13,300 research outputs found
Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks
Connected and automated vehicles will enable advanced traffic safety and
efficiency applications thanks to the dynamic exchange of information between
vehicles, and between vehicles and infrastructure nodes. Connected vehicles can
utilize IEEE 802.11p for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure
(V2I) communications. However, a widespread deployment of connected vehicles
and the introduction of connected automated driving applications will notably
increase the bandwidth and scalability requirements of vehicular networks. This
paper proposes to address these challenges through the adoption of
heterogeneous V2V communications in multi-link and multi-RAT vehicular
networks. In particular, the paper proposes the first distributed (and
decentralized) context-aware heterogeneous V2V communications algorithm that is
technology and application agnostic, and that allows each vehicle to
autonomously and dynamically select its communications technology taking into
account its application requirements and the communication context conditions.
This study demonstrates the potential of heterogeneous V2V communications, and
the capability of the proposed algorithm to satisfy the vehicles' application
requirements while approaching the estimated upper bound network capacity
Car-to-Cloud Communication Traffic Analysis Based on the Common Vehicle Information Model
Although connectivity services have been introduced already today in many of
the most recent car models, the potential of vehicles serving as highly mobile
sensor platform in the Internet of Things (IoT) has not been sufficiently
exploited yet. The European AutoMat project has therefore defined an open
Common Vehicle Information Model (CVIM) in combination with a cross-industry,
cloud-based big data marketplace. Thereby, vehicle sensor data can be leveraged
for the design of entirely new services even beyond traffic-related
applications (such as localized weather forecasts). This paper focuses on the
prediction of the achievable data rate making use of an analytical model based
on empirical measurements. For an in-depth analysis, the CVIM has been
integrated in a vehicle traffic simulator to produce CVIM-complaint data
streams as a result of the individual behavior of each vehicle (speed, brake
activity, steering activity, etc.). In a next step, a simulation of vehicle
traffic in a realistically modeled, large-area street network has been used in
combination with a cellular Long Term Evolution (LTE) network to determine the
cumulated amount of data produced within each network cell. As a result, a new
car-to-cloud communication traffic model has been derived, which quantifies the
data rate of aggregated car-to-cloud data producible by vehicles depending on
the current traffic situations (free flow and traffic jam). The results provide
a reference for network planning and resource scheduling for car-to-cloud type
services in the context of smart cities
The What-And-Where Filter: A Spatial Mapping Neural Network for Object Recognition and Image Understanding
The What-and-Where filter forms part of a neural network architecture for spatial mapping, object recognition, and image understanding. The Where fllter responds to an image figure that has been separated from its background. It generates a spatial map whose cell activations simultaneously represent the position, orientation, ancl size of all tbe figures in a scene (where they are). This spatial map may he used to direct spatially localized attention to these image features. A multiscale array of oriented detectors, followed by competitve and interpolative interactions between position, orientation, and size scales, is used to define the Where filter. This analysis discloses several issues that need to be dealt with by a spatial mapping system that is based upon oriented filters, such as the role of cliff filters with and without normalization, the double peak problem of maximum orientation across size scale, and the different self-similar interpolation properties across orientation than across size scale. Several computationally efficient Where filters are proposed. The Where filter rnay be used for parallel transformation of multiple image figures into invariant representations that are insensitive to the figures' original position, orientation, and size. These invariant figural representations form part of a system devoted to attentive object learning and recognition (what it is). Unlike some alternative models where serial search for a target occurs, a What and Where representation can he used to rapidly search in parallel for a desired target in a scene. Such a representation can also be used to learn multidimensional representations of objects and their spatial relationships for purposes of image understanding. The What-and-Where filter is inspired by neurobiological data showing that a Where processing stream in the cerebral cortex is used for attentive spatial localization and orientation, whereas a What processing stream is used for attentive object learning and recognition.Advanced Research Projects Agency (ONR-N00014-92-J-4015, AFOSR 90-0083); British Petroleum (89-A-1204); National Science Foundation (IRI-90-00530, Graduate Fellowship); Office of Naval Research (N00014-91-J-4100, N00014-95-1-0409, N00014-95-1-0657); Air Force Office of Scientific Research (F49620-92-J-0499, F49620-92-J-0334
How to Choose the Relevant MAC Protocol for Wireless Smart Parking Urban Networks?
Parking sensor network is rapidly deploying around the world and is regarded
as one of the first implemented urban services in smart cities. To provide the
best network performance, the MAC protocol shall be adaptive enough in order to
satisfy the traffic intensity and variation of parking sensors. In this paper,
we study the heavy-tailed parking and vacant time models from SmartSantander,
and then we apply the traffic model in the simulation with four different kinds
of MAC protocols, that is, contention-based, schedule-based and two hybrid
versions of them. The result shows that the packet interarrival time is no
longer heavy-tailed while collecting a group of parking sensors, and then
choosing an appropriate MAC protocol highly depends on the network
configuration. Also, the information delay is bounded by traffic and MAC
parameters which are important criteria while the timely message is required.Comment: The 11th ACM International Symposium on Performance Evaluation of
Wireless Ad Hoc, Sensor, and Ubiquitous Networks (2014
Classification of Occluded Objects using Fast Recurrent Processing
Recurrent neural networks are powerful tools for handling incomplete data
problems in computer vision, thanks to their significant generative
capabilities. However, the computational demand for these algorithms is too
high to work in real time, without specialized hardware or software solutions.
In this paper, we propose a framework for augmenting recurrent processing
capabilities into a feedforward network without sacrificing much from
computational efficiency. We assume a mixture model and generate samples of the
last hidden layer according to the class decisions of the output layer, modify
the hidden layer activity using the samples, and propagate to lower layers. For
visual occlusion problem, the iterative procedure emulates feedforward-feedback
loop, filling-in the missing hidden layer activity with meaningful
representations. The proposed algorithm is tested on a widely used dataset, and
shown to achieve 2 improvement in classification accuracy for occluded
objects. When compared to Restricted Boltzmann Machines, our algorithm shows
superior performance for occluded object classification.Comment: arXiv admin note: text overlap with arXiv:1409.8576 by other author
A Survey of Positioning Systems Using Visible LED Lights
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe
Accelerating innovation with prize rewards: History and typology of technology prizes and a new contest design for innovation in African agriculture
"This paper describes how governments and philanthropic donors could drive innovation through a new kind of technology contest. We begin by reviewing the history of technology prizes, which operate alongside private intellectual property rights and public R&D to accelerate and guide productivity growth towards otherwise-neglected social goals. Proportional “prize rewards” would modify the traditional winner-take-all approach, by dividing available funds among multiple winners in proportion to measured achievement. This approach would provide a royalty-like payment for incremental success. The paper provides concludes with a specific example for how such prizes could be implemented to reward and help scale up successful innovations in African agriculture, through payments to innovators in proportion to the value created by their technologies after adoption. " from authors' abstractProductivity growth, Technology adoption, intellectual property, Agricultural R&D, Innovation,
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