scholarly journals On the minimizers of the fusion frame potential

2017 ◽  
Vol 291 (4) ◽  
pp. 669-681
Author(s):  
Sigrid B. Heineken ◽  
Juan P. Llarena ◽  
Patricia M. Morillas
Keyword(s):  
Fusion Frame ◽  
Frame Potential

Minimizing Fusion Frame Potential

2008 ◽  
Vol 107 (1-3) ◽  
pp. 7-24 ◽  
Author(s):  
Peter G. Casazza ◽  
Matthew Fickus
Keyword(s):  
Fusion Frame ◽  
Frame Potential

Construction of g-fusion frames in Hilbert spaces

2020 ◽  
Vol 23 (02) ◽  
pp. 2050015
Author(s):  
Vahid Sadri ◽  
Gholamreza Rahimlou ◽  
Reza Ahmadi ◽  
Ramazan Zarghami Farfar
Keyword(s):  
Hilbert Spaces ◽  
Closed Range ◽  
Fusion Frames ◽  
Interesting Topic ◽  
Fusion Frame

After introducing g-frames and fusion frames by Sun and Casazza, respectively, combining these frames together is an interesting topic for research. In this paper, we introduce the generalized fusion frames or g-fusion frames for Hilbert spaces and give characterizations of these frames from the viewpoint of closed range and g-fusion frame sequences. Also, the canonical dual g-fusion frames are presented and we introduce a Parseval g-fusion frame.

Nonuniform recovery of fusion frame structured sparse signals

2017 ◽  
Vol 15 (03) ◽  
pp. 333-352
Author(s):  
Yu Xia ◽  
Song Li
Keyword(s):  
Probability Distribution ◽  
Compressed Sensing ◽  
Fourier Coefficients ◽  
A Priori ◽  
Sparse Recovery ◽  
A Priori Knowledge ◽  
Sparse Signals ◽  
Fusion Frame ◽  
Redundant Representation ◽  
Vector Valued

This paper considers the nonuniform sparse recovery of block signals in a fusion frame, which is a collection of subspaces that provides redundant representation of signal spaces. Combined with specific fusion frame, the sensing mechanism selects block-vector-valued measurements independently at random from a probability distribution [Formula: see text]. If the probability distribution [Formula: see text] obeys a simple incoherence property and an isotropy property, we can faithfully recover approximately block sparse signals via mixed [Formula: see text]-minimization in ways similar to Compressed Sensing. The number of measurements is significantly reduced by a priori knowledge of a certain incoherence parameter [Formula: see text] associated with the angles between the fusion frame subspaces. As an example, the paper shows that an [Formula: see text]-sparse block signal can be exactly recovered from about [Formula: see text] Fourier coefficients combined with fusion frame [Formula: see text], where [Formula: see text].

Frame potential minimization for clustering short time series

2011 ◽  
Vol 5 (4) ◽  
pp. 341-355 ◽  
Author(s):  
Tobias Springer ◽  
Katja Ickstadt ◽  
Joachim Stöckler
Keyword(s):  
Time Series ◽  
Short Time Series ◽  
Frame Potential ◽  
Short Time

FUSION FRAMES AND g-FRAMES IN HILBERT C*-MODULES

2008 ◽  
Vol 06 (03) ◽  
pp. 433-446 ◽  
Author(s):  
AMIR KHOSRAVI ◽  
BEHROOZ KHOSRAVI
Keyword(s):  
Hilbert Space ◽  
Tensor Product ◽  
Hilbert Spaces ◽  
Frame Theory ◽  
Fusion Frames ◽  
Perturbation Result ◽  
Fusion Frame

The notion of frame has some generalizations such as frames of subspaces, fusion frames and g-frames. In this paper, we introduce fusion frames and g-frames in Hilbert C*-modules and we show that they share many useful properties with their corresponding notions in Hilbert space. We also generalize a perturbation result in frame theory to g-frames in Hilbert spaces. We also show that tensor product of fusion frames (g-frames) is a fusion frame (g-frame) and tensor product of resolution of identity is a resolution of identity.

Challenges and Solutions When Applying Implicit Motor Learning Theory in a High Performance Sport Environment: Examples from Rugby League

2011 ◽  
Vol 6 (4) ◽  
pp. 567-575 ◽  
Author(s):  
Tim Gabbett ◽  
Rich Masters
Keyword(s):  
Motor Learning ◽  
Learning Strategies ◽  
High Performance ◽  
Rugby League ◽  
Practical Training ◽  
Implicit Motor Learning ◽  
Implicit Motor ◽  
Frame Potential ◽  
Training Activities ◽  
Stress Fatigue

Skills that are learnt implicitly (i.e., without the accumulation of task-related rules and knowledge) have been shown to result in performance that displays stability in conditions of psychological stress, fatigue, multi-tasking, and over prolonged periods of time. Despite the wealth of evidence supporting the use of implicit motor learning strategies, the majority of this evidence has been generated from studies of novice performers rather than of experts. The aim of this paper is to describe some of the challenges faced by high-performance coaches and athletes who may wish to use implicit motor learning and to frame potential solutions with respect to the elite Australian National Rugby League competition. Practical training activities and techniques (e.g., errorless learning, random practice, cues, dual-tasking, and analogies), designed to facilitate the development of implicit skills that transfer robustly to high-performance competition environments, are presented.

scholarly journals Tight J-frames in Krein space and the associated J-frame potential

2016 ◽  
Vol 10 ◽  
pp. 917-931 ◽  
Author(s):  
Sk. Monowar Hossein ◽  
Shibashis Karmakar ◽  
Kallol Paul
Keyword(s):  
Krein Space ◽  
Kreĭn Space ◽  
Frame Potential
Sign in / Sign up

Export Citation Format

Share Document