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  • 1
    Online Resource
    Online Resource
    Robot for Under Water Monitoring
    Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP ; 2019
    In:  International Journal of Engineering and Advanced Technology Vol. 9, No. 1s2 ( 2019-12-31), p. 26-32
    Details
    In: International Journal of Engineering and Advanced Technology, Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, Vol. 9, No. 1s2 ( 2019-12-31), p. 26-32
    Abstract: Unmanned underwater vehicle is the branch of robotics which comes under the autonomous underwater vehicle. These robots have the wide applications like research, collection of the samples and observation of underwater bodies. The proposed work involves underwater robot for measuring the parameters of water and surveillances for inspecting underwater structure. The contamination of the water bodies can be found out by using the light density method. The evaporation level of water bodies can be studied through temperature measurements and also the acidic and alkaline nature of the water can be seen through pH measurement. To move easily through any standard inspection doors, the robot is designed with the length of 240 mm, breadth 94.68mm and height of 130mm by considering the mass to length calculation method based on floating theory. In addition, this robot is fitted with air tubes of dimension 81.5mm which is a standard size for 5kg robot. This work makes use of Arduino UNO commands to control the motors, light density measurement and water sample collection and communication through Bluetooth communication between the user and the float which floats on the surface of water. The customized mobile application makes the robot user friendly and easily controllable. In this work, an underwater 4K ultra HD camera is fitted to the hardware. This camera is used to record the underwater view. The recorded sequences of images stored in micro SD card are used to analyze surrounding objects in the target area. All the elements connected to the Arduino are controlled by the user. The robot is designed for monitoring the under water.
    Type of Medium: Online Resource
    ISSN: 2249-8958
    URL: Journal
    URL: Article
    DOI: 10.35940/ijeat.2249-8958
    DOI: 10.35940/ijeat.A1019.1291S219
    Language: Unknown
    Publisher: Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
    Publication Date: 2019
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Digital Image Falsification Detection System for Effective Data Communication
    Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP ; 2019
    In:  International Journal of Engineering and Advanced Technology Vol. 9, No. 2 ( 2019-12-30), p. 4081-4086
    Details
    In: International Journal of Engineering and Advanced Technology, Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, Vol. 9, No. 2 ( 2019-12-30), p. 4081-4086
    Abstract: In this proposed system a digital imagefalsification can be identified using the combination of both adaptive over block based segmentation, feature keypointbased feature extraction algorithms(Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF)) and forgery region extraction algorithm. The proposed falsification detection algorithm comprises both block based falsification detection algorithm (adaptive over block based segmentation and block feature matching algorithm) and the keypoint based falsification detection algorithm(forgery region extraction algorithm). Adaptive over block based Segmentation algorithm adaptively segments the input digital image into separate(non overlapped) blocks in irregular manner. Scale Invariant Feature Transform (SIFT) algorithm and Speeded Up Robust Features (SURF) algorithms are used to draw out features from the segmentedblocks as a block features. Then the extracted features are matched with the feature points of other segmented block. If the feature key points are matched with any other feature point presents in the segmented blocks, then the matched feature points are marked as Labeled key Points (LKP), which can be doubted as a forged regions. Finally, the Forgery Region Extraction algorithm can be used to detect the forged region from the input digital image based on the extracted labeled feature points. The experimental outcomesdisplay that the novelfalsification detection system can accomplished the requirements compared with the existing digital imagefalsification detection methods.
    Type of Medium: Online Resource
    ISSN: 2249-8958
    URL: Issue
    URL: Journal
    URL: Article
    DOI: 10.35940/ijeat.9.2
    DOI: 10.35940/ijeat.2249-8958
    DOI: 10.35940/ijeat.B4950.129219
    Language: Unknown
    Publisher: Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
    Publication Date: 2019
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
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