ABSTRACT

This paper describes a control technology of wheelchair which may feel more flexible than traditional joystick controlled one. The main objective of our research is to develop new control architecture for a motorized wheelchair as well as an embedded system for monitoring critical patients. Such a smart wheelchair is designed for the disabled people in the developing countries as it will be very low-cost than existing others. Controlling is possible by android operated mobile or tab. In addition to button control, motion sensor controlling mechanism also has implemented. Moreover, bio-metric features have made wheelchair more suitable for critical patients. If the patient is in hostile condition, the wheelchair will produce an alert by raising the alarm with the measurement of the heartbeat at a particular interval.

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

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