81 research outputs found
Utility Maximization for Uplink MU-MIMO: Combining Spectral-Energy Efficiency and Fairness
Driven by green communications, energy efficiency (EE) has become a new
important criterion for designing wireless communication systems. However, high
EE often leads to low spectral efficiency (SE), which spurs the research on
EE-SE tradeoff. In this paper, we focus on how to maximize the utility in
physical layer for an uplink multi-user multiple-input multipleoutput (MU-MIMO)
system, where we will not only consider EE-SE tradeoff in a unified way, but
also ensure user fairness. We first formulate the utility maximization problem,
but it turns out to be non-convex. By exploiting the structure of this problem,
we find a convexization procedure to convert the original nonconvex problem
into an equivalent convex problem, which has the same global optimum with the
original problem. Following the convexization procedure, we present a
centralized algorithm to solve the utility maximization problem, but it
requires the global information of all users. Thus we propose a primal-dual
distributed algorithm which does not need global information and just consumes
a small amount of overhead. Furthermore, we have proved that the distributed
algorithm can converge to the global optimum. Finally, the numerical results
show that our approach can both capture user diversity for EE-SE tradeoff and
ensure user fairness, and they also validate the effectiveness of our
primal-dual distributed algorithm
Self-organization of Nodes using Bio-Inspired Techniques for Achieving Small World Properties
In an autonomous wireless sensor network, self-organization of the nodes is
essential to achieve network wide characteristics. We believe that connectivity
in wireless autonomous networks can be increased and overall average path
length can be reduced by using beamforming and bio-inspired algorithms. Recent
works on the use of beamforming in wireless networks mostly assume the
knowledge of the network in aggregation to either heterogeneous or hybrid
deployment. We propose that without the global knowledge or the introduction of
any special feature, the average path length can be reduced with the help of
inspirations from the nature and simple interactions between neighboring nodes.
Our algorithm also reduces the number of disconnected components within the
network. Our results show that reduction in the average path length and the
number of disconnected components can be achieved using very simple local rules
and without the full network knowledge.Comment: Accepted to Joint workshop on complex networks and pervasive group
communication (CCNet/PerGroup), in conjunction with IEEE Globecom 201
Abductive Action Inference
Abductive reasoning aims to make the most likely inference for a given set of
incomplete observations. In this work, we propose a new task called abductive
action inference, in which given a situation, the model answers the question
`what actions were executed by the human in order to arrive in the current
state?'. Given a state, we investigate three abductive inference problems:
action set prediction, action sequence prediction, and abductive action
verification. We benchmark several SOTA models such as Transformers, Graph
neural networks, CLIP, BLIP, end-to-end trained Slow-Fast, and Resnet50-3D
models. Our newly proposed object-relational BiGED model outperforms all other
methods on this challenging task on the Action Genome dataset. Codes will be
made available.Comment: 16 pages, 9 figure
Achieving Small World Properties using Bio-Inspired Techniques in Wireless Networks
It is highly desirable and challenging for a wireless ad hoc network to have
self-organization properties in order to achieve network wide characteristics.
Studies have shown that Small World properties, primarily low average path
length and high clustering coefficient, are desired properties for networks in
general. However, due to the spatial nature of the wireless networks, achieving
small world properties remains highly challenging. Studies also show that,
wireless ad hoc networks with small world properties show a degree distribution
that lies between geometric and power law. In this paper, we show that in a
wireless ad hoc network with non-uniform node density with only local
information, we can significantly reduce the average path length and retain the
clustering coefficient. To achieve our goal, our algorithm first identifies
logical regions using Lateral Inhibition technique, then identifies the nodes
that beamform and finally the beam properties using Flocking. We use Lateral
Inhibition and Flocking because they enable us to use local state information
as opposed to other techniques. We support our work with simulation results and
analysis, which show that a reduction of up to 40% can be achieved for a
high-density network. We also show the effect of hopcount used to create
regions on average path length, clustering coefficient and connectivity.Comment: Accepted for publication: Special Issue on Security and Performance
of Networks and Clouds (The Computer Journal
A Self-Organization Framework for Wireless Ad Hoc Networks as Small Worlds
Motivated by the benefits of small world networks, we propose a
self-organization framework for wireless ad hoc networks. We investigate the
use of directional beamforming for creating long-range short cuts between
nodes. Using simulation results for randomized beamforming as a guideline, we
identify crucial design issues for algorithm design. Our results show that,
while significant path length reduction is achievable, this is accompanied by
the problem of asymmetric paths between nodes. Subsequently, we propose a
distributed algorithm for small world creation that achieves path length
reduction while maintaining connectivity. We define a new centrality measure
that estimates the structural importance of nodes based on traffic flow in the
network, which is used to identify the optimum nodes for beamforming. We show,
using simulations, that this leads to significant reduction in path length
while maintaining connectivity.Comment: Submitted to IEEE Transactions on Vehicular Technolog
Self-Organization of Wireless Ad Hoc Networks as Small Worlds Using Long Range Directional Beams
We study how long range directional beams can be used for self-organization
of a wireless network to exhibit small world properties. Using simulation
results for randomized beamforming as a guideline, we identify crucial design
issues for algorithm design. Subsequently, we propose an algorithm for
deterministic creation of small worlds. We define a new centrality measure that
estimates the structural importance of nodes based on traffic flow in the
network, which is used to identify the optimum nodes for beamforming. This
results in significant reduction in path length while maintaining connectivity.Comment: Accepted to Joint workshop on complex networks and pervasive group
communication (CCNet/PerGroup), in conjunction with IEEE Globecom 201
DeformToon3D: Deformable 3D Toonification from Neural Radiance Fields
In this paper, we address the challenging problem of 3D toonification, which
involves transferring the style of an artistic domain onto a target 3D face
with stylized geometry and texture. Although fine-tuning a pre-trained 3D GAN
on the artistic domain can produce reasonable performance, this strategy has
limitations in the 3D domain. In particular, fine-tuning can deteriorate the
original GAN latent space, which affects subsequent semantic editing, and
requires independent optimization and storage for each new style, limiting
flexibility and efficient deployment. To overcome these challenges, we propose
DeformToon3D, an effective toonification framework tailored for hierarchical 3D
GAN. Our approach decomposes 3D toonification into subproblems of geometry and
texture stylization to better preserve the original latent space. Specifically,
we devise a novel StyleField that predicts conditional 3D deformation to align
a real-space NeRF to the style space for geometry stylization. Thanks to the
StyleField formulation, which already handles geometry stylization well,
texture stylization can be achieved conveniently via adaptive style mixing that
injects information of the artistic domain into the decoder of the pre-trained
3D GAN. Due to the unique design, our method enables flexible style degree
control and shape-texture-specific style swap. Furthermore, we achieve
efficient training without any real-world 2D-3D training pairs but proxy
samples synthesized from off-the-shelf 2D toonification models.Comment: ICCV 2023. Code: https://github.com/junzhezhang/DeformToon3D Project
page: https://www.mmlab-ntu.com/project/deformtoon3d
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Systems modelling as an approach for eliciting the mechanisms for hip fracture recovery among older adults in a participatory stakeholder engagement setting
IntroductionDue to an aging population, the rising prevalence and incidence of hip fractures and the associated health and economic burden present a challenge to healthcare systems worldwide. Studies have shown that a complex interplay of physiological, psychological, and social factors often affects the recovery trajectories of older adults with hip fractures, often complicating the recovery process.MethodsThis research aims to actively engage stakeholders (including doctors, physiotherapists, hip fracture patients, and caregivers) using the systems modeling methodology of Group Model Building (GMB) to elicit the factors that promote or inhibit hip fracture recovery, incorporating a feedback perspective to inform system-wide interventions. Hip fracture stakeholder engagement was facilitated through the Group Model Building approach in a two-half-day workshop of 25 stakeholders. This approach combined different techniques to develop a comprehensive qualitative whole-system view model of the factors that promote or inhibit hip fracture recovery.ResultsA conceptual, qualitative model of the dynamics of hip fracture recovery was developed that draws on stakeholders' personal experiences through a moderated interaction. Stakeholders identified four domains (i.e., expectation formation, rehabilitation, affordability/availability, and resilience building) that play a significant role in the hip fracture recovery journey..DiscussionThe insight that recovery of loss of function due to hip fracture is attributed to (a) the recognition of a gap between pre-fracture physical function and current physical function; and (b) the marshaling of psychological resilience to respond promptly to a physical functional loss via uptake of rehabilitation services is supported by findings and has several policy implications
HPAM : hybrid protocol for application layer multicast
This dissertation presents Hybrid Protocol for Application Layer Multicast (HPAM) which is used to stream live media over the Internet without IP multicast support. HPAM self-organizes clients to form efficient, self-improving, self-repairing, source-based overlay trees which minimize the root latency as well as loss rate for each client. HPAM exploits the simplicity and optimality of a lightweight, centralized controller, DS (Directory Server), with the robustness and scaleability of distributed clients. DS facilitates peer discovery and serves as a reliable backup should the distributed algorithm fails. Tree construction, refinement and recovery from partitions are executed independently by the clients. Other innovations of HPAM include: the JoinSource&Adopt algorithm to specially minimize the latency of clients located right below the root; the Gossip and Spiral mechanisms for tree refinement and repair; the Relative Loss Rate based heuristics for the detection of possible local congestion between a client and its parent to reduce unnecessary parent switching; the study on the impact of cheating clients who fabricate distance measurements on HPAM’s performance; the cheat detection techniques. In essence, it can build and maintain application layer multicast trees with reasonable overheads and network stress and is able to deliver high QoS to its clients.DOCTOR OF PHILOSOPHY (EEE
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