scholarly journals On a restriction problem of de Leeuw type for Laguerre multipliers

1995 ◽  
Vol 68 (1-2) ◽  
pp. 135-149
Author(s):  
G. Gasper ◽  
W. Trebels
Keyword(s):  
Restriction Problem

Research on Physical Decay Model and Location Fingerprint for WIFI Positioning Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 3936-3941
Author(s):  
Jian Hong Xu ◽  
Yan Li Feng ◽  
Wen Wen Fan
Keyword(s):  
Data Collection ◽  
Exact Match ◽  
Positioning Accuracy ◽  
Decay Model ◽  
Error Range ◽  
Positioning Algorithm ◽  
Fingerprint Algorithm ◽  
Restriction Problem

for the mutual restriction problem of precision and efficiency of current WIFI positioning technology, we propose a locating algorithm combining the Location Fingerprint with the Physical Decay Model and carry out de-noising treatment during the data collection. Use direct physical decay model to position within a tolerable error range. When the error exceeds the threshold, combined with location fingerprint algorithm, we use KNN for further exact match. Experients show that this method can effectively reduce the errors caused by unstable RSSI and improve the positioning accuracy and efficiency.

scholarly journals A symplectic restriction problem

2021 ◽  
Author(s):  
Valentin Blomer ◽  
Andrew Corbett
Keyword(s):  
Asymptotic Formula ◽  
Modular Form ◽  
Half Space ◽  
Imaginary Part ◽  
Dimensional Subspace ◽  
Siegel Modular Form ◽  
3 Dimensional ◽  
Relative Trace Formula ◽  
Lindelöf Hypothesis ◽  
Restriction Problem

AbstractWe investigate the norm of a degree 2 Siegel modular form of asymptotically large weight whose argument is restricted to the 3-dimensional subspace of its imaginary part. On average over Saito–Kurokawa lifts an asymptotic formula is established that is consistent with the mass equidistribution conjecture on the Siegel upper half space as well as the Lindelöf hypothesis for the corresponding Koecher–Maaß series. The ingredients include a new relative trace formula for pairs of Heegner periods.

scholarly journals Sets of p-restriction and p-spectral synthesis

2019 ◽  
Vol 52 (1) ◽  
pp. 397-403
Author(s):  
Michael J. Puls
Keyword(s):  
Unit Sphere ◽  
Spectral Synthesis ◽  
Sufficient Conditions ◽  
Restriction Property ◽  
Restriction Problem

AbstractIn this paper we investigate the restriction problem. More precisely, we give sufficient conditions for the failure of a set E in ℝn to have the p-restriction property. We also extend the concept of spectral synthesis to Lp(ℝn) for sets of p-restriction when p > 1. We use our results to show that there are p-values for which the unit sphere is a set of p-spectral synthesis in ℝn when n ⩾ 3.

scholarly journals A note on the discrete Fourier restriction problem

2018 ◽  
Vol 146 (9) ◽  
pp. 3839-3846 ◽  
Author(s):  
Xudong Lai ◽  
Yong Ding
Keyword(s):  
Fourier Restriction ◽  
Restriction Problem

1P1-P13 Angle Restriction Problem of Passive Sub-Crawler for Crawler Robot(Search and Rescue Robot and Mechatronics)

2013 ◽  
Vol 2013 (0) ◽  
pp. _1P1-P13_1-_1P1-P13_4
Author(s):  
Masayuki OKUGAWA ◽  
Kazuki HOSOMI ◽  
Kazuki NIWA ◽  
Soichiro SUZUKI ◽  
Satoshi HASEGAWA
Keyword(s):  
Search And Rescue ◽  
Rescue Robot ◽  
Restriction Problem

Thinking How to Live and the Restriction Problem

2006 ◽  
Vol 72 (3) ◽  
pp. 707-713 ◽  
Author(s):  
MICHAEL E. BRATMAN
Keyword(s):  
Restriction Problem

scholarly journals SAR Target Classification Based on Sample Spectral Regularization

2020 ◽  
Vol 12 (21) ◽  
pp. 3628
Author(s):  
Wei Liang ◽  
Tengfei Zhang ◽  
Wenhui Diao ◽  
Xian Sun ◽  
Liangjin Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Regularization Method ◽  
Training Data ◽  
Image Feature ◽  
Sar Image ◽  
Target Classification ◽  
Spectral Regularization ◽  
Sar Data ◽  
Restriction Problem

Synthetic Aperture Radar (SAR) target classification is an important branch of SAR image interpretation. The deep learning based SAR target classification algorithms have made remarkable achievements. But the acquisition and annotation of SAR target images are time-consuming and laborious, and it is difficult to obtain sufficient training data in many cases. The insufficient training data can make deep learning based models suffering from over-fitting, which will severely limit their wide application in SAR target classification. Motivated by the above problem, this paper employs transfer-learning to transfer the prior knowledge learned from a simulated SAR dataset to a real SAR dataset. To overcome the sample restriction problem caused by the poor feature discriminability for real SAR data. A simple and effective sample spectral regularization method is proposed, which can regularize the singular values of each SAR image feature to improve the feature discriminability. Based on the proposed regularization method, we design a transfer-learning pipeline to leverage the simulated SAR data as well as acquire better feature discriminability. The experimental results indicate that the proposed method is feasible for the sample restriction problem in SAR target classification. Furthermore, the proposed method can improve the classification accuracy when relatively sufficient training data is available, and it can be plugged into any convolutional neural network (CNN) based SAR classification models.

Linear restriction problem of Hermitian reflexive matrices and its approximation

2008 ◽  
Vol 200 (1) ◽  
pp. 341-351 ◽  
Author(s):  
Lijun Zhao ◽  
Xiyan Hu ◽  
Lei Zhang
Keyword(s):  
Linear Restriction ◽  
Restriction Problem

The $Q_{ \alpha}$-restriction problem

2019 ◽  
Vol 23 (5) ◽  
pp. 837-876
Author(s):  
Z. Wang ◽  
J. Xiao ◽  
Y. Zhou
Keyword(s):  
Restriction Problem
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