31,339 research outputs found
Stability and Control of Power Systems using Vector Lyapunov Functions and Sum-of-Squares Methods
Recently sum-of-squares (SOS) based methods have been used for the stability
analysis and control synthesis of polynomial dynamical systems. This analysis
framework was also extended to non-polynomial dynamical systems, including
power systems, using an algebraic reformulation technique that recasts the
system's dynamics into a set of polynomial differential algebraic equations.
Nevertheless, for large scale dynamical systems this method becomes
inapplicable due to its computational complexity. For this reason we develop a
subsystem based stability analysis approach using vector Lyapunov functions and
introduce a parallel and scalable algorithm to infer the stability of the
interconnected system with the help of the subsystem Lyapunov functions.
Furthermore, we design adaptive and distributed control laws that guarantee
asymptotic stability under a given external disturbance. Finally, we apply this
algorithm for the stability analysis and control synthesis of a network
preserving power system.Comment: to appear at the 14th annual European Control Conferenc
Space-based Aperture Array For Ultra-Long Wavelength Radio Astronomy
The past decade has seen the rise of various radio astronomy arrays,
particularly for low-frequency observations below 100MHz. These developments
have been primarily driven by interesting and fundamental scientific questions,
such as studying the dark ages and epoch of re-ionization, by detecting the
highly red-shifted 21cm line emission. However, Earth-based radio astronomy
below frequencies of 30MHz is severely restricted due to man-made interference,
ionospheric distortion and almost complete non-transparency of the ionosphere
below 10MHz. Therefore, this narrow spectral band remains possibly the last
unexplored frequency range in radio astronomy. A straightforward solution to
study the universe at these frequencies is to deploy a space-based antenna
array far away from Earths' ionosphere. Various studies in the past were
principally limited by technology and computing resources, however current
processing and communication trends indicate otherwise. We briefly present the
achievable science cases, and discuss the system design for selected scenarios,
such as extra-galactic surveys. An extensive discussion is presented on various
sub-systems of the potential satellite array, such as radio astronomical
antenna design, the on-board signal processing, communication architectures and
joint space-time estimation of the satellite network. In light of a scalable
array and to avert single point of failure, we propose both centralized and
distributed solutions for the ULW space-based array. We highlight the benefits
of various deployment locations and summarize the technological challenges for
future space-based radio arrays.Comment: Submitte
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
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