Modeling and simulation of nonlinear systems provide communication system designers
with a tool to predict and verify overall system performance under nonlinearity and
complex communication signals. Traditionally, RF system designers use deterministic
signals (discrete tones), which can be implemented in circuit simulators, to predict the
performance of their nonlinear circuits/systems. However, RF system designers are usually
faced with the problem of predicting system performance when the input to the system
is real-world communication signals which have a random nature.
Power Electronics is one of modern and key technologies in Electrical and
Electronics Engineering for green power, sustainable energy systems, and smart
grids. Especially, the transformation of existing electric power systems into smart
grids is currently a global trend. The gradual increase of distributed generators in
smart grids indicates a wide and important role for power electronic converters in
the electric power system, also with the increased use of power electronics devices
(nonlinear loads) and motor loadings, low cost, low-loss and high-performance
shunt current quality compensators are highly demanded by power customers to
solve current quality problems caused by those loadings.
This book describes a unifying framework to networked teleoperation systems
cutting across multiple research fields including networked control system for linear
and nonlinear forms, bilateral teleoperation, trilateral teleoperation, multilateral
teleoperation, cooperative teleoperation, and some teleoperation application
examples. Networked control has been deeply studied at the intersection of systems
& control and robotics for a long time, and many scholarly books on the topic have
been already published. Nevertheless, the approach remains active even in several
new research fields, such as bilateral teleoperation, single master and multiple
slaves, trilateral teleoperation, and multilateral teleoperation
There exist two essentially different approaches to the study of dynamical systems, based on
the following distinction:
time-continuous nonlinear differential equations ? time-discrete maps
One approach starts from time-continuous differential equations and leads to time-discrete
maps, which are obtained from them by a suitable discretization of time. This path is
pursued, e.g., in the book by Strogatz [Str94]. 1 The other approach starts from the study of
time-discrete maps and then gradually builds up to time-continuous differential equations,
see, e.g., [Ott93, All97, Dev89, Has03, Rob95]. After a short motivation in terms of nonlinear
differential equations, for the rest of this course we shall follow the latter route to dynamical
systems theory. This allows a generally more simple way of introducing the important
concepts, which can usually be carried over to a more complex and physically realistic
context.
If you are acquainted with neural networks, automatic control problems
are good industrial applications and have a dynamic or evolutionary nature
lacking in static pattern-recognition; control ideas are also prevalent in the
study of the natural neural networks found in animals and human beings.
If you are interested in the practice and theory of control, artificial neu-
ral networks offer a way to synthesize nonlinear controllers, filters, state
observers and system identifiers using a parallel method of computation.
Recent years have seen a rapid development of neural network control tech-
niques and their successful applications. Numerous simulation studies and
actual industrial implementations show that artificial neural network is a good
candidate for function approximation and control system design in solving the
control problems of complex nonlinear systems in the presence of different kinds
of uncertainties. Many control approaches/methods, reporting inventions and
control applications within the fields of adaptive control, neural control and
fuzzy systems, have been published in various books, journals and conference
proceedings.
General paradigm in solving a computer vision problem is to represent a raw image
using a more informative vector called feature vector and train a classifier on top of
feature vectors collected from training set. From classification perspective, there are
several off-the-shelf methods such as gradient boosting, random forest and support
vector machines that are able to accurately model nonlinear decision boundaries.
Hence, solving a computer vision problem mainly depends on the feature extraction
algorithm
HRVAS is a complete and self-contained heart rate variability analysis software (HRVAS) package. HRVAS offers several preprocessing options. HRVAS offers time-domeain, freq-domain, time-frequency, and nonlinear HRV analysis. All results can be exported to an Excel file. For processing many files HRVAS offers a bach processing feature. All settings/options can be saved to a .mat file and reloaded for future HRV analysis. Upon starting HRVAS all previously used settings/options are loaded.
