Mechanical Engineering Research Papers

Absorption and Scattering of Light by Small Particles

1983

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Consensus problems in networks of agents with switching topology and time-delays

2004, Automatic Control, IEEE …

In this paper, we discuss consensus problems for networks of dynamic agents with fixed and switching topologies. We analyze three cases: 1) directed networks with fixed topology; 2) directed networks with switching topology; and 3)... more

In this paper, we discuss consensus problems for networks of dynamic agents with fixed and switching topologies. We analyze three cases: 1) directed networks with fixed topology; 2) directed networks with switching topology; and 3) undirected networks with communication time-delays and fixed topology. We introduce two consensus protocols for networks with and without time-delays and provide a convergence analysis in all three cases. We establish a direct connection between the algebraic connectivity (or Fiedler eigenvalue) of the network and the performance (or negotiation speed) of a linear consensus protocol. This required the generalization of the notion of algebraic connectivity of undirected graphs to digraphs. It turns out that balanced digraphs play a key role in addressing average-consensus problems. We introduce disagreement functions for convergence analysis of consensus protocols. A disagreement function is a Lyapunov function for the disagreement network dynamics. We proposed a simple disagreement function that is a common Lyapunov function for the disagreement dynamics of a directed network with switching topology. A distinctive feature of this work is to address consensus problems for networks with directed information flow. We provide analytical tools that rely on algebraic graph theory, matrix theory, and control theory. Simulations are provided that demonstrate the effectiveness of our theoretical results. He has been a Postdoctoral Scholar in the Department of Control and Dynamical System, California Institute of Technology, Pasadena, since 2001. His research interests include distributed control of multiagent systems, formation control, consensus and synchronization problems, flocking/swarming, particle-based modeling and simulation, self-assembly, cooperative robotics, mobile sensor networks, self-organizing networks, folding and unfolding, biomolecular systems, dynamic graph theory, graph rigidity, computational geometry, nonlinear control theory, and control of aerospace vehicles and UAVs.

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Neural networks a comprehensive foundation

by aso tahu

1995

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An introduction to compressive sampling

by Emmanuel Candes

2008, Signal Processing Magazine, IEEE

Fig. 1. Original megapixel image with pixel values in the range [0,255] and its wavelet transform coefficients (arranged

Fig. 2. A sparse real valued signal (left) and its reconstruction from 60 (complex) valued Fourier coefficients by

Fig. 3. Original 25,000-sparse image (left) and its reconstruction from 96,000 undersampled incoherent measure-

Fig. 5. Random Pre-integration (RPI) system for analog-to-information conversion.

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Learning from delayed rewards

by erisa luzi

1989

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Robust and optimal control

by Daniel Moreano

1996

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Constrained Model Predictive Control: Stability and Optimality

by MAHENDRA SINGH

2000, AUTOMATICA- …

Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a "nite horizon open-loop optimal control problem, using the current state of the plant as the initial... more

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Measurement of hardness and elastic modulus by instrumented indentation: Advances in understanding and refinements to methodology

by Hygreeva Namburi

The method we introduced in 1992 for measuring hardness and elastic modulus by instrumented indentation techniques has widely been adopted and used in the characterization of small-scale mechanical behavior. Since its original... more

FIG. 1. Schematic illustration of indentation load—displacement data showing important measured parameters (after Ref. 1).

FIG. 2. Schematic illustration of the unloading process showing pa- rameters characterizing the contact geometry (after Ref. 1). Using Eq. (2) to approximate the vertical displacement of the contact periphery, it follows from the geometry of where € is a constant that depends on the geometry of the indenter. Important values are: € = 0.72 for a conical punch, € = 0.75 for a paraboloid of revolution (which approximates to a sphere at small depths), and e = 1.00 for a flat punch.** Based on the empirical observation that the unloading curves are best approximated by an indenter that behaves like a paraboloid of revolution (m = 1.5), the value € = 0.75 was recommended and has since become the standard value used for analysis. Recent analytical work has provided a more complete picture of why ¢ takes on this value.***? This is also addressed in Sec. III. A.

FIG. 3. Concepts used to understand and define the effective indenter shape (from Ref. 35). The ideas underlying the effective indenter shape are outlined in Fig. 3. The basic principles are derived from observations gleaned from finite element simulations of indentation of elastic—plastic materials by a rigid conical indenter with a half included angle of 70.3°.*° During the initial loading of the indenter [Fig. 3(a)], both elastic and plastic deformation processes occur, and the indenter conforms perfectly to the shape of the hardness impres-

FIG. 4. Topographic image of a Berkovich indentation in fused silica obtained using a quantitative imaging system that uses the indenter itself as the imaging tip: (a) complete 3D image showing the curvature of the faces; (b) cross section through an edge and the opposing face with straight lines included to accentuate the curvature. Experimental evidence for the curvature in the un- loaded hardness impressions is given in Fig. 4, where topographical images of a Berkovich indentation in fused silica are presented. The images were obtained by scan- ning the specimen laterally under the indenter tip with a very light load applied to the indenter to keep it in con- tact. The three-dimensional (3D) image in Fig. 4(a)

FIG. 5. The relation between € and m given by Eq. (17) (from Ref. 35).

FIG. 7. Dependence of normalized contact areas on the experimental parameter h,/h,,,,. The areas are normalized with respect to Aj, the area given by the area function of the indenter assuming no pile-up or sink-in (after Ref. 25).

FIG. 8. Finite element simulation of normalized contact profiles under load for spherical indentation of an elastic—perfectly-plastic material with E/oy = 200 showing the influence of the normalized penetration depth, //R, on the pile-up/sink-in behavior (after Ref. 34).

Recent studies have shown that even for perfectly elas- tic contact by a rigid cone, 8 may deviate from unity due to departures from the small strain approximation. Hay et al.*° used both finite element simulation and analytical techniques to examine indentation of an elastic half- space by a rigid conical indenter. They showed that the derivation used to obtain Eq. (6) with B = 1 is not strictly valid for the conical indenter geometry because it improperly accounts for radial material displacements in the region of contact (note: Hay et al. originally desig- nated this correction factor as y). They developed an approximate correction for the effect that gives

FIG. 10. Typical load versus displacement curve for a calibratior experiment in fused silica. FIG. 9. An indentation in fused silica made by raster scanning with a nanopositioning stage. Data obtained from this indentation were used to calibrate the load frame compliance and area function. (a) The image on the left shows the borders defining the two contact areas discussed in the text. (b) The image on the right is included for clarity.

FIG. 12. An expanded view of the load versus displacement curve at the point of contact. Although the harmonic stiffness is the preferred loca- tor of the surface in this particular situation, it should be noted that which of the measurements in Figs. 12-15 provides the best surface definition depends on the prop- erties of the material and the characteristics of the testing system. The resonant frequency of the testing system is particularly important (12 Hz for the system used here). As the stiffness of the contact increases, the resonant frequency of the combined instrument and contact sweeps toward higher values. The exact nature of the changes observed in the dynamic measurements in Figs. 13-15 then depends on whether the system moves toward resonance (as in our experiments) or away from it as contact is made. In fact, it is because the system moves toward and through resonance that the harmonic dis- placement in Fig. 13 actually increases rather than de- creasing when contact is made. Often, the most sensitive frequency for surface detection is the resonant frequency of the testing system just prior to contact; however, this is generally also the frequency at which the system is most vulnerable to noise (vibration, and so forth).

FIG. 11. A typical stiffness versus displacement curve for the cali- bration experiment. Stiffnesses were determined using the continuous stiffness measurement technique.

FIG. 14. An expanded view of the phase angle versus displacement curve at the point of contact. FIG. 13. An expanded view of the harmonic displacement amplitude versus displacement curve at the point of contact.

FIG. 16. A typical load over stiffness squared versus displacement for the calibration experiment. Note the constant value beyond 1000 nm. In Fig. 17, we demonstrate the load frame compliance measurement procedure for three different assumed val- ues of C;. The correct value, C; = 9.62 x 10° m/N, results in a flat curve for the data between 1000 nm and 2300 nm where the tip rounding is unimportant. How- ever, increasing or decreasing C; by as little as 20%

FIG. 15. An expanded view of the harmonic stiffness versus displace- ment curve at the point of contact. Note the minimum observed prior to full contact and the steep rise in stiffness at the point of contact.

FIG. 18. The change in the projected area of contact for a tilted Berkovich indenter versus the ratio of the lengths of the longest to the shortest side. For the experimental data presented here (Figs. 10 and 11), the first approach was taken to determine the area function. A back-reflection laser goniometer was used to measure the face angles to within 0.025°, giving Cy = 24.212. In precision work, this must be modified if the indenter is not aligned with its axis perfectly perpendicu- lar to the specimen surface, as such misalignments in- crease the area at a given contact depth. The degree of tilt can be assessed from measurements of the sides of the hardness impression if a good image is available. A simple but effective method is based on the ratio of the long to short side of the hardness impression. Figure 18 shows how the contact area at a given depth depends on this ratio when the indenter is tilted slightly toward an edge or a face. Other tilt directions lie in-between the two

FIG. 17 Load over stiffness squared versus displacement for three different values for the load frame stiffness. The curve with the correct load frame stiffness is flat at large depths.

FIG. 20. Difference between the areas predicted by the area function and the data used to obtain the fit. This area function works well at small depths but not at large.

FIG. 19. Difference between the areas predicted by the area function and the data used to obtain the fit. This area function can be used at large depths.

TABLE I. Values of parameters characterizing unloading curves as observed in nanoindentation experiments with a Berkovich indenter.* “Data from Ref. 1.

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Surface-Enhanced Resonance Raman Scattering: Single-Molecule Detection in a Langmuir–Blodgett Monolayer

by Duncan Graham

2003, Applied Spectroscopy

The adsorption of dye molecules on silver hydrosol particles causes an intensity enhancement of their resonance Raman scattering (RRS) spectra by several orders of magnitude [1]. This is accompanied by luminescence quenching which makes... more

Note. Frequencies that are assigned to C=C chromophore vibra- tions are marked by asterisks. TABLE 1. Molecular Structures and Absorption Maxima of tran and cis- Forms and the Most Intensive RRS Lines of the Dyes under Study

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Consensus Seeking in Multi-agent Systems Under Dynamically Changing Interaction Topologies

by Wei Ren

2003

This note considers the problem of information consensus among multiple agents in the presence of limited and unreliable information exchange with dynamically changing interaction topologies. Both discrete and continuous update schemes... more

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Introduction to robotics: mechanics and control

by Joji Trivandrum

1989

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GENERATING OPTIMAL TOPOLOGIES IN STRUCTURAL DESIGN USING A HOMOGENIZATION METHOD

by Zunyi Duan

Optimal shape design of structural elements based on boundary variations results in final designs that are topologically equivalent to the initial choice of design, and general, stable computational schemes for this approach often require... more

Finite element mesh sensitivity for the first cell

Finite element mesh sensitivity for the second cell

Fig. 4. Adapted finite element grid (cell 2). For use in shape optimization, the homogenized elasticity tensor must be obtained for a “continuously” varying size of a rectangular hole in the cell so that the density of the substance of materials can be a continuous design variable for the optimal “material distribution.” To this end, it is certainly impossible to determine the homogenized elastic

constant for all the possibilities of the material density (that is, the size of a rectangular hole in the cell). Thus, we compute the homogenized elastic tensors for certain sizes of the hole, and other values are then interpoiated by, e.g., Legendre polynomials, in order to have a continuous variation with respect to material density. An example for a square hole is shown in Figs. 5 and 6 in which four representative square holes and the interpolated components of the homogenized elastic tensor are given, respectively. It is noted that the variation of the elastic moduli is highly nonlinear, as shown in Fig. 6. This nonlinearity wii! play an important role in the shape optimization and will be examined by comparing the res: sts obtained in the case of a linearly varying elasticity tensor. It is also important to note that if the cell structure is rotated in relation to a fixed reference frame (for example the coordinate system of the shape optimization problem), the transformed elastic tensor is very different from the one for the unrotated case. The influence of the rotation of the cell becomes very strong when the size of the hole becomes large, as shown in Fig. 7, and it can be seen that a possibility of rotation

Fig. 5. Representative square holes in the unit (square) cell.

Fig. 8. Different choices of design domain 2, for a given set of surface tractions.

Gesign area tnat ?s available for the construction of the fillet. For the sake of comparison, a design optimization was also carried out for the case of rigidity that is linear in the design variable, as for variable thickness sheets, cf. Section 2. Suc design problems have been studied in detail in [51], and are very well-behaved problem: Figure 10 shows results for this type of problems and illustrates that this representation stiffness gives a good indication of the two-dimensional optimal shape for high volum fractions, while for low volume fractions it is difficult to interpret the results so as to define two-dimensional shape. This is, of course, not surprising as we are dealing with a tru three-dimensional problem that just happens to have a two-dimensional model. That is th variable thickness is a hidden three-dimensional feature. However, the design of variab] thickness sheets does indeed give well-defined shapes as three-dimensional structures. The testing of the design method using composite materials has been carried out for variou different formulations that all relate to the description of the method given in Section 4. Tw

Fig. 11. Results for two different discretizations. Case B with voids as square holes. Left-hand column for 16 x 10 mesh, right-hand column for 32 x 20 mesh. Volume is 91%, 64%, 36%, respectively. Black areas indicate material, and density of voids is illustrated as a removal of black areas. The voids are drawn element-wise as a rotated, macroscopic hole; note that the voids in reality are at microlevel.

Fig. 12. Comparison with two-variable composite design. Case B with voids as rectangular holes. Volumes are (a): 1%, (b): 64%, (c): 36%.

“MVE PUL HO ‘216 O18 SOUIN[OA ‘sUdIsap podumn] SuNNses oy) UUINTOS pueYy-7Yy3IE ‘SuIduIN] 0} JO y[NsAa1 SMOYs UUIN[OS pueY-2T] °psjej03 aq WED Jey) Sajoy arenbs se sploA YIM Y aseD “ssodoid Suiduiny ayy Jo syNsey “pT “By

Fig. 15. Results obtained by boundary variations. Case A, with volumes 91%, 64% and 369

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Current-driven excitation of magnetic multilayers

by John Slonczewski

1996, Journal of Magnetism and Magnetic Materials

A new mechanism is proposed for exciting the magnetic state of a ferromagnet. Assuming ballistic conditions and using WKB wave functions, we predict that a transfer of vectorial spin accompanies an electric current flowing perpendicular... more

Fig. 1. Bottom: Coulomb plus locally diagonalized exchange potential V_ versus position € in a five-layer system composed of paramagnets A, B, C, and ferromagnets Fl and F2. The particle flow is rightward the charged flow leftward (/, > 0). Top: Vector diagram of spin moments $,, and their current-driven velocities S$,» for magnets F1,2.

Fig. 2. Spin-transfer velocities IS, oI of ferromagnetic spin vectors S, versus included angle @, based on Eqs. (9) and (10). The units are [, /e (J, = current, ¢ = electron charge). Equal polariza- tion coefficients P of the magnets are assumed.

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Object-oriented software engineering: a use case driven approach

by Agiri Hamzat Ayodeji

1992

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Geophysical Fluid Dynamics

by shantanu ghosal

1981, Journal of Applied Mechanics-transactions of The Asme

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Finite element procedures in engineering analysis

by Michael Mera

1984, Journal of Pressure Vessel Technology

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State-space solutions to standard H/sub 2/ and H/sub infinity / control problems

by Pramod Khargonekar

1989, IEEE Transactions on Automatic Control

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Speaker verification using adapted Gaussian mixture models

by Sowmyan Kousthubadharan

2000, Digital signal processing

In this paper we describe the major elements of MIT Lincoln Laboratory's Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around... more

FIG. 1. Likelihood ratio-based speaker detection system. The single-speaker detection task can be restated as a basic hypothesis test vatwreen

FIG. 2. Data and model pooling approaches for creating a UBM. (a) Data from subpopulations are pooled prior to training the UBM via the EM algorithm. (b) Individual subpopulation models are trained then combined (pooled) to create final UBM.

FIG. 3. Pictorial example of two steps in adapting a hypothesized speaker model. (a) The training vectors (x’s) are probabilistically mapped into the UBM mixtures. (b) The adapted mixture parameters are derived using the statistics of the new data and the UBM mixture parameters. The adaptation is data dependent, so UBM mixture parameters are adapted by different amounts.

FIG. 4. Pictorial example of HNORM compensation. This picture shows log-likelihood ratic score distributions for two speakers before (left column) and after (right column) HNORM has been applied. After HNORM, the non-speaker score distribution for each handset type has been normalized to zero mean and unit standard deviation.

FIG. 5. DET curves for three UBM compositions: Pooled male and female data, separate male and female models, and pooled male and female models. Results are on the NIST 1998 summer- development single-speaker data using all scores.

FIG. 6. DET curves for systems using UBMs with 16-2048 mixtures. Results are on the NIS’ 1998 summer-development single-speaker data using all scores.

FIG. 7. DET curves for adaptation of different combinations of parameters. W = weights, M = means, V = variances. Results are on the NIST 1998 summer-development single-speaker data using all scores.

FIG. 8. Comparison of GMM-UBM system with and without HNORM. Results are on the NIST 1999 SRE single-speaker data using all scores. Reynolds, Quatieri, and Dunn: Speaker Verification Using Adapted GMMs

FIG. 9. Comparison of GMM-UBM system with and without HNORM, using different poolings of files in the 1999 NIST SRE single-speaker data set. SNST = Same-Number, Same-Type, DNST = Different-Number, Same-Type, DNDT = Different-Number, Different-Type.

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A critical evaluation of indentation techniques for measuring fracture toughness: I, direct crack measurements

by Sridhar Babu

1981, Journal of The American Ceramic Society

The application of indentation techniques to the evaluation of fracture toughness is examined critically, in two parts. In this first part, attention is focused on an approach which involves direct measurement of Vickers-produced radial... more

Fig. 1. Schematic of Vickers-produced indentation-fracture system, peak load P, showing characteristic dimensions c anda of penny-like radial/median crack and hardness impression, respectively.

Fig. 2. Variation of radial crack length, c’,, with time after indentation (P =40 N) for tests on soda-lime glass in air and oil environments. Shaded band shows immediate postindentation crack length, co.

Fig. 4. Plot of P/co*” over working range of load P for each test material for which well-defined radial cracks could be obtained. Number of indentations used to determine mean parameter is indicated in each case.

Fig. 5. Plot demonstrating correlation between toughness values determined by indentation and by conventional means. Filled symbols denote reference materials used to evaluate constant §f in Eq. (4). Vertical error bars represent uncertainty (standard deviation) in parameter P / con" obtained from Fig. 4, horizontal error bars nominal accuracy of K, values taken from Table I.

Fig. 3. Scanning electron micrographs of radial crack systems in 3 modifications of Al,O;, (A) AD999 (P =50 N), (8) Vi (P=50 N), and (C) sapphire (P=10 N), showing effect of increasing grain size on pattern definition; width of field 200 jum.

*Pyroceram, Corning Glass Works, Corning, N. Y. Commercial sheet glass. ‘National Bureau of Standards, Washington, D. C. “Coors Porcelain Co., Golden, Colo. ‘Vistal, Coors Porcelain Co. ‘Linde, Union Carbide Co., New York, N. Y. §Norton Co., Worcester, Mass. "CSIRO, Australia. ‘Texas Instruments, Inc., Dallas. ‘Rods, [0001] 30° to axis. "Disks, [111] parallel to axis. ‘DT=double torsion, DCB=double cantilever beam. “University of California, Berkeley. ‘Rockwell International Science Center, Thousand Oaks, Calif. ‘National Bureau of Standards. Table I. Materials Used in Indentation Toughness Studies

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An Overview Of Industrial Model Predictive Control Technology

by Bhau Anarase

1997

This paper provides an overview of commercially available model predictive control (MPC) technology, both linear and nonlinear, based primarily on data provided by MPC vendors. A brief history of industrial MPC technology is presented... more

Fig. 1. Approximate genealogy of linear MPC algorithms. The development of modern control concepts can be traced to the work of Kalman et al. in the early 1960s (Kalman, 1960a, b). A greatly simplified description of their results will be presented here as a reference point for the discussion to come. In the discrete-time context, Then the optimal input uy, is computed using an optimal proportional state controller:

Fig. 2. Hierarchy of control system functions in a typical processing plant. Conventional structure is shown at the left; MPC structure is shown at the right.

Fig. 3. Process structure determines the degrees of freedom available to the controller. Adapted from Froisy (1994) It also became increasingly difficult to translate control requirements into relative weights for a single objective function. Including all the required trade-offs in a single objective function means that relative weights have to be assigned to the value of output setpoint violations, output soft constraint violations, inputs moves, and optimal input target violations. For large problems it is not easy to translate control specifications into a consistent set of relative weights. In some cases it does not make sense to include these variables in the same objective function; driving the inputs to their optimal targets may lead to larger violation of output soft constraints, for example. Even when a consistent set of relative weights can be found, care must be taken to avoid scaling problems that lead to an ill-conditioned solution. Prett and Garcia (1988) commented on this problem: In practice, process inputs and outputs can be lost in real time due to signal hardware failure, valve saturation or direct operator intervention. They can just as easily come back into the control problem at any sample interval. This means that the structure of the problem and the degrees of freedom available to the control can change dynamically. This is illustrated in Fig. 3, which illustrates the shape of the process transfer function

Fig. 4. Classification of model types used in industrial MPC algorithms. “The numbers reflect a snapshot survey conducted in mid-1999 and should not be read as static. A recent update by one vendor showed 80% increase in the number of applications. > Adersa applications through January 1, 1996 are reported here. Since there are many embedded Adersa applications, it is difficult to accurately report their number or distribution. Adersa’s product literature indicates over 1000 applications of PFC alone by January 1, 1996. ©The number of applications of SMOC includes in-house applications by Shell, which are unclassified. Therefore, only a total number is estimated here.

An alternative model form is the finite step response model (FSR) (Cutler, 1983); given by:

Fig. 5. Flow of MPC calculation at each control execution. 3.4.1. Output feedback (state estimation)

Fig. 6. The three basic types of constraint; hard, soft and setpoint approximation. Hard constraints (top) should not be violated in the future. Soft constraints (middle) may be violated in the future, but the violation is penalized in the objective function. Setpoint approximation of constraint (bottom) penalizes deviations above and below the constraint. Shades areas show violations penalized in the dynamic optimization. Adapted from Froisy (1994).

Fig. 7. Four options for specifying future CV behavior; setpoint, zone, reference trajectory and funnel. Shaded areas show violations penalized in the dynamic optimization.

Fig. 8. MPC based on a funnel allows a CV to move back to the setpoint faster than a trajectory would require if a pulse disturbance releases. A trajectory based MPC would try to move away from the setpoint to follow the trajectory. The NOVA-NLC upper and lower trajectories, RMPCT funnel, and SMOC reference trajectory bounds are advantageous in that if a disturbance causes the predicted future CV to reach the setpoint more quickly Fig. 9. Output horizon options. Finite horizon (top) includes P future points. A subset of the prediction horizon, called the coincidence points (bottom) may also be used. Shaded areas show violations penalized in the dynamic optimization.

*“SMOC-I was licensed to MDC Technology and Yokogawa in the past. Shell global solutions is the organization that markets the current SMOC technology. Companies and products included in Linear MPC technology survey Table 1

Comparison of linear MPC control technology Table 4

Comparison of nonlinear MPC control technology

Summary of linear MPC applications by areas (estimates based on vendor survey; estimates do not include applications by companies who havc licensed vendor technology)* Table 6

Summary of nonlinear MPC applications by areas (estimates based on vendor survey; estimates do not include applications by companies who have licensed vendor technology)

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Linear optimal control systems

by puligorla chandrika

1972

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Standardised Nordic questionnaires for the analysis of musculoskeletal symptoms

by Umesh Gyawali

1987, Applied ergonomics

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Numerical solution of the Navier-Stokes equations

by LUCAS WANGWE

1968, Math. Comp

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Friction stir welding and processing

by siyamak mas

2005, Materials Science & Engineering R-reports

Friction stir welding (FSW) is a relatively new solid-state joining process. This joining technique is energy efficient, environment friendly, and versatile. In particular, it can be used to join high-strength aerospace aluminum alloys... more

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Review on thermal energy storage with phase change: materials, heat transfer analysis and applications

by Muhammad Nadjib

2003, Applied Thermal Engineering

Thermal energy storage in general, and phase change materials (PCMs) in particular, have been a main topic in research for the last 20 years, but although the information is quantitatively enormous, it is also spread widely in the... more

Fig. 1. Areas of research in thermal storage systems [1]. equipment or in installations with reduced prices for electrical energy consumed during off-peak hours. Nowadays, security of energy supply is often achieved with extra equipment. The use of phase change materials (PCMs) could either avoid or reduce this extra equipment. As it will be seen later in this paper, thermal inertia and thermal protection is the area where the PCMs have achieved a higher penetration in the market. Low mama wrens an oo eaviraw AP ao¢isdéan dan Geaw «oth "TEO aoadnnw whaon nhawewsa ewotacsale Tha

Fig. 2. Classification of energy storage materials [1].

Fig. 3. Enthalpy variation with temperature.

Inorganic substances with potential use as PCM

Table 1 (continued) n.a.: not available. B. Zalba et al. | Applied Thermal Engineering 23 (2003) 251-283 a a Pa as As far as concerns the storage temperature or phase change, the heat transfer in accumulators can be improved choosing the PCM in such a way that its phase change temperature optimises the thermal gradient with respect to the substance with which the heat is being exchanged (Farid [46], Hassan [64], Strub [65]). For example, with paraffins and alkanes it is possible to vary the number of carbon atoms or form different molecular alloys which allows a practically continuous varia- tion of the phase change temperature within certain ranges.

Non-eutectic mixtures of inorganic substances with potential use as PCM % in weight; n.a.: not available.

Organic substances with potential use as PCM

Organic eutectics with potential use as PCM Table 5 on observation over experimental set-ups [78,79]. Porisini [80] tested the corrosion of four com- mercially available salt hydrates used as PCMs in 1988. Recently, Cabeza et al. [81-84] studied corrosion resistance of five common metals (aluminium, brass, copper, steel and stainless steel) in contact with molten salt hydrates (zinc nitrate hexahydrate, sodium hydrogen phosphate do- decahydrate, calcium chloride hexahydrate, sodium carbonate, potassium hydrogen carbonate, potassium chloride, water, sodium acetate trihydrate, and sodium thiosulphate pentahydrate) in an immersion corrosion test. ot @ a ee a | ay i rr ee o a, a, er, ee ey

Fatty acids with potential use as PCM % in weight; n.a.: not available.

Commercial PCMs available in the market n.a.: not available.

Comparison of organic and inorganic materials for heat storage Table 8 Important characteristics of energy storage materials

B. Zalba et al. | Applied Thermal Engineering 23 (2003) 251-283 In the past, the earliest works basically concentrated on analysing pure substances, often water (for ice stores). Studies that can be cited include London [98], Goodman [162], Lazaridis [100] and Saitoh [163]. In subsequent years, and even quite recently and due to the implementation on a commercial and industrial scale of the so-called ice storage systems, abundant information re- lating to water as a storage substance has also appeared i in the literature. we ite: Pn a aa Pa ee qe

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Measures of Reliability In Sports Medicine and Science

by Will Hopkins

2000, Sports Medicine

Reliability refers to the reproducibility of values of a test, assay or other meas-urement in repeated trials on the same individuals.Better reliability implies better precision of single measurements and better tracking of changes in... more

Table I. Data from a reliability study for a variable measured twice in 5 participants studies. Bland and Altman,'+! the researchers who devised this measure, realised that the difference scores between trials give a good indication of the reliability of the test. Instead of using the standard deviation of the difference scores directly, they cal- culated the range within which an individual’s dif- ference scores would fall most (95%) of the time. In the above example of 5 individuals tested twice, the 95% limits of agreement are —10.1 and 12.5. The interpretation of these limits is as follows: on the basis of our 2 trials with 5 participants, when we test and then retest another participant, the score in the second trial has | chance in 20 of being more than 12.5 higher or less than 10.1 lower than the score in the first trial. Note that the limits in this example are not quite symmetrical, because the par- ticipants showed an average improvement of 1.2 in the second trial. It is preferable to take this im- provement out of each limit and express the limits as 1.2 £11.3.

Table Il. Factors for generating the 95% likely range of the true value of a typical error from the value observed in a reliability study consisting of different numbers of participants and trials*

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A tutorial on visual servo control

by chingwei liao

1996, IEEE Transactions on Automation Science and Engineering

This article provides a tutorial introduction to visual servo control of robotic manipulators. Since the topic spans many disciplines our goal is limited to providing a basic conceptual framework. We begin by reviewing the prerequisite... more

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Optical Constants of Water in the 200-nm to 200-µm Wavelength Region

by Marvin R

1973, Applied Optics

Extinction coefficients k(X) for water at 25°C were determined through a broad spectral region by manually smoothing a point by point graph of k(X) vs wavelength X that was plotted for data obtained from a review of the scientific... more

Fig.1. Extinction coefficients of water for the 0-2000-A spectral region. Notation and curves are described in the text.

Fig. 2, Extinction coefficients of water for the 200-1000-nm spectral region. Well known data that were in notable disagreement with the curve finally selected for k(\) are plotted as points on the graph. Notation and curves are described in the text.

Fig. 3. Extinction coefficients of water for the 0.95-2.6-4m spectral region. Curves are described in the text.

Fig. 4. Extinction coefficients of water for the 2.5-18.5-um spectral region. Curves are described in the text.

Fig.5. Extinction coefficients of water for the 10-106-ym spectral region. Notation and curves are described in the text.

Fig. 6. Index of refraction of water for the spectral regions 0.2-200 pm. Descriptions of the curves and the symbols are presented in the text.

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Handbook of biological confocal microscopy

by James Pawley

2006

CHAPTER 10: DISK-SCANNING CONFOCAL MICROSCOPY Derek Toomre and James B. Pawley

Alan R. Hibbs, Glen MacDonald, and Karl Garsha

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A new kind of science

by Mohsin Ali

2002, Applied Mechanics Reviews

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Handbook of brain theory and neural networks

by Michael A Arbib

1995

In this issue we have three reviews, all of which are directly related to expert systems. The reviews cover books on artificial neural networks, virtual reality and uncertain reasoning. We would like to take this opportunity to thank each... more

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A review on machinery diagnostics and prognostics implementing condition-based maintenance

by Dragan Banjevic

2006, Mechanical Systems and Signal Processing

Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. It consists of three main steps: data acquisition, data processing and... more

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Laser beams and resonators

by jk kj

1966, Applied Optics

This paper is a review of the theory-of laser beams and resonators. It is meant to be tutorial in nature and useful in scope. No attempt is made to be exhaustive in the treatment. Rather, emphasis is placed on formulations and derivations... more

‘ig. 19. Relative field distributions of the TEMos mode for a resonator with circular mirrors (V=1).

Fig. 20. Relative field distributions of the TEMo mode for a resonator with circular mirrors (V=1).

where the slopes are measured positive as indicated in the figure. The ABCD matrix is called the ray transfer matrix. Tte determinant is scenerally unitv The matrix elements are related to the focal length f of the system and to the location of the principal planes by

Fig. 4. Stability diagram. Unstable resonator systems lie in shaded regions.

Fig. 6. Contour of a Gaussian beam. which can be used to express wy and z in terms of wand R:

Fig. 11. Mode parameters of interest for a resonator with mirrors of unequal curvature. Fig. 10. Symmetrical laser resonator and the equivalent sequence of lenses. The beam parameters, gq: and qe, are indicated.

optical structures commonly encountered. They are the confocal parametér b and the distance ¢ which gives the waist location of the emerging beam. System No. 1 is a resonator formed by a flat mirror and a spherical mirror of radius R. System No. 2 is a resonator formed by two equal spherical mirrors. System No. 3 is a resonator formed by mirrors of unequal curvature. System No. 4

Fig. 12, The confocal parameter bz as a func- tion of the lens-waist spacing a.

Fig. 15. The Gaussian beam chart. Both W-plane and Z-plane circle diagram are combined into one. Fig. 14. Geometry for the W-plane circle diagram.

Fig. 16. Geometry of a spherical-mirror resonator with finite mirror apertures and the equivalent sequence of lenses set in opaque absorbing screens. In deriving the integral equations, it is assumed that a traveling TEM wave is reflected back and forth between the mirrors. The resonator is thus analogous to a trans- mission medium consisting of apertures or lenses set in opaque absorbing screens (see Fig. 16). The fields at the two mirrors are related by the equations [24]

Fig. 17. Linearly polarized resonator mode configurations for square and circular mirrors.

Fig. 18. Synthesis of different polarization configurations from the linearly polarized TEMn mode.

Fig. 21. Relative field distributions of four of the low order modes of a Fabry-Perot resonator with (parallel-plane) circular mirrors (N= 10). 4.7 Diffraction Losses and Phase Shifts

Fig. 22. Diffraction loss per transit (in decibels) for the TEMoo mode of a stable resonator with circular mirrors.

Fig. 23. Diffraction loss per transit (in decibels) for the TEMa mode of a stable resonator with circular mirrors. The diffraction losses for the two lowest order (TEMoo and TEMo:) modes of a stable resonator with a pair of identical, circular mirrors (a1=42, gi=g2=g) are given in Figs. 22 and 23 as functions of the Fresnel number NV and for various values of g. The curves are obtained by solving (77) numerically using the method of successive approximations [31]. Corresponding curves for the phase shifts are shown in Figs. 24 and 25. The horizontal por- tions of the phase shift curves can be calculated from the formula

Fig. 24. Phase shift per transit for the TEM mode of a stable resonator with circular mirrors.

‘ig. 25. Phase shift per transit for the TEMa mode of a stable resonator with circular mirrors.

plane BE by simply requiring that this plane coincide with a node or an antinode. Obviously the a angle of the roof must be so small that the curvature of the nodal or antinodal surfaces can be neglected. Due to the high order of the cylindrical waves, the field in the central region of the cavity approaches the form of a standing wave be- tween the two roof reflectors, while it decays so rapidly from the central region toward the vertices G and H, that the absence of the complete metal walls of the dia- mond outside the resonator will have very little impor- tance. This treatment, although approximate, allowed the author to understand how the resonator actually worked Modes, Phase Shifts, and Losses of Flat-Roof Open Resonators

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Supercontinuum generation in photonic crystal fiber

by Borghol Riham

2006, Reviews of modern physics

A topical review of numerical and experimental studies of supercontinuum generation in photonic crystal fiber is presented over the full range of experimentally reported parameters, from the femtosecond to the continuous-wave regime.... more

FIG. 2. Calculated GVD parameter 8, (top) and D (bottom) for PCFs with parameters as shown: A=1 wm, ¢=0.6 um; A=1.6 wm, ¢=1.4 wm [that used by Ranka et al. (2000a)]; and standard Corning SMF28 single-mode fiber.

The left-hand side of Eq. (2) models linear propaga- tion effects, with a the linear loss and the £;’s the dis- persion coefficients associated with the Taylor series ex- pansion of the propagation constant 6(@) about wy. In fact, given 6(w) the dispersion operator can be applied directly and in an approximation-free manner in the fre- quency domain through multiplication of the complex

The time-domain envelope is obtained from A(z,?) =(1/27)f%,,A(z,@—w)exp[—i(w—)t|]dw, and note that the amplitude is normalized such that |A(z,t)|* gives the instantaneous power in watts. With the change of vari- able T=t-,z, we transform into a comoving frame at The time-domain envelope is obtained from A(z,?)

Calculating the corrected value of 7,,,., requires only computation of the frequency dependence of Agr and the effective index nep(a).

FIG. 3. (Color online) Results from numerical simulations showing (a) spectral and (b) temporal evolution for selected propagation distances. The input sech? input pulse at 835 nm has 10 kW peak power and 50 fs FWHM. Fiber parameters are given in the text. Bottom curves show the advantage of repre- senting these results as (logarithmic) density plots. Unless oth- erwise stated, the 40 dB dynamic range density scale shown applies to all density plots.

FIG. 5. (Color online) Periodic evolution of the spectral and temporal characteristics of the higher-order N=3 soliton as de- scribed in the text. The propagation distance is in units of the soliton period, which for these parameters is Z,,);=10.6 cm.

FIG. 4. Detailed view of output (a) temporal and (b) spectral characteristics. The labels A, B, and C show correlated tempo- ral and spectral features.

FIG. 8. (Color online) Results from numerical simulations showing the spectral (left) and temporal (right) signatures of dispersive-wave generation from a fundamental soliton (a) in the absence of and (b) in the presence of Raman scattering. The fundamental soliton parameters correspond to the first ejected soliton from the fission process shown in Fig. 7. Here P )=3.48 kW and FWHM 10 fs at 835 nm.

FIG. 9. Simulation results illustrating the influence of Ramar scattering on dispersive-wave dynamics showing the evolutior with distance of (a) the mean soliton frequency (left axis) anc wavelength (right axis) and (b) the relative energy radiated tc the dispersive wave.

FIG. 10. (Color online) Simulated supercontinuum spectro- gram projected onto the temporal intensity and spectrum for input pulses at 835 nm of 10 kW peak power and 50 fs FWHM propagating in 15 cm of PCF. The gate function used is the 50 fs input pulse. An animation of the evolution over the 15 cm propagation length is available as an EPAPS Document cited in the Reference section.

FIG. 11. Comparison between experimental and simulations corresponding to the output SC spectra obtained using ~22 fs (FWHM) pulses of energy 0.9 nJ injected into 15 cm of PCF. Adapted from Corwin et al., 2003b.

FIG. 12. (Color online) Experimentally measured spectrogram of a SC generated in a 16 cm length of PCF using 25 fs pulses at 800 nm with energy around 1 nJ. Adapted from Dudley, Gu, et al., 2002.

FIG. 13. Results from numerical simulations showing the spec- tral and temporal SC characteristics observed for selected pump wavelengths as indicated. The input pulse peak power is 10 kW and duration (FWHM) is 50 fs. The dashed line shows the fiber ZDW. Spectrogram animations for the 650, 700, and 780 nm cases are available as an EPAPS Document cited in the Reference section.

FIG. 14. (Color online) Results from numerical simulations showing density plots of the spectral and temporal evolution of supercontinua generated by pulses with wavelengths of (a) 650, (b) 700, and (c) 780 nm. The input pulse peak power is 10 kW and duration (FWHM) is 50 fs. The dashed line shows the fiber ZDW.

FIG. 15. (Color online) The variation in SC spectral density with pump wavelength (left axis) shown in density plot repre- sentation. The input pulse peak power is 10 kW and duration (FWHM) is 50 fs for all cases. The SC spectra are plotted as a function of (a) wavelength and (b) relative frequency. To facili- tate comparison between these curves, the frequency axis in (b) is reversed. The dashed line in (a) shows the ZDW.

FIG. 17. (Color online) Results from numerical simulations showing density plots of the SC spectral and temporal evolu- tion generated by pulses with duration (FWHM) of (a) 100 and (b) 500 fs. The input pulse peak power is 10 kW and wave- length is 835 nm. The dashed line shows the fiber ZDW.

FIG. 16. Spectral and temporal characteristics for SC gener- ated with pulse durations in the 20-500 fs range as indicated. The input pulse peak power is 10 kW and the wavelength is 835 nm. For spectrogram animations, see the EPAPS Docu- ment cited in the Reference section.

FIG. 18. Simulation results illustrating noise sensitivity. (a) Spectral and (b) temporal intensities for five simulations using 10 kW peak power 150 fs pulses at a wavelength of 835 nm injected into 10 cm of PCF. Bottom curve in (a) shows the averaged spectrum. (d) The spectral phase at the pump wave- length evolving along the fiber length for 20 simulations. (c) The degree of coherence | gi calculated over that ensemble of simulations. The bottom curve of Fig. 18(a) shows the mean spec- trum calculated over an ensemble of 20 simulations. It is clear that there is significant averaging of the fine struc- ture that is apparent in single-shot simulations. These

FIG. 20. (Color online) Evolution along the fiber length of the spectrum of one particular simulation (left) and of the corre- sponding degree of coherence (right) for supercontinuum gen- eration obtained with 10 kW peak power pulses of (a) 100 and (b) 150 fs duration at a 835 nm pump wavelength.

FIG. 21. (Color online) Wavelength dependence of coherence characteristics, (a) Dependence of the average coherence (lei 1) on pump wavelength (left axis) for SC obtained with 150 fs pulses of 4 kW peak power for 10 cm of PCF. Right axis shows the corresponding —20 dB SC spectral width. (b) Aver- age coherence shown in a density plot representation as a func- tion of both pump wavelength and pump pulse duration for a fixed peak power of 4 kW. The dashed line indicates the posi- tion of the ZDW.

FIG. 22. Average coherence of the SC versus soliton order N calculated for pump wavelengths in the range 790-900 nm, pulse durations in the range 30—200 fs, and peak powers in the range 3—30 kW. Propagation in 10 cm of PCF is considered.

FIG. 23. (Color online) Parametric gain as a function of fre- quency offset from the pump ©,/27 (in THz). (a) Linear den- sity plot representation for different pump wavelengths (verti- cal axis). (b) Specific gain curves at two particular wavelengths as indicated. The dashed line in (a) is the ZDW, and calcula- tions assume a CW pump power of 500 W.

FIG. 24. Results from numerical simulations showing the spec- tral and temporal characteristics of picosecond supercontinua generated by selected pump wavelengths as indicated in 2 m of PCF. The input pulse peak power is 500 W and duration (FWHM) is 20 ps. Dashed line shows the fiber ZDW.

FIG. 25. Results from numerical simulations showing the spec- tral evolution at propagation distances as indicated for pump wavelengths of (a) 700 and (b) 750 nm. The input pulse peak power is 500 W and duration (FWHM) is 20 ps. FIG. 26. Results from numerical simulations showing the spec- tral evolution at propagation distances as indicated for pump wavelengths of (a) 780 and (b) 900 nm.

FIG. 28. (Color online) Evolution of spectrum and coherence. (a) Average spectrum and (b) degree of first-order coherence Feed of a SC obtained with 20 ps pulses of 500 W peak power at a 800 nm pump wavelength in a 50-cm-long fiber. (c) Evolu- tion of the spectrum along the fiber (logarithmic scale, clipped here at —80 dB for clarity) and (d) corresponding evolution of the coherence.

FIG. 27. Spectral and temporal characteristics for SC gener- ated in 2m of PCF with pump pulse durations in the 10-500 ps range as indicated. Simulations were carried out for pulses at 800 nm with peak power of 500 W. Note the different time scale that has been used to display the temporal results for the 500 ps case.

TABLE I. Taylor series expansion coefficients at 835 nm for the GVD of the PCF used in the simulations. The structure has f=1.4 wm and A=1.6 wm. The full GVD curve is shown in Fig. 2. PCF dispersion characteristics in our simulations, Table I provides the terms of a corresponding Taylor series expansion truncated at ,, that yields near-identical re- sults.

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Supervisory Control of a Class of Discrete Event Processes

by Peter j. Ramadge

1987, SIAM Journal on Control and Optimization

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The mechanical properties of nanostructured materials

by Peter Liaw

2001, Jom

In this paper, the state-of-the-art progress in research on novel mechanical properties of nanocrystalline materials and carbon nanotubes is reviewed. There is evidence that the relation between the strength of nanocrystalline materials... more

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A new method for the nonlinear transformation of means and covariances in filters and estimators

by Justin Whyte

2000, IEEE Transactions on Automatic Control

This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parameterize the mean and covariance of a (not necessarily Gaussian) probability distribution. The method yields a... more

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A joint coordinate system for the clinical description of three-dimensional motions: application to the knee

by Edward Grood

1983, Journal of biomechanical engineering

The experimental study of joint kinematics in three dimensions requires the description and measurement of six motion components. An important aspect of any method of description is the ease with which it is communicated to those who use... more

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Super-resolution image reconstruction: a technical overview

by Semsem Ali

2003, IEEE Signal Processing Magazine

Stochastic regularized methods are quite advantageous in super-resolution (SR) image reconstruction problems. In the particular techniques, the SR problem is formulated by means of two terms, the datafidelity term and the regularization... more

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Constant pressure molecular dynamics simulation: the Langevin piston method

by Richard Pastor

1995

We present in this work a novel application of constant pressure molecular dynamics method (MD) that allows shape variation of MD cells. The new pressostat method, based on metric-tensor flexiblecell algorithm that has patched the... more

where the superimposed dot denotes a time derivative, Tr(A) is the trace of the matrix A, Wx is the fictitious mass of variable x, and Nose-Poincare thermostat algorithm® can sample the NVT ensemble while keeping the symplectic structure of Hamiltonian with Poincare transformation. For simplic- ity, we take s as the additional degree of freedom in Nose-Poincare method, and g as the number of degrees of freedom of the system. The equations of motion in NPT ensemble are as follows:

Note in Eqs. (5a)-(5c), k2 = k7k = 77G'h, k-F = k'?, =n’ G, = 1-4. In the above derivation, the follow- ing relations have been used: d(detG) /dt = V?Tr(G~'G), d(detG)/dG = (detG)G~", and Agcp/98r6 = — Be, See The latter is obtained from the differentiation of GG !=I. As a final remark to Eqs. (4a)-(4b), they are both written in a matrix form, which are suitable for cer- tain predictor-corrector integral algorithms.' Were the gen- eralized leapfrog scheme (GLF)!! applied, the formulas should be rewritten in a covariant-contravariant form. Constant Pressure Molecular Dynamics Simulation for Ionic System

Fig. 1. Radial distribution function of NaCl at 300 K and 1 bar. The radial distribution function of NaCl at 300 K and 1 bar is depicted in Figure 1. Since the equations of motion are independent of the cell orientation, so are they to the spa- tial Cartesian reference frame. This implies that the scal- ing matrix h, which is relevant to coordinate system, can- not be obtained directly from the existing MD simulation algorithm. To get the reference frame, h is assigned an upper triangular matrix, resulting the same degree of free- dom with the metric tensor G and hence its evaluation is straightforward. To demonstrate the validity of the algo- rithm, some physical constants have been calculated for a comparison between simulation results and experimental data. Table II lists the simulation results. Accordingly, the J. Comput. Theor. Nanosci. 5, 1646-1650, 2008

Fig. 2. Comparison of the volume percentage at temperatures of 300-800 K and different pressures of 1 bar, 10 kbar, 20 kbar, 30 kbar with experiment values.

Fig. 3. Comparison of the volume thermal expansion coefficient at dif- ferent pressures of | bar, 10 kbar, 20 kbar, 30 kbar with Birch’s fitting.

Fig. 4. The density phase transition curve of MD simulation. The phase transition of ZrO, is very complicated, but it is a good choice for MD algorithm checking. With a heat- ing rate of 10 from 2100 to 3000 K, the phase transition from tetragonal to cubic is simulated successfully by a MD process starting with a tetragonal sample. The variations of the density are plotted in Figure 4. Note that a similar curve has been given in Ref. [17] where the pressure was controlled by a scaling of the MD cell, which could not be verified to yield the correct NPT ensemble. Figure 5 depicts the variations of the effective axial ratio of (a+ b)/2c. From the phase diagrams, transition from tetrago- nal to cubic can be identified easily. In Figure 5, due to the initialization of tetragonal phase, the axial ratio (0.922)

References Fig. 5. The effective axial ratio (a+ b)/2c phase transition curve of MD simulation. Constant Pressure Molecular Dynamics Simulation for Ionic System of (a+b)/2c is close to the experimental value of 0.97.!° Above 2800 K, the sample transforms from tetragonal to cubic, thus the axial ratio equals to 1. The cubic structure is confirmed by an analysis of the scaling matrix h, which means that a= b=c=5.22 A.

Table I. Potential parameters of NaCl used in simulations. The first example is NaCl. We take a potential function for NaCl as follows:'3

Table II. Comparison between MD results and experimental data.

Table III. Potential parameters of ZrO, used in simulation.

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Computational fluid mechanics and heat transfer

by raghu neel

1984

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Optical properties of metallic films for vertical-cavity optoelectronic devices

by Alek Sandra

1998, Applied optics

We present models for the optical functions of 11 metals used as mirrors and contacts in optoelectronic and optical devices: noble metals ͑Ag, Au, Cu͒, aluminum, beryllium, and transition metals ͑Cr, Ni, Pd, Pt, Ti, W͒. We used two simple... more

The number of harmonic oscillators per frequency interval is determined by a Gaussian function. In their original paper*! the authors suggested that the model is applicable to an amorphous solid in the far-IR part of the spectrum and constructed the model from a phenomenological point of view. How- ever, we show that this model can be used to describe optical properties of a wide range of materials includ- ing metals. Integral (4) can be solved analytically (see Appendix A) to obtain Bormann?! have proposed a model for a dielectric function of solids that replaces a Lorentz oscillator with a superposition of an infinite number of oscilla- tors, given by

When we keep in mind that special functions of com- plex arguments are now readily available through languages such as Mathematica,*? or from various We suggest that Eq. (5) also be expressed in terms of confluent hypergeometric functions, namely, the Kummer functions of the second kind U(a, b, z). By employing the relation between erfc(z) and U(a, 6, z) [cf. Ref. 41, Eq. (13.6.39)],

Fig. 2. Real and imaginary parts of the optical dielectric function of Au: solid curves, values that we calculated using the BB model; dashed curves, the LD model. Also shown are selected experi- mental data points from Dold and Mecke*¢ and Théye.*2

Fig. 1. Real and imaginary parts of the optical dielectric function of Ag: solid curves, values that we calculated using the BB model; dashed curves, the LD model. Also shown are the selected exper- imental data points from Dold and Mecke,*6 Winsemius et al.,4” and Leveque et al.48 films of various thicknesses. A detailed analysis of these samples indicated that it is possible to produce very thin but continuous films with the same prop- erties as the bulk material. The results on well- crystallized films of thicknesses from 10 to 25 nm evaporated in ultrahigh vacuum (10~'" Torr) show good agreement with data obtained on bulk sam- ples.49

Fig. 4. Real and imaginary parts of the optical dielectric function of Al: solid curves, values that we calculated using the BB model; dashed curves, the LD model. Also shown are selected data from Rakié.18

Fig. 3. Real and imaginary parts of the optical dielectric function of Cu: solid curves, values calculated by use of the BB model; dashed curves, LD model. Also shown are tabulated data from Hagemann e al.,5° Ordal et al.,26 and Nash and Sambles.*4

Fig.6. Real and imaginary parts of the optical dielectric function of Cr: solid curves, values that we calculated using the BB model; dashed curves, the LD model. Also shown are tabulated data from Bos and Lynch® and Kirillova and Noskov.®°

Fig. 5. Real and imaginary parts of the optical dielectric function of Be versus photon energy (open circles, data from Arakawa et al.57; solid curves, the BB model; dashed curves, the LD model).

Fig. 9. Real and imaginary parts of the optical dielectric function of Pt versus photon energy (open circles, data from Weaver; solid curves, BB model; dashed curves, LD model).

Fig. 8. Real and imaginary parts of the optical dielectric function of Pd: solid curves, values that we calculated using the BB model; dashed curves, the LD model. Also shown are tabulated data from Weaver and Benbow® and Johnson and Christy.®! To fit our model to data, we used the tabulation of the optical constants of Pt given in 45 based on the study of Weaver.64 Weaver used reflectance®-§? and transmittance®® data from a number of sources to

Fig. 7. Real and imaginary parts of the optical dielectric function of Ni: solid curves, values that we calculated using the BB model; dashed curves, the LD model. Also shown are tabulated data from Lynch and Hunter.**

Fig. 12. Real and imaginary parts of the optical dielectric func- tion of W versus photon energy (open circles, data from Weaver e: al.7®; solid curves, BB model; dashed curves, LD model).

Fig. 11. Real and imaginary parts of the optical dielectric func- tion of Ti: solid curves, values that we calculated using the BB model; dashed curves, the LD model. Also shown are tabulated data from Kirillova and Charikov72:”* and Bolotin et al.74

Fig. 10. Real and imaginary parts of the optical dielectric func- tion of Ti: experimental data points, Kirillova and Charikov,72:73 Bolotin e¢ al.,74 Johnson and Christy,®! and Lynch et al.7°

Fig. 14. Calculated phase changes on reflection for four GaAs/ metal interfaces: GaAs/100 nm Ag, GaAs/100 nm Au, GaAs/100 nm Al, and GaAs/100 nm Pt. wave optical thickness (QWOT), the wave reflected from interface 1, which undergoes the phase change on reflection » = 7m [see Fig. 15(a)], is in phase with the wave reflected from interface 2, which experi- ences a phase shift 6 = —a while making a round trip through the last high-index layer (GaAs) and no phase change on reflection. The additional high- index QWOT layer (the phase-matching layer) is needed between the last semiconductor layer and the perfectly conductive metallic layer [see Fig. 15(b)] to keep the waves reflected from interfaces 1 and 2 in phase. Therefore the round-trip phase shift —7 in the phase-matching layer cancels the phase change on reflection at the semiconductor—metal interface, o = 7. If we now consider the real metal with the complex refractive index N = n — ik, the phase change on reflection » is determined by Eq. (14), where » < 7 at all wavelengths. To keep the waves reflected from interfaces 1 and 2 in phase, one needs

Fig. 13. Calculated reflectivities for four GaAs—metal interfaces: GaAs/100 nm Ag, GaAs/100 nm Au, GaAs/100 nm Al, and GaAs/ 100 nm Pt.

Fig. 15. DBR mirror terminating on (a) air, (b) perfectly conduc- tive metal, and (c) real metal.

Fig. 16. Measured and calculated reflectivity of metal clad (air— GaAs—Ti/Au) and dielectric clad (air-GaAs—wax) F-P cavities: reflectance that we calculated using the transfer matrix method, solid curves; experimental curves from Ref. 77, dashed curves. knowledge, a comprehensive parameterization of the optical functions for 11 metals for applications in op- toelectronic and optical devices: noble metals (Ag, Au, and Cu), aluminum, beryllium, and some of the transition metals (Cr, Ni, Pd, Pt, Ti, and W). We used two simple phenomenological models, namely, the LD and the BB models to interpret both the free- electron and the interband parts of the dielectric re- sponse of real metals in the wide spectral range from 0.1to6eV. Our results show that the BB model was needed for an appropriate description of the onset of interband absorption in noble metals (Ag, Au, Cu), while for aluminum and the transition metals both models exhibit good agreement with the experimen- tal data. This is in accordance with our initial as- sumptions. The LD model due to the extended absorption of the Lorentz oscillators can hardly de- scribe a sharp absorption edge, unless a large number of oscillators were used to describe that transition. In such a case it is physically justified and computa- tionally simpler to employ a single Gaussian-like BB oscillator instead.

Using a similar approach, we can solve the second integral

The Brendel—Borman model requires the integration of Eq. (4) in closed form. The integral to be solved is Appendix A

Table 2. Values of the LD Model Parameters “Tn electron volts.

Table 1. Values of the Plasma Frequencies fiw, (eV)

“Tn electron volts. Table 3. Values of the BB Model Parameters

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Self-Consistent-Charge Density-Functional Tight-Binding Parameters for Cd–X (X = S, Se, Te) Compounds and Their Interaction with H, O, C, and N

by Sougata Pal

2011, Journal of Chemical Theory and Computation

Molecular dynamics simulations based on self-consistent-charge density-functional tight-binding (SCC-DFTB) energies indicate that upon a single molecular TiO 2 impact onto the amorphous TiO 2 surface the resulting structure becomes more... more

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Behavior-based formation control for multirobot teams

by Ronald C Arkin

1998, IEEE Transactions on Robotics and …

New reactive behaviors that implement formations in multirobot teams are presented and evaluated. The formation behaviors are integrated with other navigational behaviors to enable a robotic team to reach navigational goals, avoid hazards... more

Fig. 1. A team of four robotic scout vehicles manufactured for DARPA’s Demo II project. The formation techniques reported in this article were implemented on these robots. Photograph courtesy of Lockheed-Martin.

Fig. 2. An example of how scouts in formation focus their sensor assets so as to ensure complete coverage. Four robot scouts sweep from left to right in a diamond formation. The wedges indicate the sensor focus for each scout. Figure courtesy of Diane Cook of the University of Texas at Arlington [7].

Fig. 4. Formation position determined by various referencing techniques (from left to right: unit-center, leader, neighbor).

Fig. 5. Zones for the computation of maintain-formation magnitude. a position to the left of and abeam Robot 1. Robot 3 is in the controlled zone, so a moderate force toward the desired posi- tion (forward and right) is generated by maintain-formation. In general, the magnitude of the vector is computed as follows:

Fig. 6. Four robots in leader-referenced (a) diamond, (b) wedge, (c) line, and (d) column formations.

Fig. 7. Comparison of (a) leader-referenced and (b) unit-center-referenced diamond formations.

Fig. 8. Shannon and Sally, the two Nomad 150 robots used in formation experiments.

Fig. 9. Telemetry and photos of Shannon and Sally moving into and then traveling in column formation. Top row: column formation telemetry with no obstacles present. Middle row: column formation telemetry with obstacles present. Bottom row: photos of the robots in column formation with obstacles present. The photo sequence corresponds to telemetry in the middle row with obstacles (wastebaskets) present. This experiment was recorded in the foyer of the Georgia Tech Manufacturing Research Center, looking down on the robots from 20 feet above so that formation positions are more easily observed.

Fig. 10. Telemetry and photos of Shannon and Sally moving into and then traveling in wedge formation. Top row: wedge formation telemetry with no obstacles present. Middle row: wedge formation telemetry with obstacles present. Bottom row: photos of the robots in wedge formation with obstacles present. The photo sequence corresponds to the telemetry in the middle row with obstacles present.

Fig. 11. Telemetry and photos of Shannon and Sally moving into and then traveling in line formation. Top row: line formation telemetry with no obstacle present. Middle row: line formation telemetry with obstacles present. Bottom row: photos of the robots in line formation with obstacles present. The phot sequence corresponds to the telemetry in the middle row with obstacles present.

Fig. 13. Illustration of terms used in describing formation behaviors for UGV’s. In this diagram the robot is behind and to the right of the desired position in formation. The robot's position and direction are indicated by Rpos and Raiy. The desired formation position is F,o;. The formation is moving in the direction Fy;,. Fig. 12. A comparison of telemetry from actual robot formation runs (top row) and runs in in simulation (bottom row). From left to right: line, wedge, and column formations.

Fig. 16. Reconstruction of the ground track of DARPA UGV’s depicted in Fig. 17. The pair of robots are shown at three points in time as they move from right to left. They transition from column (right) to wedge (center) to line formations as they traverse the field. Fig. 15. Simulation of two DARPA UGV’s in formation. The robots are moving from left to right in a line formation. The robot at the top of the figure follows a fixed path, while the other robot utilizes behaviors described in the text to maintain a unit-center-referenced line formation.

Fig. 17. Two DARPA UGV’s in formation, from left to right: (a) column, (b) wedge, and (c) line.

afean eae MoToR SCHEMA PARAMETES FOR FORMATION NAVIGATION EXPERIMENTS IN SIMULATION

PERFORMANCE FOR A 90 DEGREE TURN FOR BOTH UNIT-CENTER AND LEADER-REFERENCED FORMATIONS, SMALLER NUMBERS ARE BETTER. THE STANDARD DEVIATION IS INDICATED WITHIN PARAMETERS TABLE II

PERFORMANCE FOR NAVIGATION Across AN OBSTACLE FIELD B. Motor Schema Performance in Turns 50 m from the unit-center. In contrast, the flanking robots in wedge, line, and column formations are 75 m from the unit center.

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An algorithm of hyperspectral remote sensing images classification is proposed based on the frequency spectrum of spectral signature. The spectral signature of each pixel in the hyperspectral image is taken as a discrete signal, and the... more

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