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Titlebook: Evolution in Signal Processing and Telecommunication Networks; Proceedings of Sixth P. Satish Rama Chowdary,Jaume Anguera,Vikrant Bhat Conf

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樓主: 傳家寶
31#
發(fā)表于 2025-3-26 21:00:48 | 只看該作者
Biodiversity and Its Conservation,a sets. Both the qualitative and quantitative results were obtained. The experimental results show that the method adjust itself according to various challenges and gives best qualitative and quantitative results.
32#
發(fā)表于 2025-3-27 02:32:44 | 只看該作者
33#
發(fā)表于 2025-3-27 07:30:52 | 只看該作者
https://doi.org/10.1057/9781137494122 subjecting them to IQ imbalances, and their performance is analysed through simulations, considering bit error probability (BEP) as the performance metric. It is also shown that pruned DFT-s FBMC is less sensitive to IQ imbalances compared to other waveform candidates.
34#
發(fā)表于 2025-3-27 10:43:12 | 只看該作者
35#
發(fā)表于 2025-3-27 15:41:00 | 只看該作者
36#
發(fā)表于 2025-3-27 17:46:22 | 只看該作者
37#
發(fā)表于 2025-3-28 01:06:45 | 只看該作者
Propagation of Data Using Free Space Under Different Weather Conditions,iques are incorporated which will reduce the attenuation in the free space link. In this work, optical link which works for 1–5?Kms are studied, and parameters such as bit error rate and quality factor are observed for different wavelengths.
38#
發(fā)表于 2025-3-28 06:02:50 | 只看該作者
39#
發(fā)表于 2025-3-28 08:45:07 | 只看該作者
Energy Detector and Diversity Techniques for Cooperative Spectrum Sensing, combining (SC) and maximal ratio combining (MRC) diversity techniques at each cognitive radio user, and it is proved that by increasing number of branches (Antennas) at each cognitive radio user, maximal ratio combining (MRC) is giving better performance by optimal reduction of the total error in a cognitive radio network.
40#
發(fā)表于 2025-3-28 14:07:37 | 只看該作者
Classification of Non-fluctuating Radar Target Using ReliefF Feature Selection Algorithm,is obtained by using the MODWPT feature extraction. To remove the extraneous and unnecessary features from the feature set, ReliefF optimal feature selection is proposed. The feature subset thus obtained is given to different classifiers, namely SVM and KNN, and their performance is observed.
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