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 CERNE , 2009, Abstract: This work evaluated the effects of particle thickness and veneer reinforced layer on the physical and mechanicalproperties of OSB made of Pinus taeda L. The boards were manufactured with particle thickness of 0.4, 0.7 and 1.0 mm and phenolformaldehyderesin in the proportion of 6% of solid content. To the veneer reinforced layer was used veneer from Pinus taeda with 2.0mm of thickness. The increase in the slenderness (length/thickness) ratio of thins particles, results in the higher values of MOE andMOR in the cross direction. The increase in the particles thickness contributed to higher values of the board internal bond. Thedifferent particles thickness did not clearly affected on the physical properties of OSB. The veneer reinforced layer results in the higheraverage values of MOE and MOR in the cross direction. All of the results of MOE and MOR obtained for boards with differentthickness attend tominimum values required per CSA 0437 (CSA, 1993). For the internal bond, the results were satisfactory to boardsmanufactured with particles thickness of 0.7 and 1.0 mm. According to the results the main conclusions were: (i) The increase in theparticles thickness contributed to lower values of MOE and MOR, and higher values of the board internal bond; (ii) the veneerreinforced layer increased MOE and MOR values in the cross direction.
 International Journal of Recent Technology and Engineering , 2012, Abstract: In this study the suitability of using core veneer made from renewable natural fiber i.e. Non-oven jute felt, which is the second most widely used natural fiber for manufacturing of plywood was investigated to minimize the gap between demand and supply of wood veneer. The renewable natural hard jute fibre was impregnated with phenolic resin and was used for the manufacture of plywood. Plywood of 4 mm, 6 mm, 12 mm and 18 mm thick were manufactured by using phenolic resin impregnated jute felt having thickness 16mm of 1850 GSM (approx.) as a core in place of the natural wood veneer. From the study, it can be inferred that PF Resin impregnated Non oven jute felt as a natural fibre can suitably replace the wooden glue core veneer to manufacture ply board up to 80% as an alternative substitute of wood. The physico-mechanical properties such as surface roughness, moisture content, density, water absorption, swelling, compressive strength, tensile strength, static bending strength, glue shear strength, of the plywood manufactured by using jute felt as core veneer with different resin dilution have been studied. Data revels that most of the physico-mechanical properties of the plywood showed satisfactory results meeting the requirement of different grades of plywood tested as per IS: 1734 - 1983. The accelerated study of the glued core after impregnation with jute felt have been carried out for three months before plywood manufacture after storing it in proper temperature and humidity. The data revealed that there is no appreciable change in bond quality and mechanical properties of the plyboard manufactured after storing the veneer up to 30 days. The study concluded that wood substituted jute composites could be an ideal solution with ever depleting forest reserves where utilization of renewable resources will be beneficiary for plywood industries to meet the challenges during scarcity of veneer by reducing the cost of imported veneer.
 Journal of Networks , 2011, DOI: 10.4304/jnw.6.3.498-504 Abstract: This paper exploits recent developments in sparse approximation and compressed sensing to efficiently perform the direction finding. The new method is proposed based on unimodal characteristic of antenna pattern and sparse property of received data. Unlike the conventional methods based peak-searching and symmetric constraint, the sparse reconstruction algorithm requires less pulse and takes advantage of compressive sampling. Simulation results validate the performance of the proposed method is better than the conventional methods.
 Mathematics , 2010, Abstract: This paper exploits recent developments in compressive sensing (CS) to efficiently perform the direction finding via amplitude comprarison. The new method is proposed based on unimodal characteristic of antenna pattern and sparse property of received data. Unlike the conventional methods based peak-searching and symmetric constraint, the sparse reconstruction algorithm requires less pulse and takes advantage of CS. Simulation results validate the performance of the proposed method is better than the conventional methods.
 Mathematics , 2009, Abstract: In this paper, we propose and study the use of alternating direction algorithms for several $\ell_1$-norm minimization problems arising from sparse solution recovery in compressive sensing, including the basis pursuit problem, the basis-pursuit denoising problems of both unconstrained and constrained forms, as well as others. We present and investigate two classes of algorithms derived from either the primal or the dual forms of the $\ell_1$-problems. The construction of the algorithms consists of two main steps: (1) to reformulate an $\ell_1$-problem into one having partially separable objective functions by adding new variables and constraints; and (2) to apply an exact or inexact alternating direction method to the resulting problem. The derived alternating direction algorithms can be regarded as first-order primal-dual algorithms because both primal and dual variables are updated at each and every iteration. Convergence properties of these algorithms are established or restated when they already exist. Extensive numerical results in comparison with several state-of-the-art algorithms are given to demonstrate that the proposed algorithms are efficient, stable and robust. Moreover, we present numerical results to emphasize two practically important but perhaps overlooked points. One point is that algorithm speed should always be evaluated relative to appropriate solution accuracy; another is that whenever erroneous measurements possibly exist, the $\ell_1$-norm fidelity should be the fidelity of choice in compressive sensing.