Real-time traffic sign detection and recognition using Raspberry Pi

Nowadays, the number of road accident in Malaysia is increasing expeditiously. One of the ways to reduce the number of road accident is through the development of the advanced driving assistance system (ADAS) by professional engineers. Several ADAS system has been proposed by taking into consideration the delay tolerance and the accuracy of the system itself. In this work, a traffic sign recognition system has been developed to increase the safety of the road users by installing the system inside the car for driver’s awareness. TensorFlow algorithm has been considered in this work for object recognition through machine learning due to its high accuracy. The algorithm is embedded in the Raspberry Pi 3 for processing and analysis to detect the traffic sign from the real-time video recording from Raspberry Pi camera NoIR. This work aims to study the accuracy, delay and reliability of the developed system using a Raspberry Pi 3 processor considering several scenarios related to the state of the environment and the condition of the traffic signs. A real-time testbed implementation has been conducted considering twenty different traffic signs and the results show that the system has more than 90% accuracy and is reliable with an acceptable delay

Development of a Tool for Assessing the Kinematics and Kinetics of Pen Skills

Pen skills are essential in the learning process, as they help to develop the brain–body coordination. This development is significant for young children, as these skills will help them learn better at school. Learning pen skills will provide an advantage in presenting their knowledge. This research focused on quantifying and interpreting kinematic and kinetic data for pen-based activities. The kinematic data were quantified using a tablet computer on the basis of a stylus pen input. The kinetic data were quantified using a specially developed stylus equipped with force sensors using a MyRIO data acquisition board. A LabVIEW code was developed to provide interaction between users and the tablet. This code was also used to record the pen stylus motion and the force applied at the stylus as a function of time. Then, the pen skills performance was investigated by extracting the kinematic and kinetic parameters from the recorded signal by using an offline algorithm. This algorithm synchronised the kinematic and kinetic signals, as both the signals were quantified using two different devices. This enabled the study of the relationship between the kinematic and the kinetic parameters involved in pen-based tasks. The system was validated by comparing the pen skills performance between the dominant and the non-dominant hand of adults. The pen skills performance was also investigated for three different writing speeds (Natural, Accurate, and Fast) and two different sizes. Moreover, the pen skills performance was evaluated between the dominant and the non-dominant hand for tracing and copying tasks