RAVINDRABABU JALADANKI

Multi stage Parallel Interference Cancellation (PIC) technique gives good performance compared to Successive Interference Cancellation (SIC) method, but biased decision statistic and complexity problems are raised due to imperfect estimation of Multiple Access Interference (MAI) as number of stages increases. Partial Parallel Interference Cancellation (PPIC) technique is proposed to cancel the interference partially stage by stage to overcome biased problem. The complexity reduction for PIC detection is based on the convergence nature of interference cancellation which is called the Difference PIC (D-PIC) detection technique. In this p aper we combine (PPIC and DPIC) these two techniques and propose a Multi stage multiuser Hybrid or PD-PIC using MMSE detector for performance improvement and complexity reduction compared to conventional PIC detector. The performance is degraded as the number of users’ increases in each technique. Here considering MMSE for first stage instead of match...

Cyber-physical systems (CPSs), which are more susceptible to a range of cyber-attacks, play an increasingly crucial role in power system security today. Digital communication has become a global phenomenon in the last decade. Sadly, cyber terrorism is on the rise, and abusers are able to hide behind the anonymity of the internet. A hybrid model for detecting instances of cyber terrorism in Twitter datasets was proposed in this study after a survey of prominent classification algorithms. Logistic regression, linear support vector classifier, and naive bayes are the methods utilised for evaluation. Four metrics were used to evaluate the performance of the classifiers in experiments: precision, F1, accuracy, and recall. The findings show how well each of the algorithms worked, along with the metrics that went along with them. Linear support vector classifier (SVC) was the least effective, while hybrid model (EM) was the most successful.

In this paper, partial pre-coding method has been developed to solve the Multiple Access Interference (MAI) and computational complexity problems. This is done by selectively pre-decorrelating users to destructive interference while allowing interference when it is expected to contribute to their signal. The resulting SNR improvement is achieved by making use of energy existent in the system so performance enhancement is attained without the need for increased transmitted power-per-user. The proposed technique applies to the downlink of cellular Code Division Multiple Access (CDMA) systems. Theoretical analysis and comparative simulations show that significant performance improvement and computational complexity can be attained with the proposed technique.