An Intelligent Vision System to Navigate a Robot Without On-Board Sensors

MASRİ, İbrahim; Erdal, Erdal

doi:10.1109/ubmk.2019.8907110

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…By using those apps there were few problems in every project application that are discussed above, Masri and Erdal (2019). And I have created an application which was solution to every proposed problem.…”

Section: Analysis From Literature Reviewmentioning

confidence: 99%

Machine Learning Based Cloud Music Application With Facial Recognition Using Android Studio (Musync)

2021

aijser

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This paper output is a music player application but when it comes to its features it will be way more than a simple music player. It is developed on Android Studio and other tools like: Firebase is used as database, Android phone camera, Music library of Android Phone are used in the development of application. When user changes his phone or reset his phone then all of his data is lost or user has to put all the data in his computer and then back to his mobile phone except data that is backed up online. Message data, photos and contacts are that things that users backed up online. But music files normally don’t get backed up and user troubles in re downloading the files or moving files in computer and back to phone. In this purposed work the targeted problem is resolved as MUSYNC application is be able to automatically backup all the mp3 data from the phone and user will get all of his data by just signing in the application in his new phone. The purposed application has a feature of sync music. Users can sync music with another one and that person will able to listen to same music instantly. Application also provides a unique feature of mood detection using digital image processing DIP. This feature is able to check your face emotion and play music according to it. User just has to take a picture and that is it, this music player plays the music according to your mood. This feature is useful when user having tough time what to listen.

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Section: Analysis From Literature Reviewmentioning

confidence: 99%

Machine Learning Based Cloud Music Application With Facial Recognition Using Android Studio (Musync)

2021

aijser

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“…In different agricultural applications, mobile chassis should ensure flexible movement and stable driving to improve the effectiveness and robustness of mobile chassis path planning [1][2][3]. In the existing studies, image processing technology achieves the path optimization from the perspective of obstacle detection and energy consumption reduction [4][5][6][7][8][9]. By improving the accuracy of geofencing, the optimal path selection in dynamic space can be provided [10], which can also solve problems such as vehicle collision in dynamic path planning [11] and improve the effectiveness of vehicle path planning in constrained environments [12].…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Mecanum Wheel Chassis Based on Improved A* Algorithm

Wang

Zhao

et al. 2023

Electronics

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This study is concerned with path planning in a structured greenhouse, in contrast to much of the previous research addressing applications in outdoor fields. The prototype mainly comprises an independently driven Mecanum wheel, a lidar measuring module, a single-chip microcomputer control board, and a laptop computer. Environmental information collection and mapping were completed on the basis of lidar and laptop computer connection. The path planning algorithm used in this paper expanded the 8-search-neighborhood of the traditional A* algorithm to a 48-search-neighborhood, increasing the search direction and improving the efficiency of path planning. The Floyd algorithm was integrated to smooth the planned path and reduced the turning points in the path. In this way, the problems of the traditional A* algorithm could be solved (i.e., slow the path planning speed and high numbers of redundant points). Tests showed that the turning points, planning path time, and distance of the improved algorithm were the lowest. Compared with the traditional 8-search-neighborhood A* algorithm, the turning point was reduced by 50%, the planning time was reduced by 13.53%, and the planning distance was reduced by 13.96%. Therefore, the improved method of the A* algorithm proposed in this paper improves the precision of the planning path and reduces the planning time, providing a theoretical basis for the navigation, inspection, and standardization construction of greenhouses in the future.

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An Intelligent Vision System to Navigate a Robot Without On-Board Sensors

Cited by 2 publications

References 7 publications

Machine Learning Based Cloud Music Application With Facial Recognition Using Android Studio (Musync)

Machine Learning Based Cloud Music Application With Facial Recognition Using Android Studio (Musync)

Path Planning of Mecanum Wheel Chassis Based on Improved A* Algorithm

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