Results 41 to 50 of about 528,580 (282)
Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle
The Scientific World Journal, 2014 As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC) has attracted much attention in synthetic aperture radar (SAR) automatic target recognition (ATR) recently ...
Xiangwei Xing +3 more
doaj +1 more source
Sparse representation-based synthetic aperture radar imaging [PDF]
, 2011 There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying ...
Cetin, Mujdat +3 more
core +1 more source
A First Step to Convolutive Sparse Representation
, 2008 In this paper an extension of the sparse decomposition problem is considered and an algorithm for solving it is presented. In this extension, it is known that one of the shifted versions of a signal s (not necessarily the original signal itself) has a ...
Babaie-Zadeh, Massoud +3 more
core +2 more sources
Sparse Representations of Random Signals [PDF]
Mathematical Methods in the Applied Sciences, 2021 Sparse (fast) representations of deterministic signals have been well studied. Among other types there exists one called adaptive Fourier decomposition (AFD) for functions in analytic Hardy spaces. Through the Hardy space decomposition of the $L^2$ space the AFD algorithm also gives rise to sparse representations of signals of finite energy.
openaire +1 more source
Visual Tracking Based on Extreme Learning Machine and Sparse Representation
Sensors, 2015 The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging ...
Baoxian Wang +4 more
doaj +1 more source
Unlearning via Sparse Representations
CoRR, 2023 Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible by existing techniques. We propose a nearly compute-free zero-shot unlearning technique based on a discrete representational bottleneck. We show that the proposed technique efficiently unlearns the forget set
Vedant Shah +7 more
openaire +2 more sources
Intravitreal GD2‐Specific Chimeric Antigen Receptor T‐Cell Therapy for Refractory Retinoblastoma
Pediatric Blood &Cancer, EarlyView.ABSTRACT Effective treatments for advanced, treatment‐resistant retinoblastoma (RB) remain limited. GD2‐specific chimeric antigen receptor (CAR) T cells show potent antitumor activity with minimal toxicity but have not previously been evaluated in RB.
Subongkoch Subhadhirasakul +13 more
wiley +1 more source
Saliency Detection Using Sparse and Nonlinear Feature Representation
The Scientific World Journal, 2014 An important aspect of visual saliency detection is how features that form an input image are represented. A popular theory supports sparse feature representation, an image being represented with a basis dictionary having sparse weighting coefficient ...
Shahzad Anwar +3 more
doaj +1 more source
Sparse representation-based SAR imaging [PDF]
, 2009 There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying ...
Cetin, Mujdat +3 more
core +1 more source
Toward a Robust Sparse Data Representation for Wireless Sensor Networks
, 2015 Compressive sensing has been successfully used for optimized operations in wireless sensor networks. However, raw data collected by sensors may be neither originally sparse nor easily transformed into a sparse data representation.
Alsheikh, Mohammad Abu +3 more
core +1 more source