scholarly journals Are low-energy data already hinting at five dimensions?

2022 ◽  
Vol 105 (1) ◽  
Author(s):  
Naoyuki Haba ◽  
Nobuchika Okada ◽  
Toshifumi Yamada
Keyword(s):  
Low Energy ◽  
Energy Data ◽  
Five Dimensions

scholarly journals p-Wave Strength in the Low-energy 7Li(p,?0)8Be Cross Section

1995 ◽  
Vol 48 (5) ◽  
pp. 813 ◽  
Author(s):  
FC Barker
Keyword(s):  
Angular Distribution ◽  
Shell Model ◽  
Cross Section ◽  
Low Energy ◽  
P Wave ◽  
Model Calculations ◽  
Energy Data

Recent fits to low-energy 7Li(p, "Yo)8Be angular distribution and analysing power data suggested a large p-wave strength. It is shown that acceptable fits to the data can be obtained by attributing the p-wave Ml contributions to the tails of the 17 �64 and 18 �15 MeV 1+ levels of 8Be, with p-wave strengths much less than those obtained previously, but only if some of the spectroscopic amplitudes have signs opposite to those suggested by shell model calculations and/or a fit to higher-energy data.

Mobile telemedicine sensor networks with low-energy data query and network lifetime considerations

2006 ◽  
Vol 5 (4) ◽  
pp. 404-417 ◽  
Author(s):  
Fei Hu ◽  
Yu Wang ◽  
Hongyi Wu
Keyword(s):  
Sensor Networks ◽  
Network Lifetime ◽  
Low Energy ◽  
Energy Data ◽  
Data Query

scholarly journals Reconstructing aZ′Lagrangian using the LHC and low-energy data

2009 ◽  
Vol 80 (5) ◽  
Author(s):  
Li Ye ◽  
Frank Petriello ◽  
Seth Quackenbush
Keyword(s):  
Low Energy ◽  
Energy Data

LEPTOGENESIS AND LOW ENERGY DATA

2005 ◽  
Author(s):  
K. R. S. BALAJI
Keyword(s):  
Low Energy ◽  
Energy Data

scholarly journals Possibilities of Using Kalman Filters in Indoor Localization

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1564
Author(s):  
Katerina Fronckova ◽  
Pavel Prazak
Keyword(s):  
Object Tracking ◽  
Stochastic Models ◽  
Kalman Filters ◽  
Indoor Environment ◽  
Moving Objects ◽  
Indoor Localization ◽  
Low Energy ◽  
Localization Error ◽  
Energy Data ◽  
The Mean

Kalman filters are a set of algorithms based on the idea of a filter described by Rudolf Emil Kalman in 1960. Kalman filters are used in various application domains, including localization, object tracking, and navigation. The text provides an overview and discussion of the possibilities of using Kalman filters in indoor localization. The problems of static localization and localization of dynamically moving objects are investigated, and corresponding stochastic models are created. Three algorithms for static localization and one algorithm for dynamic localization are described and demonstrated. All algorithms are implemented in the MATLAB software, and then their performance is tested on Bluetooth Low Energy data from a real indoor environment. The results show that by using Kalman filters, the mean localization error of two meters can be achieved, which is one meter less than in the case of using the standard fingerprinting technique. In general, the presented principles of Kalman filters are applicable in connection with various technologies and data of various nature.

scholarly journals Making Ends Meet: String Unification and Low-Energy Data

1995 ◽  
Vol 75 (14) ◽  
pp. 2646-2649 ◽  
Author(s):  
Keith R. Dienes ◽  
Alon E. Faraggi
Keyword(s):  
Low Energy ◽  
Energy Data

scholarly journals New physics in the third family and its effect on low-energy data

1999 ◽  
Vol 61 (1) ◽  
Author(s):  
Ehab Malkawi ◽  
C.-P. Yuan
Keyword(s):  
New Physics ◽  
Low Energy ◽  
Energy Data ◽  
The Third

scholarly journals The use of unitarity bounds for a stable extrapolation of low energy data

1973 ◽  
Vol 30 (3) ◽  
pp. 249-260 ◽  
Author(s):  
J. Fischer
Keyword(s):  
Low Energy ◽  
Energy Data

Low-energy data of the Sherman function of Hg, Tl and Pb

1992 ◽  
Vol 25 (20) ◽  
pp. 4281-4285 ◽  
Author(s):  
M Dummler ◽  
M Bartsch ◽  
H Geesmann ◽  
G F Hanne ◽  
J Kessler
Keyword(s):  
Low Energy ◽  
Energy Data
Sign in / Sign up

Export Citation Format

Share Document