Conferences related to Differential equations

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2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore

2019 IEEE 58th Conference on Decision and Control (CDC)

The CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, systems and control, and related areas.The 58th CDC will feature contributed and invited papers, as well as workshops and may include tutorial sessions.The IEEE CDC is hosted by the IEEE Control Systems Society (CSS) in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).

2019 IEEE Applied Power Electronics Conference and Exposition (APEC)

APEC focuses on the practical and applied aspects of the power electronics business. The conference addresses issues of immediate and long term importance to practicing power electronics engineer.

2019 IEEE Industry Applications Society Annual Meeting

The Annual Meeting is a gathering of experts who work and conduct research in the industrial applications of electrical systems.

2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

This conference is the annual premier meeting on the use of instrumentation in the Nuclear and Medical fields. The meeting has a very long history of providing an exciting venue for scientists to present their latest advances, exchange ideas, renew existing collaboration and form new ones. The NSS portion of the conference is an ideal forum for scientists and engineers in the field of Nuclear Science, radiation instrumentation, software engineering and data acquisition. The MIC is one of the most informative venues on the state-of-the art use of physics, engineering, and mathematics in Nuclear Medicine and related imaging modalities, such as CT and increasingly so MRI, through the development of hybrid devices

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Periodicals related to Differential equations

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.

Applied Superconductivity, IEEE Transactions on

Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission

Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...

Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.

Circuits and Systems I: Regular Papers, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.

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Most published Xplore authors for Differential equations

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Xplore Articles related to Differential equations

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[{u'author_order': 1, u'authorUrl': u'', u'full_name': u'J.G. Brainerd', u'id': 37314777100}, {u'author_order': 2, u'authorUrl': u'', u'full_name': u'T.K. Sharpless', u'id': 37370973400}] Proceedings of the IEEE, 1999


On the hybrid dynamic programming principle

[{u'author_order': 1, u'full_name': u'M.S. Sheikh'}] International Multi Topic Conference, 2002. Abstracts. INMIC 2002., 2002

Summary form only given, as follows. A Class of the hybrid optimal problem is formulated and a hybrid dynamic programming principle (DPP) is presented which constitutes a generalization of the celebrated dynamic programming principle of Richard Bellman. It is shown that similarly to the case of continuous dynamic programming, which leads to the well-known Hamilton-Jacobi-Bellman (HJB) functional partial differential equation, ...

Nonlinear neural field filters

[{u'author_order': 1, u'affiliation': u"King's Coll. London, London Univ., UK", u'authorUrl': u'', u'full_name': u'H.T. Sherief', u'id': 38220980900}, {u'author_order': 2, u'affiliation': u"King's Coll. London, London Univ., UK", u'authorUrl': u'', u'full_name': u'H.A. Fatmi', u'id': 37375078600}] [Proceedings] 1991 IEEE International Joint Conference on Neural Networks, 1991

Design, stability and implementation of nonlinear neural field filters are examined. The input and output of the neural field filters are vector fields. A neural transform is used to represent the input, output signals and the transfer function of the neural field filter. It is concluded that the Lyapunov conditions for such fields are taken care of by a novel ...

A general approach to charge transport in semiconductors

[{u'author_order': 1, u'affiliation': u'Los Alamos Scientific Lab., Los Alamos, N. Mex.', u'full_name': u'R.A. Gore'}] Proceedings of the IEEE, 1966


Decometer: A Digital Computer for the Diver's Wrist

[{u'author_order': 1, u'affiliation': u'Naval Undersea Center, Hawaii Laboratory, Kailua, HI, USA', u'authorUrl': u'', u'full_name': u'K. Jennings', u'id': 38227576400}] OCEANS '76, 1976

The Digital Decompression Computer (DECOMETER) is a wrist-worn accessory that enables the diver to maximize his time in the water safely and reliably. The computer is programmed with the U. S. Navy Diving Tables and continually calculates his inert gas pressure at each depth to determine his decompression status. The calculations occur every two seconds and the diver's status is ...

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Educational Resources on Differential equations

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No eLearning Articles are currently tagged "Differential equations" Videos

IMS 2015: Maxwells Legacy: The Heart and Soul of the EM Discipline
Understanding Differential Evolution
IMS 2015: Maxwell's Equation - The Genesis
How Symmetry Constrains Evolutionary Optimizers: A Black Box Differential Evolution Case Study - IEEE Congress on Evolutionary Computation 2017
MicroApps: Measurement Advances for Differential and I/Q Devices (Agilent Technologies)
Approximate Dynamic Programming Methods A Unified Framework
Ponnuthurai Nagaratnam Suganthan - Differential Evolution
IMS 2015: Four scientists who saved Maxwells Theory
From Maxwell's Equations to Modern Electromagnetics and Antenna Engineering Marvels
Maxwell's Equations: The Tip of an Iceberg (Peter Higgs)
Micro-Apps 2013: Determining Circuit Material Dielectric Constant from Phase Measurements
When Do We Resort to EC in the Communications Industry, and What is Needed in the Future? - IEEE Congress on Evolutionary Computation 2017
Hardware Detection in Implantable Media Devices Using Adiabatic Computing - S. Dinesh Kumar - ICRC 2018
A High-Efficiency Linear Power Amplifier for 28GHz Mobile Communications in 40nm CMOS: RFIC Interactive Forum 2017
Micro-Apps 2013: Breaking the RF Carrier Barrier - 0 to 200 in Under a Second
FinSAL: A Novel FinFET Based Secure Adiabatic Logic for Energy-Efficient and DPA Resistant IoT Devices - Himanshu Thapliyal: 2016 International Conference on Rebooting Computing
Micro-Apps 2013: Design and Simulation of Phased Arrays in VSS
Larson Collection interview with John V. Atanasoff



    Theoretical modeling‐based analysis is a process where a model is set up based on laws of nature and logic, using mostly mathematics, physics, and engineering‐ initially with simplified assumptions about their processes and aiming at finding an input/output model. Integrators and function generation can accomplish simulation of an ordinary differential equation (ODE). A state‐space formulation allows mathematical implementation with ODE solvers that can be computed by MATLAB and supports the definition of a block diagram for signal modeling simulators, such as Simulink. State variables are directly related to the energy storage elements of a system, and the ODEs can be derived from nodal or mesh analysis. ECAP was the first general program for solving time‐varying circuit equations. Differential equation‐based systems are developed and simulated from practical examples that focus typical electrical circuit applications, energy conversion, renewable energy sources, interconnection of distributed generation, power electronics, power systems, and power quality problems.

  • II Dynamics

    Dynamics concerns representing and reasoning about how continuous properties of things change over time. As we saw in two of the motivating examples from chapter 1 (i.e., what happens when heating water on a stove and whether warm or cold water freezes faster), subtle conclusions can be drawn even without numerical parameters or differential equations. This section describes how qualitative representations can be used to reason about change in continuous systems. It summarizes a rich set of representations and reasoning techniques developed by researchers in qualitative reasoning (and other areas as well). It also explores their implications for understanding commonsense reasoning, aspects of expert reasoning, causality, and natural-language semantics.

  • Three-Phase EPLL-II

    This chapter presents the second member of the three-phase enhanced phase- locked loop (3EPLL) structures. This structure, called the 3EPLL-II, is a direct extension of the 3EPLL-I in order to obviate its major shortcoming with regard to input signal unbalance. The 3EPLL-II inherits all properties of the 3EPLL-I, and in addition to those, it avoids the double-frequency error caused by the negative-sequence component. The 3EPLL-II is comprised of a 3EPLL-I on top and another modified 3EPLL-I on the bottom. The chapter explains the derivation of 3EPLL-II and the modular representation of 3EPLL-II. Representation of the 3EPLL-II in stationary domain is developed and a linear time invariant (LTI) model for the 3EPLL-II is derived for design purposes. The chapter highlights that the performance of the 3EPLL-II is controlled by two gains which makes its design stage very simple.

  • 6 Relationships between Quantities

    As the last chapter illustrated, there are a variety of useful and plausible representations for continuous properties. To reason about such properties requires representing how they can be related. A very simple way to use quantities is in the form of rules, either learned by instruction or via induction (e.g., "To boil a cup of water in the microwave, set the power to 900 watts and the time to 1 minute 30 seconds"). Although such rules are clearly used by people, they are far from the only way that people reason about quantities. We have intuitive notions of how quantities change over time, as our examples of boiling water and freezing ice cubes illustrate. Moreover, we can reason causally about such changes: if the temperature of the stove is higher, the kettle will boil sooner, all else being equal. Qualitative reasoning research has identified forms of qualitative mathematics that capture many aspects of such reasoning. This chapter describes two systems of qualitative mathematics, because each captures aspects of human causal reasoning about continuous systems. We begin with a look at traditional mathematics and why qualitative mathematics is needed in the first place. Then we examine the qualitative mathematics of qualitative process theory (Forbus, 1984), showing how it provides a representation system that captures partial knowledge about causal relationships between quantities and how it can be used to reason with partial information. Next we examine de Kleer and Brown's (1984) idea of confluences, showing how it can be used for causal reasoning in circumstances where QP theory breaks down. Finally, we discuss some of the limitations of these models.

  • The Continuum Model for Linear Arrays

    This chapter contains sections titled:The Linear Array without External InjectionThe Linear Array with External InjectionBeam‐Steering via End DetuningBeam‐Steering via End InjectionConclusion

  • Linear Optimal Filters and Predictors

    Estimation problem is the problem of estimating the state of a linear stochastic system by using measurements that are linear functions of the state. The Wiener filter is defined for stationary systems in continuous time, and the Kalman filter is defined for either stationary or nonstationary systems in either discrete time or continuous time, but with finite-state dimension. A brief discussion of solution methods for the Riccati differential equation for the Kalman-Bucy filter is presented in this chapter. An analogous treatment of the discrete-time problem for the Kalman filter is also presented. Prediction is equivalent to filtering when measurements (system outputs) are not available. The chapter gives the implementation equations for continuous-time and discrete-time predictors. Finally, the problem of missing data is discussed in detail.

  • Notes

    This chapter contains sections titled: Introduction, Chapter 1, Chapter 2, Chapter 3, Chapter 4, Chapter 5, Chapter 6, Chapter 7, Chapter 8, Chapter 9, Chapter 10, Chapter 11, Chapter 12, Conclusion

  • A Topology-Aware Evolutionary Algorithm for Reverse-Engineering Gene Regulatory Networks

    This chapter is concerned with modeling and simulating the dynamics of gene regulatory networks (GRNs). It explains the process of reverse-engineering GRNs from time-series gene expression data sets. The idea is to discover an optimal set of parameters for a computational model of the network that is able to adequately simulate the behavior described by the gene expression data sets. The chapter investigates three different mathematical methods used in computational models that are based on ordinary differential equations. These methods include Artificial Neural Network (ANN) method, S-System (SS) method and General Rate Law of Transcription (GRLOT) method. The mathematical models investigated in the chapter require a significant number of parameters to be fine-tuned in order for the models to accurately simulate real biological network behavior. In order to take advantage of available computational resources, parallel evolutionary algorithms are implemented using QosCosGrid- OpenMPI (QCG-OMPI). neural nets; reverse engineering

  • Linear Dynamic Systems

    This chapter discusses the dynamic models used in Kalman filtering, and especially those represented by systems of linear differential equations. It demonstrates, using specific examples, how one goes about building such models and how one can go from a model using differential equations to one suitable for Kalman filtering. The chapter characterizes the measurable outputs of dynamic systems as functions of the internal states and inputs of the system. The treatment is deterministic, in order to define functional relationships between inputs and outputs. Observability is the issue of whether the state of a dynamic system with a known model is uniquely determinable from its inputs and outputs. It is essentially a property of the given system model. A given linear dynamic system model with a given linear input/output model is considered observable if and only if its state is uniquely determinable from the model definition, its inputs, and its outputs.

  • Enhanced Phase-Locked Loop

    This chapter deals with the basic enhanced phase-locked loop (EPLL) structure. The EPLL enhances the standard PLL by removing its main drawback, which is the presence of double-frequency errors. EPLL achieves this task by means of estimating the amplitude of the input signal and using it within a new loop to remove the error. The EPLL provides an estimate of the input signal magnitude and also provides a filtered version of the input signal. It serves as a core and a building block for numerous developments. The chapter focuses on the derivation, principles of operation, linear model, and design guidelines pertaining to the EPLL. The droop control method (DCM) is widely used to control the operation of synchronous generators (SGs) in a power system. Three scenarios are considered to study the dynamic performance of the EPLL: the step jumps in the input signal variables, amplitude modulations, and phase- angle modulations.

Standards related to Differential equations

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Jobs related to Differential equations

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