Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 4 Articles
Active contours are used in the image processing application including edge detection,\nshape modeling, medical image-analysis, detectable object boundaries, etc. Shape is\none of the important features for describing an object of interest. Even though it is\neasy to understand the concept of 2D shape, it is very difficult to represent, define and\ndescribe it. In this paper, we propose a new method to implement an active contour\nmodel using Daubechies complex wavelet transform combined with B-Spline based on\ncontext aware. To show the superiority of the proposed method, we have compared\nthe results with other recent methods such as the method based on simple discrete\nwavelet transform, Daubechies complex wavelet transform and Daubechies complex\nwavelet transform combined with B-Spline....
Software as a Service (SaaS) in Cloud Computing offers reliable access to software\napplications for end users over the Internet without direct investment in infrastructure\nand software. SaaS providers utilize resources of internal datacenters or rent resources\nfrom a public Infrastructure as a Service (IaaS) provider in order to serve their customers.\nInternal hosting can increase cost of administration and maintenance, whereas hiring\nfrom an IaaS provider can impact quality of service due to its variable performance.\nTo surmount these challenges, we propose a knowledge-based admission control\nalong with scheduling algorithms for SaaS providers to effectively utilize public\nCloud resources in order to maximize profit by minimizing cost and improving customers�\nsatisfaction level. In the proposed model, the admission control is based on Service Level\nAgreement (SLA) and uses different strategies to decide upon accepting user requests for\nthat minimal performance impact, avoiding SLA penalties that are giving higher profit.\nHowever, because the admission control can make decisions optimally, there is a\nneed of machine learning methods to predict the strategies. In order to model\nprediction of sequence of strategies, a customized decision tree algorithm has been\nused. In addition, we conducted several experiments to analyze which solution in which\nscenario fit better to maximize SaaS provider�s profit. Results obtained through our\nsimulation shows that our proposed algorithm provides significant improvement\n(up to 38.4 % cost saving) compared to the previous research works...
This article is aimed to describe the method of testing the implementation of voice\ncontrol over operating and technical functions of Smart Home Come. Custom control\nover operating and technical functions was implemented into a model of Smart Home\nthat was equipped with KNX technology. A sociological survey focused on the needs of\nseniors has been carried out to justify the implementation of voice control into Smart\nHome Care. In the real environment of Smart Home Care, there are usually unwanted\nsignals and additive noise that negatively affect the voice communication with the\ncontrol system. This article describes the addition of a sophisticated system for filtering\nthe additive background noise out of the voice communication with the control system.\nThe additive noise significantly lowers the success of recognizing voice commands to\ncontrol operating and technical functions of an intelligent building. Within the scope\nof the proposed application, a complex system based on fuzzy-neuron networks,\nspecifically the ANFIS (Adaptive Neuro-Fuzzy Interference System) for adaptive\nsuppression of unwanted background noises was created. The functionality of the\ndesigned system was evaluated both by subjective and by objective criteria\n(SSNR, DTW). Experimental results suggest that the studied system has the potential\nto refine the voice control of technical and operating functions of Smart Home Care\neven in a very noisy environment....
Head-mounted displays and otherwearable devices open up for innovative types of interaction forwearable augmented reality (AR).\nHowever, to design and evaluate these new types of AR user interfaces, it is essential to quickly simulate undeveloped components\nof the system and collect feedback frompotential users early in the design process. One way of doing this is the wizard of Oz (WOZ)\nmethod. The basic idea behind WOZ is to create the illusion of a working system by having a human operator, performing some\nor all of the system�s functions.WozARd is a WOZ method developed for wearable AR interaction. The presented pilot study was\nan initial investigation of the capability of theWozARd method to simulate an AR city tour. Qualitative and quantitative data were\ncollected from 21 participants performing a simulated AR city tour. The data analysis focused on seven categories that can have\nan impact on how the WozARd method is perceived by participants: precision, relevance, responsiveness, technical stability, visual\nfidelity, general user-experience, and human-operator performance. Overall, the results indicate that the participants perceived\nthe simulated AR city tour as a relatively realistic experience despite a certain degree of technical instability and human-operator\nmistakes....
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