124 research outputs found
A precise bare simulation approach to the minimization of some distances. Foundations
In information theory -- as well as in the adjacent fields of statistics,
machine learning, artificial intelligence, signal processing and pattern
recognition -- many flexibilizations of the omnipresent Kullback-Leibler
information distance (relative entropy) and of the closely related Shannon
entropy have become frequently used tools. To tackle corresponding constrained
minimization (respectively maximization) problems by a newly developed
dimension-free bare (pure) simulation method, is the main goal of this paper.
Almost no assumptions (like convexity) on the set of constraints are needed,
within our discrete setup of arbitrary dimension, and our method is precise
(i.e., converges in the limit). As a side effect, we also derive an innovative
way of constructing new useful distances/divergences. To illustrate the core of
our approach, we present numerous examples. The potential for widespread
applicability is indicated, too; in particular, we deliver many recent
references for uses of the involved distances/divergences and entropies in
various different research fields (which may also serve as an interdisciplinary
interface)
Design and Performance Analysis of Next Generation Heterogeneous Cellular Networks for the Internet of Things
The Internet of Things (IoT) is a system of inter-connected computing devices, objects and mechanical and digital machines, and the communications between these devices/objects and other Internet-enabled systems. Scalable, reliable, and energy-efficient IoT connectivity will bring huge benefits to the society, especially in transportation, connected self-driving vehicles, healthcare, education, smart cities, and smart industries.
The objective of this dissertation is to model and analyze the performance of large-scale heterogeneous two-tier IoT cellular networks, and offer design insights to maximize their performance. Using stochastic geometry, we develop realistic yet tractable models to study the performance of such networks. In particular, we propose solutions to the following research problems:
-We propose a novel analytical model to estimate the mean uplink device data rate utility function under both spectrum allocation schemes, full spectrum reuse (FSR) and orthogonal spectrum partition (OSP), for uplink two-hop IoT networks. We develop constraint gradient ascent optimization algorithms to obtain the optimal aggregator association bias (for the FSR scheme) and the optimal joint spectrum partition ratio and optimal aggregator association bias (for the OSP scheme).
-We study the performance of two-tier IoT cellular networks in which one tier operates in the traditional sub-6GHz spectrum and the other, in the millimeter wave (mm-wave) spectrum. In particular, we characterize the meta distributions of the downlink signal-to-interference ratio (sub-6GHz spectrum), the signal-to-noise ratio (mm-wave spectrum) and the data rate of a typical device in such a hybrid spectrum network. Finally, we characterize the meta distributions of the SIR/SNR and data rate of a typical device by substituting the cumulative moment of the CSP of a user device into the Gil-Pelaez inversion theorem.
-We propose to split the control plane (C-plane) and user plane (U-plane) as a potential solution to harvest densification gain in heterogeneous two-tier networks while minimizing the handover rate and network control overhead. We develop a tractable mobility-aware model for a two-tier downlink cellular network with high density small cells and a C-plane/U-plane split architecture. The developed model is then used to quantify effect of mobility on the foreseen densification gain with and without C-plane/U-plane splitting
Autonomous Swarm Navigation
Robotic swarm systems attract increasing attention in a wide variety of applications, where a multitude of self-organized robotic entities collectively accomplish sensing or exploration tasks. Compared to a single robot, a swarm system offers advantages in terms of exploration speed, robustness against single point of failures, and collective observations of spatio-temporal processes.
Autonomous swarm navigation, including swarm self-localization, the localization of external sources, and swarm control, is essential for the success of an autonomous swarm application. However, as a newly emerging technology, a thorough study of autonomous swarm navigation is still missing.
In this thesis, we systematically study swarm navigation systems, particularly emphasizing on their collective performance. The general theory of swarm navigation as well as an in-depth study on a specific swarm navigation system proposed for future Mars exploration missions are covered.
Concerning swarm localization, a decentralized algorithm is proposed, which achieves a near-optimal performance with low complexity for a dense swarm network.
Regarding swarm control, a position-aware swarm control concept is proposed. The swarm is aware of not only the position estimates and the estimation uncertainties of itself and the sources, but also the potential motions to enrich position information. As a result, the swarm actively adapts its formation to improve localization performance, without losing track of other objectives, such as goal approaching and collision avoidance.
The autonomous swarm navigation concept described in this thesis is verified for a specific Mars swarm exploration system. More importantly, this concept is generally adaptable to an extensive range of swarm applications
The Ensemble Kalman Filter: A Signal Processing Perspective
The ensemble Kalman filter (EnKF) is a Monte Carlo based implementation of
the Kalman filter (KF) for extremely high-dimensional, possibly nonlinear and
non-Gaussian state estimation problems. Its ability to handle state dimensions
in the order of millions has made the EnKF a popular algorithm in different
geoscientific disciplines. Despite a similarly vital need for scalable
algorithms in signal processing, e.g., to make sense of the ever increasing
amount of sensor data, the EnKF is hardly discussed in our field.
This self-contained review paper is aimed at signal processing researchers
and provides all the knowledge to get started with the EnKF. The algorithm is
derived in a KF framework, without the often encountered geoscientific
terminology. Algorithmic challenges and required extensions of the EnKF are
provided, as well as relations to sigma-point KF and particle filters. The
relevant EnKF literature is summarized in an extensive survey and unique
simulation examples, including popular benchmark problems, complement the
theory with practical insights. The signal processing perspective highlights
new directions of research and facilitates the exchange of potentially
beneficial ideas, both for the EnKF and high-dimensional nonlinear and
non-Gaussian filtering in general
Industrialization of hybrid and additive manufacturing - Implementation to Finnish industry (HYBRAM)
Industrialization of hybrid and additive manufacturing - Implementation to Finnish industry (HYBRAM)
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