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Gliomas are the largest prevalent and destructive of brain tumors and have crucial parts for the diagnosing and treating of MRI brain tumors during segmentation using computerized methods. Recently, U-Net architecture has achieved impressive brain tumor segmentation, but this role remains challenging due to the differing severity and appearance of gliomas. Therefore, we proposed a novel encoder-decoder architecture called Multi Inception Residual Attention U-Net (MIRAU-Net) in this work. It integrates residual, inception modules with attention gates into U-Net to further enhance brain tumor segmentation performance. Encoderdecoder is connected in this architecture through Inception Residual pathways to decrease the distance between their maps of features. We use the weight crossentropy and generalized Dice (GDL) with focal Tversky loss functions to resolve the class imbalance problem. The evaluation performance of MIRAU-Net checked with Brats 2019 and obtained mean dice similarities of 0.885 for the whole tumor, 0.879 for the core area, and 0.818 for the enhancement tumor. Experiment results reveal that the suggested MIRAU-Net beats its baselines and provides better efficiency than recent techniques for brain tumor segmentation.This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020).info:eu-repo/semantics/publishedVersio

Multi-Agent Pursuit-Evasion Game Based on Organizational Architecture

Multi-agent coordination mechanisms are frequently used in pursuit-evasion games with the aim of enabling the coalitions of the pursuers and unifying their individual skills to deal with the complex tasks encountered. In this paper, we propose a coalition formation algorithm based on organizational principles and applied to the pursuit-evasion problem. In order to allow the alliances of the pursuers in different pursuit groups, we have used the concepts forming an organizational modeling framework known as YAMAM (Yet Another Multi Agent Model). Specifically, we have used the concepts Agent, Role, Task, and Skill, proposed in this model to develop a coalition formation algorithm to allow the optimal task sharing. To control the pursuers\u27 path planning in the environment as well as their internal development during the pursuit, we have used a Reinforcement learning method (Q-learning). Computer simulations reflect the impact of the proposed techniques

HTTU-Net: Hybrid Two Track U-Net for Automatic Brain Tumor Segmentation

Brain cancer is one of the most dominant causes of cancer death; the best way to diagnose and treat brain tumors is to screen early. Magnetic Resonance Imaging (MRI) is commonly used for brain tumor diagnosis; however, it is a challenging problem to achieve higher accuracy and performance, which is a vital problem in most of the previously presented automated medical diagnosis. In this paper, we propose a Hybrid Two-Track U-Net(HTTU-Net) architecture for brain tumor segmentation. This architecture leverages the use of Leaky Relu activation and batch normalization. It includes two tracks; each one has a different number of layers and utilizes a different kernel size. Then, we merge these two tracks to generate the final segmentation. We use the focal loss, and generalized Dice (GDL), loss functions to address the problem of class imbalance. The proposed segmentation method was evaluated on the BraTS’2018 datasets and obtained a mean Dice similarity coefficient of 0.865 for the whole tumor region, 0.808 for the core region and 0.745 for the enhancement region and a median Dice similarity coefficient of 0.883, 0.895, and 0.815 for the whole tumor, core and enhancing region, respectively. The proposed HTTU-Net architecture is sufficient for the segmentation of brain tumors and achieves highly accurate results. Other quantitative and qualitative evaluations are discussed, along with the paper. It confirms that our results are very comparable expert human-level performance and could help experts to decrease the time of diagnostic.This work was supported in part by the Robotics and Internet-of-Things Laboratory of Prince Sultan University, Saudi Arabia, and in part by the National Natural Science Foundation of China under Grant 61375081.info:eu-repo/semantics/publishedVersio

Bio-inspired locomotion control for UBot self-reconfigurable modular robot

This paper first presents a mathematic CPG (central pattern generator) model which has been developed based on the characteristics of a self-reconfigurable modular robot (UBot)'s modules with universal joints. Then, a bionic motion neural control network based on the CPG is proposed to solve the problem of multi-mode locomotion control problem in the complex environment. The bionic network is composed of perceptual neurons, CPG phase modulation network and motor neurons, so it can coordinate the walking and creeping gait of the modular robot before and after deformation, and adapt to autonomous movement in the complex environment with challenging features, such as steps, slopes and obstacles. Finally, the proposed motion control algorithm is verified by experiments

Learning 3D Bipedal Walking with Planned Footsteps and Fourier Series Periodic Gait Planning

Reinforcement learning provides a general framework for achieving autonomy and diversity in traditional robot motion control. Robots must walk dynamically to adapt to different ground environments in complex environments. To achieve walking ability similar to that of humans, robots must be able to perceive, understand and interact with the surrounding environment. In 3D environments, walking like humans on rugged terrain is a challenging task because it requires complex world model generation, motion planning and control algorithms and their integration. So, the learning of high-dimensional complex motions is still a hot topic in research. This paper proposes a deep reinforcement learning-based footstep tracking method, which tracks the robot’s footstep position by adding periodic and symmetrical information of bipedal walking to the reward function. The robot can achieve robot obstacle avoidance and omnidirectional walking, turning, standing and climbing stairs in complex environments. Experimental results show that reinforcement learning can be combined with real-time robot footstep planning, avoiding the learning of path-planning information in the model training process, so as to avoid the model learning unnecessary knowledge and thereby accelerate the training process

A Master-Slave Separate Parallel Intelligent Mobile Robot Used for Autonomous Pallet Transportation

This work reports a master-slave separate parallel intelligent mobile robot for the fully autonomous transportation of pallets in the smart factory logistics. This separate parallel intelligent mobile robot consists of two independent sub robots, one master robot and one slave robot. It is similar to two forks of the forklift, but the slave robot does not have any physical or mechanical connection with the master robot. A compact driving unit was designed and used to ensure access to the narrow free entry under the pallets. It was also possible for the mobile robot to perform a synchronous pallet lifting action. In order to ensure the consistency and synchronization of the motions of the two sub robots, high-gain observer was used to synchronize the moving speed, the lifting speed and the relative position. Compared with the traditional forklift AGV (Automated Guided Vehicle), the mobile robot has the advantages of more compact structure, higher expandability and safety. It can move flexibly and take zero-radius turn. Therefore, the intelligent mobile robot is quite suitable for the standardized logistics factory with small working space