Simultaneous Localization and Sampled Environment Mapping Based on a Divide-and-conquer Ideology

2011 ◽  
Vol 36 (12) ◽  
pp. 1697-1705 ◽  
Author(s):  
Rong-Chuan SUN ◽  
Shu-Gen MA ◽  
Bin LI ◽  
Ming-Hui WANG ◽  
Yue-Chao WANG
Keyword(s):  
2014 ◽  
Vol 12 (2) ◽  
pp. 124-130 ◽  
Author(s):  
Cosme Santiesteban-Toca ◽  
Gerardo Casanola-Martin ◽  
Jesus Aguilar-Ruiz

Author(s):  
Anany Levitin ◽  
Maria Levitin

While many think of algorithms as specific to computer science, at its core algorithmic thinking is defined by the use of analytical logic to solve problems. This logic extends far beyond the realm of computer science and into the wide and entertaining world of puzzles. In Algorithmic Puzzles, Anany and Maria Levitin use many classic brainteasers as well as newer examples from job interviews with major corporations to show readers how to apply analytical thinking to solve puzzles requiring well-defined procedures. The book's unique collection of puzzles is supplemented with carefully developed tutorials on algorithm design strategies and analysis techniques intended to walk the reader step-by-step through the various approaches to algorithmic problem solving. Mastery of these strategies--exhaustive search, backtracking, and divide-and-conquer, among others--will aid the reader in solving not only the puzzles contained in this book, but also others encountered in interviews, puzzle collections, and throughout everyday life. Each of the 150 puzzles contains hints and solutions, along with commentary on the puzzle's origins and solution methods. The only book of its kind, Algorithmic Puzzles houses puzzles for all skill levels. Readers with only middle school mathematics will develop their algorithmic problem-solving skills through puzzles at the elementary level, while seasoned puzzle solvers will enjoy the challenge of thinking through more difficult puzzles.


2021 ◽  
pp. 1-21
Author(s):  
Christopher Tomlins

The Cambridge Handbook of US Labor Law for the Twenty-First Century decries federal labor law for forsaking American workers and undermining American unions. Its contributors seek a reformed labor law for the current century. In this review essay, I examine the handbook’s contention that federal labor law has failed. To assess the merits of the claim, we must test the foundations of its contributors’ assumptions—about the labor movement, about the place of the labor movement in the political economy of American capitalism envisaged by labor law, and, indeed, about law itself. To do so, I turn to earlier, critical research on the character of American labor laws, notably Joel Rogers’s seminal 1990 essay “Divide and Conquer,” and also to work of my own. To put it crudely, I ask how much labor law reform actually matters.


Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2236
Author(s):  
Sichun Du ◽  
Qing Deng

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.


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