## What is/are Blind Identification?

Blind Identification - At present, the recognition of FEC codes is mainly concentrated in the field of semi-blind identification with known types of codes.^{[1]}Consequently, blind identification of the shaping rate for the PS-QAM signals is in an urgent demand for the flexible transceiver.

^{[2]}By using mathematical derivation and empirical corroboration, the results suggest that the improved moment matching (IMM) is capable of reducing OAs effectively and reserving the EEG waveform information on the greatest extent compared to existing methods, such as independent component analysis (ICA) and second-order blind identification.

^{[3]}This paper addresses the problem of blind identification of multichannel systems.

^{[4]}However, the role of chaos in blind identification of a sparse system has not been investigated.

^{[5]}The study presented in this paper demonstrates that the cumulant-based algorithms are more adequate if the data inputs are not available (blind identification), but the kernel- and binary-measurement-based methods are more adequate if the noise is not important (SNR≥16 dB).

^{[6]}Here using a combination of high-density EEG, second order blind identification (SOBI), and a standard visual oddball task, we test, in humans, a two-stage novelty processing hypothesis which states that two distinct stages of novelty processing exist, one involves early-occurring domain-specific neural activity in the sensory processing areas of the brain and the other involves later-occurring domain-general neural activity involving brain regions beyond the sensory cortices.

^{[7]}A modal identification technique called second-order blind identification (SOBI) is discussed in this paper to estimate the modal parameters of tall structures subjected to ambient loading.

^{[8]}This paper is concerned with the blind identification of graph filters from graph signals.

^{[9]}The proposed technique is shown to improve blind identification performance at low signal-to-noise ratio (SNR) when the system is driven by both chaotic numeric and symbolic signals.

^{[10]}In this work, we formulate a blind identification method based on long short-term memory neural network (LSTM-NN) model.

^{[11]}Several papers have been published on blind identification of binary FEC codes but papers reported on the identification of non-binary error correcting codes are less.

^{[12]}To address the issues such as less sensors than the targeted modal modes (under-determinate problem), repeated natural frequencies as well as systems with complex mode shapes, this paper proposed a complex wavelet modified second order blind identification method (CWMSOBI) by transforming the time domain problem into time-frequency domain.

^{[13]}Therefore, this paper deals with the blind and semi-blind identification of nonlinear SIMO/MIMO channels.

^{[14]}Compared with the existing second-order blind identification and spatial smoothing SOBI algorithms, the proposed algorithm does not need the information of the first order Bragg frequency of sea clutter, so it can improve the output signal-to-clutter-noise ratio when there is an error in the first order Bragg frequency of sea clutter.

^{[15]}Accordingly, the blind identification techniques of encoders have drawn much research interest.

^{[16]}Blind identification of encoders has received increasing attention in recent years.

^{[17]}While DL has been intensively studied for modulation recognition, there are very few investigations for blind identification of Space-Time Block Codes (STBCs).

^{[18]}In this work, we study the blind identification of Hammerstein-like system with memory nonlinearity.

^{[19]}The blind identification of scrambling codes is an important issue in intelligent reception of unknown synchronous digital hierarchy (SDH) lines.

^{[20]}Based on clinical consensus for three typical DCE-MRI curve patterns, three characteristic curves as regularization constraints were introduced to the extended Tofts model (ETM) using clustering strategy, and the clustering-based blind identification of multichannel (CBM) framework was then proposed for pharmacokinetic parameter estimation.

^{[21]}Firstly, based on a new double-constrained single source points (SSP) detection criterion, a fuzzy mean clustering underdetermined blind identification (UBI) algorithm is proposed which got the high precision estimation of the mixing matrix.

^{[22]}The four ICA algorithms are: Second Order Blind Identification (SOBI), Hyvarinen's Fixed Point Algorithm (FastICA), Infomax and Joint Approximation Diagonalization of Eigenmatrices (JADE).

^{[23]}Blind identification of channel codes is becoming increasingly important in signal interception and intelligent communication systems.

^{[24]}For the issue, this paper proposes a method with inverse-sparse transform and second order blind identification (SOBI) for the separation of the activations.

^{[25]}The proposed approach reduces the problem of statistical blind image deconvolution to the problem of blind identification of one-dimensional signals.

^{[26]}This work proposes the utilization of signal processing techniques to decompose the electrical voltage and/or current signals into its harmonic and interhamonic component waveforms through a Blind Source Separation (BSS) algorithm named Second Order Blind Identification (SOBI).

^{[27]}Therefore, we propose a blind identification method for mixed numerologies.

^{[28]}A blind identification of adaptive nonlinear control for inverse nonlinear transform is proposed.

^{[29]}Second, to obtain an accurate mixing matrix estimation, a blind identification method is designed by identifying single source data.

^{[30]}Three popular dimension reduction methods, namely principal component analysis (PCA), fourth order blind identification (FOBI) and sliced inverse regression (SIR) are considered in detail and the first two moments of subsets of the eigenvalues are used to test for the dimension of the signal space.

^{[31]}At present, the recognition of FEC codes is mainly concentrated in the field of semi-blind identification with known types of codes.

^{[32]}Blind identification for modulations is an important issue in signal processing and wireless communications.

^{[33]}In this paper, we are interested in the blind identification of binary linear block codes from received noisy data.

^{[34]}Thus, DMD is a time series blind source separation algorithm in disguise, but is different from closely related second order algorithms such as the Second-Order Blind Identification (SOBI) method and the Algorithm for Multiple Unknown Signals Extraction (AMUSE).

^{[35]}DWSAE is compared with two other methods that are Second order blind identification (SOBI) and Wavelet neural network (WNN).

^{[36]}The blind identification algorithm of nonlinear model is applied for the digital calibration of weak nonlinear circuit.

^{[37]}We then tested ADMIRE’s performance using full, GSO, SVD, and ICA–fourth-order blind identification (ICA-FOBI) models.

^{[38]}We successfully trained and tested various multilayer perceptron-based models for blind identification, in real-time, using our implemented agricultural IoT implementation.

^{[39]}We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations.

^{[40]}In terms of non-cooperative communication, blind identification of signal interleaving type is an important part of channel coding identification.

^{[41]}In this paper we consider the blind identification problem of a single input single output (SISO) linear system.

^{[42]}Key Findings: Participants with the most experience with the reference standards performed best on a blind identification of reference standards.

^{[43]}To get useful information from intercepted data, in noncooperative context, it is necessary to have algoritihms for blind identification of FEC code and interleaver parameters.

^{[44]}This method is a combination of singular spectrum analysis (SSA) and second-order blind identification (SOBI) method.

^{[45]}Simulation results show that the proposed algorithm significantly separate the source signals with better performance measures as compared with the state-of-the-art approaches such as second-order blind identification and fast independent component analysis.

^{[46]}SOBI(second order blind identification)algorithm is applied to solve the practical problem of separating multiple targets in space.

^{[47]}In the first step, independent components are extracted from nine macroeconomic time series using second order blind identification (SOBI).

^{[48]}Further validation of the platform was obtained through the blind identification of D-Cycloserine, a molecule scheduled to enter phase IV clinical trials for SCI.

^{[49]}Diagonalization methods for IVA in the proposed system were reworked based on SCHUR decomposition offering a faster second order blind identification algorithm that can be used on time demanding applications.

^{[50]}

## Order Blind Identification

By using mathematical derivation and empirical corroboration, the results suggest that the improved moment matching (IMM) is capable of reducing OAs effectively and reserving the EEG waveform information on the greatest extent compared to existing methods, such as independent component analysis (ICA) and second-order blind identification.^{[1]}Here using a combination of high-density EEG, second order blind identification (SOBI), and a standard visual oddball task, we test, in humans, a two-stage novelty processing hypothesis which states that two distinct stages of novelty processing exist, one involves early-occurring domain-specific neural activity in the sensory processing areas of the brain and the other involves later-occurring domain-general neural activity involving brain regions beyond the sensory cortices.

^{[2]}A modal identification technique called second-order blind identification (SOBI) is discussed in this paper to estimate the modal parameters of tall structures subjected to ambient loading.

^{[3]}To address the issues such as less sensors than the targeted modal modes (under-determinate problem), repeated natural frequencies as well as systems with complex mode shapes, this paper proposed a complex wavelet modified second order blind identification method (CWMSOBI) by transforming the time domain problem into time-frequency domain.

^{[4]}Compared with the existing second-order blind identification and spatial smoothing SOBI algorithms, the proposed algorithm does not need the information of the first order Bragg frequency of sea clutter, so it can improve the output signal-to-clutter-noise ratio when there is an error in the first order Bragg frequency of sea clutter.

^{[5]}The four ICA algorithms are: Second Order Blind Identification (SOBI), Hyvarinen's Fixed Point Algorithm (FastICA), Infomax and Joint Approximation Diagonalization of Eigenmatrices (JADE).

^{[6]}For the issue, this paper proposes a method with inverse-sparse transform and second order blind identification (SOBI) for the separation of the activations.

^{[7]}This work proposes the utilization of signal processing techniques to decompose the electrical voltage and/or current signals into its harmonic and interhamonic component waveforms through a Blind Source Separation (BSS) algorithm named Second Order Blind Identification (SOBI).

^{[8]}Three popular dimension reduction methods, namely principal component analysis (PCA), fourth order blind identification (FOBI) and sliced inverse regression (SIR) are considered in detail and the first two moments of subsets of the eigenvalues are used to test for the dimension of the signal space.

^{[9]}Thus, DMD is a time series blind source separation algorithm in disguise, but is different from closely related second order algorithms such as the Second-Order Blind Identification (SOBI) method and the Algorithm for Multiple Unknown Signals Extraction (AMUSE).

^{[10]}DWSAE is compared with two other methods that are Second order blind identification (SOBI) and Wavelet neural network (WNN).

^{[11]}We then tested ADMIRE’s performance using full, GSO, SVD, and ICA–fourth-order blind identification (ICA-FOBI) models.

^{[12]}We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations.

^{[13]}This method is a combination of singular spectrum analysis (SSA) and second-order blind identification (SOBI) method.

^{[14]}Simulation results show that the proposed algorithm significantly separate the source signals with better performance measures as compared with the state-of-the-art approaches such as second-order blind identification and fast independent component analysis.

^{[15]}SOBI(second order blind identification)algorithm is applied to solve the practical problem of separating multiple targets in space.

^{[16]}In the first step, independent components are extracted from nine macroeconomic time series using second order blind identification (SOBI).

^{[17]}Diagonalization methods for IVA in the proposed system were reworked based on SCHUR decomposition offering a faster second order blind identification algorithm that can be used on time demanding applications.

^{[18]}The Second Order Blind identification (SOBI) algorithm is widely used in blind source separation (BSS) processing and has achieved many satisfactory results.

^{[19]}In this article, the use of Second-Order Blind Identification is proposed.

^{[20]}The estimator is however already known since 1989 as FOBI (Fourth order blind identification), and it indeed has many nice properties, even outside the independent component model.

^{[21]}An algorithm called Second Order Blind Identification (SOBI) has been utilized for source separation and validated using simulation.

^{[22]}Secondly, weights-adjusted second-order blind identification (WASOBI) is utilized to separate the jamming and target echo.

^{[23]}The results show that the second order blind identification (SOBI) algorithm is the best one to separate the BVP signal.

^{[24]}

## blind identification method

In this work, we formulate a blind identification method based on long short-term memory neural network (LSTM-NN) model.^{[1]}To address the issues such as less sensors than the targeted modal modes (under-determinate problem), repeated natural frequencies as well as systems with complex mode shapes, this paper proposed a complex wavelet modified second order blind identification method (CWMSOBI) by transforming the time domain problem into time-frequency domain.

^{[2]}Therefore, we propose a blind identification method for mixed numerologies.

^{[3]}Second, to obtain an accurate mixing matrix estimation, a blind identification method is designed by identifying single source data.

^{[4]}As such, our method is a blind identification method for linear dynamics in a stochastic Wiener system with a quadratic nonlinearity at the output and a phase retrieval method that uses a time-evolution-model constraint and a single image at every time step.

^{[5]}

## blind identification algorithm

The blind identification algorithm of nonlinear model is applied for the digital calibration of weak nonlinear circuit.^{[1]}Diagonalization methods for IVA in the proposed system were reworked based on SCHUR decomposition offering a faster second order blind identification algorithm that can be used on time demanding applications.

^{[2]}

## blind identification problem

In this paper we consider the blind identification problem of a single input single output (SISO) linear system.^{[1]}4 considers a blind identification problem with prior knowledge about the input in the form of a linear time-invariant autonomous system.

^{[2]}