KEYWORDS: Electromagnetism, Resistance, Microelectromechanical systems, Magnetism, Sensors, Energy harvesting, Inductance, Actuators, Electromechanical design, Control systems
There is going on a flurry of research activity in the development of effcient energy harvesters from all branches of energy conversion. The need for developing self-powered wireless sensors and actuators to be employed in unmanned combat vehicles also seems to grow steadily. These vehicles are inducted into perilous war zones for silent watch missions. Energy management is sometimes carried out using misson-aware energy expenditure strategies. Also, when there is a requirement for constant monitoring of events, the sensors and the subsystems of combat vehicles require energy harvesters that can operate over a discrete set of spot frequencies. This paper attempts to review some of the recent techniques and the energy harvesting devices based on electromagnetic and electromechanical principles. In particular, we shall discuss the design and performance of a MEMS-harvester that exhibits multiple resonances. Frequency response of a simulated electromagnetic harvester is plotted. It has three dominant peaks at three different resonant frequencies. Variation in the load power in the normalized units as a function of load is found, which determines the matched load resistance.
Biochemical pathways characterize various biochemical reaction schemes that involve a set of species and the
manner in which they are connected. Determination of schematics that represent these pathways is an important
task in understanding metabolism and signal transduction. Examples of these Pathways are: DNA and protein
synthesis, and production of several macro-molecules essential for cell survival. A sustained feedback mechanism arises in gene expression and production of mRNA that lead to protein synthesis if the protein so synthesized serves as a transcription factor and becomes a repressor of the gene expression. The cellular regulations are carried out through biochemical networks consisting of reactions and regulatory proteins.
Systems biology is a relatively new area that attempts to describe the biochemical pathways analytically and
develop reliable mathematical models for the pathways. A complete understanding of chemical reaction kinetics
is prohibitively hard thanks to the nonlinear and highly complex mechanisms that regulate protein formation,
but attempting to numerically solve some of the governing differential equations seems to offer significant insight about their biochemical picture. To validate these models, one can perform simple experiments in the lab.
This paper introduces fundamental ideas in biochemical signaling and attempts to take first steps into the understanding of biochemical oscillations. Initially, the two-pool model of calcium is used to describe the
dynamics behind the oscillations. Later we present some elementary results showing biochemical oscillations
arising from solving differential equations of Elowitz and Leibler using MATLAB software.
The extremum of a real-valued nonanalytic function of a complex variable that defies Cauchy-Riemann equations can be found using formal derivatives. This letter shows how the first and second derivative tests can be recast via formal gradient and formal Hessian. The elegance of the method is illustrated with examples.
KEYWORDS: Solar cells, Photovoltaics, Resistance, Detection and tracking algorithms, Information operations, Electronic circuits, Solar energy, Complex systems, MATLAB, Capacitors
The voltage and current characteristic of a photovoltaic (PV) cell is highly nonlinear and operating a PV cell for maximum power transfer has been a challenge for a long time. Several techniques have been proposed to estimate and track the maximum power point (MPP) in order to improve the overall efficiency of a PV panel. A strategic use of the mean value theorem permits obtaining an analytical expression for a point that lies in a close neighborhood of the true MPP. But hitherto, an exact solution in closed form for the MPP is not published. This problem can be formulated analytically as a constrained optimization, which can be solved using the Lagrange method. This method results in a system of simultaneous nonlinear equations. Solving them directly is quite difficult. However, we can employ a recursive algorithm to yield a reasonably good solution. In graphical terms, suppose the voltage current characteristic and the constant power contours are plotted on the same voltage current plane, the point of tangency between the device characteristic and the constant power contours is the sought for MPP. It is subject to change with the incident irradiation and temperature and hence the algorithm that attempts to maintain the MPP should be adaptive in nature and is supposed to have fast convergence and the least misadjustment. There are two parts in its implementation. First, one needs to estimate the MPP. The second task is to have a DC-DC converter to match the given load to the MPP thus obtained. Availability of power electronics circuits made it possible to design efficient converters. In this paper although we do not show the results from a real circuit, we use MATLAB to obtain the MPP and a buck-boost converter to match the load. Under varying conditions of load resistance and irradiance we demonstrate MPP tracking in case of a commercially available solar panel MSX-60. The power electronics circuit is simulated by PSIM software.
Stability is an important factor in the study of electrostatic MEMS switches and sensors. Their response is
signicantly improved by either applying a large dc bias or by depositing a prescribed value of charge on the
oating electrodes. This charge is related to the pull-in voltages. Measurement of charge without causing
loading is recommended; so instead of incorporating any eld operated transistor circuitry for this purpose,
methods are developed to relate the charge magnitude to the dynamical response of the actuators. Elata et al.
developed ecient and reliable ways of charge monitoring without causing loading to the device. These methods
rely on energy of the system instead of performing integration in the time domain. Based on their work, this
paper examines the alterations in the dynamic response of actuators. The positive and negative pull-in voltages
in the voltage displacement plane are symmetrically located with respect to charge on the
oating electrode.
This fact is exploited to carry out indirect charge measurement from the average of the two pull-in values. A
regression scheme is proposed that predicts the charge from the voltage shift based on limited measurements of
capacitance of the actuator.
Electrostatic MEMS switches have become prevalent because of low power consumption and ease of integration in
micro-fabrication technology. The equations governing their dynamic response obtained by energy methods are
nonlinear differential equations. Even the unit-step response of these devices requires numerical computation.
Depending on the magnitude of the applied step voltage and the presence of dielectric in the actuator, the
response could be recurring or non-recurring. Estimating the period time and the switching time in these cases
proves to be hard because one has to solve the energy equation numerically which could be time consuming or
difficult to converge if it is not posed properly. Elata et al. have developed excellent methods to obtain these
times on a logarithmic scale of voltage more easily for the undamped case. This paper extends their work for
the case when the bottom plate is covered with a dielectric layer. The stagnation time occurring before dynamic
pull-in, and the switching time thereafter are first shown as nonlinear graphs with the dielectric permittivity as
a parameter. They are also linearized on an exponential scale and made useful for quick look up and convenience
of designers.
Performance of electrostatic actuators used in MEMS devices is severely limited by the stability considerations
that are related to the pull-in parameters. The static and dynamic responses of electrostatic actuators driven
by single as well as multiple voltage excitations are studied with an aim of estimating these pull-in voltage
and distance parameters. A normalized Hamiltonian formulation is adopted and the resulting equations are
solved analytically and also numerically using an iterative scheme. Recently a numerical α-line method has been
proposed to extract the pull-in parameters. Scanning along the α-lines by voltage and displacement iteration
schemes were studied. Estimating the intersection of the α-lines with the pull-in hypersurface indicates maximal
voltage variable. We revisit these two iteration schemes and propose few insights to improve the convergence.
Convergence of the parameters to the theoretical values is found to be smooth. This approach helps us to
generalize the technique for more complicated geometries.
Surface waves (SWs) are localized waves that travel along the planar interface between two different mediums
when certain dispersion relations are satisfied. If both mediums have purely dielectric constitutive properties,
the characteristics of SW propagation are determined by the anisotropy of both mediums. Surface waves are then
called Dyakonov SWs (DSWs), after Dyakonov who theoretically established the possibility of SW propagation
at the planar interface of an isotropic dielectric and a positive uniaxial dielectric. Since then, DSW propagation
guided by interfaces between a variety of dielectrics has been studied. With an isotropic dielectric on one
side, the dielectric on the other side of the interface can be not only positive uniaxial but also biaxial. DSW
propagation can also occur along an interface between two uniaxial or biaxial dielectrics that are twisted about
a common axis with respect to each other but are otherwise identical.
Recently, DSW propagation has been studied taking (i) uniaxial dielectrics such as calomel and dioptase
crystals; (ii) biaxial dielectrics such as hemimorphite, crocoite, tellurite, witherite, and cerussite; and (iii)
electro-optic materials such as potassium niobate. With materials that are significantly anisotropic, the angular
regime of directions for DSW propagation turns out to be narrow. In the case of naturally occurring crystals,
one has to accept the narrow angular existence domain (AED). However, exploiting the Pockels effect not only
facilitates dynamic electrical control of DSW propagation, but also widens the AED for DSW propagation.
Surface-wave propagation at the planar interface of a columnar thin film (CTF) -- a biaxial dielectric medium -- and an isotropic dielectric substrate may occur over a narrow range of propagation directions, range being dependent on (i) the bulk matgerial that is evaporated to deposit the CTF, (ii) the vapor incidence angle used for the deposition, and (iii) the refractive index of the substrate material.
A new constant modulus algorithm (CMA) type of error function for fast phase recovery of QAM signals was recently proposed in which the cost function of the error function recovers the phase simultaneously with the equalization. In this paper we propose variable step (VS) in the update equations of new error function to enhance the performance of convergence speed and residual inter-symbol interference (ISI). Effectiveness of the error function for well-behaved channel, under-modeled channel and channel disparity are presented. Simulation results are presented to compare the performance of VS, constant step (CS) and the modified CMA (MCMA) using 16-QAM signal constellation.
The problem of reconstructing an irregularly sampled discrete-time band-limited signal with unknown sampling locations can be analyzed using both geometric and algebraic approaches. This problem can be solved using iterative and non-iterative techniques including the cyclic coordinate approach and the random search method. When the spectrum of the given signal is band-limited to L coefficients, the algebraic structure underlying the signal can be dealt using subspace techniques and a method is suggested to classify the solutions based on this approach. We numerically solve the Irregular Sampling at Unknown Locations (ISUL) problem by considering it as a combinatorial optimization problem. The exhaustive search method to determine the optimum solution is computationally intensive. The need for a more efficient optimization technique to save computational complexity leads us to propose Evolutionary Programming as a stochastic optimization technique. Evolutionary algorithms, based on the models of natural evolution were originally developed as a method to evolve finite-state machines for solving time series prediction tasks and were later extended to parameter optimization problems. The solution space is modeled as a population of individuals, and the search for the optimum solution is obtained by evolving to the best individual in the population. We propose an Evolutionary Programming (EP) based method to converge to the global optimum and obtain the set of sampling locations for the given irregularly sampled signal. The results obtained by EP are compared with the Random Search and Cyclic Coordinate descent algorithms.
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