Speaker Recognition Based on Mutated Monarch Butterfly Optimization Configured Artificial Neural Network

Speaker recognition is the process of extracting speaker-specific details from voice waves to validate the features asserted by system users; in other words, it allows voice-controlled access to a range of services. The research initiates with extraction features from voice signals and employing those features in Artificial Neural Network (ANN) for speaker recognition. Increasing the number of hidden layers and their associated neurons reduces the training error and increases the computational process\u27s complexity. It is essential to have an optimal number of hidden layers and their corresponding, but attaining those optimal configurations through a manual or trial and the process takes time and makes the process more complex. This urges incorporating optimization approaches for finding optimal hidden layers and their corresponding neurons. The technique involve in configuring the ANN is Mutated Monarch Butterfly Optimization (MMBO). The proposed MMBO employed for configuring the ANN achieves the sensitivity of 97.5% in a real- time database that is superior to contest techniques

An Improved ICI Self Cancellation Scheme for OFDM Systems Under Various Channels

Inter Carrier Interference (ICI) is being introduced in OFDM due to the carrier frequency offset (CFO), which will degrade the system performance and efficiency at higher modulation levels and it decreases the performance of power amplifiers. Hence, here in this paper, we introduced a novel ICI reduction algorithms cancellation under the various channel environments such as AWGN, Rayleigh and also Rician. Simulation results have been compared with existing and proposed schemes under these channel specifications and concluded that the Rayleigh has performed far better than the AWGN and Rician channel distributions in terms of Bit Error Rate (BER) and Carrier interference Ration (CIR) performance

Optimization of cooperative secondary users in cognitive radio networks

In this paper, we identified the optimal number of secondary users in a cooperative spectrum sensing by maximizing the energy efficiency. We obtain the mathematical expressions and simulation results for the optimal number of secondary users using OR and AND fusion rules. We conducted the simulation for both OR and AND rules in two categories, One by keeping signal to noise ratio constant and second by keeping the detection threshold constant. Based on the analysis we showed that the performance obtained for OR rule is better than the AND rule. We hope that our results will be useful for improving the energy efficiency in identifying the un-utilized spectrum. Keywords: Cognitive radio, Cooperative spectrum sensing, Optimization, Energy efficienc