134 research outputs found
Assembling strategies in extrinsic evolvable hardware with bi-directional incremental evolution
Bidirectional incremental evolution (BIE) has been proposed as a technique to overcome the āstallingā effect in evolvable hardware applications. However preliminary results show perceptible dependence of performance of BIE and quality of evaluated circuit on assembling strategy applied during reverse stage of incremental evolution. The purpose of this paper is to develop assembling strategy that will assist BIE to produce relatively optimal solution with minimal computational effort (e.g. the minimal number of generations)
Key perennial weeds in arable crops in the Nordic countries
Our review on the most common perennial weeds in the Nordic countries draws on 1) a Nordic/Baltic joint desk-top study done in 1997-99, 2) information from national weed surveys and 3) expert opinions from Denmark, Finland, Norway and Sweden
Rafting Towards Consensus: Formation Control of Distributed Dynamical Systems
In this paper, we introduce a novel adaptation of the Raft consensus
algorithm for achieving emergent formation control in multi-agent systems with
a single integrator dynamics. This strategy, dubbed "Rafting," enables robust
cooperation between distributed nodes, thereby facilitating the achievement of
desired geometric configurations. Our framework takes advantage of the Raft
algorithm's inherent fault tolerance and strong consistency guarantees to
extend its applicability to distributed formation control tasks. Following the
introduction of a decentralized mechanism for aggregating agent states, a
synchronization protocol for information exchange and consensus formation is
proposed. The Raft consensus algorithm combines leader election, log
replication, and state machine application to steer agents toward a common,
collaborative goal. A series of detailed simulations validate the efficacy and
robustness of our method under various conditions, including partial network
failures and disturbances. The outcomes demonstrate the algorithm's potential
and open up new possibilities in swarm robotics, autonomous transportation, and
distributed computation. The implementation of the algorithms presented in this
paper is available at https://github.com/abbas-tari/raft.git
A new project to address run-time reconfigurable hardware systems
Last autumn, we started a new project named Context Switching Reconfigurable Hardware for Communication Systems (COSRECOS). In this talk, I would like to present how we plan to address the challenge of changing hardware configurations while a system is in operation. The overall goal of the project is to contribute in making run-time reconfigurable systems more feasible in general.
This includes introducing architectures for reducing reconfiguration time as well as undertaking tool development. Case studies by applications in network and communication systems will be a part of the project. Comments to the planned outline are much welcome
Differences of Human Perceptions of a Robot Moving using Linear or Slow in, Slow out Velocity Profiles When Performing a Cleaning Task
We investigated how a robot moving with different velocity profiles affects a person's perception of it when working together on a task. The two profiles are the standard linear profile and a profile based on the animation principles of slow in, slow out. The investigation was accomplished by running an experiment in a home context where people and the robot cooperated on a clean-up task. We used the Godspeed series of questionnaires to gather people's perception of the robot. Average scores for each series appear not to be different enough to reject the null hypotheses, but looking at the component items provides paths to future areas of research. We also discuss the scenario for the experiment and how it may be used for future research into using animation techniques for moving robots and improving the legibility of a robot's locomotion
Data Driven Analysis of Tiny Touchscreen Performance with MicroJam
The widespread adoption of mobile devices, such as smartphones and tablets,
has made touchscreens a common interface for musical performance. New mobile
musical instruments have been designed that embrace collaborative creation and
that explore the affordances of mobile devices, as well as their constraints.
While these have been investigated from design and user experience
perspectives, there is little examination of the performers' musical outputs.
In this work, we introduce a constrained touchscreen performance app, MicroJam,
designed to enable collaboration between performers, and engage in a novel
data-driven analysis of more than 1600 performances using the app. MicroJam
constrains performances to five seconds, and emphasises frequent and casual
music making through a social media-inspired interface. Performers collaborate
by replying to performances, adding new musical layers that are played back at
the same time. Our analysis shows that users tend to focus on the centre and
diagonals of the touchscreen area, and tend to swirl or swipe rather than tap.
We also observe that while long swipes dominate the visual appearance of
performances, the majority of interactions are short with limited expressive
possibilities. Our findings are summarised into a set of design recommendations
for MicroJam and other touchscreen apps for social musical interaction
Guiding Neuroevolution with Structural Objectives
The structure and performance of neural networks are intimately connected,
and by use of evolutionary algorithms, neural network structures optimally
adapted to a given task can be explored. Guiding such neuroevolution with
additional objectives related to network structure has been shown to improve
performance in some cases, especially when modular neural networks are
beneficial. However, apart from objectives aiming to make networks more
modular, such structural objectives have not been widely explored. We propose
two new structural objectives and test their ability to guide evolving neural
networks on two problems which can benefit from decomposition into subtasks.
The first structural objective guides evolution to align neural networks with a
user-recommended decomposition pattern. Intuitively, this should be a powerful
guiding target for problems where human users can easily identify a structure.
The second structural objective guides evolution towards a population with a
high diversity in decomposition patterns. This results in exploration of many
different ways to decompose a problem, allowing evolution to find good
decompositions faster. Tests on our target problems reveal that both methods
perform well on a problem with a very clear and decomposable structure.
However, on a problem where the optimal decomposition is less obvious, the
structural diversity objective is found to outcompete other structural
objectives -- and this technique can even increase performance on problems
without any decomposable structure at all
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