Big Data and Big Data analytics have attracted major interest in research and industry and continue to do so. The high demand for capable and scalable analytics in combination with the ever increasing number and volum...
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Big Data and Big Data analytics have attracted major interest in research and industry and continue to do so. The high demand for capable and scalable analytics in combination with the ever increasing number and volume of application scenarios and data has lead to a large and intransparent landscape full of versions, variants and individual algorithms. As this zoo of methods lacks a systematic way of description, understanding is almost impossible which severely hinders effective application and efficient development of analytic algorithms. To solve this issue we propose our concept of modular analytics that abstracts the essentials of an analytic domain and turns them into a set of universal building blocks. As arbitrary algorithms can be created from the same set of blocks, understanding is eased and development benefits from reusability.
A large proportion of publications in the field of evolutionary computation describe algorithm specialisation and experimentation. algorithms are variously described using text, tables, flowcharts, functions or pseudo...
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ISBN:
(纸本)9781450305570
A large proportion of publications in the field of evolutionary computation describe algorithm specialisation and experimentation. algorithms are variously described using text, tables, flowcharts, functions or pseudocode. However, ambiguity that can limit the efficiency of communication is common. Evolutionary System Definition Language (ESDL) is a conceptual model and language for describing evolutionary systems efficiently and with reduced ambiguity, including systems with multiple populations and adaptive parameters. ESDL may also be machine-interpreted, allowing algorithms to be tested without requiring a hand-coded implementation, as may already be done using the esec framework. The style is distinct from existing notations used within the field and is easily recognisable. This paper describes the case for ESDL, provides an overview of ESDL and examples of its use.
In this study, we propose a novel approach to dual-energy contrast-enhanced digital mammography, with the development of a dual-energy recombination algorithm based on an image chain model and the determination of the...
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ISBN:
(纸本)9780819466280
In this study, we propose a novel approach to dual-energy contrast-enhanced digital mammography, with the development of a dual-energy recombination algorithm based on an image chain model and the determination of the associated optimal low and high-energy techniques. Our method produces clutter-free iodine-equivalent images and includes thickness correction near the breast border. After the algorithm description, the optimal low and high-energy acquisition techniques are determined to obtain a compromise between image quality and glandular dose. The low and high-energy techniques were chosen to minimize the glandular dose for a target Signal Difference to Noise Ratio (SDNR) in the dual-energy recombined image. The theoretical derivation of the iodine SDNR in the recombined image allowed the prediction of the optimal low and high-energy techniques. Depending on the breast thickness and glandular percentage, the optimal low-energy kVp and mAs ranged from 24kVp (Mo/Mo or Mo/Rh) to 35kVp (Rh/Rh), and from 60 to 90mAs respectively, and the high-energy kVp and mAs ranged from 40kVp to 47kVp (Mo/Cu), and from 80mAs to 290mAs. We proved the better :performance of our algorithm compared to the classic weighted logarithmic subtraction method in terms of patient dose and also in terms of texture cancelation, through the use of artificial textured images. Values of iodine contrast measured on phantom were close to the expected iodine thickness. Good correlation was found between the measured and theoretical iodine SDNR in the dual-energy images, which validates our theoretical optimization of the acquisition techniques.
Mathematical morphology clarified geometrical properties of shape analysis algorithms for binary pictures. Results of labelling, distance transform, and adjacent numbering are. however, coded pictures. For full descri...
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Mathematical morphology clarified geometrical properties of shape analysis algorithms for binary pictures. Results of labelling, distance transform, and adjacent numbering are. however, coded pictures. For full descriptions of shape analysis algorithms in the framework of mathematical morphology, it is necessary to extend morphological operations to code-labelled pictorial data. Nevertheless, extensions of morphology to code-labelled pictures have never discussed though the theory of gray morphology is well studied by several authors. Hence, this paper proposes a theory of the coded morphology which is based on the binary scaling of labels of pixels. The method uses n-layered binary sub-pictures for the processing of a picture with 2n labels. By introducing morphological operations for the coded point sets, we express some coding functions in the manner of the mathematical morphology. We also derive multi-dimensional array registers and gates which store and process coded pictures and morphological operations to them by proposing basic gates which compute parallelly logical operations for elements of Boolean layered arrays. These gates and registers are suitable for the implementation of the shape analysis processors on the three-dimensional VLSI and ULSI.
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