Engineering Commons^™

Breaking-Down And Parameterising Wave Energy Converter Costs Using The Capex And Similitude Methods, Ophelie Choupin, Michael Henriksen, Amir Etemad-Shahidi, Rodger Tomlinson

Research outputs 2014 to 2021

Wave energy converters (WECs) can play a significant role in the transition towards a more renewable-based energy mix as stable and unlimited energy resources. Financial analysis of these projects requires WECs cost and WEC capital expenditure (CapEx) information. However, (i) cost information is often limited due to confidentiality and (ii) the wave energy field lacks flexible methods for cost breakdown and parameterisation, whereas they are needed for rapid and optimised WEC configuration and worldwide site pairing. This study takes advantage of the information provided by Wavepiston to compare different costing methods. The work assesses the Froude-Law-similarities-based “Similitude method” for cost-scaling …

Detection And Recognition Of Moving Video Objects: Kalman Filtering With Deep Learning, Hind Rustum Mohammed, Zahir M. Hussain

Research outputs 2014 to 2021

© 2021. All rights reserved. Research in object recognition has lately found that Deep Convolutional Neuronal Networks (CNN) provide a breakthrough in detection scores, especially in video applications. This paper presents an approach for object recognition in videos by combining Kalman filter with CNN. Kalman filter is first applied for detection, removing the background and then cropping object. Kalman filtering achieves three important functions: predicting the future location of the object, reducing noise and interference from incorrect detections, and associating multi-objects to tracks. After detection and cropping the moving object, a CNN model will predict the category of object. The …

Deep Learning Versus Spectral Techniques For Frequency Estimation Of Single Tones: Reduced Complexity For Software-Defined Radio And Iot Sensor Communications, Hind R. Almayyali, Zahir M. Hussain

Research outputs 2014 to 2021

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can. While filling the gap in the existing literature, the study is comprehensive, analyzing errors under different signal-to-noise ratios (SNRs), numbers of nodes, and numbers of input samples under missing SNR information. DL-based FE is not significantly affected by SNR bias or number of nodes. …

Rgb-D Data-Based Action Recognition: A Review, Muhammad Bilal Shaikh, Douglas Chai

Research outputs 2014 to 2021

Classification of human actions is an ongoing research problem in computer vision. This review is aimed to scope current literature on data fusion and action recognition techniques and to identify gaps and future research direction. Success in producing cost-effective and portable vision-based sensors has dramatically increased the number and size of datasets. The increase in the number of action recognition datasets intersects with advances in deep learning architectures and computational support, both of which offer significant research opportunities. Naturally, each action-data modality—such as RGB, depth, skeleton, and infrared (IR)—has distinct characteristics; therefore, it is important to exploit the value of …

Green Underwater Wireless Communications Using Hybrid Optical-Acoustic Technologies, Kazi Y. Islam, Iftekhar Ahmad, Daryoush Habibi, M. Ishtiaque A. Zahed, Joarder Kamruzzaman

Research outputs 2014 to 2021

Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology – underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe …

Short Word-Length Entering Compressive Sensing Domain: Improved Energy Efficiency In Wireless Sensor Networks, Nuha A. S. Alwan, Zahir M. Hussain

Research outputs 2014 to 2021

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows …

A Range Error Reduction Technique For Positioning Applications In Sports, Adnan Waqar, Iftekhar Ahmad, Daryoush Habibi, Quoc V. Phung

Research outputs 2014 to 2021

In recent times, ultra-wideband (UWB)-based positioning systems have become popular in sport performance monitoring. UWB positioning system uses time of arrival to calculate the range data between devices (i.e. anchors, tags), and then use trilateration algorithms to estimate position coordinates. In practical applications, non-line-of-sight transmissions and multipath propagations lead to inaccurate range data and lower positioning accuracy. This paper introduces a range error minimisation algorithm to address this limitation of error in range data in UWB-based positioning system. The proposed solution analyses the range error for each anchor and sequentially reduces this error based on the distance between each anchor …

Hybrid Mamdani Fuzzy Rules And Convolutional Neural Networks For Analysis And Identification Of Animal Images, Hind R. Mohammed, Zahir M. Hussain

Research outputs 2014 to 2021

Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, …

Realization And Optimization Of Optical Logic Gates Using Bias Assisted Carrier-Injected Triple Parallel Microring Resonators, Fayza Kizhakkakath, Sooraj Ravindran, Kwangwook Park, Kamal Alameh, Yong Tak Lee

Research outputs 2014 to 2021

We propose a p-i-n diode embedded parallel triple microring resonator (MRR) configuration to simultaneously realize optical OR and AND, or NAND and NOR logic gates using a bias-assisted carrier injection mechanism. The applied bias on the rings induces refractive index change in the intrinsic region through bandfilling, bandgap shrinkage and free carrier absorption effects, leading to intensity variation at the output ports of the MRR due to respective resonant wavelength shift. The optical logic gate operational outputs are represented as the light intensities at the output ports of the MRR with the wavelength of the input optical signal launched into …

Optimal Sizing Of Energy Storage System To Reduce Impacts Of Transportation Electrification On Power Distribution Transformers Integrated With Photovoltaic, Pravakar Pradhan, Iftekhar Ahmad, Daryoush Habibi, Asma Aziz, Bassam Al-Hanahi, Mohammad A S Masoum

Research outputs 2014 to 2021

Transportation systems are one of the leading sectors that contribute to greenhouse gas emissions that lead to enhance global warming. The electrification of vehicles is a promising solution to this widespread problem; however, integrating electric vehicles (EVs) into existing grid systems on a large scale creates several problems, both for consumers and for utilities. Accelerated aging of expensive grid assets, such as power transformers, is one of the primary issues that these utilities are facing. This problem can be addressed with battery energy storage systems (BESS), which acts as buffer between demand and supply. Accordingly, this paper proposes a novel …

Deep Learning Control For Digital Feedback Systems: Improved Performance With Robustness Against Parameter Change, Nuha A. S. Alwan, Zahir M. Hussain

Research outputs 2014 to 2021

Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a …

Voltage Stability Of Power Systems With Renewable-Energy Inverter-Based Generators: A Review, Nasser Hosseinzadeh, Asma Aziz, Apel Mahmud, Ameen Gargoom, Mahbub Rabbani

Research outputs 2014 to 2021

© 2021 by the authors. The main purpose of developing microgrids (MGs) is to facilitate the integration of renewable energy sources (RESs) into the power grid. RESs are normally connected to the grid via power electronic inverters. As various types of RESs are increasingly being connected to the electrical power grid, power systems of the near future will have more inverter-based generators (IBGs) instead of synchronous machines. Since IBGs have significant differences in their characteristics compared to synchronous generators (SGs), particularly concerning their inertia and capability to provide reactive power, their impacts on the system dynamics are different compared to …

Class Distribution-Aware Adaptive Margins And Cluster Embedding For Classification Of Fruit And Vegetables At Supermarket Self-Checkouts, Khurram Hameed, Douglas Chai, Alexander Rassau

Research outputs 2014 to 2021

The complex task of vision based fruit and vegetables classification at a supermarket self-checkout poses significant challenges. These challenges include the highly variable physical features of fruit and vegetables i.e. colour, texture shape and size which are dependent upon ripeness and storage conditions in a supermarket as well as general product variation. Supermarket environments are also significantly variable with respect to lighting conditions. Attempting to build an exhaustive dataset to capture all these variations, for example a dataset of a fruit consisting of all possible colour variations, is nearly impossible. Moreover, some fruit and vegetable classes have significant similar physical …

Frequency Estimation From Compressed Measurements Of A Sinusoid In Moving‐Average Colored Noise, Nuha A. S. Alwan, Zahir M. Hussain

Research outputs 2014 to 2021

Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two disci-plines by studying the effects of compressed measurements of a single sinusoid in moving‐average colored noise on its frequency estimation accuracy. CCS techniques can recover the second‐order statistics of the original uncompressed signal from the compressed measurements, thereby enabling correlation‐based frequency estimation of single tones in colored noise using higher order lags. Ac-ceptable accuracy is achieved for …

Power Network And Smart Grids Analysis From A Graph Theoretic Perspective, Hossein Parast Vand

Theses: Doctorates and Masters

The growing size and complexity of power systems has given raise to the use of complex network theory in their modelling, analysis, and synthesis. Though most of the previous studies in this area have focused on distributed control through well established protocols like synchronization and consensus, recently, a few fundamental concepts from graph theory have also been applied, for example in symmetry-based cluster synchronization. Among the existing notions of graph theory, graph symmetry is the focus of this proposal. However, there are other development around some concepts from complex network theory such as graph clustering in the study.

In spite …