Tunnel Field Effect Transistor can be introduced as an emerging alternate to MOSFET which is energy efficient and can be used in low power applications. Due to the challenge involved in integration of band to band tunneling generation rate, the existing drain current models are inaccurate. A compact analytical model for simple tunnel FET and pnpn tunnel FET is proposed which is highly accurate. The numerical integration of tunneling generation rate in the tunneling region is performed using Simpson's rule. Integration is done using both Simpson's 1/3 rule and 3/8 rule and the models are validated against numerical device simulations. The models are compared with existing models and it is observed that the proposed models show excellent agreement with device simulations in the entire region of operation with Simpson's 3/8 rule exhibiting the maximum accuracy.

The project aims to prevent animal-train collisions by using an image processing system that can identify obstacles on the train tracks, particularly animals. The project utilizes machine learning algorithms to identify the obstacles and send a notification to the train if an obstacle is detected. The system uses an IoT application and Bluetooth transmitter to send a message to the train and trigger a buzzer notification. By utilizing technology, the project hopes to reduce the number of animal-train collisions and improve safety for both humans and animals. The system utilizes machine learning algorithms to identify the obstacles in real-time and send a notification to the train via an IoT application and Bluetooth transmitter. If an obstacle is detected, a buzzer notification will be triggered on the train, alerting the driver to slow down or stop. The image processing system is designed to be highly accurate and efficient in identifying animals on the tracks, even in challenging lighting or weather conditions. The system is trained using a large dataset of images of animals and non- animal objects on train tracks, enabling it to recognize animals of various sizes, shapes, and colors. Overall, the goal of this project is to reduce the number of animal-train collisions and improve the safety of train travel. By leveraging the power of machine learning and IoT technology, this system can provide an effective solution to a pressing safety issue." To accomplish this, the project utilizes an image processing system that is highly accurate and efficient in identifying animals on the tracks, even in challenging lighting or weather conditions. The system is trained using a large dataset of images of animals and non-animal objects on train tracks, enabling it to recognize animals of various sizes, shapes, and colors. The goal of this project is to reduce the number of animal- train collisions and improve the safety of train travel. By leveraging the power of machine learning and IoT technology, this system can provide an effective solution to a pressing safety issue.

Tunnel Field Effect Transistor can be introduced as an emerging alternate to MOSFET which is energy efficient and can be used in low power applications. Due to the challenge involved in integration of band to band tunneling generation rate, the existing drain current models are inaccurate. A compact analytical model for simple tunnel FET and pnpn tunnel FET is proposed which is highly accurate. The numerical integration of tunneling generation rate in the tunneling region is performed using Simpson’s rule. Integration is done using both Simpson’s 1/3 rule and 3/8 rule and the models are validated against numerical device simulations. The models are compared with existing models and it is observed that the proposed models show excellent agreement with device simulations in the entire region of operation with Simpson’s 3/8 rule exhibiting the maximum accuracy.

Cloud computing has emerged to meet the requirements of large, Internet based and data intensive applications. Commonly clouds are implemented on large data centers consisting of thousands of servers. All application processing and resources are centralized in these data centers. As the number of users increases, this centralized approach produce bottlenecks and affect the quality of cloud services. This brings inconvenience to users. Fortunately centralized approach is not the only way to provide cloud services. Other possible architectures for cloud computing are federated approach and peer to peer approach. Federated cloud combines independent clouds and provides interoperability between them. P2P clouds are a low cost model for cloud computing. This paper reviews centralized, federated and peer to peer approaches to cloud computing.