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For overlay cognitive radio (CR), this paper proposes a knowledge-based spectrum prediction technique using artificial neural network. This is targeted for CR-based emergency sensor networks in real life applications, where extremely reliable data transmission is mandatory. The technique operates in two steps: 1) identification of idle states in primary user bands; 2) prediction of noise level of the idle bands. The goal is to select a less noisy vacant band among the detected idle bands. The system is analysed under both Gaussian mixer noise (GMN) distribution and additive white Gaussian noise (AWGN) by predicting the current state of the channel in low SNR environment. Simulation results using MATLAB R2015b show satisfactory values for probability of false alarm (0.17 and 0.3), and probability of misdetection (0.12 and 0.25) for AWGN and GMN, respectively.

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Objective of this paper is to enhance the throughput of Blowfish block cipher by designing a pipelined architecture of the same followed by implementation and evaluation of its performance in Field Programmable Gate Array. The proposed architecture was implemented by using Verilog HDL and was synthesized, placed and routed in Spartan3E chip XC3s500e-5fg320 using ISE Design Suite 12.1. Performance analysis of the proposed pipelined design shows a throughput of 6.3 Gbps as compared to 588.255 Mbps for non-pipelined design.