Engineering Commons^™

Ua84 Sigma Chi, Wku Archives

WKU Archives Collection Inventories

Records created by and about the WKU chapter of Sigma Xi.

Ua66/1/5 Ogden College Of Science & Engineering Dean's Office Centers, Wku Archives

WKU Archives Collection Inventories

Records created by and about centers associated with the Dean's office in the Ogden College of Science & Engineering.

Ua1c2/37 Facilities Services & Heating Plant Photos, Wku Archives

WKU Archives Collection Inventories

Images of Facilities Services and the Heating Plant.

Ua1c4/7 Student Groups & Association Photos, Wku Archives

WKU Archives Collection Inventories

Images of student groups and associations not otherwise listed.

A Note On Kriging And Gaussian Processes, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther

Electrical and Computer Engineering Faculty Publications

An introduction to gaussian processes and kriging.

Details On Gaussian Process Regression (Gpr) And Semi-Gpr Modeling, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther

Electrical and Computer Engineering Faculty Publications

This report tends to provide details on how to perform predictions using Gaussian process regression (GPR) modeling. In this case, we represent proofs for prediction using non-parametric GPR modeling for noise-free predictions as well as prediction using semi-parametric GPR for noisy observations.

On The Stability Analysis Of Perturbed Continuous T-S Fuzzy Models, Mohammad Shekaramiz, Farid Sheikholeslam

Electrical and Computer Engineering Faculty Publications

This paper deals with the stability problem of continuous-time Takagi-Sugeno (T-S) fuzzy models. Based on the Tanaka and Sugeno theorem, a new systematic method is introduced to investigate the asymptotic stability of T-S models in case of having second-order and symmetric state matrices. This stability criterion has the merit that selection of the common positive-definite matrix P is independent of the sub-diagonal entries of the state matrices. It means for a set of fuzzy models having the same main diagonal state matrices, it suffices to apply the method once. Furthermore, the method can be applied to T-S models having certain …

Details On Amp-B-Sbl: An Algorithm For Recovery Of Clustered Sparse Signals Using Approximate Message Passing [1-3], Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther

Electrical and Computer Engineering Faculty Publications

Solving the inverse problem of compressive sensing in the context of single measurement vector (SMV) problem with an unknown block-sparsity structure is considered. For this purpose, we propose a sparse Bayesian learning (SBL) algorithm simplified via the approximate message passing (AMP) framework. In order to encourage the block-sparsity structure, we incorporate the concept of total variation, called Sigma-Delta, as a measure of block-sparsity on the support set of the solution. The AMP framework reduces the computational load of the proposed SBL algorithm and as a result makes it faster compared to the message passing framework. Furthermore, in terms of the …

[Accepted Article Manuscript Version (Postprint)] Distributed Data-Gathering And -Processing In Smart Cities: An Information-Centric Approach, Reza Tourani, Abderrahmen Mtibaa, Satyajayant Misra

Computer Science Faculty Works

The technological advancements along with the proliferation of smart and connected devices (things) motivated the exploration of the creation of smart cities aimed at improving the quality of life, economic growth, and efficient resource utilization. Some recent initiatives defined a smart city network as the interconnection of the existing independent and heterogeneous networks and the infrastructure. However, considering the heterogeneity of the devices, communication technologies, network protocols, and platforms the interoperability of these networks is a challenge requiring more attention. In this paper, we propose the design of a novel Information-Centric Smart City architecture (iSmart), focusing on the demand of …

Ignatian Pedagogy For Sustainability To Support Community-Based Projects: Client-Focused Sustainable Energy Solutions, Andrew Baruth

Jesuit Higher Education: A Journal

Seeing the words of Laudato Si’ as a call to action, we are engaging students in Ignatian Pedagogy for Sustainability through a series of community-based projects with the goal of client-focused sustainable energy solutions and associated dialogue. We outline the development of a purpose-created Energy Technology undergraduate program housed in the College of Arts and Sciences at Creighton University, born from Ignatian Sensibilities, and highlight the role of client engagement to engross students in a client-focused design process to deliver sustainable energy initiatives that become practically feasible with student leadership. For the senior capstone of this program, students engage in …

A Low-Area, Energy-Efficient 64-Bit Reconfigurable Carry Select Modified Tree-Based Adder For Media Signal Processing, Priscilla Sharon Allwin

Browse all Theses and Dissertations

Multimedia systems play an essential part in our daily lives and have drastically improved the quality of life over time. Multimedia devices like cellphones, radios, televisions, and computers require low-area and low-power reconfigurable adders to process greedy computation algorithms for the real-time audio/video signal and image processing such as discrete cosine transform, inverse discrete cosine transform, and fast Fourier transform, etc. In this thesis, a novel 64-bit reconfigurable adder is proposed and implemented to reduce the area and power consumption. This adder can be run-time reconfigured to different reconfigurable word lengths, i.e., one 64- bit, two 32-bits, four 16-bits or …

Strategic Decision Facilitation: An Exploration Of Alternative Anchoring And Scale Distortion Optimization In Multi-Attribute Group Decision Making, Joseph Patrick Kristbaum

Browse all Theses and Dissertations

Choosing between alternatives, regardless of the decision context in an organizational group setting is a difficult task. The integrity of the decision is under constant scrutiny and rarely are we ever able to characterize the magnitude of that with which we are consciously or subconsciously concerned: personal bias. This leads to long, drawn out timelines and loosely trusted decisions. This dissertation research focuses on using a traditionally, negatively viewed anchoring bias strategically in a series of experiments with a hypothesis that it can be used to positively reduce personal biases such as preference bias and judgement rooted in ambiguous or …

Knowledge-Enabled Entity Extraction, Hussein S. Al-Olimat

Browse all Theses and Dissertations

Information Extraction (IE) techniques are developed to extract entities, relationships, and other detailed information from unstructured text. The majority of the methods in the literature focus on designing supervised machine learning techniques, which are not very practical due to the high cost of obtaining annotations and the difficulty in creating high quality (in terms of reliability and coverage) gold standard. Therefore, semi-supervised and distantly-supervised techniques are getting more traction lately to overcome some of the challenges, such as bootstrapping the learning quickly. This dissertation focuses on information extraction, and in particular entities, i.e., Named Entity Recognition (NER), from multiple domains, …

The Performance Of Concrete Containing Recycled Plastic Aggregates, Jason T. Manning

Undergraduate Honors Theses

Currently, our planet faces an issue with plastic waste and even with current recycling methods, there is still a large amount of it found in landfills and bodies of water. The goal of this research is to find a practical solution for the use of recycled plastic. This will be done by adding various amounts of recycled plastic aggregates into concrete mixes. The aggregate will act as a replacement (by volume) for sand and will be added in amounts of 0, 10, 30, 50, and 70 percent. There will be a 28-day compressive strength test, using 3 samples for each …

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.