Scielo RSS <![CDATA[SAIEE Africa Research Journal]]> vol. 113 num. 4 lang. en <![CDATA[SciELO Logo]]> <![CDATA[<b>Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa</b>]]> Monitoring and assessing the distribution of economic areas in East Africa such as low and high income neighborhoods, has typically relied on the use of structured data and traditional survey approaches for collecting information such as questionnaires, interviews and field visits. These types of surveys are slow, costly and prone to human error. With the digital revolution, a lot of unstructured data is generated daily that is likely to contain useful proxy data for many economic variables. In this research we focus on satellite imagery data with applications in East Africa. Recently East African cities have been developing at a fast pace by building new infrastructure and constructing innovative economic zones. Moreover with increased urban population, cities have been expanding in multiple directions affecting the overall distribution of areas with economic activity. Automatic detection and classification of these areas could be used to inform a number of policies such as land usage and could also assist with policy enforcement monitoring. On the other hand, the distribution of different economic areas in a specific city could provide proxies for various economic development variables such as income distribution and poverty metrics. In this research, we apply deep learning techniques to satellite imagery to classify and assess the distribution of various economic areas of a specific region for urban planning. By benchmarking performance against various state-of-art models, results show that the proposed deep learning techniques yielded superior performance with an f1-score of 99%. <![CDATA[<b>Sustainable Smart City to Society 5.0: State-of-the-Art and Research Challenges</b>]]> With the growth of data traffic, demand of huge number of digital devices and their interconnection to establish a reliable communication, the internet has become a potential demand of the society. To develop a system that securely connects the internet to real-world space would aid in the advancement of a human-centered society that balances economic progress with the resolution of social issues. This paper provides a detailed aspect of Society 5.0 with its requirements, architecture, and components. We have proceeded extensively with the state-of-the-art Society 5.0 and its link with Industry 4.0/5.0. Furthermore, the role of Society 5.0 in the sustainable development goals of the United Nations is well elaborated. Several emerging communication and computing technologies such 5G/5G-Internet of Things (IoT), edge computing/ cloud computing/ fog computing, Internet of everything, blockchain, and beyond networks have been also well explored to fulfill the demands of Society 5.0. The potential application of super smart cities (Society 5.0) with some real-time experience of inhabitants is thoroughly discussed. Finally, we highlighted several open research challenges with opportunities. OPEN LICENSE: CC BY-NC-ND <![CDATA[<b>Controlling a Low Cost Bang Bang Pneumatic Monopod</b>]]> To date there have been great advances in the legged robotics community. However, these platforms are extremely costly to develop and require complex controllers to perform agile motion, limiting their research to well funded institutions, or purely simulation based studies. This research focuses on an extremely low cost robotic monopod platform that consists of a high powered servo motor as well as a pneumatic actuator. Due to the on/off (bang bang) nature of pneumatics, the platform is challenging to mathematically model. Using a reduced order model of the pneumatic actuator, trajectory optimization methods were implemented to generate acceleration, steady-state and deceleration trajectories. These were then analyzed and a simple state machine controller was developed to implement these trajectories on the robotic platform, with comparisons to the simulation results. In order to test the capabilities of the monopod robot, the above method was further extended with the robot running on multiple different surfaces (hard surface as well as two different gravel surfaces). Results are promising and reveal that simple models and controllers are sufficient to generate stable transient motions for a legged robot running on nonuniform terrain.