[Virtual Physics]

number 09, September 1, 1996

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a forum for virtual meetings of scientists and students involved in a research activity on
CONTEMPORARY PHYSICS

Editors:
Marcel Ausloos, ausloos@gw.unipc.ulg.ac.be, Institut de Physique, Université de Liège, Belgium,
Kenneth Holmlund, Kenneth.Holmlund@TP.UmU.SE, Umeå University, Sweden
Cameron L. Jones, cjones@swin.edu.au, Swinburne University of Technology, Australia
Zbigniew J.Koziol, (Editor-in-Chief) webex@ra.isisnet.com, WebExperts Inc., Canada
Michal Spalinski, Michal.Spalinski@fuw.edu.pl, Institute of Theoretical Physics, Warsaw University, Poland
Krzysztof P. Wroblewski, chris@nmr.biophys.upenn.edu, University of Pennsylvania, U.S.A.
Copyright (C) 1996 by Zbigniew Koziol.
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IN THIS ISSUE:

[0] Letter from the Editor
[0] Multifractal Scaling With Wavelet Packets, by Cameron L. Jones
[0] Animated GIFS - A Simple Tutorial Example, by Zbigniew Koziol
[0] "Lawrence and His Laboratory" - a history of physics site

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LETTER FROM THE EDITOR

Dear Readers of Virtual Physics,

Since the recent edition of Virtual Physics has been published, several important organizational events took place.

We are pleased to inform you that a new mirror site of Virtual Physics, http://www.tp.umu.se/vp.html, has been made available for browsing. The site is located at Department of Theoretical Physics at Umeå University in Sweden, at the TIPTOP server, which is the largest and the best known place with the collection of materials related to physics. We do believe that the new site will significantly improve the access to our journal by the European users of WWW.

The new mirror site is administered by Dr. Kenneth Holmlund. We were pleased when Dr. Holmlund accepted our invitation to join the Editorial Board of Virtual Physics. Dr. Holmlund's work in theoretical physics is already well known, especially the study of certain electrodynamic properties of superconductors. This is a subject which has a long and appreciated tradition of research in Sweden. Right now, Dr. Kenneth Holmlund is strongly involved in a research activity which is related to the implementation of the Internet technology in education and the exchange of information between physicists.

It is great pleasure and honor to introduce to you yet another member of the Editorial Board. Dr. Krzysztof Wroblewski is a Research Assistant Professor of Biochemistry and Biophysics and the director of the NMR Facility at the University of Pensylvania School of Medicine in Philadelphia. His research interests include implementation of multi-dimensional NMR spectroscopy to medical sciences, nuclear relaxation, techniques of image processing, and artificial intelligence. We believe that Virtual Physics will benefit from collaboration with Dr. Wroblewski also due to his excellent experience with the Internet.

Sincerely yours,
Zbigniew Koziol

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Multifractal Scaling With Wavelet Packets

Cameron L. Jones

Centre for Applied Colloid and BioColloid Science
Swinburne University of Technology, School of Chemical Sciences
Hawthorn, Victoria, 3122, Australia
CJONES@swin.edu.au

Abstract

A multifractal Wavelet Packet approach is described for characterising one dimensional signals using the Hurst exponent. The Hurst exponent quantifies scaling behaviour between data points, and classifies these trends as persistent, antipersistent, or random walk. A multifractal approach provides additional information regarding local scaling behaviour. This algorithm uses commercially available software to perform the Wavelet Packet Transform, with additional manipulation carried out using an iterative spreadsheet function to determine the regression slope in lagged windows. The method is demonstrated in a biophysical reaction- diffusion system where image-analysis was used to collect raw data concerning the spatial distribution of local chemical (enzyme) concentrations. These one dimensional signals (Enzyme Walks) display antipersistent scaling at small lags, but random walk and persistent scaling at longer lags. This emphasises the importance of local coordination during enzyme secretion, which breaks down at longer lags as a function of the advancing diffusion front.

Introduction

A straightforward approach to quantify, interpret or visualise correlations within one dimensional (1-D) sequences can be achieved with some partition function. We consider here the partition function called Wavelet Packet Analysis (WPA) which can identify local and global ordering in 1-D signals with respect to position, frequency and scale. Previous efforts have applied this technique to estimate the global Hurst exponent in 1-D signals [1}. The Hurst exponent was developed to estimate the fluctuations which occurred in the reservoir water level in the Nile River Dam [2]. The Hurst exponent is a time-series method named after the hydrologist who measured the daily water discharge levels over a 40 year period. He found a positive correlation between the 'peaks' and 'troughs' which fluctuate about an average value across the time-series of water level measurements. This statistic was used to quantify the persistence or antipersistence of feature details (in this original case, the day-to-day or month- to-month water levels). We note that in 1-D the Hurst exponent, H falls in the range of: 0 < H < 1. A persistent trend is characterised by repetitive behaviour. For example, if a high value occurs at time, tx then at time tx+1 one would expect the probability of another high value to be greater than 0.5. Persistent trends fall in the range 0.5 < H <=1.0. Note that a random walk process which exhibits no correlation between values has a Hurst exponent H=0.5. This contrasts with an antipersistent trend where successive values are likely to alternate. For example, if a high value occurs at time, tx then at time tx+1 one would be more likely to see a low value. Similarly, antipersistent scaling has a Hurst exponent, 0 <= H < 0.5.

This paper uses WPA to estimate the Hurst exponent. In addition, we demonstrate the method by examining a complex biophysical system which displays fractal-like scaling trends during enzyme secretion1. This gives rise to one dimensional signals called 'Enzyme Walks', which can be characterised using time series tools. Enzyme Walks are an example of a diffusion controlled spatial deposition process. From a practical viewpoint, we also extend the global method to estimate multifractal, or local scaling trends. This therefore extends the utility of this method to heterogeneous systems which display complex behaviours at multiple length or time scales. It is hoped that this method will find widespread application for the rapid analysis of complex, multifractal type signals encountered in physics, chemistry, biology or in other signal analysis situations.

Global versus Local Scaling

A global method provides a mean overview of statistical scaling behaviour. This may be useful as a first attempt at sequence characterisation, but is essentially best suited for homogeneous signals which do not display abrupt or complex feature fluctuations. A global analysis method only considers 'macro-level' organisation [3]. This approach is therefore limited to simple systems, although the computational effort is usually small. This contrasts with a multifractal approach which looks for local feature fluctuations and therefore provides a 'micro-level' interpretation of scaling behaviour. However it is computationally more intensive than the global approach. This analytical method is best suited for heterogeneous signals which display complex feature fluctuations.

Self-affine Scaling

The application of the Hurst approach to characterise digital elevation profiles is straightforward in theory and in practice. Two dimensional images contain pixel information, which is defined by a grey-level range in the z-axis: 0 <= z <= 255, as well as positional localization using cartesian coordinates. Using image-analysis, a one dimensional line (or lines) can be overlaid onto the image, and the grey-scale values which fall under the line generates an elevation profile series which constitute the 'Enzyme Walk'. These are examples of self-affine 1-D signals, where the x and y positions are well defined, but the z-axis is a fluctuating quantity. Magnified portions of such signals do not scale equally in all directions, resulting in scalar anisotropy. This violates the property of true self-similarity where finer feature details, under a magnification scale transform, should occur isotropically in all directions. The magnification factor used to rescale self-affine functions therefore depends on the direction.

Biological Background

We consider a biochemical synthesis system in a filamentous fungus. Fungal colonies can be grown on solid nutrient media in Petri-dishes. They can be germinated from a single spore, and differentiate into an interconnected mycelium composed of fine filaments called hyphae (see for example reference 3). These microorganisms derive their nutrition by secreting a range of exo-enzymes which enable them to breakdown substrate constituents for synthesis of new cell material. From a practical viewpoint, many of these exo-enzymes are useful industrially since the catalytic reactions carried out by enzymes such as laccase, Mn-peroxidase or lignin peroxidase are relatively non-specific and therefore valuable for breakdown of industrial dyes [4], bioremediation of pesticide residues [5] or lignocellulose bio-processing [6] for paper manufacture. From a theoretical viewpoint, the way in which these enzymes diffuse out and away from the hyphae gives rise to interesting chemical feedback reactions which generate spatial patterns via chemical diffusion, oscillations, travelling waves or multistability. Experimental details have been given previously [1], but we are considering the active secretion and diffusion of laccase enzyme during growth on a nutrient rich medium. Figure 1 illustrates both the radially symmetric growth of the fungal colony, and the histochemical stain (Orange colour) which has been used to identify the enzyme. Normally the spatial secretion zone of this enzyme is invisible. Figure 2 displays the image-captured colony with a single digital line profile (in White) placed across the colony. Note that the colony germinated at time zero from the centre-point of the image. Figure 3 shows a pseudocoloured view of the grey-scale distribution which indexed the local, symmetrical fluctuation in laccase enzyme concentration. The rest of this paper considers the statistical scaling behaviour of a series of line profiles (Enzyme Walks) which were placed across different regions of the colony. Details regarding the global Hurst exponent are detailed in reference 1, while this paper extends this work by analysing for multifractal scaling.

[ Image: Figures 1,2, and 3 ]
Figure 1. Three day old colony of Pycnoporus cinnabarinus grown on a microporous membrane overlaid onto Malt Extract Agar, and stained to reveal the exo-enzyme laccase.
Figure 2. Single digital line profile overlaid on the image [ALL].
Figure 3. Pseodocolour representation to reveal the radial double banding pattern of enzyme activity concentration. The arrow indicates the centre-point inoculum, and the letter a and b indicate the high laccase expression region. Scale bar == 10mm.

Numerical Approach

Each radially symmetric colony grows outwards from the centre-point. Therefore we have two ways of approaching the analysis of this spatially extended system. Firstly, we can consider enzyme fluctuation from edge-to-edge across the plate [ALL], as in Figure 2. Secondly, we can consider growth only in one half of the circle [HALF], i.e. from centre-point to the edge. This second approach provides some indication of the importance of symmetrical growth in filamentous fungi. Furthermore this may reveal that the 'natural' antipersistence provided by the 'two-halves' of the radially symmetric colony is also present at very small scales in only one half of the whole. Note that in these experiments scaling is considered in short signal lengths of, L=256. Figure 4a shows a typical single digital line profile extracted from across the middle of the whole colony [ALL]. Figure 4b shows the frequency partitioning for the wavelet transform with the d.4 wavelet. The best basis entropy algorithm was used to compute the best basis from the wavelet packet table and these coefficients blocks are shown in black.

[ Image: Figure 4 ]
Figure 4. (a) Single laccase enzyme walk for ALL. (b) Frequency partitioning for the Wavelet Packet Transform for ALL shown in (a), plotting the best basis (x-axis) versus scale level (y-axis). (c) Enzyme walk for HALF. (d) Wavelet Packet Transform frequency partition with the best basis coefficients identified as black blocks.

To examine sensitivity to resolution scale changes, one can double the magnification and take a line profile from the centre-point to the right hand edge [HALF]. Figure 4c and 4d shows the respective 1-D profile and frequency partition with the d.4 wavelet packet.

Computer Software and Methodology

One dimensional wavelet packet analysis (WPA) was performed on ascii data obtained via image processing which was imported into PC compatible software [7]. Example data sets are shown in Figures 4a and 4c. The best basis function was computed for at least 15 line profiles for each magnification (ALL and HALF). This listing of wavelet packet best basis coefficients, [Cmn] was then used to estimate multifractal scaling behaviour. Note that the best basis coefficient listing is output in raw or unsorted format. Normally to compute the global fractal dimension we sort this list of coefficient energies from Highest to Lowest. However, to estimate local scaling trends we require the unsorted list of coefficient energies - which is equivalent to the order in which they were generated. If you look closely at the spatial relationship between Figures 4 a and b or Figure 4c and d it is clear that the 'black' blocks have significant spatial significance. In moving from left to right across the signal space, blocks of nodes are highlighted in black, which completely tile the position-frequency plane. This indicates the relative importance of individual features at different scales necessary to describe the signal. To estimate local Hurst exponents, Hlocal we can process these naturally ordered best basis coefficients to quantify scaling behaviour in windowed regions. This gives rise to the identification of local scaling trends. The global monofractal Hurst exponent was estimated using all coefficients with a windowed lag equal to one. In contrast, the degree of correlation between best basis coefficients separated by different lags can be estimated as follows. For local exponents, Hlocal one takes the listing of naturally ordered best basis coefficients and retains all coefficients at different lagged positions, e.g. 2,3,4,...etc. A second vector listing of coefficient index positions at each lag is also constructed and retained. One then sorts the best basis coefficients for each lag versus the lagged coefficient index positions, N, in descending order of magnitude.

Mathematical Notation

Wavelet Packet best basis coefficients are referred to by the notation, [Cmn]. The sub- and superscripts index location and scale respectively. A power-law has been found [1] to describe the scaling shown between the numbers of coefficients, Nr having a particular energy magnitude, and index position, N in a sorted listing from highest to lowest on log-log axes, following:

[ Equation 1 ]

(1)

The slope, [delta] can then be used to index the Hurst exponent. From here, a straightforward algebraic arrangement of terms can be used to extract the Hurst exponent following:

[ Equation 2 ]

(2)

Note that in 2-D, the following relationship holds between the Fractal Dimension, D and the Hurst exponent, H: D=2-H.

Multifractal Data Manipulation

A simple spreadsheet function was used to estimate multifractal Hurst exponents (Hlocal) using Sigma-Plot (Jandel Scientific, ver. 1.02). The list of unsorted best basis coefficients should be placed into column 1 - [e.g. notation is col(1), col(2),...]. A second list of length L, i.e. 1,2,3,...Lmax should be placed into col(2). Here Lmax=256. These 2 columns are the reference columns, and all subsequent calculations are performed by manipulating these two columns and placing output lagged data into adjacent columns to the right. In this example we estimate multifractal scaling over 12 different lag positions, however additional lagged positions can be added by extending this protocol. Sigma-Plot allows the data in columns 1 and 2 to be manipulated. Essentially we need to isolate different data points belonging to the unsorted best basis in column 1, and isolate the respective coefficient index position from column 2 for each lag. This will allow us to estimate the global Hurst exponent. Then to carry out a local Hurst exponent analysis at lag=2, we need to identify and copy those coefficients in col(1) which are at a lagged position of 2, and do the same in col(2). In Sigma-Plot this function is performed by taking the nth value in a particular sequence (Figure 5a). In practice, we first operate on col(1) and col(2) by taking the nth data points at lag=1 for both columns and placing this data set into col(4) and (3). Later we will plot (x vs y) col(3) vs col(4) to estimate the regression [see Figure 5b]. Then we again operate on col(1) and (2) but take the nth data points at lag=2 and place this paired data set into col(6) and (5) [see the first 8 lines of Figure 5a]. This process is continued until all lagged data sets have been created. Once all 12 lagged data sets are in place, we then plot a series of x vs y least-squares linear regressions on log-log axes. Figure 6 details an example of this plotting scheme with the linear regressions plotted for all 12 lag positions. One can then use the slope exponent of the regression to estimate the individual Hlocal values for each lag position following equations 1 and 2. The necessary data manipulations in a spreadsheet environment are detailed below:

[ Image: Figure 5 ] Figure 5. (a) Spreadsheet calculation of the nth data set for specified lags. (b) x versus y plotting scheme between coefficient energy magnitude and index position for different lags.

Results and Discussion

Since we had a minimum of fifteen digital line profiles for each enzyme walk (i.e. ALL and HALF), a series of graphs were generated similar to the one shown in Figure 6.

[ Image: Figure 6 ] Figure 6. Sample plot for HALF showing the 12 regressions for each lag position.

By taking the average slope for all data sets for the two treatments, it was possible to obtain mean statistics for a multifractal description of local enzyme activity concentration. This provides an overview of the scaling behaviour between nearest neighbour regions separated by a defined lag position (measured in pixel units). This data is plotted for both ALL and HALF in Figure 7.

[ Image: Figure 7 ]
Figure 7. Mean values for Hlocal for different lagged positions at each magnification scale (ALL or HALF).

Clearly the global Hurst exponent at a lag=1 returns an Hglobal=0.378 for ALL, and for HALF, the Hglobal=0.345. However the multifractal description shows a more complicated picture for the scaling behaviour. For example in the ALL data set, an antipersistent trend occurs up to a lag of 6, with longer lags showing approximate random walk behaviour (H=0.5). For HALF, there is a rapid degeneration towards random walk behaviour, although there is a pronounced sinusoidal oscillation from antipersistent to random walk to antipersistent to persistent across the lags. This general trend is less clear for the ALL data set, with random walk behaviour dominating at lags of 7 and above. Notice too that the error bars for Hlocal for both data sets are quite large. This demonstrates the large variability in convergence towards a mean local scaling exponent. This is partly a function of small data sets, with L=256 and the fact that at longer lags the cross-correlation falls away sharply between nearest neighbour regions separated by a defined lag.

Biological Implications

The biological implications of antipersistent scaling at short lags suggests one hypothesis that nearest neighbour hyphae are more likely to have alternate 'high' and 'low' local enzyme activity concentrations. This would be an adaptive advantage during enzyme expression by limiting release out of selected hyphae. Once in the external medium, local diffusion distributes the enzyme where it can perform useful work. This scaling correlation may also index nearest neighbour signalling fields, where hyphae in close proximity don't overproduce exo-enzymes already released by their neighbours. The breakdown in scaling (random walk) at shorter lags for HALF reveals a realistic distance estimate for the cross-correlation between nearest neighbour hyphae and their role in enzyme secretion. The small persistent scaling regions may be due to the linear nature of the diffusion front (out of and away from the hyphal tips) in a fixed area volume.

Conclusions

One should note that multifractal WPA is an alternative to established techniques such as the local second moment method to quantify the Hurst exponent in lagged windows [8]. The WPA method has been demonstrated using commercially available software (see reference 7), and although the data manipulations have been performed externally in a spreadsheet environment, it is anticipated that this algorithm will be ported to a common operating environment such as S-Plus running S+Wavelets. In addition, this multifractal algorithm is currently being adapted to operate on two dimensional images - extending the practical utility of the global 2-D Wavelet Packet method for Fractal Analysis outlined in recent papers [3,9,10].

References

  1. Jones, C.L., Lonergan, G.T. and Mainwaring, D.E. (1996). Wavelet Packet Computation of the Hurst Exponent. J. Phys. A: Math. Gen. 29: 2509-2527.
  2. Peters, E.E. (1991). Chaos and Order in the Capital Markets - A New View of Cycles, Prices, and Market Volatility. John Wiley & Sons, New York. pp. 61-80.
  3. Jones, C.L. (1996). Fractal Dimension Estimation with Wavelet packets. Virtual Physics. August 1 ,No. 7. URL: http://www.isisnet.com/MAX/science/physics/vp/vp07.html
  4. Lonergan, G.T., Panow, A., Jones, C.L., Schliephake, K., Ni, C.J. and Mainwaring, D.E. (1995). Physiological and Biodegradative Behaviour of the White-rot Fungus, Pycnoporus cinnabarinus in a 200 litre Packed Bed Bioreactor. Australasian Biotechnology. 5(2), March-April: 107-111.
  5. Field, J.A., de Jong, E., Feijoo-Costa, G. and de Bont, J.A.M. (1993). Screening for Ligninolytic Fungi Applicable to the Biodegradation of Xenobiotics. Tibtech. 11: 44-49.
  6. Jurasek, L. and Paice, M.G. (1988). Biological Treatment of Pulps. Biomass. 15: 103-108.
  7. Coifman, R.R. and Wickerhauser, M.V. (1993). Wavelets and Adapted Waveform Analysis - A Toolkit for Signal Processing and Numerical Analysis. [Computer program and Book] -Wavelet Packet Laboratory for Windows (Digital Diagnostics Corporation - Yale University; Email to: dcc@cs.yale.edu). A. K. Peters, Massachusetts.
  8. Hastings, H.M and Sugihara, G. (1993). Fractals - A User's Guide for the Natural Sciences. Oxford University Press, New York.
  9. Jones, C.L. (1996) 2-D Wavelet Packet Analysis of Structural Self-Organization and Morphogenic Regulation in Filamentous Fungal Colonies. Complex Systems Conference - "From Local Interactions to Global Phenomena" July 14-17, 1996 Charles Sturt University; Albury - Wodonga, N.S.W, Australia. Also in: Complex Systems 96 - From Local Interactions to Global Phenomena. (Stocker, R., Jelinek, H., Durnota, B. and Bossomaier, T. eds.) IOS Press, Amsterdam, 1996. pp. 12-23. Available online: http://www.swin.edu.au/chem/bio/cs96/camjones.htm
  10. Jones, C.L. (1996) Wavelet Packet Fractal Analysis - Software Operating Instructions. [A series of 7 software functions which calculate the mean or global Fractal Dimension of 2-D objects in digital images. Requirements include: S-Plus and S+Wavelets for Windows. Programs available for the following image sizes: 64x64, 128x128, 192x192, 256x256, 384x384, 512x512, and 300x200.] Available online: http://www.swin.edu.au/chem/bio/s+code/wpafrac1.htm

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Animated GIFS - a simple tutorial example

Zbigniew Koziol, WebEx@ra.isisnet.com

CONTENTS

  • Nonlinear Diffusion - a One-Dimensional Case
    • Animated demonstration
    • Numerical Modelling of Nonlinear Diffusion
    • References
  • EVERYTHING ABOUT GIF89a-based Animation for the WWW
  • SELECTED TOOLS
    • GIFTOOL
    • GIF Construction Set for Windows
  • MORE EXAMPLES

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Animation presentation, based on the GIF89a image format (one of the versions of the GIF format) become very popular, recently. Netscape Navigator 2.0 and its later versions, and the recent versions of Microsoft's Explorer support GIF89a format. That means multi-image support, allowing presentations or animations to be encoded within a single GIF file.

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Nonlinear Diffusion - a One-Dimensional Case

Animated demonstration

In this demonstration, solutions of the following nonlinear differential equation is modelled:

[image: a nonlinear diffusion equation ]

where beta is a function of time (t) and space-coordinate (x). The parameter kappa governs the nonlinearity: when kappa is equal to 0, a usual linear diffusion equation is recovered which has very well known solutions. This time, only that simplest case will be illustated. The quantity beta may represent, for instance, the magnetic field penetrating into a superconductor.

Here, the following cases can be viewed, corresponding to various time-dependences of the external magnetic field, H(t), at the surface of a flat superconductor (see also the figures):

  • MODE 0: External field H is a step function of time
  • MODE 1: External field has a triangle-dependence on time
  • MODE 2: External field is a periodic function of time

Magnetic field penetration into an infinite slab of a superconductor,
for kappa = 0 (classical, linear diffusion limit)

[ animation for kappa=0, mode No 0 ] [.]

time from 0 to 2500
images taken every 100 steps of iteration
[ animation for kappa=0, mode No 1 ] [.]

time from 0 to 2500
images taken every 100 steps of iteration
[ animation for kappa=0, mode No 2 ] [.]

time from 1000 to 2000
images taken every 20 steps of iteration

Numerical Modelling of Nonlinear Diffusion

A listing of the essential part of a Pascal program to perform numerical modelling of the nonlinear diffusion equation. This is an example only, not a complete source code of a ready-to-use program. 

CONST maxSTEP=5000;  { number of loops, 50 iterations each }
      kappa=3;       { parameter determining nonlinearity  }
      PERIOD = 25000 { time-period of external AC field    }
      a = 0.5;       { 'a' must be less or equal than about}   
                     { 0.5, to have calculations stable    }
VAR f: array[0..200,0..50] of real; xs, ts, step : integer;
BEGIN             { magnetic field =0 inside of the sample }
for xs:=1 to 199 do f[xs,0]:=0; 
step:=0;
  REPEAT
  FOR ts:=0 to 50 do
    BEGIN  { field = sin(2*(*time/PERIOD) on both surfaces }
           { any other function could be placed there      }
    f[0,ts]:= sin(2*3.14159*(50*step+ts)/PERIOD); 
    f[200,ts]:=f[0,ts];
    END; 
    FOR ts:=0 to 49 do
       FOR xs:=1 to 199 DO
 f[xs,ts+1]:=f[xs,ts] +a *( pwr(f[xs+1,ts]-f[xs,ts],kappa+1)
                     -pwr(f[xs,ts] - f[xs-1,ts], kappa+1) );
{The function pwr(x,z) is x to power z; 
                        this function is defined elsewhere }
{Here, the data can be stored to the disk. Now,the magnetic}
{field distribution at t=50 is assumed to be the starting  }
{condition for the next 50 steps of iteration              }
  FOR xs:=0 to 200 do f[xs,0]:=f[xs,50]; 
  inc(step);
  UNTIL step=maxSTEP;
END.

References

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EVERYTHING ABOUT GIF89a-based Animation for the WWW

The site http://members.aol.com/royalef/gifanim.htm, maintained by Royal E. Frazier is the largest and the best source of information about all the aspects related to the implementation of animated gifs on www pages. Here is a few short fragments from this page.
What is GIF89a. Well, its the technical rules from 1989 that explain how GIFs can be put together. You see most GIFs over the years have only one image per file. According to the technical specifications from 1987, a GIF could have had more than one image per file, making it like a slide show presentation and not a single image. However, most programs that work with GIF are designed around the idea of one image per file. So the multi-image aspect of GIFs was forgotten. In 1989, they added timing and various other abilities to the GIF format, including transparency. Nobody used these new additions either. Then the Web took off. Transparency and interlacing became features people wanted to use and software companies began supporting those features.

[ The site ] contains a tutorial of over 35 printed pages, 80K of readable text, animated illustrations, 1 megabyte of images and data, bugs reports and more. Please check back at the Overview and look to the table of contents for more info.

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SELECTED TOOLS

  • GIFTOOL

    It is available from http://www.homepages.com/tools/giftool/.

    This tool does a variety of things, add/remove comments and interlace GIF images. It can do batch convertion of large sets of images.

    Binaries are available for all the major platforms known: Sun Sparc Solaris 2.X (SunOS 5.X) and 1.X (SunOS 4.X), Dec Alpha OSF 2.0, Hewlett Packard HPUX 9.05, IBM RS6000, SGI Irix 4.0.5 and 5.2, x86 Linux, PC MS-DOS. The source code is also available.

    A source code of a perl script for animation is also available at the same site.

  • GIF Construction Set for Windows

    It is available from http://www.mindworkshop.com/alchemy/gifcon.html, a site of Alchemy Mindworks

    GIF Construction Set for Windows is a powerful collection of tools to work with multiple-block GIF files. It will allow you to assemble GIF files containing image blocks, plain text blocks, comment blocks and control blocks. It includes facilities to manage palettes and merge multiple GIF files together.

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MORE EXAMPLES

____________________________________________________________

Zbigniew Koziol

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"Lawrence and His Laboratory" - a history of physics site

Received: August 29, 1996
A new physics/history of science site on the Berkeley Lab Web titled "Lawrence and His Laboratory,"

http://www.lbl.gov/Science-Articles/Research-Review/Magazine/1981/

chronicles Berkeley Lab's "Lawrence Years," beginning with the Ernest Lawrence's founding of the Lab in 1931. It features hundreds of scientific images from the Lab's historical photo archive. Images include:

- photos of the first cyclotron and later particle-accelerating machines

- handwritten pages from lab notebooks describing Nobel Prize-winning discoveries

- engineer's sketches for devices that made possible probing the secrets of the atomic nucleus

If you have further questions, please contact Jeffery Kahn at jbkahn@lbl.gov or (510) 486-4019.

Berkeley Lab is a U.S. Department of Energy national laboratory located in Berkeley, California. It conducts unclassified research and is managed by the University of California.

Mike Wooldridge
Berkeley Lab Public Information

____________________________________________________________
Virtual Physics: a forum for virtual meetings of scientists and students involved in a research activity on CONTEMPORARY PHYSICS

Editors:

Marcel Ausloos, ausloos@gw.unipc.ulg.ac.be, Institut de Physique B5,
Université de Liège, Sart Tilman, B-4000 Liège, Belgium, tel. (+32 41) 66 37 52
Kenneth Holmlund, Kenneth.Holmlund@TP.UmU.SE, Department of Theoretical Physics
Umeå University, S-907 42 Umeå, Sweden, tel. +46-(0)90-167717
Cameron L. Jones, cjones@swin.edu.au, Centre for Applied Colloid and BioColloid Science
Swinburne University of Technology, P.O. Box 218 Hawthorn, Victoria, 3122 Australia, tel. +613 9214 8935, fax +613 9819 0834
Zbigniew J. Koziol (Editor-in-Chief), WebEx@ra.isisnet.com, WebExperts Inc.,
2-6032 Compton Ave., Halifax, Nova Scotia, B3H 1E7 Canada, tel. (902) 423 2149
Michal Spalinski, Michal.Spalinski@fuw.edu.pl, Institute of Theoretical Physics,
Warsaw University, Hoza 69, 00-681 Warsaw, Poland, tel. (+48 2) 628 3031
Krzysztof P. Wroblewski, chris@nmr.biophys.upenn.edu, School of Medicine
University of Pennsylvania, Rm. C-501 Richards Bldg., Philadelphia, PA 19104-6089, U.S.A., tel. (215) 898-6396
Virtual Physics URL addresses:

CANADA - http://www.isisnet.com/MAX/vp.html
AUSTRALIA - http://www.swin.edu.au/chem/complex/vp.html
SWEDEN - http://www.tp.umu.se/vp.html

To subscribe a F R E E e-mail version or submit materials for publication, write to Zbigniew Koziol.
Copyright (C) 1996 by Zbigniew Koziol.
this copyright notice concerns the whole of the Virtual Physics edition but not specific articles published there which are property of their respective copyright owners
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