Accepted Papers
Improved Navigation System of Marine Unmanned Robot Based on Sensor Fusion

Improving navigation systems for unmanned marine robots is one of the challenging issues in recent decades. In this paper, an Unmanned Surface Vehicle (USV) is introduced, which has an improved navigation system to carry out its various missions in the sea. This marine robot works with wind, wave, and solar energy, and the robot's navigation includes a global navigation satellite system (GNSS), compass sensors, and inertial measurement unit (IMU). Also, an algorithm is designed to improve navigation, which combines sensor information with classical filters such as Extended Kalman Filter (EKF) and Particle Filter (PF). Finally, the simulation results showed that EKF and PF filters have very similar and acceptable results in determining the robot's position. However, due to the ease of implementing EKF on the USV control board, in the implementation part, the combination of sensors' information based on EKF was investigated.


USV, Sensor Fusion, IMU, EKF, PF.

Bridging the Gap: Selenium and Rpa for Unparalleled Automation

RohitKhankhoje, Independent Researcher Avon, Indiana, USA


In the ever-changing technological landscape of today, the incorporation of test automation has become an essential element in the realm of software development and quality assurance. Selenium, known for its robust capabilities in web application testing, has long served as a fundamental pillar in the field of test automation. Simultaneously, Robotic Process Automation (RPA) has experienced increased utilization in order to streamline repetitive business processes across various industries. However, the true potential of these two automation approaches lies in their integration, which provides an opportunity for unparalleled efficiency, productivity, and comprehensive test coverage. This article delves into the synergistic relationship between Selenium and RPA, examining how their combination ushers in a new era of test automation. We explore the seamless integration of Selenium within RPA frameworks and demonstrate how it expands automation beyond web applications, encompassing desktop applications and diverse systems. Our research outlines practical use cases, technical implementation, and the benefits of this amalgamation. Furthermore, we discuss its potential to revolutionize not only software testing but also broader business processes. The fusion of Selenium and RPA signifies a transformative shift in test automation, enabling organizations to bridge the gap between isolated testing efforts and comprehensive automation, ultimately resulting in unparalleled efficiency and reliability.


Test Automation, RPA,Selenium, Integration,Software testing.

Comparison Between Traditional Detection Techniques and Deep Learning Methods

Nagi Ould Taleb1, Moustapha Mohamed Saleck2, Mohamedade Farouk3, 1Higher Institute of Digital, Nouakchott, Mauritania, 2University of Chouaïb Doukkali, EL Jadida, Morocco, 3University of Nouakchott, Nouakchott, Mauritania


People detection has attracted considerable interest from the computer vision community in recent years. The detection of people in images and videos is a very popular research topic within the computer vision community. It is a particularly difficult subject, in particular because of the great variability of appearances and possible situations. In this approach, we will use two traditional detectors Haar Adaboost, Hog adaboost and those of deep learning Faster RCNN, SSD to detect people in the images. The research work proposed in this manuscript aims to contribute to the modelling of pattern (or object) recognition methods by classifying descriptors containing the most relevant information of an object and to apply the models found to the detection of the human silhouette (people or pedestrians) in images or multimedia streams (video)


Pedestrian detection, Convolutional Neural Network, Deep Learning, Haar like features, HOG descriptor.

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