Computer Engineering Commons^™

Poster: Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutiso Mutua, Ruairí De Fréin, Ali Malik, Kibanza Eliel, Sahbane Marco, Pantel Maxime

Conference papers

Range anxiety is a significant challenge affecting electric vehicles use as drivers fear running out of charge without finding a charging point on time. We develop methods to optimise the distribution of charging points. EV portacharge and GEECharge solutions distribute charging points in a city by considering the population density and Points Of Interest (POI) or road traffic. This paper focuses on (1) developing and evaluating methods to distribute Charging Points (CPs) in Dublin city; (2) optimising CP allocation; (3) visualising paths in the graph network to show the most used roads and points of interest; (4) describing a way …

List Of 121 Papers Citing One Or More Skin Lesion Image Datasets, Neda Alipour

Other resources

No abstract provided.

Round Trip Time Measurement Over Microgrid Power Network, Yasin Emir Kutlu, Ruairí De Fréin, Malabika Basu, Ali Malik

Conference papers

A focus of the Power Systems and Networking communities is the design and deployment of Microgrid (MG) integration systems that ensure that quality of service targets are met for load sharing systems at different endpoints. This paper presents an integrated Microgrid testbed that allows Microgrids endpoints to share their current, voltage and power values using a Network Published Shared Variable (NPSV) approach. We present Round Trip Time (RTT) measurements for time sensitive Microgrid control traffic in the presence of varying background traffic as an example quality of service measurement. Numerical results are presented using a range of different background traffic …

Energy-Aware Ai-Driven Framework For Edge-Computing-Based Iot Applications, Muhammad Zawish, Nouman Ashraf, Rafay Iqbal Ansari, Steven Davy

Conference papers

The significant growth in the number of Internet of Things (IoT) devices has given impetus to the idea of edge computing for several applications. In addition, energy harvestable or wireless-powered wearable devices are envisioned to empower the edge intelligence in IoT applications. However, the intermittent energy supply and network connectivity of such devices in scenarios including remote areas and hard-to-reach regions such as in-body applications can limit the performance of edge computing-based IoT applications. Hence, deploying state-of-the-art convolutional neural networks (CNNs) on such energy-constrained devices is not feasible due to their computational cost. Existing model compression methods, such as network …

Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutua Mutiso, Ruairí De Fréin, Ali Malik, Eliel Kibanza, Marco Sahbane, Maxime Pantel

Conference papers

Range anxiety poses a hurdle to the adoption of Electric Vehicles (EVs), as drivers worry about running out of charge without timely access to a Charging Point (CP). We present novel methods for optimising the distribution of CPs, namely, EV portacharge and GEECharge. These solutions distribute CPs in Dublin, in this paper, by considering the population density and Points Of Interest (POIs) or road traffic. The object of this paper is to (1) develop and evaluate methods to distribute CPs in Dublin city; (2) optimise CP allocation; (3) visualise paths in the graph network to show the most used roads …

Subnetwork Ensembling And Data Augmentation: Effects On Calibration, A. Çağrı Demir, Simon Caton, Pierpaolo Dondio

Articles

Deep Learning models based on convolutional neural networks are known to be uncalibrated, that is, they are either overconfident or underconfident in their predictions. Safety-critical applications of neural networks, however, require models to be well-calibrated, and there are various methods in the literature to increase model performance and calibration. Subnetwork ensembling is based on the over-parametrization of modern neural networks by fitting several subnetworks into a single network to take advantage of ensembling them without additional computational costs. Data augmentation methods have also been shown to enhance model performance in terms of accuracy and calibration. However, ensembling and data augmentation …

Work In Progress: A Virtual Educational Robotics Coding Club Framework To Improve K-6 Students Emotional Engagement In Stem, Kate Carmody, Julie Booth, Jospehine Bleach, Pramod Pathak, Paul Styles

Conference papers

The growing popularity and deployment of Internet of Things (IoT) devices has led to serious security concerns. The integration of a security operations center (SOC) becomes increasingly important in this situation to ensure the security of IoT devices. In this article, we will present a summary of IoT device security issues, their vulnerabilities, a review of current challenges to keep these devices secure, and discuss the role that SOC can bring in protecting IoT devices while considering the challenges encountered and the directions to consider when implementing a reliable SOC for IoT monitoring.

Survey Of Routing Techniques-Based Optimization Of Energy Consumption In Sd-Dcn, Mohammed Nsaif, Gergely Kovásznai, Ali Malik, Ruairí De Fréin

Articles

The increasing power consumption of Data Center Networks (DCN) is becoming a major concern for network operators. The object of this paper is to provide a survey of state-of-the-art methods for reducing energy consumption via (1) enhanced scheduling and (2) enhanced aggregation of traffic flows using Software-Defined Networks (SDN), focusing on the advantages and disadvantages of these approaches. We tackle a gap in the literature for a review of SDN-based energy saving techniques and discuss the limitations of multi-controller solutions in terms of constraints on their performance. The main finding of this survey paper is that the two classes of …

Gated Deep Reinforcement Learning With Red Deer Optimization For Medical Image Classification, Narayanan Ganesh, Sambandan Jayalakshmi, Rama Chandran Narayanan, Miroslav Mahdal, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

The brain is one of the most important and complex organs in the body, consisting of billions of individual cells. Uncontrolled growth and expansion of aberrant cell populations within or around the brain are the main causes of brain tumors. These cells have the potential to harm healthy cells and impair brain function [1]. Tumors can be detected using medical imaging techniques, which are considered the most popular and accurate way to classify different types of cancer, and this procedure is even more crucial as it is noninvasive [2]. Magnetic resonance imaging (MRI) is one such medical imaging technique that …

Setransformer: A Transformer-Based Code Semantic Parser For Code Comment Generation, Zheng Li, Yonghao Wu, Bin Peng, Xiang Chen, Zeyu Sun, Yong Liu, Paul Doyle

Conference Papers

Automated code comment generation technologies can help developers understand code intent, which can significantly reduce the cost of software maintenance and revision. The latest studies in this field mainly depend on deep neural networks, such as convolutional neural networks and recurrent neural network. However, these methods may not generate high-quality and readable code comments due to the long-term dependence problem, which means that the code blocks used to summarize information are far from each other. Owing to the long-term dependence problem, these methods forget the previous input data’s feature information during the training process. In this article, to solve the …

Co-Design Of An Interactive Wellness Park: Exploring Design Requirements For A Multimodal Outdoor Physical Web Installation With Older Adults, Fatima Badmos

Academic Posters Collection

The global demographic landscape is experiencing a notable shift, characterised by a growing proportion of adults over 60. According to projections, the proportion of individuals aged 60 and above is expected to reach one-sixth of the global population by 2030. Furthermore, by 2050, this demographic is projected to exceed a staggering two billion people. Amidst this shift, there is an urgent need to develop interactive and innovative solutions to address older adults' unique challenges, particularly in outdoor physical activity.

A co-design methodology involving older adults’ participation from the idea generation to the application development process will be adopted to address …

Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo

Articles

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain …

Graph-Based Heuristic Solution For Placing Distributed Video Processing Applications On Moving Vehicle Clusters, Kanika Sharma, Bernard Butler, Brendan Jennings

Articles

Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of onboard sensors, video cameras, and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on selected vehicular fog nodes. We handle the unstable vehicular environment …

Ml-Based Online Traffic Classification For Sdns, Mohammed Nsaif, Gergely Kovasznai, Mohammed Abboosh, Ali Malik, Ruairí De Fréin

Articles

Traffic classification is a crucial aspect for Software-Defined Networking functionalities. This paper is a part of an on-going project aiming at optimizing power consumption in the environment of software-defined datacenter networks. We have developed a novel routing strategy that can blindly balance between the power consumption and the quality of service for the incoming traffic flows. In this paper, we demonstrate how to classify the network traffic flows so that the quality of service of each flow-class can be guaranteed efficiently. This is achieved by creating a dataset that encompasses different types of network traffic such as video, VoIP, game …

Human Mental Workload: A Survey And A Novel Inclusive Definition, Luca Longo, Christopher D. Wickens, Gabriella Hancock, P.A. Hancock

Articles

Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and …

Bayesian Adaptive Path Allocation Techniques For Intra-Datacenter Workloads, Ali Malik, Ruairí De Fréin, Chih-Heng Ke, Hasanen Alyasiri, Obinna Izima

Conference papers

Data center networks (DCNs) are the backbone of many cloud and Internet services. They are vulnerable to link failures, that occur on a daily basis, with a high frequency. Service disruption due to link failure may incur financial losses, compliance breaches and reputation damage. Performance metrics such as packet loss and routing flaps are negatively affected by these failure events. We propose a new Bayesian learning approach towards adaptive path allocation that aims to improve DCN performance by reducing both packet loss and routing flaps ratios. The proposed approach incorporates historical information about link failure and usage probabilities into its …

On The Mandelbrot Set For I**2 = ±1 And Imaginary Higgs Fields, Jonathan Blackledge

Articles

We consider the consequence of breaking with a fundamental result in complex analysisby lettingi2=±1wherei=√−1is the basic unit of all imaginary numbers. An analysis of theMandelbrot set for this case shows that a demarcation between a Fractal and a Euclidean object ispossible based oni2=−1andi2= +1, respectively. Further, we consider the transient behaviourassociated with the two cases to produce a range of non-standard sets in which a Fractal geometricstructure is transformed into a Euclidean object. In the case of the Mandelbrot set, the Euclideanobject is a square whose properties are investigate. Coupled with the associated Julia sets and othercomplex plane mappings, this …

A Proactive-Restoration Technique For Sdns, Ali Malik, Ruairí De Fréin

Conference papers

Failure incidents result in temporarily preventing the network from delivering services properly. Such a deterioration in services called service unavailability. The traditional fault management techniques, i.e. protection and restoration, are inevitably concerned with service unavailability due to the convergence time that is required to achieve the recovery when a failure occurs. However, with the global view feature of software-defined networking a failure prediction is becoming attainable, which in turn reduces the service interruptions that originated by failures. In this paper, we propose a proactive restoration technique that reconfigure the vulnerable routes which are likely to be affected if the …

Rapid Restoration Techniques For Software-Defined Networks, Ali Malik, Ruairí De Fréin, Benjamin Aziz

Articles

There is increasing demand in modern day business applications for communication networks to be robust and reliable due to the complexity and critical nature of such applications. As such, data delivery is expected to be reliable and secure even in the harshest of environments. Software-Defined Networking (SDN) is gaining traction as a promising approach for designing network architectures which are robust and flexible. One reason for this is that separating the data plane from the control plane, increases the controller’s ability to configure the network rapidly. When network failure events occur, the network manager may trade-off the optimality of the …

Applications Of Artificial Intelligence To Cryptography, Jonathan Blackledge, Napo Mosola

Articles

This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI). It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) to analyze and encrypt data. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs for generating unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of finite binary strings for applications in Cryptanalysis. The aim of the paper is to provide an overview on …

Using Bluetooth Low Energy Devices To Monitor Visitor Activity In Remote Amenity Spaces, Ahlam Al Anbouri, David Powell, Damon Berry, John Mcgrory, Niall Holmes, Lorraine D'Arcy

Conference Papers

Tracking of pedestrian behaviour, particularly route selection and temporal behaviours, can be difficult to undertake. This is especially true of studies at a community or campus level where the anonymity of pedestrians can be difficult to protect. The introduction of the EU’s General Data Protection Regulations 2016 (GDPR) has increased the complexity of this challenge. Advances in Bluetooth Low Energy (BLE) technology in recent years have increased the potential to monitor human behaviour by tracking and triangulating pedestrians. This paper describes an experiment undertaken along The Great South Wall at the Port of Dublin, which is considered a leading amenity …

Distance-Based Cluster Head Election For Mobile Sensing, Ruairí De Fréin, Liam O'Farrell

Conference papers

Energy-efficient, fair, stochastic leader-selection algorithms are designed for mobile sensing scenarios which adapt the sensing strategy depending on the mobile sensing topology. Methods for electing a cluster head are crucially important when optimizing the trade-off between the number of peer-to- peer interactions between mobiles and client-server interactions with a cloud-hosted application server. The battery-life of mobile devices is a crucial constraint facing application developers who are looking to use the convergence of mobile computing and cloud computing to perform environmental sensing. We exploit the mobile network topology, specifically the location of mobiles with respect to the gateway device, to stochastically …

State Acquisition In Computer Networks, Ruairí De Fréin

Conference papers

We establish that State Acquisition should be per- formed in networks at a rate which is consistent with the rate-of-change of the element or service being observed. We demonstrate that many existing monitoring and service-level prediction tools do not acquire network state in an appropriate manner. To address this challenge: (1) we define the rate-of- change of different applications; (2) we use methods for analysis of unevenly spaced time series, specifically, time series arising from video and voice applications, to estimate the rate-of-change of these services; and finally, (3) we demonstrate how to acquire network state accurately for a number …

Review Of The Effectiveness Of Impulse Testing For The Evaluation Of Cable Insulation Quality And Recommendations For Quality Testing, Adrian Coughlan, Joseph Kearney, Tom Looby

Conference papers

Abstract— This project investigates impulse breakdown testing as a means of determining the as constructed standard of MV power cable. A literature survey is undertaken to elucidate the place of this test in an overall cable test regime and to determine the factors that impact on the performance of the test method. Testing was undertaken on ESB Networks cables to establish if a merit order ranking was feasible based on this test and to determine if the test could detect defects in the inner semiconducting layer. Based on this, conclusions and recommendations are made regarding the overall applicability and usefulness …

Ambiqual – A Full Reference Objective Quality Metric For Ambisonic Spatial Audio, Miroslaw Narbutt, Andrew Allen, Jan Skoglund, Michael Chinen, Andrew Hines

Conference papers

Streaming spatial audio over networks requires efficient encoding techniques that compress the raw audio content without compromising quality of experience. Streaming service providers such as YouTube need a perceptually relevant objective audio quality metric to monitor users’ perceived quality and spatial localization accuracy. In this paper we introduce a full reference objective spatial audio quality metric, AMBIQUAL, which assesses both Listening Quality and Localization Accuracy. In our solution both metrics are derived directly from the B-format Ambisonic audio. The metric extends and adapts the algorithm used in ViSQOLAudio, a full reference objective metric designed for assessing speech and audio quality. …

Can Threshold-Based Sensor Alerts Be Analysed To Detect Faults In A District Heating Network?, Liam Cantwell

Dissertations

Older IoT “smart sensors” create system alerts from threshold rules on reading values. These simple thresholds are not very flexible to changes in the network. Due to the large number of false positives generated, these alerts are often ignored by network operators. Current state-of-the-art analytical models typically create alerts using raw sensor readings as the primary input. However, as greater numbers of sensors are being deployed, the growth in the number of readings that must be processed becomes problematic. The number of analytic models deployed to each of these systems is also increasing as analysis is broadened. This study aims …

Tiled Time Delay Estimation In Mobile Cloud Computing Environments, Ruairí De Fréin

Conference papers

We present a tiled delay estimation technique in the context of Mobile Cloud Computing (MCC) environments. We examine its accuracy in the presence of multiple sources for (1) sub-sample delays and also (2) in the presence of phase-wrap around. Phase wrap-around is prevalent in MCC because the separation of acoustic sources may be large. We show that tiling a histogram of instantaneous phase estimates can improve delay estimates when phase-wrap around is sig- nificantly present and also when multiple sources are present. We report that error in the delay estimator is generally less than 5% of a sample, when the …

Chaos-Based Cryptography For Cloud Computing, Paul Tobin, Lee Tobin, Michael Mckeever, Jonathan Blackledge

Conference papers

Cloud computing and poor security issues have quadrupled over the last six years and with the alleged presence of backdoors in common encryption ciphers, has created a need for personalising the encryption process by the client. In 2007, two Microsoft employees gave a presentation ``On the Possibility of a backdoor in the NIST SP800-90 Dual Elliptic Curve Pseudo Random Number Generators'' and was linked in 2013 by the New York Times with notes leaked by Edward Snowden. This confirmed backdoors were placed, allegedly, in a number of encryption systems by the National Security Agency, which if true creates an urgent …

One-To-Cloud One-Time Pad Data Encryption: Introducing Virtual Prototyping With Pspice, Paul Tobin, Lee Tobin, Roberto Gandia Blanquer Dr, Michael Mckeever, Jonathan Blackledge

Conference papers

In this paper, we examine the design and application of a one-time pad encryption system for protecting data stored in the Cloud. Personalising security using a one-time pad generator at the client-end protects data from break-ins, side-channel attacks and backdoors in public encryption algorithms. The one-time pad binary sequences were obtained from modified analogue chaos oscillators initiated by noise and encoded client data locally. Specific ``one-to-Cloud'' storage applications returned control back to the end user but without the key distribution problem normally associated with one-time pad encryption. Development of the prototype was aided by ``Virtual Prototyping'' in the latest version …

On The Development Of A One-Time Pad Generator For Personalising Cloud Security, Paul Tobin, Lee Tobin, Michael Mckeever, Jonathan Blackledge

Conference papers

Cloud computing security issues are being reported in newspapers, television, and on the Internet, on a daily basis. Furthermore, in 2013, Edward Snowden alleged backdoors were placed in a number of encryption systems by the National Security Agency causing confidence in public encryption to drop even further. Our solution allows the end-user to add a layer of unbreakable security by encrypting the data locally with a random number generator prior to uploading data to the Cloud. The prototype one-time pad generator is impervious to cryptanalysis because it generates unbreakable random binary sequences from chaos sources initiated from a natural noise. …