A Novel Blind mmWave Channel Estimation Algorithm Based on ML-ELM

Rapid Recognition of Different Sources of Heroin Drugs by Using a Hand-Held Near-Infrared Spectrometer Based on a Multi-Layer Extreme Learning Machine Algorithm

“…Accuracy is the proportion of the total number of predictions that were correct: (11) Precision is a measure of the accuracy provided that a specific class has been predicted. It is defined by: (12) Specificity is the proportion of actual negatives measured that were correct: (13) In equation 13, TN means true negative and FP means false positive.…”

“…Considering the above discussion and analysis, ML-ELM is very suitable for the processing of NIR spectral data. 11 In this study, a novel classification method using handheld NIR technology and ML-ELM algorithm was put forward to recognize the sources of heroin drugs rapidly and non-destructively. The proposed technique was applied to establish the heroin source recognition model and testified by practical-seized heroin samples.…”

Machine Learning: A Catalyst for THz Wireless Networks

“…In this direction, several researcher have focused on presenting ML-related solutions for automatic modulation recognition (AMR) (Khan et al, 2016;Li et al, 2018;Wu et al, 2018;Iqbal et al, 2019;Shah et al, 2019;Yang et al, 2019;Bu et al, 2020), channel estimation (Satyanarayana et al, 2019;Zhu et al, 2019;Liu S. et al, 2020;Ma et al, 2020a;Ma et al, 2020b;Moon et al, 2020;Mai et al, 2021;Wang et al, 2021), and signal detection (Jeon et al, 2018;Aoudia and Hoydis, 2019;Samuel et al, 2019;Katla et al, 2020;Satyanarayana et al, 2020). In more detail, AMR has been identified as an important task for several wireless systems, since it enables dynamic spectrum access, interference monitoring, radio fault self-detection as well as other civil, government, and military applications.…”

“…The results indicate that this approach can achieve a minimum mean square error that is comparable with the one of the orthogonal matching pursuit scheme. Likewise, in (Mai et al, 2021), a manifold learning-extreme learning machine was presented for estimating high-directional channels. In more detail, manifold learning was employed for dimentionality reduction, whereas the extreme learning algorithm with one-shot training was employed for channel state information estimation.…”

“…Similarly, since an inherent characteristic of THz channels is the high temporal correlation, the ML methodology presented in (Wu et al, 2018) is expected to find application in THz wireless systems. Additionally, despite the fact that the ML algorithms in (Zhu et al, 2019;Ma et al, 2020a;Liu S. et al, 2020;Ma et al, 2020b;Katla et al, 2020;Moon et al, 2020;Satyanarayana et al, 2020;Mai et al, 2021;Wang et al, 2021) were applied to mmW systems and exploit the spatio-temporal and directional characteristics of the channels in this band, the same approach can be used in THz systems that have the similar particularities. Finally, the ML approach presented in Jeon et al (2018) can be applied in any MIMO regardless the operating frequency.…”