Efficient search strategy for coarse synchronization of UWB signals without channel knowledge

2006 ◽  
Author(s):  
Eva Arias-de-Reyna ◽  
Jose Acha-catalina
Keyword(s):  
Search Strategy ◽  
Efficient Search ◽  
Channel Knowledge

scholarly journals Efficient blind adaptive Karhunen–Loéve transform via parallel search

2018 ◽  
Vol 14 (6) ◽  
pp. 155014771878237
Author(s):  
Wenbiao Tian ◽  
Guosheng Rui ◽  
Daoguang Dong ◽  
Jian Kang
Keyword(s):  
Numerical Simulation ◽  
Data Compression ◽  
Search Strategy ◽  
Original Data ◽  
Parallel Search ◽  
Efficient Search ◽  
Blind Adaptive ◽  
Simulation Results ◽  
Karhunen Loeve Transform ◽  
Minimal Information

This article introduces a new algorithm that constructs an efficient search strategy, called parallel search, for blind adaptive Karhunen–Loéve transform. Unlike anterior Karhunen–Loéve transform, the proposed algorithm converges quickly by searching for solutions in different directions simultaneously. Moreover, the process is “blind,” which means that minimal information about the original data is used. The new algorithm also avoids repeating the Karhunen–Loéve transform basis learning step in data compression applications. Numerical simulation results verify the validity of the theory and illustrate the capability of the proposed algorithm.

LEARNING IN NAVIGATION: GOAL FINDING IN GRAPHS

1996 ◽  
Vol 10 (05) ◽  
pp. 429-446 ◽  
Author(s):  
PETER CUCKA ◽  
NATHAN S. NETANYAHU ◽  
AZRIEL ROSENFELD
Keyword(s):  
Random Graphs ◽  
Search Strategy ◽  
Search Strategies ◽  
Complex Environment ◽  
Efficient Search ◽  
Discrete Spaces ◽  
Robotic Agent

A robotic agent operating in an unknown and complex environment may employ a search strategy of some kind to perform a navigational task such as reaching a given goal. In the process of performing the task, the agent can attempt to discover characteristics of its environment that enable it to choose a more efficient search strategy for that environment. If the agent is able to do this, we can say that it has "learned to navigate" — i.e., to improve its navigational performance. This paper describes how an agent can learn to improve its goal-finding performance in a class of discrete spaces, represented by graphs embedded in the plane. We compare several basic search strategies on two different classes of "random" graphs and show how information collected during the traversal of a graph can be used to classify the graph, thus allowing the agent to choose the search strategy best suited for that graph.

scholarly journals A more efficient search strategy for aging genes based on connectivity

2004 ◽  
Vol 21 (3) ◽  
pp. 338-348 ◽  
Author(s):  
L. Ferrarini ◽  
L. Bertelli ◽  
J. Feala ◽  
A. D. McCulloch ◽  
G. Paternostro
Keyword(s):  
Search Strategy ◽  
Efficient Search

scholarly journals An Efficient Search Strategy for Site-Selection Decisions in an Expert System

2010 ◽  
Vol 28 (2) ◽  
pp. 126-146 ◽  
Author(s):  
Theo A. Arentze ◽  
Aloys W. J. Borgers ◽  
Harry J. P. Timmermans
Keyword(s):  
Expert System ◽  
Site Selection ◽  
Search Strategy ◽  
Efficient Search ◽  
Selection Decisions
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