KEYWORDS: Control systems, Quantum computing, Optimization (mathematics), Chemical species, Quantum physics, Data processing, Quantum information, Mathematical modeling, Electromagnetism, Complex systems
This paper deals with the progress made in applications of quantum
computing in control and optimization. It concentrates on applying
the geometric technique in order to investigate a finite control
problem of a two-level quantum system, resonance control of a
three-level system, simulation of bilinear quantum control systems,
and optimal control using the Bellman principle. We show that a
quantum object described by a Schroedinger equation can be
controlled in an optimal way by electromagnetic modes. We also
demonstrate an application of these techniques and an
algebra-geometric approach to the study of dynamic processes in
nonlinear systems. The information processing by means of controlled
quantum lattices is discussed: we present new mathematical models of
classical (CL) and quantum-mechanical lattices (QML) and their
application to information processing. system-theoretical results on
the observability, controllability and minimal realizability
theorems are formulated for cl. The cellular dynamaton (CD) based on
quantum oscillators is presented. Cellular's quantum computational
search procedure can provide the basis for implementing adaptive
global optimization algorithms. A brief overview of the procedure is
given and a framework called lattice adaptive search is set up. A
method of Yatsenko and one introduced by the authors fit into this
framework and are compared.
This paper describes a new approach to global optimization and
control uses geometric methods and modern quantum mathematics.
Polynomial extremal problems (PEP) are considered. PEP
constitute one of the most important subclasses of nonlinear
programming models. Their distinctive feature is that an objective
function and constraints can be expressed by polynomial functions
in one or several variables. A general approach to optimization
based on quantum holonomic computing algorithms and instanton
mechanism. An optimization method based on geometric Lie -
algebraic structures on Grassmann manifolds and related with Lax
type flows is proposed. Making use of the differential geometric
techniques it is shown that associated holonomy groups properly
realizing quantum computation can be effectively found concerning
polynomial problems. Two examples demonstrating calculation
aspects of holonomic quantum computer and maximum clique problems
in very large graphs,
are considered in detail.
It is known that leaf reflectance spectra can be used to estimate the contents of chemical components in vegetation. Recent novel applications include the detection of harmful biological agents that can originate from agricultural bioterrorism attacks. Such attacks have been identified as a major threat to the United States’ agriculture. Nevertheless, the usefulness of such approach is currently limited by distorting factors, in particular soil reflectance.
The quantitative analysis of the spectral curves from the reflection of plant leaves may be the basis for the development of new methods for interpreting the data obtained by the remote measurement of plants. We consider the problem of characterizing the chemical composition from noisy spectral data using an experimental optical method.
Using our experience in signal processing and optimization of complex systems we propose a new mathematical model for sensing of chemical components in vegetation. Estimates are defined as minimizers of penalized cost functionals with sequential quadratic programming (SQR) methods. A deviation measure used in risk analysis is also considered.
This framework is demonstrated for different agricultural plants using adaptive filtration, principal components analysis, and optimization techniques for classification of spectral curves of chemical components. Various estimation problems will be considered to illustrate the computational aspects of the proposed method.
Using our experience in signal processing and optimization of complex systems we propose a new method to adaptive sensing of chemical content of vegetations. This framework is demonstrated for different agricultural plants using the neural network algorithm for classification of spectral curves and adaptive filtration. Utilization of characteristics of leaf reflectance spectrum, which are a relative characteristic of the light reflected from canopies, makes it possible to avoid the necessity of measuring the 100% reflectance standard and to provide the high resistance of the method to distorting factors in particular to soil reflectance contribution. For utilization of the method the numerical algorithms is proposed. Various estimation problems will be considered to illustrate the computational aspects of the proposed method. The software is based on digital filter, optimization approach and neural network algorithm for classification of chemical components. Supporting software for data management, storage, signal processing will be development. A concept of an intelligent sensor is considered.
In this paper we consider the problem of estimating chlorophyll content in vegetation using an experimental optical method from noisy spectral data. It is shown that the quantitative analysis of the spectral curves for the reflection of plant leaves may be the basis for development of new methods for interpretation of the data obtained by the remote measurement of plants. A mathematical model of vegetation reflectance is proposed to estimate the chlorophyll content from spectral data. Estimates are defined as minimizers of penalized cost functionals with sequential quadratic programming (SQR) methods. An estimation is related to the local scoring procedure for the generalized additive model. A deviation measurement in risk analysis of vegetation is considered. The role of deviation and risk measures in optimization is analyzed. Experimental and simulation results are shown for different agricultural plants using a functional-parametric representation of spectral curves.
KEYWORDS: Superconductors, Sensors, Magnetism, Signal processing, Chemical elements, Dynamical systems, Signal detection, Mathematical modeling, Chaos, Digital signal processing
A concept of the cryogenic-optical sensor based on competitive
adaptive sensitive elements applicable to a gravity meter sensor
is considered. The sensor element is based on a magnetic
levitation phenomenon, high-precision optical registration of
levitating body mechanical coordinates, and robust signal
processing tools. A controlled self-bearing probe dynamics is
analyzed. An optimization approach to highly sensitive
measurement of weak signal is presented. An optimization method
which allows the extraction of the Lyapunov exponents from
nonlinear chaotic dynamics of a macroscopic superconducting probe
is described. Simulation results support the mathematical, and the
system characteristics are thus optimized.
In this paper we consider the problem of estimating chlorophyll content in vegetation using an experimental optical method from noisy spectral data. It is shown that the quantitative analysis of the spectral curves for the reflection of plant leaves may be the basis for development of new methods for interpretation of the data obtained by the remote measurement of plants. A mathematical model of vegetation reflectance is proposed to estimate the chlorophyll concentration from spectral data. Estimates are defined as minimizers of penalized cost functionals with sequential quadratic programming (SQP) methods. An estimation tool is related to the local scoring procedure for an generalized additive model. Experimental and simulation results are shown for different agricultural plants using a functional parametric fitting of spectral curves.
KEYWORDS: Sensors, Mathematical modeling, Digital filtering, Superconductors, Magnetism, Magnetic sensors, Electronic filtering, Control systems, Signal detection, Interference (communication)
We describe a phenomenon in which a macroscopic superconducting probe, as large as 2 - 6 cm, is chaotically and magnetically levitated. We have found that, when feedback is used, the probe chaotically moves near an equilibrium state. The global optimization approach to highly sensitive measurement of weak signal is considered. Furthermore an accurate mathematical model of asymptotically stable estimation of a limiting weak noisy signal using the stochastic measurement model is considered.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.