Enhanced WDM-OFDM-PON System Based on Higher Data Transmitted with Modulation Technique

ABSTRACT:- Studies among the field communication system existing technique and proposes and by experimentation demonstrate a multiuser wavelengthdivision-multiplexing passive optical network (WDM-PON) system combining with orthogonal frequency division multiple (OFDM) technique. A tunable multiwavelength optical comb is intended to provide flat optical lines for helping the configuration of the multiple source-free optical network units WDM-OFDM-PON system supported normal single-mode fiber (SSMF). In WDM based on fiber, optical network communications using wavelength with multiplex or demultiplex may be a technology that multiplexes a variety of optical carrier signals onto one fiber by victimization completely different wavelengths of optical device lightweight. this system allows bidirectional communications over one strand of fiber, also as multiplication of capability and calculate BER (Bit Error Rate) and OSNR (optical signal noise ratio) finally; a comparison of by experimentation achieved receiver sensitivities and transmission distances victimization these receivers is given. The very best spectral potency and longest transmission distance at the very best bit rate. WDM based applications like transmission data, medical imaging data, and digital audio data and video conferencing data are information measure-intensive with the Advance in optical technology providing verdant bandwidth, it's natural to increase the multicast construct to optical networks so as to realize increased performance. Our projected scheme (PGA) based on information load transmitted capability improve supported higher information transmitted over these channels and high data up to develop in Matlab tool and using optical Interleaved the OFDM model and analysis the performance of the WDM-PON system

Attention at SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS)

In this paper, we have worked on interpretability, trust, and understanding of the decisions made by models in the form of classification tasks. The task is divided into 3 subtasks. The first task consists of determining Binary Sexism Detection. The second task describes the Category of Sexism. The third task describes a more Fine-grained Category of Sexism. Our work explores solving these tasks as a classification problem by fine-tuning transformer-based architecture. We have performed several experiments with our architecture, including combining multiple transformers, using domain adaptive pretraining on the unlabelled dataset provided by Reddit and Gab, Joint learning, and taking different layers of transformers as input to a classification head. Our system (with team name Attention) was able to achieve a macro F1 score of 0.839 for task A, 0.5835 macro F1 score for task B and 0.3356 macro F1 score for task C at the Codalab SemEval Competition. Later we improved the accuracy of Task B to 0.6228 and Task C to 0.3693 in the test set