329 research outputs found
WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery
Virtual reality (VR) over wireless is expected to be one of the killer
applications in next-generation communication networks. Nevertheless, the huge
data volume along with stringent requirements on latency and reliability under
limited bandwidth resources makes untethered wireless VR delivery increasingly
challenging. Such bottlenecks, therefore, motivate this work to seek the
potential of using semantic communication, a new paradigm that promises to
significantly ease the resource pressure, for efficient VR delivery. To this
end, we propose a novel framework, namely WIreless SEmantic deliveRy for VR
(WiserVR), for delivering consecutive 360{\deg} video frames to VR users.
Specifically, deep learning-based multiple modules are well-devised for the
transceiver in WiserVR to realize high-performance feature extraction and
semantic recovery. Among them, we dedicatedly develop a concept of semantic
location graph and leverage the joint-semantic-channel-coding method with
knowledge sharing to not only substantially reduce communication latency, but
also to guarantee adequate transmission reliability and resilience under
various channel states. Moreover, implementation of WiserVR is presented,
followed by corresponding initial simulations for performance evaluation
compared with benchmarks. Finally, we discuss several open issues and offer
feasible solutions to unlock the full potential of WiserVR.Comment: This article has been submitted to IEEE Wireless Communications
Magazine (after major revisions) for possible publicatio
National freight transport planning: towards a Strategic Planning Extranet Decision Support System (SPEDSS)
This thesis provides a `proof-of-concept' prototype and a design architecture for a
Object Oriented (00) database towards the development of a Decision Support
System (DSS) for the national freight transport planning problem. Both governments
and industry require a Strategic Planning Extranet Decision Support System
(SPEDSS) for their effective management of the national Freight Transport Networks
(FTN).
This thesis addresses the three key problems for the development of a SPEDSS to
facilitate national strategic freight planning: 1) scope and scale of data available and
required; 2) scope and scale of existing models; and 3) construction of the software.
The research approach taken embodies systems thinking and includes the use of:
Object Oriented Analysis and Design (OOA/D) for problem encapsulation and
database design; artificial neural network (and proposed rule extraction) for
knowledge acquisition of the United States FTN data set; and an iterative Object
Oriented (00) software design for the development of a `proof-of-concept'
prototype. The research findings demonstrate that an 00 approach along with the use
of 00 methodologies and technologies coupled with artificial neural networks
(ANNs) offers a robust and flexible methodology for the analysis of the FTN problem
domain and the design architecture of an Extranet based SPEDSS.
The objectives of this research were to: 1) identify and analyse current problems and
proposed solutions facing industry and governments in strategic transportation
planning; 2) determine the functional requirements of an FTN SPEDSS; 3) perform a
feasibility analysis for building a FTN SPEDSS `proof-of-concept' prototype and
(00) database design; 4) develop a methodology for a national `internet-enabled'
SPEDSS model and database; 5) construct a `proof-of-concept' prototype for a
SPEDSS encapsulating identified user requirements; 6) develop a methodology to
resolve the issue of the scale of data and data knowledge acquisition which would act
as the `intelligence' within a SPDSS; 7) implement the data methodology using
Artificial Neural Networks (ANNs) towards the validation of it; and 8) make recommendations for national freight transportation strategic planning and further
research required to fulfil the needs of governments and industry.
This thesis includes: an 00 database design for encapsulation of the FTN; an
`internet-enabled' Dynamic Modelling Methodology (DMM) for the virtual
modelling of the FTNs; a Unified Modelling Language (UML) `proof-of-concept'
prototype; and conclusions and recommendations for further collaborative research
are identified
Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors
Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies. To make the best out of the event data, we introduce three different event-stream encoding methods based on Frequency, Surface of Active Event (SAE) and Leaky Integrate-and-Fire (LIF). We further integrate them into the state-of-the-art neural network architectures with two fusion approaches: the channel-level fusion of the raw feature space and decision-level fusion with the probability assignments. We present a qualitative and quantitative explanation why different encoding methods are chosen to evaluate the pedestrian detection and which method performs the best. We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736 event frames. This work paves the way of more fascinating perception applications with neuromorphic vision sensors
Analytical study and computational modeling of statistical methods for data mining
Today, there is tremendous increase of the information available on electronic form. Day by day it is increasing massively. There are enough opportunities for research to retrieve knowledge from the data available in this information. Data mining and app
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