DITRWSL D13125710

, ACO algorithms have been tested on a large number of
academic problems. These include problems related to the traveling salesman, as well
as assignment, scheduling, subset, and constraint satisfaction problems. For many of
these, world-class performance has been achieved. For example, ACO algorithms
are, at the time of writing, state-of-the-art (i.e., their performance is comparable to,
or better than, that of the best existing methods other than ACO) for the sequential
ordering problem (Gambardella & Dorigo, 2000), the vehicle routing problem with
time window constraints (Gambardella et al., 1999), the quadratic assignment problem
(Maniezzo, 1999; Stu¨ tzle & Hoos, 2000), the group shop scheduling problem
(Blum, 2003a), the arc-weighted l-cardinality tree problem (Blum & Blesa, 2003), and
the shortest common supersequence problem (Michel & Middendorf, 1999). Additionally,
very good performance has been obtained by AntNet (see chapter 6) on
network routing problems (Di Caro & Dorigo, 1998c).
This success with academic problems has raised the attention of a number of
companies that have started to use ACO algorithms for real-world applications.
Among the first to exploit algorithms based on the ACO metaheuristic is EuroBios
(www.eurobios.com). They have applied ACO to a number of di¤erent scheduling

Paraphrase:

The words highlighted in blue contains a paraphrase of (Huaxin and Shuai, 2011)
Link to Article.

The manipulation of images in the spectrum analyser software will be implemented by the structural decorator pattern for adding various plots to the image without inheriting from the image class.” Inheriting a border from another class puts a border around every subclass instance” (Gamma, 1995 Page 175). This is clearly what we do not want to do as an image class may have ten or more decorations classes applied, whereas another image class instance could only have one decoration. Fundamentally the decorator pattern dynamically encloses the image class adding or removing additional functionality at runtime without changing the overall structure of the class. (Huaxin and Shuai, 2011) Although can also be error prone as you can add the same functionality more than once. Therefore the decorator pattern must be implemented carefully.

Original Source (Huaxin and Shuai, 2011)
We can use one or more decorators to wrap an object. Given that the decorator has the same super type as the object it decorates, we can pass around a decorated object in place of the original object. The decorator adds its own behaviour either before and/or after delegating to the object it decorates to do the rest of the job. Objects can be decorated at any time, so we can decorate objects dynamically at runtime with as many decorators as we like.

Quotation:

The words highlighted in blue contain a quotation of (Prechelt et al., 2001)

Link to Article.

The importance of these design patterns are recognised by many researchers. “it is useful to design programs with design patterns even if the actual design problem is simpler than that solved by the design patterns”(Prechelt et al., 2001). The nature of the spectrum analyser software and how it handles images makes this pattern ideal. Therefore the decorator pattern provides a alternative solution to inheritance especially in the case of our program as we require to add multiple plots of images at run-time, wavelet transform, principal components, histogram, scattered plot, fast Fourier transformation etc.

The abstract window factory will be implemented for determining what kind of operating system we need to display the image and plots essentially decoupling this software from the concrete operating system at run-time. Most operating systems contain frameworks for image manipulation......

Summary of a Article: (He et al., 2007)

The words highlighted in blue contains a part summary of (He et al., 2007)

Link to Article.

Spectroscopy, a physics theory and the basis was first discovered by Sir Isaac Newton with his famous splitting of white light via a glass prism to show primary colours in 1666 Isaac Newton proved that the prism did not produce the colours but was the light itself. Every element or molecule emits or absorbs a unique spectra . The spectra is a measure of wavelengths in the electromagnetic spectrum mostly in the visible to infra-red light (Born, 1999) Spectroscopy can be used to ascertain the elements in distance stars Fraunhofer lines. Remote sensing of earth from satellites in space. NASA landsat1 satellite was first system of producing multi-spectral data in digital form.

However in industry spectrometry is generally a complex procedure that need to be carried out in a Lab requiring exact measurements. In the early 2000s with the advancement of ingenious smart phones and Artificial Intelligence as applied to face recognition and voice ( Hsu et al., 2002) . This research intends at joining Artificial Intelligence with Spectrometry.

The main problem with using a neural network as applied to spectroscopy is basically the noise of the spectrum image and the complexity. A small spectrum image can be 640 x 480 pixels and in true colour image this complexity is just too big for a neural net to learn.

(He et al., 2007) addresses the issue by using a wavelet transform to reduce the size of the image and as a byproduct removes noise at the same time. States that such an spectrum image was reduced to 77 wavelet transforms and that this can reconstruct 99.97% of the original image. These wavelets are then described by eight principal components. Making the overall image very easy for the neural network to process. The principal components are used as inputs and adjusting the neural network to having 12 neurons in the the hidden level , three as output. Claiming a 100% recognition rate of 40 unknown samples of tea.

References:

Born, M., 1999. Principles of optics: electromagnetic theory of propagation, interference and diffraction of light, 7th expanded ed. ed. Cambridge University Press, Cambridge ; New York.

Gamma, E., 1995. Design patterns: elements of reusable object-oriented software. Addison-Wesley, Reading, Mass.

He, Y., Li, X., Deng, X., 2007. Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model. J. Food Eng. 79, 1238–1242.

Hsu, R.-L., Abdel-Mottaleb, M., Jain, A.K., 2002. Face detection in color images. IEEE Trans. Pattern Anal. Mach. Intell. 24, 696–706.

Huaxin, M., Shuai, J., 2011a. Design patterns in software development, in: 2011 IEEE 2nd International Conference on Software Engineering and Service Science (ICSESS). Presented at the 2011 IEEE 2nd International Conference on Software Engineering and Service Science (ICSESS), pp. 322–325.

Kay, S.M., Marple, S.L., J., 1981. Spectrum analysis #8212;A modern perspective. Proc. IEEE 69, 1380–1419.

Prechelt, L., Unger, B., Tichy, W.F., Brossler, P., Votta, L.G., 2001. A controlled experiment in maintenance: comparing design patterns to simpler solutions. IEEE Trans. Softw. Eng. 27, 1134–1144.

DITRWSL D13125710

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Assignment 2

Paraphrase:

Quotation:

Summary of a Article: (He et al., 2007)

References:

Final Summary:

Continuous Assessment - Assignment Part I

Journal of Computer and System Sciences

ICSE, the International Conference on Software Engineering,

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