Accurate modeling and prediction of energy availability in energy harvesting real-time embedded systems

2010 ◽  
Author(s):  
Jun ◽  
Shaobo Liu ◽  
Qing Wu ◽  
Qinru Qiu
Keyword(s):  
Energy Harvesting ◽  
Embedded Systems ◽  
Real Time ◽  
Energy Availability ◽  
Modeling And Prediction

scholarly journals A Novel Rail-Network Hardware Simulator for Embedded System Programming

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Balaji M ◽  
Chandrasekaran M ◽  
Vaithiyanathan Dhandapani
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Real Time ◽  
Learning Outcomes ◽  
Real World ◽  
Time Constraints ◽  
Network Simulator ◽  
Practical Applications ◽  
Rail Network ◽  
Knowledge Enhancement

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.

scholarly journals NetQoPE: A Model-Driven Network QoS Provisioning Engine for Distributed Real-time and Embedded Systems

2008 ◽  
Author(s):  
Jaiganesh Balasubramanian ◽  
Sumant Tambe ◽  
Balakrishnan Dasarathy ◽  
Shrirang Gadgil ◽  
Frederick Porter ◽  
...  
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Qos Provisioning ◽  
Model Driven
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