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Background: The presence of speckle noise in synthetic aperture radar (SAR) images makes the images of low quality in terms of textural features and spatial resolution which are required for processing issues such as image classification and clustering. Already, there are many adaptive filters to remove noise in SAR images. ENVI software is a fully applicable tool for this purpose which has a good library including several filters in the classes of adaptive, orderstatistics and non-linear filters. Materials and Methods: In this study, the toolbox of ENVI is reviewed, analyzed and then numerically evaluated based on several single-band images along with multi-band polarimetric SAR (Pol-SAR) images achieved from SAR sensors such as TerraSAR-X. For evaluation, two metrics including Equivalent Number of Looks (ENL) and Edge Preservation Index (EPI) are used which show the ability of the filters in preserving jointly spatial/textural features based on general information and edges quality, respectively. Results: It is notable that both metrics illustrate that some classic filters are better in comparison to newer filters. Conclusion: The experiments can help us in selecting a better filter towards our aims. In this respect, attention to the results of commercial filters of ENVI software and their analysis can guide us to find the best case in order to process commercial data of SAR sensors in the applications of environmental monitoring, geo-science studies, industrial usages and so on.

Background: The paper aims to review existing solutions for smart building to increase hospitality levels in locations like hospitals. The answers could support features such as online appointments, smart navigation, and queue management in the building through mobile phone and navigation to the desired location by highlighting the point of interest and facilities and checking the spaces' occupancy. Methods: Such a solution addresses all mentioned issues regarding the smart building by integrating and utilizing various data sources collected by the internet of things (IoT) sensors. This solution mainly deals with location determining, also known as positioning, using Bluetooth Low Energy radio. The goal is to implement a low-power, low-cost indoor positioning system that utilizes existing hardware. Results: Positions determining is considered a significant section of these kinds of solutions. Previous attempts with indoor positioning systems intensify statistical fingerprinting methods, mainly using IEEE 802.11 (WLAN) as the platform. Conclusion: Some efforts have been made with purely signal strength-based positioning, but indoor environments have been shown to work inauspiciously for these kinds of methods.