As we all know fingerprint recognition is one of the secure and accurate Biometric Technologies. If think about it in deep even with the Biometric system the virus can be spread during these situations. To overcome this, we need to come up with a secure and contactless way of authentication. So, let’s update to some contactless remedies like Iris authentication which are unique for every individual and they don’t need to have any physical contact. So, we can use this Iris detection for a secure and contactless authentication system. The main aim of this research is to provide contactless remedies for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. Coming to the outline of the attendance management system, we will first collect the data from the Kaggle repository. Next, we split the data into training and testing, then we will train the data using transfer learning techniques and test the model by using test data. Finally, we integrated the trained model with the flask. If the Iris matches then the attendance of a particular person will be posted. If not matched then we train the model by adding new person’s data. For the construction of modern electronic security systems, real-time face recognition is crucial. Face detection, feature extraction, and face recognition are the three procedures involved. After recognizing the face, it will check whether the person’s face matches the collected database. If it matches it will show the person’s name, the number of books he took, and what those books are for Library transactions and in the same way the locker will be open if the person’s data is matched. The proposed methods are secure and unique contactless ways of authentication for every individual. So, we can use these detection and authentication systems for secure and contactless applications. It can be successfully used for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. The Covid-19 infection in society will undoubtedly decline if the proposed argument is implemented.

No abstract

We present an interface-trapped-charge (ITC) vandalized threshold voltage (VTH) divergence model for omega-gate (Ω-G) MOSFETs using a partial 3-D scaling equation. To account the impact, the model comprises the equivalent number of gates, gate dielectric and silicon film thickness, channel limitations. The impacts of analogous oxide charges on the flat-band voltage are also examined for short-channel-free operation. A thin gate oxide is essential to prevent VTH value divergence caused by the ITC charges. The ITC vandalized device with a thick silicon sheet is altered with wee VTH value variations caused by trapped charges but rapidly increases in the thin silicon sheet device. We can reduce the VTH value by changing the value of oxide-to-gate underlap coverage factor (OUCF). Large underlap coverage factor value is desirable for positive trapped charges and small value for negative trapped charges. A damaged device with negative trapped charges performs better in short-channel conditions than one with positive trapped charges. Due to its 3-D nature, the proposed model may be efficiently used to investigate the VTH behavior and the device operating characteristics of omega-gate (Ω-G) MOSFET. It may also be competently used in device memory cell applications. Hence, the proposed model provides a deep understanding of device physics along with its computational ability, effectiveness and simplicity.