Marzieh Ajirak,Tülay Adali,Saeid Sanei et al.
Marzieh Ajirak et al.
Machine learning (ML) has transformed neuroscience research by providing powerful tools to analyze neural data, uncover brain connectivity, and guide therapeutic interventions. This paper presents core mathematical frameworks in ML that add...
Decision-making algorithms for learning and adaptation with application to COVID-19 data [0.03%]
应用于COVID-19数据学习和自适应的决策算法
Stefano Marano,Ali H Sayed
Stefano Marano
This work focuses on the development of a new family of decision-making algorithms for adaptation and learning, which are specifically tailored to decision problems and are constructed by building up on first principles from decision theory...
Laplacian feature detection and feature alignment for multimodal ophthalmic image registration using phase correlation and Hessian affine feature space [0.03%]
基于Hessian仿射特征空间和相位相关的多模眼底图像配准中的拉普拉斯特征检测与特征对齐研究
Shan Suthaharan,Ethan A Rossi,Valerie Snyder et al.
Shan Suthaharan et al.
Advances in multimodal imaging have revolutionized diagnostic and treatment monitoring in ophthalmic practice. In multimodal ophthalmic imaging, geometric deformations are inevitable and they contain inherent deformations arising from heter...
Active contours driven by edge entropy fitting energy for image segmentation [0.03%]
基于边缘熵拟合能量的活动轮廓图像分割方法
Lei Wang,Guangqiang Chen,Dai Shi et al.
Lei Wang et al.
Active contour models have been widely used for image segmentation purposes. However, they may fail to delineate objects of interest depicted on images with intensity inhomogeneity. To resolve this issue, a novel image feature, termed as lo...
A unified framework for sparse non-negative least squares using multiplicative updates and the non-negative matrix factorization problem [0.03%]
基于乘法更新的稀疏非负最小二乘统一框架及其在非负矩阵分解中的应用
Igor Fedorov,Alican Nalci,Ritwik Giri et al.
Igor Fedorov et al.
We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS occurs naturally in a wide variety of applications where an unknown, non-negative quantity must be recovered from linear measurements. We present a unified framework fo...
Real-Time Filtering with Sparse Variations for Head Motion in Magnetic Resonance Imaging [0.03%]
基于磁共振实时头部运动稀疏变化的滤波方法研究
Daniel S Weller,Douglas C Noll,Jeffrey A Fessler
Daniel S Weller
Estimating a time-varying signal, such as head motion from magnetic resonance imaging data, becomes particularly challenging in the face of other temporal dynamics such as functional activation. This paper describes a new Kalman filter-like...
A fast algorithm for vertex-frequency representations of signals on graphs [0.03%]
图上信号的顶点-频率表示的快速算法
Iva Jestrović,James L Coyle,Ervin Sejdić
Iva Jestrović
The windowed Fourier transform (short time Fourier transform) and the S-transform are widely used signal processing tools for extracting frequency information from non-stationary signals. Previously, the windowed Fourier transform had been ...
P Gonzalez-Navarro,M Moghadamfalahi,M Akcakaya et al.
P Gonzalez-Navarro et al.
Multichannel electroencephalography (EEG) is widely used in non-invasive brain computer interfaces (BCIs) for user intent inference. EEG can be assumed to be a Gaussian process with unknown mean and autocovariance, and the estimation of par...
Junbo Duan,Charles Soussen,David Brie et al.
Junbo Duan et al.
This paper studies the intrinsic connection between a generalized LASSO and a basic LASSO formulation. The former is the extended version of the latter by introducing a regularization matrix to the coefficients. We show that when the regula...
Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse Recovery [0.03%]
基于非负约束的稀疏恢复贪婪算法
Daeun Kim,Justin P Haldar
Daeun Kim
This work proposes a family of greedy algorithms to jointly reconstruct a set of vectors that are (i) nonnegative and (ii) simultaneously sparse with a shared support set. The proposed algorithms generalize previous approaches that were des...