29,233 research outputs found
Bipartite electronic SLA as a business framework to support cross-organization load management of real-time online applications
Online applications such as games and e-learning applications fall within the broader category of real-time online interactive applications (ROIA), a new class of âkillerâ application for the Grid that is being investigated in the edutain@grid project. The two case studies in edutain@grid are an online game and an e-learning training application. We present a novel Grid-based business framework that makes use of bipartite service level agreements (SLAs) and dynamic invoice models to model complex business relationships in a massively scalable and flexible way. We support cross-organization load management at the business level, through zone migration. For evaluation we look at existing and extended value chains, the quality of service (QoS) metrics measured and the dynamic invoice models that support this work. We examine the causal links from customer quality of experience (QoE) and service provider quality of business (QoBiz) through to measured quality of service. Finally we discuss a shared reward business ecosystem and suggest how extended service level agreements and invoice models can support this
Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders
Radar images of humans and other concealed objects are considerably distorted
by attenuation, refraction and multipath clutter in indoor through-wall
environments. While several methods have been proposed for removing target
independent static and dynamic clutter, there still remain considerable
challenges in mitigating target dependent clutter especially when the knowledge
of the exact propagation characteristics or analytical framework is
unavailable. In this work we focus on mitigating wall effects using a machine
learning based solution -- denoising autoencoders -- that does not require
prior information of the wall parameters or room geometry. Instead, the method
relies on the availability of a large volume of training radar images gathered
in through-wall conditions and the corresponding clean images captured in
line-of-sight conditions. During the training phase, the autoencoder learns how
to denoise the corrupted through-wall images in order to resemble the free
space images. We have validated the performance of the proposed solution for
both static and dynamic human subjects. The frontal radar images of static
targets are obtained by processing wideband planar array measurement data with
two-dimensional array and range processing. The frontal radar images of dynamic
targets are simulated using narrowband planar array data processed with
two-dimensional array and Doppler processing. In both simulation and
measurement processes, we incorporate considerable diversity in the target and
propagation conditions. Our experimental results, from both simulation and
measurement data, show that the denoised images are considerably more similar
to the free-space images when compared to the original through-wall images
Green Cellular Networks: A Survey, Some Research Issues and Challenges
Energy efficiency in cellular networks is a growing concern for cellular
operators to not only maintain profitability, but also to reduce the overall
environment effects. This emerging trend of achieving energy efficiency in
cellular networks is motivating the standardization authorities and network
operators to continuously explore future technologies in order to bring
improvements in the entire network infrastructure. In this article, we present
a brief survey of methods to improve the power efficiency of cellular networks,
explore some research issues and challenges and suggest some techniques to
enable an energy efficient or "green" cellular network. Since base stations
consume a maximum portion of the total energy used in a cellular system, we
will first provide a comprehensive survey on techniques to obtain energy
savings in base stations. Next, we discuss how heterogeneous network deployment
based on micro, pico and femto-cells can be used to achieve this goal. Since
cognitive radio and cooperative relaying are undisputed future technologies in
this regard, we propose a research vision to make these technologies more
energy efficient. Lastly, we explore some broader perspectives in realizing a
"green" cellular network technologyComment: 16 pages, 5 figures, 2 table
Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
Visual robot navigation within large-scale, semi-structured environments
deals with various challenges such as computation intensive path planning
algorithms or insufficient knowledge about traversable spaces. Moreover, many
state-of-the-art navigation approaches only operate locally instead of gaining
a more conceptual understanding of the planning objective. This limits the
complexity of tasks a robot can accomplish and makes it harder to deal with
uncertainties that are present in the context of real-time robotics
applications. In this work, we present Topomap, a framework which simplifies
the navigation task by providing a map to the robot which is tailored for path
planning use. This novel approach transforms a sparse feature-based map from a
visual Simultaneous Localization And Mapping (SLAM) system into a
three-dimensional topological map. This is done in two steps. First, we extract
occupancy information directly from the noisy sparse point cloud. Then, we
create a set of convex free-space clusters, which are the vertices of the
topological map. We show that this representation improves the efficiency of
global planning, and we provide a complete derivation of our algorithm.
Planning experiments on real world datasets demonstrate that we achieve similar
performance as RRT* with significantly lower computation times and storage
requirements. Finally, we test our algorithm on a mobile robotic platform to
prove its advantages.Comment: 8 page
3D Point Capsule Networks
In this paper, we propose 3D point-capsule networks, an auto-encoder designed
to process sparse 3D point clouds while preserving spatial arrangements of the
input data. 3D capsule networks arise as a direct consequence of our novel
unified 3D auto-encoder formulation. Their dynamic routing scheme and the
peculiar 2D latent space deployed by our approach bring in improvements for
several common point cloud-related tasks, such as object classification, object
reconstruction and part segmentation as substantiated by our extensive
evaluations. Moreover, it enables new applications such as part interpolation
and replacement.Comment: As published in CVPR 2019 (camera ready version), with supplementary
materia
3D Point Capsule Networks
In this paper, we propose 3D point-capsule networks, an auto-encoder designed
to process sparse 3D point clouds while preserving spatial arrangements of the
input data. 3D capsule networks arise as a direct consequence of our novel
unified 3D auto-encoder formulation. Their dynamic routing scheme and the
peculiar 2D latent space deployed by our approach bring in improvements for
several common point cloud-related tasks, such as object classification, object
reconstruction and part segmentation as substantiated by our extensive
evaluations. Moreover, it enables new applications such as part interpolation
and replacement
Spectral analysis for long-term robotic mapping
This paper presents a new approach to mobile robot mapping in long-term scenarios. So far, the environment models used in mobile robotics have been tailored to capture static scenes and dealt with the environment changes by means of âmemory decayâ. While these models keep up with slowly changing environments, their utilization in dynamic, real world
environments is difficult.
The representation proposed in this paper models the environmentâs spatio-temporal dynamics by its frequency spectrum. The spectral representation of the time domain allows to identify, analyse and remember regularly occurring environment processes in a computationally efficient way. Knowledge of the periodicity of the different environment processes constitutes the model predictive capabilities, which are especially useful for long-term mobile robotics scenarios.
In the experiments presented, the proposed approach is applied to data collected by a mobile robot patrolling an indoor
environment over a period of one week. Three scenarios are investigated, including intruder detection and 4D mapping. The results indicate that the proposed method allows to represent arbitrary timescales with constant (and low) memory requirements, achieving compression rates up to 106 . Moreover, the representation allows for prediction of future environmentâs state with ⌠90% precision
Control and Communication Protocols that Enable Smart Building Microgrids
Recent communication, computation, and technology advances coupled with
climate change concerns have transformed the near future prospects of
electricity transmission, and, more notably, distribution systems and
microgrids. Distributed resources (wind and solar generation, combined heat and
power) and flexible loads (storage, computing, EV, HVAC) make it imperative to
increase investment and improve operational efficiency. Commercial and
residential buildings, being the largest energy consumption group among
flexible loads in microgrids, have the largest potential and flexibility to
provide demand side management. Recent advances in networked systems and the
anticipated breakthroughs of the Internet of Things will enable significant
advances in demand response capabilities of intelligent load network of
power-consuming devices such as HVAC components, water heaters, and buildings.
In this paper, a new operating framework, called packetized direct load control
(PDLC), is proposed based on the notion of quantization of energy demand. This
control protocol is built on top of two communication protocols that carry
either complete or binary information regarding the operation status of the
appliances. We discuss the optimal demand side operation for both protocols and
analytically derive the performance differences between the protocols. We
propose an optimal reservation strategy for traditional and renewable energy
for the PDLC in both day-ahead and real time markets. In the end we discuss the
fundamental trade-off between achieving controllability and endowing
flexibility
- âŠ