Engineering Commons^™

Good Similar Patches For Image Denoising (Poster), Si Lu

Computer Science Faculty Publications and Presentations

Patch-based denoising algorithms like BM3D have achieved outstanding performance. An important idea for the success of these methods is to exploit the recurrence of similar patches in an input image to estimate the underlying image structures....

The Chirp Function Revisited: A Uniqueness Conjecture For Chirplet Modulation, Jonathan Blackledge, Paul Tobin

Conference papers

The chirp function (a unit amplitude quadratic phase-only function with linear frequency modulation) is well known and is used in a wide range of applications including radar, digital communications, information coding and hiding and many other forms of signal and image processing. This is because the chirp provides an optimal solution to the problem of retrieving information from low energy signals with a low Signal-to-Noise Ratio (SNR). The chirp function also occurs in the solution to many mathematical models used to describe the propagation and scattering of waves (in the Fresnel zone), in quantum mechanics (the quantum shutter problem) and …

Series Compensation To Increase Power Flow: A Case Study On The Irish Transmission System, Aidan Heffernan, Jane Courtney

Conference papers

Ireland presents an interesting case study for transmission network strengthening. The majority of load in the country is located at the nation's capital, Dublin, in the East, while most of the new conventional generation and renewable generation are found in the South-West. Power is transferred between the two via a 400 kV network. This leads to large cross- country power flows. This power distribution disparity is due to increase. A large thermal generating station which is connected to the 400 kV system in the West, will close by 2025. This generation will be replaced partially with wind generation in the …

Detection Of Grid Voltage Anomalies Via Broadband Subspace Decomposition, Samet Biricik, Soydan Redif

Conference papers

Due to the increase of sensitive loads on the mains power grid, measurement and monitoring of the power quality (PQ) have become an important factor for both consumers and operators. As is well-known, PQ problems occur in a very short time period with specific characteristics. In transmission or distribution systems, power quality data are collected from monitoring devices such as digital fault recorders, power quality and dynamic system monitors, etc. The recorded data has to be analysed in order to understand system anomalies. These anomalies may be due to sources of broadband noise. In this study, we employ broadband subspace …

Demand Response And Consumer Inconvenience, Chittesh Veni Chandran, Malabika Basu, Keith Sunderland

Conference papers

Balancing the energy demand and generation using the latest load management technologies is considered as an immediate requirement for peak demand management and to improve the operation of electrical distribution networks. However, load management technologies depriving consumers of utilizing their personal resources could be perceived as a consumer right violation by many consumers, and thus, the success of the program is significantly dependent on consumer satisfaction. This paper probes consumer engagement plans through an algorithm to minimize the consumer inconvenience caused by the load management/demand response (DR) program. Four different consumer engagement plans are proposed for consumers with different tolerance …

Impact Of Consumer Profiles On A Consumer Convenience Prioritised Demand Response, Chittesh Veni Chandran, Keith Sunderland, Malabika Basu

Conference papers

Distribution network (DN) load flexibility has simultaneously created challenges and opportunities. The major challenge is to meet the demand-supply balance while maintaining a positive profit-loss ratio. Further, Government enforced climate change policies attract low carbon technology (LCT) distributed energy resources (DER), which further complicate matters. Along with DN, the domestic appliance industry has undergone drastic modernization leading to appliances with advanced control and power efficient technologies as well as automation capabilities. This paper proposes a demand response (DR) program that facilitates these advancements while micromanaging the domestic load consumption pattern so as to manage peak demand in the network. This …

Building A Risk Model For The Patient-Centred Care Of Multiple Chronic Diseases, Stephane Deparis, Pierpaolo Tommasi, Alessandra Pascale, Hicham Rifai, Julie Doyle, John Dinsmore

Conference papers

With the increase of multimorbidity due to population ageing, managing multiple chronic health conditions is a rising challenge. Machine-learning can contribute to a better understanding of persons with multimorbidity (PwMs) and how to design an effective framework of care and support for them. We present a risk model of older PwMs that was derived from the TILDA dataset, a longitudinal study of the ageing Irish population. This model is based on a 26-nodes Bayesian network that represents patients possibly having one or more chronic conditions among diabetes, chronic obstructive pulmonary disease and arthritis, through a joint probability distribution of demographic, …

Sliding Mode Control Strategy For Three-Phase Three-Level T-Type Shunt Active Power Filters, Hasan Komurcugil, Sertac Bayhan, Samet Biricik

Conference papers

In this paper, a sliding mode control (SMC) strategy is proposed for three-phase three-level T-type shunt active power filters (SAPF). The proposed control strategy has the ability to balance the capacitor voltages with respect to the neutral-point. The proposed SMC strategy is formulated in the natural frame which eliminates abc/dq transformation and two PI controllers compared to the design in the dq frame. In natural frame, only one PI controller is needed to generate the amplitude of grid current reference. The output of the PI controller is multiplied by the unity sinusoidal waveforms, obtained from the grid voltages, so as …

Sediment Patterns From Fluid-Bed Interactions: A Direct Numerical Simulations Study On Fluvial Turbulent Flows, ‪Nadim Zgheib, S. Balachandar

Mechanical Engineering Faculty Publications and Presentations

We present results on the initial formation of ripples from an initially flattened erodible bed. We use direct numerical simulations (DNS) of turbulent open channel flow over a fixed sinusoidal bed coupled with hydrodynamic stability analysis. We use the direct forcing immersed boundary method to account for the presence of the sediment bed. The resolved flow provides the bed shear stress and consequently the sediment transport rate, which is needed in the stability analysis of the Exner equation. The approach is different from traditional linear stability analysis in the sense that the phase lag between the bed topology, and the …

Analysis Of Bus Ride Comfort Using Smartphone Sensor Data, Hoong-Chor Chin, Xingting Pang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Passenger comfort is an important indicator that is often used to measure the quality of public transport services. It may also be a crucial factor in the passenger’s choice of transport mode. The typical method of assessing passenger comfort is through a passenger interview survey which can be tedious. This study aims to investigate the relationship between bus ride comfort based on ride smoothness and the vehicle’s motion detected by the smartphone sensors. An experiment was carried out on a bus fixed route within the University campus where comfort levels were rated on a 3-point scale and recorded at 5-second …

A State Aggregation Approach For Stochastic Multiperiod Last-Mile Ride-Sharing Problems, Lucas Agussurja, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

The arrangement of last-mile services is playing an increasingly important role in making public transport more accessible. We study the use of ridesharing in satisfying last-mile demands with the assumption that demands are uncertain and come in batches. The most important contribution of our paper is a two-level Markov decision process framework that is capable of generating a vehicle-dispatching policy for the aforementioned service. We introduce state summarization, representative states, and sample-based cost estimation as major approximation techniques in making our approach scalable. We show that our approach converges and solution quality improves as sample size increases. We also apply …

Routing And Scheduling For A Last-Mile Transportation System, Hai Wang

Research Collection School Of Computing and Information Systems

The last-mile problem concerns the provision of travel services from the nearest public transportation node to a passenger’s home or other destination. We study the operation of an emerging last-mile transportation system (LMTS) with batch demands that result from the arrival of groups of passengers who desire last-mile service at urban metro stations or bus stops. Routes and schedules are determined for a multivehicle fleet of delivery vehicles, with the objective of minimizing passenger waiting time and riding time. An exact mixed-integer programming (MIP) model for LMTS operations is presented first, which is difficult to solve optimally within acceptable computational …

Microstructural Evaluation Of Aluminium Alloy A365 T6 In Machining Operation, Bankole I. Oladapo, S. Abolfazl Zahedi, Francis T. Omigbodun, Edwin A. Oshin, Victor A. Adebiyi, Olaoluwa B. Malachi

Electrical & Computer Engineering Faculty Publications

The optimum cutting parameters such as cutting depth, feed rate, cutting speed and magnitude of the cutting force for A356 T6 was determined concerning the microstructural detail of the material. Novel test analyses were carried out, which include mechanical evaluation of the materials for density, glass transition temperature, tensile and compression stress, frequency analysis and optimisation as well as the functional analytic behaviour of the samples. The further analytical structure of the particle was performed, evaluating the surface luminance structure and the profile structure. The cross-sectional filter profile of the sample was extracted, and analyses of Firestone curve for the …

On Hybrid Temporal Basis Functions For Stable Numerical Solution Of Time Domain Boundary Integral Equations, Fang Q. Hu

Mathematics & Statistics Faculty Publications

Problems in unsteady aerodynamics and aeroacoustics can sometimes be formulated as integral equations, such as the boundary integral equations. Numerical discretization of integral equations in the time domain often leads to so-called March-On-in-Time (MOT) schemes. In the literature, the temporal basis functions used in MOT schemes have been largely limited to low-order shifted Lagrange basis functions. In order to evaluate the accuracy and effectiveness of the temporal basis functions, a Fourier analysis of the temporal interpolation schemes is carried out. Based on the Fourier analysis, the spectral resolutions of various temporal basis functions are quantified. It is argued that hybrid …

The Effect Of Tube Geometry On The Chiral Plasma, S. Jin, D. Zou, X. Lu, Mounir Laroussi

Electrical & Computer Engineering Faculty Publications

A chiral plasma plume has recently been reported inside a circular quartz tube without the use of an external magnetic field. It is believed that the quartz tube plays an important role in the formation of the chiral plasma plume. In this paper, to better understand how this interesting structure is generated, the effect of the tube geometry on the chiral plasma is investigated. First, the effect of the thickness of the tube wall on the chiral plasma is investigated. It is interesting to find that a too thin or too thick tube wall is not favorable for generating the …

Computational Modeling Of Trust Factors Using Reinforcement Learning, C. M. Kuzio, A. Dinh, C. Stone, L. Vidyaratne, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous …

Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a …

Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep …

A Survey Of Techniques For Mobile Service Encrypted Traffic Classification Using Deep Learning, Pan Wang, Xuejiao Chen, Feng Ye, Zhixin Sun

Electrical and Computer Engineering Faculty Publications

The rapid adoption of mobile devices has dramatically changed the access to various net- working services and led to the explosion of mobile service traffic. Mobile service traffic classification has been a crucial task that attracts strong interest in mobile network management and security as well as machine learning communities for past decades. However, with more and more adoptions of encryption over mobile services, it brings a lot of challenges about mobile traffic classification. Although classical machine learning approaches can solve many issues that port and payload-based methods cannot solve, it still has some limitations, such as time-consuming, costly handcrafted …

Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai

Electrical and Computer Engineering Faculty Publications

To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic …

Temporal Variations In Methane Emissions From An Unconventional Well Site, Derek Johnson, Robert Heltzel, Dakota Oliver

Faculty & Staff Scholarship

Studies have aimed to quantify methane emissions associated with the growing natural gas infrastructure. Quantification is completed using direct or indirect methods—both of which typically represent only a snapshot in time. Most studies focused on collecting emissions data from multiple sites to increase sample size, thus combining the effects of geospatial and temporal variability (spatio-temporal variability). However, we examined the temporal variability in methane emissions from a single unconventional well site over the course of nearly 2 years (21 months) by conducting six direct quantification audits. We used a full flow sampling system that quantified methane mass emissions with an …

An Elastic-Net Logistic Regression Approach To Generate Classifiers And Gene Signatures For Types Of Immune Cells And T Helper Cell Subsets, Arezo Torang, Paraag Gupta, David J. Klinke Ii

Faculty & Staff Scholarship

Background: Host immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view. Yet, leveraging transcriptomics data to identify immune cell subtypes presents challenges for extracting informative gene signatures hidden within a high dimensional transcriptomics space characterized by low sample numbers with noisy and missing values. To address these challenges, we explore using machine learning methods to select gene subsets and estimate gene coefficients simultaneously. Results: Elastic-net logistic regression, a type of …

From Statistical Correlations To Stochasticity And Size Effects In Sub-Micron Crystal Plasticity, Hengxu Song, Stefanos Papanikolaou

Faculty & Staff Scholarship

Metals in small volumes display a strong dependence on initial conditions, which translates into size effects and stochastic mechanical responses. In the context of crystal plasticity, this amounts to the role of pre-existing dislocation configurations that may emerge due to prior processing. Here, we study a minimal but realistic model of uniaxial compression of sub-micron finite volumes. We show how the statistical correlations of pre-existing dislocation configurations may influence the mechanical response in multi-slip crystal plasticity, in connection to the finite volume size and the initial dislocation density. In addition, spatial dislocation correlations display evidence that plasticity is strongly influenced …

How To Balance Intuitive And Analytical Functions Of Brain: A Neutrosophic Way Of Scientific Discovery Process, Florentin Smarandache, Victor Christianto, Robert Neil Boyd

Branch Mathematics and Statistics Faculty and Staff Publications

Initially this article stems from our discussion on math and mysticism, inspired by an article by Ralph Abraham. But it becomes a discussion on the role of intuition and inspiration in scientific discovery process. Hopefully this article will help anyone who aspires to be good scientists or engineers.

Person Re-Identification Over Encrypted Outsourced Surveillance Videos, Hang Cheng, Huaxiong Wang, Ximeng Liu, Yan Fang, Meiqing Wang, Xiaojun Zhang

Research Collection School Of Computing and Information Systems

Person re-identification (Re-ID) has attracted extensive attention due to its potential to identify a person of interest from different surveillance videos. With the increasing amount of the surveillance videos, high computation and storage costs have posed a great challenge for the resource-constrained users. In recent years, the cloud storage services have made a large volume of video data outsourcing become possible. However, person Re-ID over outsourced surveillance videos could lead to a security threat, i.e., the privacy leakage of the innocent person in these videos. Therefore, we propose an efFicient privAcy-preseRving peRson Re-ID Scheme (FARRIS) over outsourced surveillance videos, which …

End-To-End Learning Via A Convolutional Neural Network For Cancer Cell Line Classification, Darlington A. Akogo, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Purpose: Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine vision algorithms. The purpose of this work is to explore and demonstrate the ability of a Convolutional Neural Network (CNN) to classify cells pictured via brightfield microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell identification.

Design/methodology/approach: The methodology involved a quantitative measure of the performance of a Convolutional Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and …

Detecting Special-Cause Variation 'Events' From Process Data Signatures, Timothy M. Young, Olga Khaliukova, Nicolas André, Alexander Petutschnigg, Timothy G. Rials, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The ability to detect the special-cause variation of incoming feedstocks from advanced sensor technology is invaluable to manufacturers. Many on-line sensors produce data signatures that require further off-line statistical processing for interpretation by operational personnel. However, early detection of changes in variation in incoming feedstocks may be imperative to promote early-stage preventive measures. A method is proposed in this applied study for developing control bands to quantify the variation of data signatures in the context of statistical process control (SPC). Control bands based on pointwise prediction intervals constructed from the Bonferroni Inequality and Bayesian smoothing splines are developed. Applications using …

1995-2005: A Decade Of Innovation In Low Temperature Plasma And Its Applications, Mounir Laroussi

Electrical & Computer Engineering Faculty Publications

Scientific breakthroughs tend to come in spurts when unique societal, economical, and political circumstances conspire (knowingly or unknowingly) and create an environment ripe for creativity. The field of low temperature plasma (LTP) recently experienced such an upheaval, which this paper attempts to relate in some details. There have been “roadmap” papers published before, which look towards the future of the field, but all roads start somewhere and even “new” roads are often paved over older roads that were discovered and traveled by early pioneers. With the sharp decrease in funding for fusion research in the USA in the early 1990s …

Ieee Access Special Section Editorial: Wirelessly Powered Networks, And Technologies, Theofanis P. Raptis, Nuno B. Carvalho, Diego Masotti, Lei Shu, Cong Wang, Yuanyuan Yang

Computer Science Faculty Publications

Wireless Power Transfer (WPT) is, by definition, a process that occurs in any system where electrical energy is transmitted from a power source to a load without the connection of electrical conductors. WPT is the driving technology that will enable the next stage in the current consumer electronics revolution, including battery-less sensors, passive RF identification (RFID), passive wireless sensors, the Internet of Things and 5G, and machine-to-machine solutions. WPT-enabled devices can be powered by harvesting energy from the surroundings, including electromagnetic (EM) energy, leading to a new communication networks paradigm, the Wirelessly Powered Networks.

A Survey Of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data, S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders among children, that affects different areas in the brain that allows executing certain functionalities. This may lead to a variety of impairments such as difficulties in paying attention or focusing, controlling impulsive behaviours and overreacting. The continuous symptoms may have a severe impact in the long-term. This paper explores the ADHD identification studies using eye movement data and functional Magnetic Resonance Imaging (fMRI). This study discusses different machine learning techniques, existing models and analyses the existing literature. We have identified the current challenges and possible future directions …