10,537 research outputs found
Digital implementation of the cellular sensor-computers
Two different kinds of cellular sensor-processor architectures are used nowadays in various
applications. The first is the traditional sensor-processor architecture, where the sensor and the
processor arrays are mapped into each other. The second is the foveal architecture, in which a
small active fovea is navigating in a large sensor array. This second architecture is introduced
and compared here. Both of these architectures can be implemented with analog and digital
processor arrays. The efficiency of the different implementation types, depending on the used
CMOS technology, is analyzed. It turned out, that the finer the technology is, the better to use
digital implementation rather than analog
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
Collective animal behaviors are paradigmatic examples of fully decentralized
operations involving complex collective computations such as collective turns
in flocks of birds or collective harvesting by ants. These systems offer a
unique source of inspiration for the development of fault-tolerant and
self-healing multi-robot systems capable of operating in dynamic environments.
Specifically, swarm robotics emerged and is significantly growing on these
premises. However, to date, most swarm robotics systems reported in the
literature involve basic computational tasks---averages and other algebraic
operations. In this paper, we introduce a novel Collective computing framework
based on the swarming paradigm, which exhibits the key innate features of
swarms: robustness, scalability and flexibility. Unlike Edge computing, the
proposed Collective computing framework is truly decentralized and does not
require user intervention or additional servers to sustain its operations. This
Collective computing framework is applied to the complex task of collective
mapping, in which multiple robots aim at cooperatively map a large area. Our
results confirm the effectiveness of the cooperative strategy, its robustness
to the loss of multiple units, as well as its scalability. Furthermore, the
topology of the interconnecting network is found to greatly influence the
performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous
Computing, Electronics & Mobile Communication Conferenc
An Agent-Based Simulation API for Speculative PDES Runtime Environments
Agent-Based Modeling and Simulation (ABMS) is an effective paradigm to model systems exhibiting complex interactions, also with the goal of studying the emergent behavior of these systems. While ABMS has been effectively used in many disciplines, many successful models are still run only sequentially. Relying on simple and easy-to-use languages such as NetLogo limits the possibility to benefit from more effective runtime paradigms, such as speculative Parallel Discrete Event Simulation (PDES). In this paper, we discuss a semantically-rich API allowing to implement Agent-Based Models in a simple and effective way. We also describe the critical points which should be taken into account to implement this API in a speculative PDES environment, to scale up simulations on distributed massively-parallel clusters. We present an experimental assessment showing how our proposal allows to implement complicated interactions with a reduced complexity, while delivering a non-negligible performance increase
Advantages of versatile neural-network decoding for topological codes
Finding optimal correction of errors in generic stabilizer codes is a
computationally hard problem, even for simple noise models. While this task can
be simplified for codes with some structure, such as topological stabilizer
codes, developing good and efficient decoders still remains a challenge. In our
work, we systematically study a very versatile class of decoders based on
feedforward neural networks. To demonstrate adaptability, we apply neural
decoders to the triangular color and toric codes under various noise models
with realistic features, such as spatially-correlated errors. We report that
neural decoders provide significant improvement over leading efficient decoders
in terms of the error-correction threshold. Using neural networks simplifies
the process of designing well-performing decoders, and does not require prior
knowledge of the underlying noise model.Comment: 11 pages, 6 figures, 2 table
Creating green space sustainability through low-budget and upcycling strategies
Frugality is a core notion of sustainability, and responsible resource management should be prioritized in urban planning and landscape architecture. Low-budget strategies as a deliberate means of creating valuable, attractive, well-used, sociable public spaces are recognized by some influential designers using the Light, cheap, quick methodology. Unused spaces, just like objects and waste, can be creatively changed, reinvented with little resource input through a circular solution of upcycling. Case study methodology was predominantly used in the inquiry with three new parks, built after the year 2004, in Faro, Portugal. The study examined how the success rate and the current state of these public green areas correlates with the amount of financial resources invested in each of the projects. The case studies show key aspects in the building of the three spaces including: urban context, management and community participation. The success rate of a place is established based on user activity observations, user counts and questionnairesconveyed amongst both experts and local residents. Results illustrate how low-budget strategies and limited use of funds and resources can be translated into a successful project of a public greenery. Comparative studies from Warsaw and Berlin further extend the discussion to the concept of upcycling as a sustainable solution for landscape architecture.Horizon 2020, European Cooperation in Science and Technology, COST Action RESTORE [CA16114
Implementation and evaluation of the sensornet protocol for Contiki
Sensornet Protocol (SP) is a link abstraction layer between the network layer and the link layer for sensor networks. SP was proposed as the core of a future-oriented sensor node architecture that allows flexible and optimized combination between multiple coexisting protocols. This thesis implements the SP sensornet protocol on the Contiki operating system in order to: evaluate the effectiveness of the original SP services; explore further requirements and implementation trade-offs uncovered by the original proposal. We analyze the original SP design and the TinyOS implementation of SP to design the Contiki port. We implement the data sending and receiving part of SP using Contiki processes, and the neighbor management part as a group of global routines. The evaluation consists of a single-hop traffic throughput test and a multihop convergecast test. Both tests are conducted using both simulation and experimentation. We conclude from the evaluation results that SP's link-level abstraction effectively improves modularity in protocol construction without sacrificing performance, and our SP implementation on Contiki lays a good foundation for future protocol innovations in wireless sensor networks
Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO
The recent introduction of powerful embedded graphics processing units (GPUs)
has allowed for unforeseen improvements in real-time computer vision
applications. It has enabled algorithms to run onboard, well above the standard
video rates, yielding not only higher information processing capability, but
also reduced latency. This work focuses on the applicability of efficient
low-level, GPU hardware-specific instructions to improve on existing computer
vision algorithms in the field of visual-inertial odometry (VIO). While most
steps of a VIO pipeline work on visual features, they rely on image data for
detection and tracking, of which both steps are well suited for
parallelization. Especially non-maxima suppression and the subsequent feature
selection are prominent contributors to the overall image processing latency.
Our work first revisits the problem of non-maxima suppression for feature
detection specifically on GPUs, and proposes a solution that selects local
response maxima, imposes spatial feature distribution, and extracts features
simultaneously. Our second contribution introduces an enhanced FAST feature
detector that applies the aforementioned non-maxima suppression method.
Finally, we compare our method to other state-of-the-art CPU and GPU
implementations, where we always outperform all of them in feature tracking and
detection, resulting in over 1000fps throughput on an embedded Jetson TX2
platform. Additionally, we demonstrate our work integrated in a VIO pipeline
achieving a metric state estimation at ~200fps.Comment: IEEE International Conference on Intelligent Robots and Systems
(IROS), 2020. Open-source implementation available at
https://github.com/uzh-rpg/vili
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