Hiding biometric data

With the wide spread utilization of biometric identification systems, establishing the authenticity of biometric data itself has emerged as an important research issue. The fact that biometric data is not replaceable and is not secret, combined with the existence of several types of attacks that are possible in a biometric system, make the issue of security/integrity of biometric data extremely critical. We introduce two applications of an amplitude modulation-based watermarking method, in which we hide a user's biometric data in a variety of images. This method has the ability to increase the security of both the hidden biometric data (e.g., eigen-face coefficients) and host images (e.g., fingerprints). Image adaptive data embedding methods used in our scheme lead to low visibility of the embedded signal. Feature analysis of host images guarantees high verification accuracy on watermarked (e.g., fingerprint) images.     

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.     

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.     

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.     

MobileCDP: A mobile framework for the consumer decision process

The consumer decision process is a widely accepted model covering consumer activities, and accordingly contains five interrelated stages: problem recognition, information search, evaluation of alternatives, purchase, and post-purchase evaluation. In order to help consumers deal with challenges associated with all these stages, mobile information systems bring significant capabilities, as in other application domains. However, related prior research is mostly restricted to the individual stages of the process. Since the stages are interrelated, and the data collected in one are also valuable for another, we propose a mobile framework designed to provide assistance in all stages of the Consumer Decision Process, named MobileCDP. A prototype is also implemented and evaluated to show the applicability of the framework. Experiments show that the functions provided by the prototype are useful, well integrated, and easy to use. Moreover, statistical analysis of the results proves that the prototype reduces time, costs, and cognitive effort of the user.     

Robust estimation in flat fading channels under bounded channel uncertainties

We investigate channel equalization problem for time-varying flat fading channels under bounded channel uncertainties. We analyze three robust methods to estimate an unknown signal transmitted through a time-varying flat fading channel. These methods are based on minimizing certain mean-square error criteria that incorporate the channel uncertainties into their problem formulations instead of directly using the inaccurate channel information that is available. We present closed-form solutions to the channel equalization problems for each method and for both zero mean and nonzero mean signals. We illustrate the performances of the equalization methods through simulations.     

Photo-electrochemistry of metallic titanium/mixed phase titanium oxide

In this study, the effect of potassium hydroxide concentration in anodization bath, anodization time, and calcination temperature on the photo-electrochemical behavior of metallic titanium/mixed phase titanium oxide is investigated. Further, the phase structure of a titanium oxide photocatalyst prepared on a titanium electrode through a high-voltage anodization method is examined. The study exploits photo-electrochemical, Fourier transform infrared spectroscopy attenuated total reflectance (FTIR–ATR), X-ray diffraction, and Raman spectroscopic methods to obtain better insights into the mechanism of mixed-phase titanium oxide formation. In this regard, the photo-electrochemical properties of the photocatalysts prepared in single excitation energy, violet light (410 nm), were investigated. The anodization time and the potassium hydroxide concentration in the anodization bath have significant effects on the photo-electrochemical properties of the photocatalysts. The experiments show that the effect of potassium hydroxide concentration is a function of the anodization potential applied, demonstrating different patterns as the anodization potential changes. Furthermore, FTIR-ATR, X-ray diffraction, and Raman spectroscopic studies reveal that the extended anodization times decrease the population of OH-containing groups, leading to lower photo-electrochemical performance. On the other hand, the formation of anatase phases becomes more favorable only in the extended anodization times before application of the calcination process. Additionally, the calcination temperature has a significant impact on the anatase to rutile ratio. Finally, increasing potassium hydroxide concentration leads to the formation of an amorphous titanium oxide layer. It can be concluded that the obtained information might have a significant impact on the preparation of titanium oxide and other metal oxide photocatalysts through the high voltage anodization process.     

Prioritization methodology of dangerous substances for water quality monitoring with scarce data

Monitoring and control of dangerous substances discharged into receiving waters have attracted more attention lately. Since it is not possible to analyze every single substance, a prioritization methodology is needed for the selection of those to be monitored. Existing well-developed models require significant amount of data for reliable outcomes. This paper presents a methodology to prioritize the dangerous substances having adverse effects on freshwaters in Turkey, where data are scarce. Such a methodology will also serve as a solid model for other countries with limited background data. The adopted methodology enabled the elimination of chemicals to generate a candidate list composed of 608 substances among more than 5000 substances. Further screening and prioritization were conducted using different assessment methods (i.e., Total Hazard Value, Total Impact Value, Combined Monitoring-based, and Modelling-based Priority Setting) to obtain a proposed Final Candidate Specific Pollutants List of 150 dangerous substances. The proposed Candidate National Pollutant List of Turkey was established by combining 45 priority pollutants of the European Union with a list of candidate specific pollutants. According to the outcomes of this study, monitoring and controlling of 195 dangerous substances in freshwaters are recommended. Further detailed studies should be conducted in order to observe the actual levels of these dangerous substances in freshwaters followed by a review of the monitoring list accordingly. Moreover, further revisions might be required in the proposed list due to some possible versatile conditions in terms of sampling points (i.e., change in the location of industries).     

Synthesis and characterization of PMMA composites activated with starch for immobilization of L‐asparaginase

Poly (methyl methacrylate) (PMMA)–starch composites were prepared by emulsion polymerization technique for L‐asparaginase (L‐ASNase) immobilization as highly activated support. The hydroxide groups on the prepared composites offer a very simple, mild and firm combination for enzyme immobilization. The pure PMMA and PMMA‐starch composites were characterized as structural, thermal and morphological. PMMA‐starch composites were found to have better thermal stability and more hydrophilic character than pure PMMA. L‐ASNase was immobilized onto PMMA‐starch composites contained the different ratio of starch (1, 3, 5, and 10 wt %). Immobilized L‐ASNase showed better performance as compared to the native enzyme in terms of thermal stability and pH. K_m value of immobilized enzyme decreased approximately eightfold compared with the native enzyme. In addition to, immobilized L‐ASNase was found to retain 60% of activity after 1‐month storage period at 4 °C. Therefore, PMMA‐starch composites can be provided more advantageous in terms of enzymatic affinity, thermal, pH and storage stability as L‐ASNase immobilization matrix. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43421.