دانشگاه صنعتی نوشیروانی بابلمجله علمی رایانش نرم و فناوری اطلاعات2383-10061420121221A Hybrid Statistical IHS Image Fusion MethodA Hybrid Statistical IHS Image Fusion Method294080868FAAli RezaAfaryBabol Noshirvani University of TechnologyMohammad JavadValdan ZoejK.N. Toosi University of Technology, Tehran, IranHassanEmamiUniversity of Tabriz, Tabriz, Iran0000-0002-0171-6487Journal Article20181226IHS method for image fusion is one of the conventional methods with a simple theory. Implementation of this<br />method is simple and effective from computational point of view. But this method causes the spectral content of<br />the fused image to get disturbed in comparison with the spectral criteria of the main multispectral image. In this<br />article a statistical image fusion method is combined with the conventional IHS method to improve its spectral<br />quality and overcome its deficiency. Some spectral indices were used for evaluation of this combined method and<br />compared with the conventional IHS and the used statistical fusion methods. This combined method improves the<br />spectral quality of the fused image and eliminates the existing shortcomings of the spectral disturbing in fused<br />image produced by IHS method.IHS method for image fusion is one of the conventional methods with a simple theory. Implementation of this<br />method is simple and effective from computational point of view. But this method causes the spectral content of<br />the fused image to get disturbed in comparison with the spectral criteria of the main multispectral image. In this<br />article a statistical image fusion method is combined with the conventional IHS method to improve its spectral<br />quality and overcome its deficiency. Some spectral indices were used for evaluation of this combined method and<br />compared with the conventional IHS and the used statistical fusion methods. This combined method improves the<br />spectral quality of the fused image and eliminates the existing shortcomings of the spectral disturbing in fused<br />image produced by IHS method.http://jscit.nit.ac.ir/article_80868_c24c0ef92e8ff96d58d1a58c2bebedba.pdfدانشگاه صنعتی نوشیروانی بابلمجله علمی رایانش نرم و فناوری اطلاعات2383-10061420121221A New Upper Bound for the Capacity of Free Space Optical Intensity Channel by Using a Simple Mathematical InequalityA New Upper Bound for the Capacity of Free Space Optical Intensity Channel by Using a Simple Mathematical Inequality495484484FAکبریاکبریDepartment of Electrical Engineering, Sadjad Institute for Higher EducationقوشهعابدهدتنیDepartment of Electrical Engineering, Ferdowsi University of Mashhad,Journal Article20121231<em>Farid-Hranilovic (FH), in an interesting way, found a capacity-achieving discrete input distribution for free space optical (FSO) channel by numerically maximizing the input-parameter (β) dependent mutual information between channel input and the scaled output. In this paper, first, by using a simple mathematical inequality, we find an upper bound for FH input-scaled output mutual information and then maximize the obtained upper bound to reach to a third order equation for the optimum β as β<sup>*</sup>. Our equation (i) determines β<sup>*</sup> exactly in contrary to the FH work where β<sup>*</sup> is found numerically through an exhaustive search and also, (ii) is consistent with the estimated equation for β<sup>*</sup> in the FH work. Our upper bound is shown to be tighter than the proposed upper bound in the FH work that is found through sphere packing argument at very high SNRs. Using numerical illustrations at different SNRs, we compare our β<sup>*</sup>s, mass point spacing as </em> <em><sup>*</sup></em><em>, and upper bound with previous works.</em><em>Farid-Hranilovic (FH), in an interesting way, found a capacity-achieving discrete input distribution for free space optical (FSO) channel by numerically maximizing the input-parameter (β) dependent mutual information between channel input and the scaled output. In this paper, first, by using a simple mathematical inequality, we find an upper bound for FH input-scaled output mutual information and then maximize the obtained upper bound to reach to a third order equation for the optimum β as β<sup>*</sup>. Our equation (i) determines β<sup>*</sup> exactly in contrary to the FH work where β<sup>*</sup> is found numerically through an exhaustive search and also, (ii) is consistent with the estimated equation for β<sup>*</sup> in the FH work. Our upper bound is shown to be tighter than the proposed upper bound in the FH work that is found through sphere packing argument at very high SNRs. Using numerical illustrations at different SNRs, we compare our β<sup>*</sup>s, mass point spacing as </em> <em><sup>*</sup></em><em>, and upper bound with previous works.</em>http://jscit.nit.ac.ir/article_84484_c7809eb20ccad3fa5f2247f99c2e185f.pdf