Traffic congestion and regulating traffic in traffic signals are major issues in cities. Nowadays, in most of the cities, traffic management centers installed numerous cameras all over the roads and traffic signals. Such cameras can be effectively used for the automation of traffic signals. The objective is to develop a real time system that can automatically monitor real time traffic and make the system intelligent using artificial intelligence techniques. Specifically, Deep Convolutional Neural Networks are employed to perform the task. From statistical traffic data, it determines count, type of vehicle, average speed, distance between vehicles, etc. Based on traffic, the algorithm instructs to stop vehicle or queue or move. It can also record a wrong-way driver. Using license plate recognition, security applications such as unauthorized vehicles are identified. If there is violation of traffic rules, they are recorded with registration number. It can detect ambulances and give first preference. The proposed algorithm identifies VIP vehicles and clear traffics in automated ways. Ambulances are given priority to pass the road. The entire system have been developed using a standalone-Graphical User Interface (GUI). We have implemented successfully and the proposed framework performs satisfactorily.
An incubator is a biomedical apparatus used to keep neonates if they are ill and in need of further investigations. This is also useful for observation and care of newborn babies who were premature or of low birth weight. There will be loss of lives of neonates if the incubator is not appropriately monitored and this may even lead to accidents. In view of these, it has been proposed to implement an Intelligent Monitoring System for Neonatal Intensive Care Unit (NICU). For monitoring the premature infants continuously it requires sensors. It has been learnt that the temperature, breathing rate, body movements, loudness, or cry are some of the features to be monitored in incubator. Hence, in this paper, a wireless communication and body area network based system that constantly examine the vital physiological parameters of the baby kept in the NICU has been discussed. The analog signals from the sensors are processed using a Raspberry Pi. The system has been developed with Raspberry Pi B+ module as the controller. Sensors are used to monitor the physiological parameters and Pi camera is used to detect the baby movements. Global System for Mobile Communications (GSM) module is incorporated in the proposed system to transmit the parameters via a short message service (SMS). Hence, this system offers a suitable method for parents and nurses to monitor premature infants.
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