Introduction to Efficient Blind

What is/are Efficient Blind?

Efficient Blind - We consider the problem of efficient blind parameter estimation in terahertz (THz)-band ultra-massive multiple-input multiple-output (UM-MIMO) systems. ^[1] Here, we propose a hardware-efficient blind FOE solution for the DSM signals by monitoring spectral dips in the frequency domain. ^[2] In this paper, two new subband structures, composed of cosine-modulated filter banks (CMFB) with critical or oversampled sampling and low-order adaptive subfilters, are proposed for efficient blind source separation approach in convolutive mixtures of speech signals. ^[3] Independent component analysis (ICA) algorithm is the most classical and efficient blind statistical signal processing technique. ^[4] Independent component analysis (ICA) is an efficient blind source separation technique, but the extracted independent components are randomly permuted in classic ICA algorithms, and subsequent identification is required to find the desired component. ^[5] To ensure these properties we present here in this paper a more secure and comparatively efficient blind signcryption scheme using the Elliptic Curve Cryptosystem (ECC). ^[6] Therefore, an efficient blind quality assessment method, which is specifically designed for 360-degree images, is urgently desired. ^[7] This paper presents an efficient blind watermarking algorithm for double color images using Walsh Hadamard transform (WHT). ^[8] In this paper, an accurate and efficient blind source separation method based on local sparsity and K-means (LSK-BSS) is proposed. ^[9] We present a highly efficient blind restoration method to remove mild blur in natural images. ^[10] Different from the existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image from a corrupted input. ^[11] This results in an efficient blind approach that has better performance and lower complexity compare with the traditional ALS. ^[12] The main aim of this paper is to provide a detailed analysis of the properties of the syndromes that could be useful to design more efficient blind reconstruction methods. ^[13] We propose an efficient blind batch encryption scheme from the CDH assumption, which can be proven to be suitably secure and blind. ^[14] In this letter, we propose an efficient blind detection scheme of polar codes compliant with the 5G standard. ^[15] This method requires an efficient blind detection of clusters from surveys with a well-known selection function and robust mass estimates, which is particularly challenging at high redshift. ^[16] Our review also shows that the quality of the included trials is generally poor with suspicion of inefficient blinding. ^[17] Fourth, to benefit from diversity gain with this differential scheme, we consider the use of sufficiently different wavelengths along with sufficient spatial separation between the transmitters and/or the receivers, where we propose an efficient blind detection method. ^[18]

efficient blind detection

In this letter, we propose an efficient blind detection scheme of polar codes compliant with the 5G standard. ^[1] This method requires an efficient blind detection of clusters from surveys with a well-known selection function and robust mass estimates, which is particularly challenging at high redshift. ^[2] Fourth, to benefit from diversity gain with this differential scheme, we consider the use of sufficiently different wavelengths along with sufficient spatial separation between the transmitters and/or the receivers, where we propose an efficient blind detection method. ^[3]

efficient blind source

In this paper, two new subband structures, composed of cosine-modulated filter banks (CMFB) with critical or oversampled sampling and low-order adaptive subfilters, are proposed for efficient blind source separation approach in convolutive mixtures of speech signals. ^[1] Independent component analysis (ICA) is an efficient blind source separation technique, but the extracted independent components are randomly permuted in classic ICA algorithms, and subsequent identification is required to find the desired component. ^[2] In this paper, an accurate and efficient blind source separation method based on local sparsity and K-means (LSK-BSS) is proposed. ^[3]