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Thanuja, T. C., Daman, K. A., Patil, A. S..  2020.  Optimized Spectrum sensing Techniques for Enhanced Throughput in Cognitive Radio Network. 2020 International Conference on Emerging Smart Computing and Informatics (ESCI). :137–141.
The wireless communication is a backbone for a development of a nation. But spectrum is finite resource and issues like spectrum scarcity, loss of signal quality, transmission delay, raised in wireless communication system due to growth of wireless applications and exponentially increased number of users. Secondary use of a spectrum using Software Defined Radio (SDR) is one of the solutions which is also supported by TRAI. The spectrum sensing is key process in communication based on secondary use of spectrum. But energy consumption, added delay, primary users security are some threats in this system. Here in this paper we mainly focused on throughput optimization in secondary use of spectrum based on optimal sensing time and number of Secondary users during cooperative spectrum sensing in Cognitive radio networks.
Hermawan, Indra, Ma’sum, M. Anwar, Riskyana Dewi Intan, P, Jatmiko, Wisnu, Wiweko, Budi, Boediman, Alfred, Pradekso, Beno K..  2019.  Temporal feature and heuristics-based Noise Detection over Classical Machine Learning for ECG Signal Quality Assessment. 2019 International Workshop on Big Data and Information Security (IWBIS). :1–8.
This study proposes a method for ECG signals quality assessment (SQA) by using temporal feature, and heuristic rule. The ECG signal will be classified as acceptable or unacceptable. Seven types of noise were able to be detected by the prosed method. The noises are: FL, TVN, BW, AB, MA, PLI and AWGN. The proposed method is aimed to have better performance for SQA than classical machine learning method. The experiment is conducted by using 1000 instances ECG signal. The experiment result shows that db8 has the best performance with 0.86, 0.85 and 85.6% on lead-1 signal and 0.69, 0.79, and 74% on lead-5 signal for specificity, sensitivity and accuracy respectively. Compared to the classical machine learning, the proposed heuristic method has same accuracy but has 48% and 31% better specificity for lead-1 and lead-5. It means that the proposed method has far better ability to detect noise.