Deep Learning Based Sentiment Analysis from Bangla Text Using Glove Word Embedding along with Convolutional Neural Network

Abstract

Unique key generation is essential for encryption purposes between Internet of Things (IoT) devices. To produce a unique key for this encryption, Physical Unclonable Functions (PUFs) might be employed. Also, the Random Number Generator (RNG) is used in many different domains; nonetheless, security is one of the most important areas that require the best RNG. In this article, We investigate the quality of random numbers generated by Physical Unclonable Functions (PUFs). We have analyzed three Figures of Merit (FoMs), Uniqueness, Randomness, and Reliability of PUFs implemented on different FPGAs. In our experiments, we have operated the test devices at different temperatures (20°F, 40°F, 60°F, 80°F, 120°F, 140°F). In the PUF that we have analyzed, the key is generated in 1 second on average. We also have analyzed and described the essential properties of random number generator that is most vital considering things to secure our Internet of Things(IoT) devices.