## What is/are Adaptive Blind?

Adaptive Blind - In order to reduce the PE estimating workload of the controller, an adaptive blind clipping (ABC) scheme is proposed subsequently to approximate the PEs contaminated LLR with different decoding trials.^{[1]}Wireless Communications and Mobile Computing has retracted the article titled “Adaptive Blind Channel Estimation for MIMO-OFDM Systems Based on PARAFAC” [1], due to a high level of similarity identified with a previously published article, as confirmed by the editorial board [2]: Ruo-Nan Yang, Wei-Tao Zhang, Shun-Tian Lou, "Joint Adaptive Blind Channel Estimation and Data Detection for MIMO-OFDM Systems", Wireless Communications and Mobile Computing, vol.

^{[2]}We find that by combining an adaptive blind deconvolution approach with the CLEAN algorithm, we obtain an SNR improvement of over 10 dB for both synthetic and experimental data.

^{[3]}Finally, the novel adaptive blind separation algorithm in this paper is formed by introducing the adaptive step size to traditional EASI algorithm.

^{[4]}We consider the problem of adaptive blind separation of two sources from their instantaneous mixtures.

^{[5]}After that, the adaptive blind equalization with constant modulus algorithm (CMA) is applied to carry out channel equalization effectively with the prerequisite that the channel input and output signals are consistent with the high order statistics.

^{[6]}In particular, we apply the extended analysis model to the multiuser Shalvi–Weinstein algorithm, chosen due to its inherent advantages in adaptive blind equalization, namely high convergence rate and numerical robustness.

^{[7]}The paper aims to give considerable insights into how the use of the well-known second-order Newton method along with a whitening filter may help adaptive blind decision feedback equalizer (DFE) achieve excellent performances.

^{[8]}Therefore, the adaptive blind source separation (BSS) problem is firstly formally expressed and compared with tradition BSS problem.

^{[9]}A novel background subtraction approach using an RGB-D camera and an adaptive blind updating policy is introduced.

^{[10]}This paper addresses the problem of adaptive blind sparse source separation in the time domain of an over-determined instantaneous noisy mixture.

^{[11]}We propose a type of channel-hopping, adaptive blind rendezvous algorithm that integrates spectrum sensing and spectrum prediction using a non-parametric model of channel occupancy.

^{[12]}

## adaptive blind separation

Finally, the novel adaptive blind separation algorithm in this paper is formed by introducing the adaptive step size to traditional EASI algorithm.^{[1]}We consider the problem of adaptive blind separation of two sources from their instantaneous mixtures.

^{[2]}

## adaptive blind equalization

After that, the adaptive blind equalization with constant modulus algorithm (CMA) is applied to carry out channel equalization effectively with the prerequisite that the channel input and output signals are consistent with the high order statistics.^{[1]}In particular, we apply the extended analysis model to the multiuser Shalvi–Weinstein algorithm, chosen due to its inherent advantages in adaptive blind equalization, namely high convergence rate and numerical robustness.

^{[2]}