Engineering Mathematics

Author(s):  
K.A. Stroud ◽  
Dexter Booth
2020 ◽  
Vol 12 (2) ◽  
pp. 104
Author(s):  
Siti Hannah Padliyyah

Indonesia is ranked 56th out of 65 participating countries in the Program for International Student Assessment (PISA) based on data 2015. According to PISA results, the average science score of Indonesian students is 403, where this number is categorized as low. This is because students are still in the process of understanding and have not yet fully recognized the location of their mistakes. Students can diagnose the location of their mistakes through self-diagnosis activities. Self-diagnosis activities require the active role of students during the learning process. One approach that can increase the active role of students is STEM (Science Technology Engineering Mathematics). However, research at this time is still rarely found self-diagnosis activities that are applied to the STEM approach. Therefore, this research has the aim to find out the increase in mastery of physical concepts and self-diagnosis of students on the STEM learning approach to the theory of poscal law class XI High School.This study uses a One-Group pretest-posttest design with a sample of 30 ini 11th grade highschool from one schools in Bandung. . Based on the findings, there is an increase in mastery of concepts [<g> = 0.51] from pre-test to post-test. In self-diagnosis activities identified that there are differences in scores [z = 1.75; p = 0.9599] student assessment results of researchers and self-scoring results. Deeper self-diagnosis triggers a series of implicit steps that encourage them to rearrange their cognition by correcting the mistakes they make when solving problems. So that learning activities using the STEM approach that involves self-diagnosis activities can improve students' mastery of concepts.


2020 ◽  
Vol 61 ◽  
Author(s):  
Christopher C Tisdell ◽  
Zlatko Jovanoski ◽  
William Guo ◽  
Judith Bunder

  EMAC 2019 UNSW Canberra, Australia 26th Nov–29th Nov 2019 This Special Section of the ANZIAM Journal (Electronic Supplement) contains the refereed papers from the 14th Engineering Mathematics and Applications Conference (EMAC2019), which was held at the UNSW Canberra, Australia from 26th November to 29th November 2019. EMAC is held under the auspices of the Engineering Mathematics Group (EMG), which is a special interest group of the Australian and New Zealand Industrial and Applied Mathematics division of the Australian Mathematics Society. This conference provides a forum for researchers interested in the development and use of mathematical methods in engineering and applied mathematics, and aims to foster interactions between mathematicians and engineers, from both academia and industry. A further theme of the conference is the mathematical education of applied mathematicians and engineers. The event attracted participants from around the globe, including: New Zealand, Saudi Arabia, United Kingdom, Japan and Australia. The invited speakers at the 2019 meeting crossed the spectrum of specialities in engineering, mathematics, education and industry. They were: Alexander Kalloniatis (Defence Science and Technology Group), Robert K. Niven (UNSW Canberra), Katherine Seaton (La Trobe University) and Antoinette Tordesillas (University of Melbourne). All of the articles included in the EMAC 2019 Proceedings have been critically peer reviewed to the usual standards of the ANZIAM Journal. EMAC 2019 Organising Committee The conference organising committee were Fiona Richmond, Zlatko Jovanoski (Director), Leesa Sidhu, Duncan Sutherland, Fangbao Tian, Isaac Towers, Timothy Trudgian and Simon Watt. The invited speakers were chosen by a committee of experts including Alys Clark, Jennifer Flegg, Bronwyn Hajek (EMG Chair), Zlatko Jovanoski, Dann Mallet, Robert Niven, Brandon Pincombe, Melanie Roberts (Chair) and Harvinder Sidhu.


2016 ◽  
Vol 57 ◽  
Author(s):  
Mark Nelson ◽  
Dan Mallet ◽  
Brandon Pincombe ◽  
Judith Bunder

2010 ◽  
Vol 10 (3) ◽  
pp. 7-10
Author(s):  
Carol Robinson ◽  
Barbara Jaworski

Author(s):  
Ulf Grenander ◽  
Michael I. Miller

Pattern Theory provides a comprehensive and accessible overview of the modern challenges in signal, data, and pattern analysis in speech recognition, computational linguistics, image analysis and computer vision. Aimed at graduate students in biomedical engineering, mathematics, computer science, and electrical engineering with a good background in mathematics and probability, the text includes numerous exercises and an extensive bibliography. Additional resources including extended proofs, selected solutions and examples are available on a companion website. The book commences with a short overview of pattern theory and the basics of statistics and estimation theory. Chapters 3-6 discuss the role of representation of patterns via condition structure. Chapters 7 and 8 examine the second central component of pattern theory: groups of geometric transformation applied to the representation of geometric objects. Chapter 9 moves into probabilistic structures in the continuum, studying random processes and random fields indexed over subsets of Rn. Chapters 10 and 11 continue with transformations and patterns indexed over the continuum. Chapters 12-14 extend from the pure representations of shapes to the Bayes estimation of shapes and their parametric representation. Chapters 15 and 16 study the estimation of infinite dimensional shape in the newly emergent field of Computational Anatomy. Finally, Chapters 17 and 18 look at inference, exploring random sampling approaches for estimation of model order and parametric representing of shapes.


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