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Found 20 papers, 6 papers with code
Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning
Ultrasound (US) is widely used for clinical imaging applications thanks to its real-time and non-invasive nature.
Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN
In contrast to 2-D ultrasound (US) for uniaxial plane imaging, a 3-D US imaging system can visualize a volume along three axial planes.
Performance Analysis of Fractional Learning Algorithms
Fractional learning algorithms are trending in signal processing and adaptive filtering recently.
AFP-SRC: Identification of Antifreeze Proteins Using Sparse Representation Classifier
In this research, we propose a computational framework for the prediction of AFPs which is essentially based on a sample-specific classification method using the sparse reconstruction.
Switchable Deep Beamformer
Recent proposals of deep beamformers using deep neural networks have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers.
OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN
Unfortunately, in the current deep learning approaches, a dedicated neural network should be trained with matched training data for each specific artifact type.
Multi-Kernel Fusion for RBF Neural Networks
A simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks.
Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal
Experimental results for various tasks such as deconvolution, speckle removal, limited data artifact removal, etc.
E3-targetPred: Prediction of E3-Target Proteins Using Deep Latent Space Encoding
Understanding E3 ligase and target substrate interactions are important for cell biology and therapeutic development.
Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks
The proposed RBF architecture is explored for the prediction of Mackey-Glass time series and results are compared with the standard RBF.
Spatio-Temporal RBF Neural Networks
Herein, we propose a spatio-temporal extension of RBFNN for nonlinear system identification problem.
Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound
In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and contrast-to-noise ratio of the delay and sum (DAS) beamformers.
A Novel Adaptive Kernel for the RBF Neural Networks
In this paper, we propose a novel adaptive kernel for the radial basis function (RBF) neural networks.
Deep Learning-based Universal Beamformer for Ultrasound Imaging
In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays.
Universal Deep Beamformer for Variable Rate Ultrasound Imaging
In particular, we design an end-to-end deep learning framework that can directly process sub-sampled RF data acquired at different subsampling rate and detector configuration to generate high quality ultrasound images using a single beamformer.
q-LMF: Quantum Calculus-based Least Mean Fourth Algorithm
The proposed $q$-least mean fourth ($q$-LMF) is an extension of least mean fourth (LMF) algorithm and it is based on the $q$-calculus which is also known as Jackson derivative.
RAFP-Pred: Robust Prediction of Antifreeze Proteins using Localized Analysis of n-Peptide Compositions
In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs) to counter the otherwise lethal intracellular formation of ice.
Comments on "Momentum fractional LMS for power signal parameter estimation"
The purpose of this paper is to indicate that the recently proposed Momentum fractional least mean squares (mFLMS) algorithm has some serious flaws in its design and analysis.
Comments on "Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization"
The purpose of this note is to highlight some critical flaws in recently proposed fractional-order variants of complex least mean square (CLMS) and normalized least mean square (NLMS) algorithms in "Design of Fractional-order Variants of Complex LMS and Normalized LMS Algorithms for Adaptive Channel Equalization" [Non-linear Dyn.
Optimization and Control
Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning
In portable, three dimensional, and ultra-fast ultrasound imaging systems, there is an increasing demand for the reconstruction of high quality images from a limited number of radio-frequency (RF) measurements due to receiver (Rx) or transmit (Xmit) event sub-sampling.
