Engineering Commons^™

All Fibre Q-Switched Thulium-Doped Fibre Laser Incorporating Thulium–Holmium Co-Doped Fibre As A Saturable Absorber, Shi Li, Elfed Lewis, Gerald Farrell, Ahmad Haziq Aiman Rosol, A.A. Latiff, Sulaiman Wadi Harun, Bingang Guo, Pengfei Wang

Articles

A novel all fibre Q-switched Thulium-doped fibre laser (TDFL) is reported which includes a short length of a Thulium–Holmiumco-doped fibre (THDF) as a saturable absorber. A high repetition rate (27.26 kHz) coupled with a low pulse width (19.06μs) is obtained for single wavelength Q-switched pulse operation at an output wavelength of 1911.5 nm using a pump power of 200 mW. Increasing the pump power from 200 mW to 700 mW results in the repetition rate increasing from 27.26 kHz to 99.67 kHz and the pulse width decreasing from 19.06μs to 920 ns. The centre wavelength of the single Q-switched pulse …

Ultra-High-Resolution Detection Of Pb2+ Ions Using A Black Phosphorus Functionalized Microfiber Coil Resonator, Yu Yin, Shunbin Wang, Shijie Jia, Jing Ren, Gerald Farrell, Elfed Lewis, Pengfei Wang

Articles

A black phosphorus (BP) functionalized optical fiber sensor based on a microfiber coil resonator (MCR) for Pb2+ ion detection in an aquatic environment is presented and experimentally demonstrated. The MCR-BP sensor is manufactured by winding a tapered microfiber on a hollow rod composed of a low-refractive-index polycarbonate (PC) resin with the BP deposited on the internal wall of the rod. Based on the propagation properties of the MCR, the chemical interaction between the Pb2+ ions and the BP alters the refractive index of the ambient environment and thus results in a detectable shift in the transmission spectrum. The resonance wavelength …

In-Fiber Temperature Sensor Based On Green Up-Conversion Luminescence In An Er3+-Yb3+ Co-Doped Tellurite Glass Microsphere, Meng Zhang, Angzhen Li, Jibo Yu, Xiaosong Lu, Shunbin Wang, Elfed Lewis, Gerald Farrell, Libo Yuan, Pengfei Wang

Articles

A novel, to the best of our knowledge, in-fiber temperature sensor based on green up-conversion (UC) luminescence in an Er3+-Yb3+" role="presentation" style="box-sizing: border-box; display: inline; font-size: 12.88px; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">Er3+-Yb3+Er3+-Yb3+ co-doped tellurite glass microsphere is described. The tellurite glass microsphere is located firmly inside a suspended tri-core hollow-fiber (STCHF) structure. The pump light launched via a single-mode fiber (SMF) is passed through a section of multimode fiber, which is fusion spliced between the SMF …

Analyzing Twitter Feeds To Facilitate Crises Informatics And Disaster Response During Mass Emergencies, Arshdeep Kaur

Dissertations

It is a common practice these days for general public to use various micro-blogging platforms, predominantly Twitter, to share ideas, opinions and information about things and life. Twitter is also being increasingly used as a popular source of information sharing during natural disasters and mass emergencies to update and communicate the extent of the geographic phenomena, report the affected population and casualties, request or provide volunteering services and to share the status of disaster recovery process initiated by humanitarian-aid and disaster-management organizations. Recent research in this area has affirmed the potential use of such social media data for various disaster …

An Investigation Of Three Subjective Rating Scales Of Mental Workload In Third Level Education, Nha Vu Thanh Nguyen

Dissertations

Mental Workload assessment in educational settings is still recognized as an open research problem. Although its application is useful for instructional design, it is still unclear how it can be formally shaped and which factors compose it. This paper is aimed at investigating a set of features believed to shape the construct of mental workload and aggregating them together in models trained with supervised machine learning techniques. In detail, multiple linear regression and decision trees have been chosen for training models with features extracted respectively from the NASA Task Load Index and the Workload Profile, well-known self-reporting instruments for assessing …

Multi-Sensory Deep Learning Architectures For Slam Dunk Scene Classification, Paul Minogue

Dissertations

Basketball teams at all levels of the game invest a considerable amount of time and effort into collecting, segmenting, and analysing footage from their upcoming opponents previous games. This analysis helps teams identify and exploit the potential weaknesses of their opponents and is commonly cited as one of the key elements required to achieve success in the modern game. The growing importance of this type of analysis has prompted research into the application of computer vision and audio classification techniques to help teams classify scoring sequences and key events using game footage. However, this research tends to focus on classifying …

Coastal Dune Vegetation Mapping Using A Multispectral Sensor Mounted On An Uas, Chen Suo, Eugene Mcgovern, Alan Gilmer

Articles

Vegetation mapping, identifying the type and distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effectsof environmental changes and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper presents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an unmanned aerial system with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. Unmanned aerial systems (UAS), also known as unmanned aerial vehicles (UAV) or …

Predicting Customer Retention Of An App-Based Business Using Supervised Machine Learning, Jeswin Jose

Dissertations

Identification of retainable customers is very essential for the functioning and growth of any business. An effective identification of retainable customers can help the business to identify the reasons of retention and plan their marketing strategies accordingly. This research is aimed at developing a machine learning model that can precisely predict the retainable customers from the total customer data of an e-learning business. Building predictive models that can efficiently classify imbalanced data is a major challenge in data mining and machine learning. Most of the machine learning algorithms deliver a suboptimal performance when introduced to an imbalanced dataset. A variety …

An Evaluation Of Learning Employing Natural Language Processing And Cognitive Load Assessment, Mrunal Tipari

Dissertations

One of the key goals of Pedagogy is to assess learning. Various paradigms exist and one of this is Cognitivism. It essentially sees a human learner as an information processor and the mind as a black box with limited capacity that should be understood and studied. With respect to this, an approach is to employ the construct of cognitive load to assess a learner's experience and in turn design instructions better aligned to the human mind. However, cognitive load assessment is not an easy activity, especially in a traditional classroom setting. This research proposes a novel method for evaluating learning …

Task Based Risk Assessment For Human Factors Impact On Aviation Maintenance Deviations: A Case Study On Three Specific Tasks And Their Operational Implications, Stephen Caffrey, Laís Veloso Lara, Christopher Murtagh, Maria Chiara Leva, Victor Hrymak

Conference papers

This paper investigates human performance on a set of selected tasks in an aviation maintenance organisation, where a significant prevalence of unsafe acts and conditions were observed. The methodology began with visual inspections of the work environment. Semi structured interviews were then undertaken to gain an insight into the job demands and critical issues reported by front line operators. Three representative critical tasks were then selected for a detailed task analysis. This fed into a task based risk assessment that mapped out safety critical deviations together with timelines for nominal and deviation paths. The likelihood and severity of potential outcomes …

What Does A Drone See?: How Aerial Data Resolution Impacts Data Protection, Jonathan Ryan, David Maloney, Brian Quinn

Session 4: 2D, 3D Scene Analysis and Visualisation

The introduction of General Data Protection Regulation (GDPR) means that organisations are responsible for data protection for all individuals within the E.U. One area of operation that is unclear is to what degree aerial camera systems capture personal data. Mega-pixels and ground sampling distance are normally used as metrics for camera resolution but they ignore a multitude of factors and do not re ect the actual resolving power of the camera system. This work examines the resolution gap by detailing what is actually captured in the image output and how this can be used as an objective measure when addressing …

Ghost Towns: Semantically Labelled Object Removal From Video, William Clifford, Charles Markham

Session 4: 2D, 3D Scene Analysis and Visualisation

This paper describes a method used to produce a video of a road in which the foreground itemswhich obstruct the view of the road have been removed i.e. other vehicles. Once these regions have been identified they are replaced using suitable images that closely resemble the original background. The work considers an approach that uses multiple video sequences of the same road (C1...Cn). One video is identified as video Cp , that requires the least repair. All instances of vehicles in each frame of video were identified using a Convolutional Neural Network (CNN). The regions associated with each vehicle were …

Fisheyemodnet: Moving Object Detection On Surround-View Cameras For Autonomous Driving, Marie Yahiaoui, Hazem Rashed, Letizia Mariotti, Ganesh Sistu, Ian Clancy, Lucie Yahiaoui, Senthil Yogamani

Session 6: Applications, Architecture and Systems Integration

Moving Object Detection (MOD) is an important task for achieving robust autonomous driving. An autonomous vehicle has to estimate collision risk with other interacting objects in the environment and calculate an optional trajectory. Collision risk is typically higher for moving objects than static ones due to the need to estimate the future states and poses of the objects for decision making. This is particularly important for near-range objects around the vehicle which are typically detected by a fisheye surroundview system that captures a 360± view of the scene. In this work, we propose a CNN architecture for moving object detection …

Fisheyemultinet: Real-Time Multi-Task Learning Architecture For Surround-View Automated Parking System., Pullaro Maddu, Wayne Doherty, Ganesh Sistu, Isabelle Leang, Michal Uricar, Sumanth Chennupati, Hazem Rashed, Jonathan Horgan, Ciaran Hughes, Senthil Yogamani

Session 6: Applications, Architecture and Systems Integration

Automated Parking is a low speed manoeuvring scenario which is quite unstructured and complex, requiring full 360° near-field sensing around the vehicle. In this paper, we discuss the design and implementation of an automated parking system from the perspective of camera based deep learning algorithms. We provide a holistic overview of an industrial system covering the embedded system, use cases and the deep learning architecture. We demonstrate a real-time multi-task deep learning network called FisheyeMultiNet, which detects all the necessary objects for parking on a low-power embedded system. FisheyeMultiNet runs at 15 fps for 4 cameras and it has three …

An Efficient Approach To Automatic Generation Of Time-Lapse Video Sequence, Javier Calero De Torres, Bryan Gardiner, Ilias Dahi, Sandra Moffett, Marco Herbst, Joan Condell

Session 6: Applications, Architecture and Systems Integration

Time-lapse video sequences have recently become a highly utilised asset for marketing and advertising, particularly within the field of construction and landscape development. However, the manual generation of these videos, at a quality that can be used for marketing purposes, can be quite time-consuming. In this paper, a novel application for generating time-lapse videos is proposed, which will automatically select the optimal frames for time-lapse video generation, enhance these frames by applying a number of image pre- processing and machine learning techniques such as FAST super-resolution to improve the frames quality, and finally, provide an intuitive user interface to allow …

Assessing The Time Synchronisation Of Eeg Systems, Yongxiang Wang, Charles Markham, Catherine Deegan

Conference Papers

This study compared the synchronisation of a medical grade Electroencephalography (EEG) system, the g.Tec, and a consumer grade EEG system, the Emotiv. Data was collected from both systems using the lab streaming layer (LSL). Both EEG systems recorded an electric signal from the surface of a customised gel phantom. The electric signal was generated using a solar cell which was illuminated by a monitor presenting a sequence of black and white images. Test results show that the g.Tec had a mean delay of 51.22 ms from the stimulus onset and the Emotiv had a mean delay of 162.69 ms from …

The Role Of Role-Play In Student Awareness Of The Social Dimension Of The Engineering Profession, Diana Adela Martin, Eddie Conlon, Brian Bowe

Articles

The article aims to expand upon traditional case based instruction through role-play and to explore the effectiveness of the approach in raising students’ awareness of the social dimension of the engineering profession. For this purpose, we added a contextual description to the case study Cutting Roadside Trees driven by a macroethical outlook. Our contribution draws on an exercise based on the contextualised case study in which 80 students at Technological University Dublin participated. The results gathered show that role-playing contributed to complex student responses to the scenario and an awareness of the social factors that are part of engineering practice …

The Effect Of Using A Project-Based Learning (Pbl) Approach To Improve Engineering Students' Understanding Of Statistics, Fionnuala Farrell, Michael Carr

Articles

Over the last number of years we have gradually been introducing a project based learning approach to the teaching of engineering mathematics inDublin Institute of Technology. Several projects are now in existence for the teaching of both second-order differential equations and first order differential equations.We intend to incrementally extend this approach acrossmore of the engineering mathematics curriculum. As part of this ongoing process, practical realworld projects in statistics were incorporated into a second year ordinary degree mathematics module. This paper provides an overview of these projects and their implementation. As a means to measure the success of this initiative, we …

Research & Development Tax Credit, Kevin Delaney, Bernard Doherty, James Mc Mahon, William Coffey, Ger Nagle

Other resources

Ireland’s R&D tax credit system is ofmajor benefit to both multinational companies and SMEs operating in Ireland. The R&D tax credit was first introduced in Finance Act 2004 and offers a company undertaking R&D in Ireland a significant tax break, representing a potential 25% refund of costs incurred.This expenditure is also allowable as a Corporation Tax deduction, giving an effective deduction of 37.5% in a company’s tax liability.

No Room For Squares: Using Bitmap Masks To Improve Pedestrian Detection Using Cnns., Adam Warde, Hamza Yous, David Gregg, David Moloney

Session 3: Deep Learning for Computer Vision

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In this paper we investigate a method to reduce the number of computations and associated activations in Convolutional Neural Networks (CNN) by using bitmaps. The bitmaps are used to mask the input images to the network that fall within a rectangular window but do not fall within the boundaries of the objects the network is being trained upon. The mask has the effect of rendering the operations on these portions of the training images trivial. The thesis is that applying this approach to CNNs will not degrade accuracy while at the same time reducing the computational workload and reducing …

Solid Spherical Energy (Sse) Cnns For Efficient 3d Medical Image Analysis, Vincent Andrearczyk, Valentin Oreiller, Julien Fageot, Xavier Montet, Adrien Depeursinge

Session 2: Deep Learning for Computer Vision

Invariance to local rotation, to differentiate from the global rotation of images and objects, is required in various texture analysis problems. It has led to several breakthrough methods such as local binary patterns, maximum response and steerable filterbanks. In particular, textures in medical images often exhibit local structures at arbitrary orientations. Locally Rotation Invariant (LRI) Convolutional Neural Networks (CNN) were recently proposed using 3D steerable filters to combine LRI with Directional Sensitivity (DS). The steerability avoids the expensive cost of convolutions with rotated kernels and comes with a parametric representation that results in a drastic reduction of the number of …

Denoising Renoir Image Dataset With Dbsr, Fatma Albluwi, Vladimir A. Krylov, Rozenn Dahyot

Session 2: Deep Learning for Computer Vision

Noise reduction algorithms have often been evaluated using images degraded by artificially synthesised noise. The RENOIR image dataset [3] provides an alternative way for testing noise reduction algorithms on real noisy images and we propose in this paper to assess our CNN called De-Blurring Super-Resolution (DBSR) [2] to reduce the natural noise due to low light conditions in a RENOIR dataset.

Entropic Regularisation Of Robust Optimal Transport, Rozenn Dahyot, Hana Alghamdi, Mairead Grogan

Session 2: Deep Learning for Computer Vision

Grogan et al. [11, 12] have recently proposed a solution to colour transfer by minimising the Euclidean distance L2 between two probability density functions capturing the colour distributions of two images (palette and target). It was shown to be very competitive to alternative solutions based on Optimal Transport for colour transfer. We show that in fact Grogan et al’s formulation can also be understood as a new robust Optimal Transport based framework with entropy regularisation over marginals.

Deep Cnn Frameworks For Comparison For Malaria Diagnosis, Priyadarshini Adyasha Pattanaik, Zelong Wang, Patrick Horain

Session 2: Deep Learning for Computer Vision

Abstract We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstrained images.

Micro Expression Classification Accuracy Assessment, Pratikshya Sharma, Sonya Coleman, Pratheepan Yogarajah, Laurenc Taggart

Session 1: Active Vision, Tracking, Motion Analysis

The ability to identify and draw appropriate implications from non-verbal cues is a challenging task in facial expression recognition and has been investigated by various disciplines particularly social science, medical science, psychology and technological sciences beyond three decades. Non-verbal cues often last a few seconds and are obvious (macro) whereas others are very short and difficult to interpret (micro). This research is based on the area of micro expression recognition with the main focus laid on understanding and exploring the combined effect of various existing feature extraction techniques and one of the most renowned machine learning algorithms identified as Support …

Comparison Of Activity Recognition Using 2d And 3d Skeletal Joint Data, Fiona Marshall, Shuai Zhang, Bryan Scotney

Session 1: Active Vision, Tracking, Motion Analysis

No abstract provided.

Theoretical Analysis Of A Volume Holographic Lens Using Matlab, Sanjay Keshri, Kevin Murphy, Izabela Naydenova, Suzanne Martin

Conference Papers

Volume holographic lenses have great potential for different types of applications requiring light redirection and beam shaping such as solar light collection and LED light management. For lighting applications using LEDs, it is essential to make a highly efficient optical element to be placed in front of the LED in order to decrease energy losses. For that reason, a careful theoretical analysis of the properties and operation regime of the lens must be carried out at the design stage. The characteristics of focusing Holographic Optical Elements (HOE) depend on many factors including their thickness, spatial frequency, the angular range of …

Calculation Methods Of Radon-222 Radiological Activity For Norm Plant With Ventilation, Pedro M. Dieguez-Elizondo, Paul G. O'Donohoe, Tomas Gil-Lopez, Juan Castejon-Navas, Miguel A. Gálvez-Huerta

Articles

A procedure is proposed to determine the radiological activity of air present in the working area of a NORM (Naturally-Occurring Radioactive Materials) plant. This NORM plant is located in the United Arab Emirates and is dedicated to the recycling of material and equipment used in oil extraction facilities. Substantial quantities of NORM waste will be present inside the enclosed plant facilities and will exhale significant amounts of Radon-222 into the working environment. The continued inhalation of this gas and its progeny has been shown to cause lung cancer. In order to reduce the concentration level of the aforementioned gas to …

Air Diffusion System Design In Large Assembly Halls. Case Study Of The Congress Of Deputies Parliament Building, Madrid, Spain, Paul G. O'Donohoe, Miguel A. Gálvez-Huerta, Tomas Gil-Lopez, Pedro M. Dieguez-Elizondo, Juan Castejon-Navas

Articles

The paper describes and analyses the air distribution solution adopted in the refurbishment of the Spanish Congress of Deputies assembly hall. A new approach is proposed for the design of displacement ventilation systems. The system performance is methodically validated both with laboratory tests, computational fluid dynamics analysis and experimental field measurements related to the environmental performance of the built environment. In assembly halls, microclimate and displacement ventilation systems, which supply air by means of floor mounted outlets, have been shown to perform adequately. In this case study floor air supply is not feasible. The solution adapted uses parapet mounted diffusers, …

Sensing Of Multiple Parameters With Whispering Gallery Mode Optical Fiber Micro-Resonators, Arun Kumar Mallik Dr, Vishnan Kavungal, Gerald Farrell, Yuliya Semenova

Conference Papers

Monitoring of multiple physical parameters, such as humidity, temperature, strain, concentrations of certain chemicals or gases in various environments is of great importance in many industrial applications both for minimizing adverse effects on human health as well as for maintaining production levels and quality of products. In this paper we demonstrate two different approaches to the design of multi-parametric sensors using coupled whispering gallery mode (WGM) optical fiber micro-resonators. In the first approach, a small array of micro-resonators is coupled to a single fiber taper, while in the second approach each of the micro-resonators within an array is coupled to …