filters

We introduce a new combination approach for the mixture of adaptive filters based on the set-membership filtering (SMF) framework. We perform SMF to combine the outputs of several parallel running adaptive algorithms and propose unconstrained, affinely constrained and convexly constrained combination weight configurations. Here, we achieve better trade-off in terms of the transient and steady-state convergence performance while providing significant computational reduction. Hence, through the introduced approaches, we can greatly enhance the convergence performance of the constituent filters with a slight increase in the computational load. In this sense, our approaches are suitable for big data applications where the data should be processed in streams with highly efficient algorithms. In the numerical examples, we demonstrate the superior performance of the proposed approaches over the state of the art using the well-known datasets in the machine learning literature.