1,721,079 research outputs found
Prof. Sim Kok Swee and FET team won Kristal Awards
No full textWe are proud to announce that Ir. Prof. Dr. Sim Kok Swee from Faculty of Engineering and Technology (FET) with his team members, Desmond Kho Teck Kiang, EE Chung Sheng, Victor Teh, Nicholas Koh, Ting Fung Fung and Lim Zheng You had won the Kristal Awards Q2/2016 Telekom Malaysia (TM), for Group Category. The announcement was made during the Jom Bersama Tan Sri Zam Isa on 1st September 2016 at TM Convention Centre. This award recognises, acknowledges and honour the team that developed and implemented the most impactful initiative that promote innovation and productivity and lives by Life Made Easier (LME), embodies the Convergence mindset with Life Made Easier (LME) and Kristal values deliver COOL with Integrity
MMU Researcher is Listed in the Top Research Scientist Malaysia Once Again
No full textIr. Professor Dr. Sim Kok Swee, from the Faculty of Engineering & Technology was recently named the "Top Research Scientist in Malaysia (TRSM)" by the Academy of Scientist Malaysia (ASM)
Signal -To-Noise Ratio Estimation In Scanning Electron Microscope Imaging System
No full textTwo new methods, the Autoregressive(AR) model and the Mixed Lagrange Time Delay Estimation Autoregressive (MLTDEAR) model, are developed to estimate the Signal-to-Noise Ratio (SNR) from a single image for the Scanning Electron Microscope (SEM) Imaging System application
Nonlinear least squares regression for single image scanning electron microscope signal-to-noise ratio estimation
No full textA new method based on nonlinear least squares regression (NLLSR) is formulated to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The estimation of SNR value based on NLLSR method is compared with the three existing methods of nearest neighbourhood, first-order interpolation and the combination of both nearest neighbourhood and first-order interpolation. Samples of SEM images with different textures, contrasts and edges were used to test the performance of NLLSR method in estimating the SNR values of the SEM images. It is shown that the NLLSR method is able to produce better estimation accuracy as compared to the other three existing methods. According to the SNR results obtained from the experiment, the NLLSR method is able to produce approximately less than 1% of SNR error difference as compared to the other three existing methods
Autoregressive linear least square single scanning electron microscope image signal-to-noise ratio estimation
No full textA technique based on linear Least Squares Regression (LSR) model is applied to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. In order to test the accuracy of this technique on SNR estimation, a number of SEM images are initially corrupted with white noise. The autocorrelation function (ACF) of the original and the corrupted SEM images are formed to serve as the reference point to estimate the SNR value of the corrupted image. The LSR technique is then compared with the previous three existing techniques known as nearest neighbourhood, first-order interpolation, and the combination of both nearest neighborhood and first-order interpolation. The actual and the estimated SNR values of all these techniques are then calculated for comparison purpose. It is shown that the LSR technique is able to attain the highest accuracy compared to the other three existing techniques as the absolute difference between the actual and the estimated SNR value is relatively small
Deep convolutional networks for magnification of DICOM Brain Images
No full textConvolutional neural networks have recently achieved great success in Single Image Super-Resolution (SISR). SISR is the action of reconstructing a high-quality image from a low-resolution one. In this paper, we propose a deep Convolutional Neural Network (CNN) for the enhancement of Digital Imaging and ommunications in Medicine (DICOM) brain images. The network learns an end-to-end mapping between the low and high resolution images. We first extract features from the image, where each new layer is connected to all previous layers. We then adopt residual learning and the mixture of convolutions to reconstruct the image. Our network is designed to work with grayscale images, since brain images are originally in grayscale. We further compare our method
with previous works, trained on the same brain images, and show that our method outperforms them
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
MMU professors receive prestigious awards
No full textWe are proud to annouce that two of Multimedia University (MMU) professors, Prof. Dr. Murali Raman, the Dean of Faculty of Management (FOM) and Prof. Dr. Sim Kok Swee from Faculty of Engineering and Technology (FET) received their prestigious awards recently. Prof. Dr. Murali Raman received the National Award for Outstanding Educator (Management of Information Technology) in the Education Cooperation of Private Universities in Malaysia (EDUCOOP) on the 28 May 2016 at Petaling Jaya. The award was presented by the Chairman EDUCOOP, Tan Sri Yahya Ibrahim to recognise the outstanding contributions made by individuals in the Private Sector Education Scene in Malaysia. This year, more than 240 nominations were received for different categories from many private sector education institutions in Malaysia, at different levels. Prof. Mural Raman was nominated by Prof. Dr. Hishamuddin Ismail, the Vice President (Academic) of MMU for this award
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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