26,026 research outputs found
On Benchmarking Embedded Linux Flash File Systems
Due to its attractive characteristics in terms of performance, weight and
power consumption, NAND flash memory became the main non volatile memory (NVM)
in embedded systems. Those NVMs also present some specific
characteristics/constraints: good but asymmetric I/O performance, limited
lifetime, write/erase granularity asymmetry, etc. Those peculiarities are
either managed in hardware for flash disks (SSDs, SD cards, USB sticks, etc.)
or in software for raw embedded flash chips. When managed in software, flash
algorithms and structures are implemented in a specific flash file system
(FFS). In this paper, we present a performance study of the most widely used
FFSs in embedded Linux: JFFS2, UBIFS,and YAFFS. We show some very particular
behaviors and large performance disparities for tested FFS operations such as
mounting, copying, and searching file trees, compression, etc.Comment: Embed With Linux, Lorient : France (2012
Evolutionary Theory and Computerised Genetic Algorithms
Neo-Darwinism can be usefully studied with the help of a Computerised Genetic Algorithm. Only a mathematical approach can reveal the shortcomings of the current dogma and point the way to a revised definition of the theory of evolution
Those who can, teach
Book review for: "Darwin's unfinished symphony: How culture made the human mind" By Kevin N. Laland. Princeton: Princeton University Press. xiv, 450 p. $35.00 (hardcover). ISBN: 978-0-691-15118-2
An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems
In this work we introduce an evolutionary strategy to solve combinatorial
optimization tasks, i.e. problems characterized by a discrete search space. In
particular, we focus on the Traveling Salesman Problem (TSP), i.e. a famous
problem whose search space grows exponentially, increasing the number of
cities, up to becoming NP-hard. The solutions of the TSP can be codified by
arrays of cities, and can be evaluated by fitness, computed according to a cost
function (e.g. the length of a path). Our method is based on the evolution of
an agent population by means of an imitative mechanism, we define `partial
imitation'. In particular, agents receive a random solution and then,
interacting among themselves, may imitate the solutions of agents with a higher
fitness. Since the imitation mechanism is only partial, agents copy only one
entry (randomly chosen) of another array (i.e. solution). In doing so, the
population converges towards a shared solution, behaving like a spin system
undergoing a cooling process, i.e. driven towards an ordered phase. We
highlight that the adopted `partial imitation' mechanism allows the population
to generate solutions over time, before reaching the final equilibrium. Results
of numerical simulations show that our method is able to find, in a finite
time, both optimal and suboptimal solutions, depending on the size of the
considered search space.Comment: 18 pages, 6 figure
Active Self-Assembly of Algorithmic Shapes and Patterns in Polylogarithmic Time
We describe a computational model for studying the complexity of
self-assembled structures with active molecular components. Our model captures
notions of growth and movement ubiquitous in biological systems. The model is
inspired by biology's fantastic ability to assemble biomolecules that form
systems with complicated structure and dynamics, from molecular motors that
walk on rigid tracks and proteins that dynamically alter the structure of the
cell during mitosis, to embryonic development where large-scale complicated
organisms efficiently grow from a single cell. Using this active self-assembly
model, we show how to efficiently self-assemble shapes and patterns from simple
monomers. For example, we show how to grow a line of monomers in time and
number of monomer states that is merely logarithmic in the length of the line.
Our main results show how to grow arbitrary connected two-dimensional
geometric shapes and patterns in expected time that is polylogarithmic in the
size of the shape, plus roughly the time required to run a Turing machine
deciding whether or not a given pixel is in the shape. We do this while keeping
the number of monomer types logarithmic in shape size, plus those monomers
required by the Kolmogorov complexity of the shape or pattern. This work thus
highlights the efficiency advantages of active self-assembly over passive
self-assembly and motivates experimental effort to construct general-purpose
active molecular self-assembly systems
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