Identifying teachers' perceptions for integrating ICT into teaching and learning processes through the implementation of One Laptop Per Child (OLPC) program in primary schools of Rwanda was the main target of this study. The study employed qualitative approach where thirty primary schools' teachers participated into this study through interviews and group discussions designed for the research questions. Questions and discussions were related to benefits of ICT in education; requirements to integrate ICT into teaching and learning practices; challenges hindering the implementation of OLPC program and the contributions of different stakeholders for implementing OLPC program in primary schools of Rwanda. Through thematic analysis of data, the program was found to be influential to teachers, learners and stakeholders of primary schools in Rwanda. In order to be fruitful, the integration of ICT through implementation of OLPC program requires to help teachers to acquire skills related to Technological Pedagogical Content Knowledge (TPACK). The study also suggested different solutions and strategies related to all identified challenges.

Collision avoidance of Arm Robot is designed for the robot to collide objects, colliding environment, and colliding its body. Self-collision avoidance was successfully trained using Generative Adversarial Networks (GANs) and Particle Swarm Optimization (PSO). The Inverse Kinematics (IK) with 96K motion data was extracted as the dataset to train data distribution of  3.6K samples and 7.2K samples. The proposed method GANs-PSO can solve the common GAN problem such as Mode Collapse or Helvetica Scenario that occurs when the generator  always gets the same output point which mapped to different input  values. The discriminator  produces the random samples' data distribution in which present the real data distribution (generated by Inverse Kinematic analysis).  The PSO was successfully reduced the number of training epochs of the generator  only with 5000 iterations. The result of our proposed method (GANs-PSO) with 50 particles was 5000 training epochs executed in 0.028ms per single prediction and 0.027474% Generator Mean Square Error (GMSE).