Faculty of Design and Creative Technologies (Te Ara Auaha)
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The Faculty of Design and Creative Technologies (Te Ara Auaha) is comprised of four school; Colab, the School of Art and Design, the School of Communication Studies and the School of Engineering, Computer and Mathematical Sciences. It also has Institutes, Centres and Labs across the Arts and Sciences in a mix that blends the traditional and the new, praxis and theory.
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Browsing Faculty of Design and Creative Technologies (Te Ara Auaha) by Subject "0299 Other Physical Sciences"
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- ItemA Monitoring Campaign (2013–2020) of ESA’s Mars Express to Study Interplanetary Plasma Scintillation(Cambridge University Press (CUP), 2023-04-12) Kummamuru, P; Molera Calvés, G; Cimò, G; Pogrebenko, SV; Bocanegra-Bahamón, TM; Duev, DA; Md Said, MD; Edwards, J; Ma, M; Quick, J; Neidhardt, A; De Vicente, P; Haas, R; Kallunki, J; MacCaferri, G; Colucci, G; Yang, WJ; Hao, LF; Weston, S; Kharinov, MA; Mikhailov, AG; Jung, TThe radio signal transmitted by the Mars Express (MEX) spacecraft was observed regularly between the years 2013-2020 at X-band (8.42 GHz) using the European Very Long Baseline Interferometry (EVN) network and University of Tasmania's telescopes. We present a method to describe the solar wind parameters by quantifying the effects of plasma on our radio signal. In doing so, we identify all the uncompensated effects on the radio signal and see which coronal processes drive them. From a technical standpoint, quantifying the effect of the plasma on the radio signal helps phase referencing for precision spacecraft tracking. The phase fluctuation of the signal was determined for Mars' orbit for solar elongation angles from 0 to 180 deg. The calculated phase residuals allow determination of the phase power spectrum. The total electron content of the solar plasma along the line of sight is calculated by removing effects from mechanical and ionospheric noises. The spectral index was determined as which is in agreement with Kolmogorov's turbulence. The theoretical models are consistent with observations at lower solar elongations however at higher solar elongation ($ ]]>160 deg) we see the observed values to be higher. This can be caused when the uplink and downlink signals are positively correlated as a result of passing through identical plasma sheets.
- ItemAnomaly Detection in Text Data Sets Using Character-Level Representation(Institute of Physics (IoP), 2021-04-28) Mohaghegh, Mahsa; Abdurakhmanov, AmantayThis paper proposes a character-level representation of unsupervised text data sets for anomaly detection problems. An empirical examination of the character-level text representation was conducted to demonstrate the ability to separate outlying and normal records using an ensemble of multiple classic numerical anomaly classifiers. Experimental results obtained on two different data sets confirmed the applicability of the developed unsupervised model to detect outlying instances in various real-world scenarios, providing the opportunity to quickly assess a large amount of textual data in terms of information consistency and conformity without knowledge of the data content itself.
- ItemAutomated Biometric Identification using Dorsal Hand Images and Convolutional Neural Networks(Institute of Physics (IoP), 2021-04-01) Mohaghegh, Mahsa; Ash, PayneThe identification of perpetrators, present in Child Sexual Abuse Imagery (CSAI), is a significant challenge due to the use of anonymisation techniques that mask their identities. Consequently, researchers have investigated the use of uncommon biometric identifiers such as knuckle patterns, palmprints and the dorsal side of the hand. This research proposes a Convolutional Neural Network (CNN) based, fully automated approach to biometric identification using dorsal hand images. The identification performance of three different CNN architectures, AlexNet, ResNet50 and ResNet152, is experimentally determined against two similar datasets, the 11k Hands and IITD dorsal hand databases. A transfer learning approach is used and the final output layers of the CNNs are modified to match the number of classes present in the datasets. The results showed that ResNet CNNs achieved identification accuracies greater than 99.9% on both datasets, whereas the AlexNet CNN achieved between 80.1% and 93.7%. These results demonstrate that it is feasible to use deep, off-the-shelf CNNs, such as ResNets, for automated biometric identification using dorsal hand images. This highlights the potential of using dorsal hand images to identify perpetrators of child sexual abuse from CSAI.