Noor J. Jihad; Sinan M. Abdul Satar
Volume 20, Issue 3 , July 2020, , Page 42-49
Abstract
Recently, optical wireless communication (OWC) technologies focused on a camera or an image sensor receiver have drawn specific attention in areas like the internet, indoor localization, motion detection, and intelligent transportation systems. Besides, panorama sensors are the subject of communications ...
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Recently, optical wireless communication (OWC) technologies focused on a camera or an image sensor receiver have drawn specific attention in areas like the internet, indoor localization, motion detection, and intelligent transportation systems. Besides, panorama sensors are the subject of communications from picture sensors as receptors as the high-speed OWC strategy do not need any change to the existing network, so the difficulty and expense of deployment are very limited. So in this paper, a detailed review of the techniques of optical camera communication (OCC)has been presented. In addition to their function of localizing, tracking and recording motion. Through addressing several facets of OCC and their different implementations, this study varies from the latest literature on this topic. The first section of the current article is on standardization, Path classification, modulation, scripting, synchronization, and signal processing methods for OCC networks whereas the second section of the research discusses OCC-based localization, navigation, motion detection, and smart transport systems literature .Finally , OCC's problems and potential work directions have been addressed in the final section of the research.
Ahmed H. Hadi; Waleed F. Shareef
Volume 20, Issue 2 , April 2020, , Page 33-46
Abstract
Due to the recent advancements in the fields of Micro Electromechanical Sensors (MEMS), communication, and operating systems, wireless remote monitoring methods became easy to build and low cost option compared to the conventional methods such as wired cameras and vehicle patrols. Pipeline Monitoring ...
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Due to the recent advancements in the fields of Micro Electromechanical Sensors (MEMS), communication, and operating systems, wireless remote monitoring methods became easy to build and low cost option compared to the conventional methods such as wired cameras and vehicle patrols. Pipeline Monitoring Systems (PMS) benefit the most of such wireless remote monitoring since each pipeline would span for long distances up to hundreds of kilometers. However, precise monitoring requires moving large amounts of data between sensor nodes and base station for processing which require high bandwidth communication protocol. To overcome this problem, In-Situ processing can be practiced by processing the collected data locally at each node instead of the base station. This Paper presents the design and implementation of In-situ pipeline monitoring system for locating damaging activities based on wireless sensor network. The system built upon a WSN of several nodes. Each node contains high computational 1.2GHz Quad-Core ARM Cortex-A53 (64Bit) processor for In-Situ data processing and equipped in 3-axis accelerometer. The proposed system was tested on pipelines in Al-Mussaib gas turbine power plant. During test knocking events are applied at several distances relative to the nodes locations. Data collected at each node are filtered and processed locally in real time in each two adjacent nodes. The results of the estimation is then sent to the supervisor at base-station for display. The results show the proposed system ability to estimate the location of knocking event.
Zainab Mohammed Resan; Muayad Sadik Croock
Abstract
Robust and accurate indoor localization has been the goal of several researchefforts over the past decade. In the building where the GPS is not available, this projectcan be utilized. Indoor localization based on image matching techniques related to deeplearning was achieved in a hard environment. So, ...
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Robust and accurate indoor localization has been the goal of several researchefforts over the past decade. In the building where the GPS is not available, this projectcan be utilized. Indoor localization based on image matching techniques related to deeplearning was achieved in a hard environment. So, if it wanted to raise the precision ofindoor classification, the number of image dataset of the indoor environment should be aslarge as possible to satisfy and cover the underlying area. In this work, a smartphonecamera is used to build the image-based dataset of the investigated building. In addition,captured images in real time are taken to be processed with the proposed model as a testset. The proposed indoor localization includes two phases the first one is the offlinelearning phase and the second phase is the online processing phase. In the offline learningphase, here we propose a convolutional neural network (CNN) model that sequences thefeatures of image data from some classis's dataset composed with a smartphone camera.In the online processing phase, an image is taken by the camera of a smartphone in real–time to be tested by the proposed model. The obtained results of the prediction can appointthe expected indoor location. The proposed system has been tested over variousexperiments and the obtained experimental results show that the accuracy of the predictionis almost 98.0%.
