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Hafiz Suliman Munawar
School of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, Australia

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Perspective
Published: 26 August 2021 in Sustainability
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Coronavirus disease 2019 (COVID-19) is an infectious disease characterised by symptoms that are like the common cold. The current pandemic situation in anticipation of a vaccine has posed serious threats to the health and economic sectors of countries worldwide. To overcome the quick transmission of the virus, the government of Australia has also taken drastic measures to prevent its spread. These policies include an international and interstate travel ban, social distancing rules, lockdown, shutdown of educational institutes and work-from-home policies. Such rules have affected people on both behavioural and psychological levels. This study aims to analyse the effect of COVID-19 on Australian citizens, and therefore, the changed behaviour of citizens concerning their mobility patterns, transport preferences and shopping methods under the pandemic have been studied. A detailed literature search was adopted for gathering data related to the study theme, along with real-time evidence of changes in the behaviour of people following the pandemic. The socioeconomic impact of the pandemic on social inequality and thereby the effect on the vulnerable people of the population are also studied. Authentic surveys and statistical data are consulted to figure out how the new lifestyle choices of people will linger in the post-pandemic era. It was found that people in Australia have adopted the work-from-home regime, and new habits suiting the nationwide restrictions have become routine for many people.

ACS Style

Hafiz Suliman Munawar; Sara Imran Khan; Zakria Qadir; Yusra Sajid Kiani; Abbas Z. Kouzani; M. A. Parvez Mahmud. Insights into the Mobility Pattern of Australians during COVID-19. Sustainability 2021, 13, 9611 .

AMA Style

Hafiz Suliman Munawar, Sara Imran Khan, Zakria Qadir, Yusra Sajid Kiani, Abbas Z. Kouzani, M. A. Parvez Mahmud. Insights into the Mobility Pattern of Australians during COVID-19. Sustainability. 2021; 13 (17):9611.

Chicago/Turabian Style

Hafiz Suliman Munawar; Sara Imran Khan; Zakria Qadir; Yusra Sajid Kiani; Abbas Z. Kouzani; M. A. Parvez Mahmud. 2021. "Insights into the Mobility Pattern of Australians during COVID-19." Sustainability 13, no. 17: 9611.

Review
Published: 14 August 2021 in Infrastructures
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Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.

ACS Style

Hafiz Suliman Munawar; Ahmed W. A. Hammad; Assed Haddad; Carlos Alberto Pereira Soares; S. Travis Waller. Image-Based Crack Detection Methods: A Review. Infrastructures 2021, 6, 115 .

AMA Style

Hafiz Suliman Munawar, Ahmed W. A. Hammad, Assed Haddad, Carlos Alberto Pereira Soares, S. Travis Waller. Image-Based Crack Detection Methods: A Review. Infrastructures. 2021; 6 (8):115.

Chicago/Turabian Style

Hafiz Suliman Munawar; Ahmed W. A. Hammad; Assed Haddad; Carlos Alberto Pereira Soares; S. Travis Waller. 2021. "Image-Based Crack Detection Methods: A Review." Infrastructures 6, no. 8: 115.

Case report
Published: 26 July 2021 in Fire
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Australia is a regular recipient of devastating bushfires that severely impacts its economy, landscape, forests, and wild animals. These bushfires must be managed to save a fortune, wildlife, and vegetation and reduce fatalities and harmful environmental impacts. The current study proposes a holistic model that uses a mixed-method approach of Geographical Information System (GIS), remote sensing, and Unmanned Aerial Vehicles (UAV)-based bushfire assessment and mitigation. The fire products of Visible Infrared Imager Radiometer Suite (VIIRS) and Moderate-resolution Imaging Spectroradiometer (MODIS) are used for monitoring the burnt areas within the Victorian Region due to the 2020 bushfires. The results show that the aggregate of 1500 m produces the best output for estimating the burnt areas. The identified hotspots are in the eastern belt of the state that progressed north towards New South Wales. The R2 values between 0.91–0.99 indicate the fitness of methods used in the current study. A healthy z-value index between 0.03 to 2.9 shows the statistical significance of the hotspots. Additional analysis of the 2019–20 Victorian bushfires shows a widespread radius of the fires associated with the climate change and Indian Ocean Dipole (IOD) phenomenon. The UAV paths are optimized using five algorithms: greedy, intra route, inter route, tabu, and particle swarm optimization (PSO), where PSO search surpassed all the tested methods in terms of faster run time and lesser costs to manage the bushfires disasters. The average improvement demonstrated by the PSO algorithm over the greedy method is approximately 2% and 1.2% as compared with the intra route. Further, the cost reduction is 1.5% compared with the inter-route scheme and 1.2% compared with the intra route algorithm. The local disaster management authorities can instantly adopt the proposed system to assess the bushfires disasters and instigate an immediate response plan.

ACS Style

Hafiz Munawar; Fahim Ullah; Sara Khan; Zakria Qadir; Siddra Qayyum. UAV Assisted Spatiotemporal Analysis and Management of Bushfires: A Case Study of the 2020 Victorian Bushfires. Fire 2021, 4, 40 .

AMA Style

Hafiz Munawar, Fahim Ullah, Sara Khan, Zakria Qadir, Siddra Qayyum. UAV Assisted Spatiotemporal Analysis and Management of Bushfires: A Case Study of the 2020 Victorian Bushfires. Fire. 2021; 4 (3):40.

Chicago/Turabian Style

Hafiz Munawar; Fahim Ullah; Sara Khan; Zakria Qadir; Siddra Qayyum. 2021. "UAV Assisted Spatiotemporal Analysis and Management of Bushfires: A Case Study of the 2020 Victorian Bushfires." Fire 4, no. 3: 40.

Journal article
Published: 22 July 2021 in Cleaner Engineering and Technology
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The magnetic levitation (MAGLEV) train uses magnetic field to suspend, guide, and propel vehicle onto the track. The MAGLEV train provides a sustainable and cleaner solution for train transportation by significantly reducing the energy usage and greenhouse gas emissions as compared to traditional train transportation systems. In this paper, we propose an advanced control mechanism using an Arduino microcontroller that selectively energizes the electromagnets in a MAGLEV train system to provide dynamic stability and energy efficiency. We also design the prototype of an energy-efficient MAGLEV train that leverages our proposed control mechanism. In our MAGLEV train prototype, the levitation is achieved by creating a repulsive magnetic field between the train and the track using magnets mounted on the top-side of the track and bottom-side of the vehicle. The propulsion is performed by creating a repulsive magnetic field between the permanent magnets attached on the sides of the vehicle and electromagnets mounted at the center of the track using electrodynamic suspension (EDS). The electromagnets are energized via a control mechanism that is applied through an Arduino microcontroller. The Arduino microcontroller is programmed in such a way to propel and guide the vehicle onto the track by appropriate switching of the electromagnets. We use an infrared-based remote-control device for controlling the power, speed, and direction of the vehicle in both the forward and the backward direction. The proposed MAGLEV train control mechanism is novel, and according to the best of our knowledge is the first study of its kind that uses an Arduino-based microcontroller system for control mechanism. Experimental results illustrate that the designed prototype consumes only 144 W-hour (Wh) of energy as compared to a conventionally designed MAGLEV train prototype that consumes 1200 Wh. Results reveal that our proposed control mechanism and prototype model can reduce the total power consumption by 8.3 × as compared to the traditional MAGLEV train prototype, and can be applied to practical MAGLEV trains with necessary modifications. Thus, our proposed prototype and control mechanism serves as a first step towards cleaner engineering of train transportation systems.

ACS Style

Zakria Qadir; Arslan Munir; Tehreem Ashfaq; Hafiz Suliman Munawar; Muazzam A. Khan; Khoa Le. A prototype of an energy-efficient MAGLEV train: A step towards cleaner train transport. Cleaner Engineering and Technology 2021, 4, 100217 .

AMA Style

Zakria Qadir, Arslan Munir, Tehreem Ashfaq, Hafiz Suliman Munawar, Muazzam A. Khan, Khoa Le. A prototype of an energy-efficient MAGLEV train: A step towards cleaner train transport. Cleaner Engineering and Technology. 2021; 4 ():100217.

Chicago/Turabian Style

Zakria Qadir; Arslan Munir; Tehreem Ashfaq; Hafiz Suliman Munawar; Muazzam A. Khan; Khoa Le. 2021. "A prototype of an energy-efficient MAGLEV train: A step towards cleaner train transport." Cleaner Engineering and Technology 4, no. : 100217.

Review
Published: 15 July 2021 in Sustainability
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Rapid advances that improve flood management have facilitated the disaster response by providing first aid services, finding safe routes, maintaining communication and developing flood maps. Different technologies such as image processing, satellite imagery, synthetic imagery and integrated approaches have been extensively analysed in the literature for disaster operations. There is a need to review cutting-edge technologies for flood management. This paper presents a review of the latest advancements in the flood management domain based on image processing, artificial intelligence and integrated approaches with a focus on post-disaster. It answers the following research questions: (1) What are the latest developments in image processing for flood management in a post-disaster scenario? (2) What are the latest techniques for flood management based on artificial intelligence in a post-disaster scenario? (3) What are the existing gaps in the selected technologies for post-disaster? (4) How can the authorities improve the existing post-disaster management operation with cutting-edge technologies? A novel framework has been proposed to optimise flood management with the application of a holistic approach.

ACS Style

Hafiz Munawar; Ahmed Hammad; S. Waller; Muhammad Thaheem; Asheem Shrestha. An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review. Sustainability 2021, 13, 7925 .

AMA Style

Hafiz Munawar, Ahmed Hammad, S. Waller, Muhammad Thaheem, Asheem Shrestha. An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review. Sustainability. 2021; 13 (14):7925.

Chicago/Turabian Style

Hafiz Munawar; Ahmed Hammad; S. Waller; Muhammad Thaheem; Asheem Shrestha. 2021. "An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review." Sustainability 13, no. 14: 7925.

Journal article
Published: 08 July 2021 in Buildings
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Detecting buildings from high-resolution satellite imagery is beneficial in mapping, environmental preparation, disaster management, military planning, urban planning and research purposes. Differentiating buildings from the images is possible however, it may be a time-consuming or complicated process. Therefore, the high-resolution imagery from satellites needs to be automated to detect the buildings. Additionally, buildings exhibit several different characteristics, and their appearance in these images is unplanned. Moreover, buildings in the metropolitan environment are typically crowded and complicated. Therefore, it is challenging to identify the building and hard to locate them. To resolve this situation, a novel probabilistic method has been suggested using local features and probabilistic approaches. A local feature extraction technique was implemented, which was used to calculate the probability density function. The locations in the image were represented as joint probability distributions and were used to estimate their probability distribution function (pdf). The density of building locations in the image was extracted. Kernel density distribution was also used to find the density flow for different metropolitan cities such as Sydney (Australia), Tokyo (Japan), and Mumbai (India), which is useful for distribution intensity and pattern of facility point f interest (POI). The purpose system can detect buildings/rooftops and to test our system, we choose some crops with panchromatic high-resolution satellite images from Australia and our results looks promising with high efficiency and minimal computational time for feature extraction. We were able to detect buildings with shadows and building without shadows in 0.4468 (seconds) and 0.5126 (seconds) respectively.

ACS Style

Hafiz Munawar; Riya Aggarwal; Zakria Qadir; Sara Khan; Abbas Kouzani; M. Mahmud. A Gabor Filter-Based Protocol for Automated Image-Based Building Detection. Buildings 2021, 11, 302 .

AMA Style

Hafiz Munawar, Riya Aggarwal, Zakria Qadir, Sara Khan, Abbas Kouzani, M. Mahmud. A Gabor Filter-Based Protocol for Automated Image-Based Building Detection. Buildings. 2021; 11 (7):302.

Chicago/Turabian Style

Hafiz Munawar; Riya Aggarwal; Zakria Qadir; Sara Khan; Abbas Kouzani; M. Mahmud. 2021. "A Gabor Filter-Based Protocol for Automated Image-Based Building Detection." Buildings 11, no. 7: 302.

Journal article
Published: 06 July 2021 in Sustainability
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Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91%. Disaster management organizations can use this model to assess the damages to critical city infrastructure and other assets worldwide to instigate proper disaster responses and minimize the damages. This can help with the smart governance of the cities where all emergent disasters are addressed promptly.

ACS Style

Hafiz Munawar; Fahim Ullah; Siddra Qayyum; Sara Khan; Mohammad Mojtahedi. UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection. Sustainability 2021, 13, 7547 .

AMA Style

Hafiz Munawar, Fahim Ullah, Siddra Qayyum, Sara Khan, Mohammad Mojtahedi. UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection. Sustainability. 2021; 13 (14):7547.

Chicago/Turabian Style

Hafiz Munawar; Fahim Ullah; Siddra Qayyum; Sara Khan; Mohammad Mojtahedi. 2021. "UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection." Sustainability 13, no. 14: 7547.

Review
Published: 29 June 2021 in Energies
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Sound localization is a vast field of research and advancement which is used in many useful applications to facilitate communication, radars, medical aid, and speech enhancement to but name a few. Many different methods are presented in recent times in this field to gain benefits. Various types of microphone arrays serve the purpose of sensing the incoming sound. This paper presents an overview of the importance of using sound localization in different applications along with the use and limitations of ad-hoc microphones over other microphones. In order to overcome these limitations certain approaches are also presented. Detailed explanation of some of the existing methods that are used for sound localization using microphone arrays in the recent literature is given. Existing methods are studied in a comparative fashion along with the factors that influence the choice of one method over the others. This review is done in order to form a basis for choosing the best fit method for our use.

ACS Style

Muhammad Liaquat; Hafiz Munawar; Amna Rahman; Zakria Qadir; Abbas Kouzani; M. Mahmud. Localization of Sound Sources: A Systematic Review. Energies 2021, 14, 3910 .

AMA Style

Muhammad Liaquat, Hafiz Munawar, Amna Rahman, Zakria Qadir, Abbas Kouzani, M. Mahmud. Localization of Sound Sources: A Systematic Review. Energies. 2021; 14 (13):3910.

Chicago/Turabian Style

Muhammad Liaquat; Hafiz Munawar; Amna Rahman; Zakria Qadir; Abbas Kouzani; M. Mahmud. 2021. "Localization of Sound Sources: A Systematic Review." Energies 14, no. 13: 3910.

Journal article
Published: 10 June 2021 in Energies
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Sound localization is a field of signal processing that deals with identifying the origin of a detected sound signal. This involves determining the direction and distance of the source of the sound. Some useful applications of this phenomenon exists in speech enhancement, communication, radars and in the medical field as well. The experimental arrangement requires the use of microphone arrays which record the sound signal. Some methods involve using ad-hoc arrays of microphones because of their demonstrated advantages over other arrays. In this research project, the existing sound localization methods have been explored to analyze the advantages and disadvantages of each method. A novel sound localization routine has been formulated which uses both the direction of arrival (DOA) of the sound signal along with the location estimation in three-dimensional space to precisely locate a sound source. The experimental arrangement consists of four microphones and a single sound source. Previously, sound source has been localized using six or more microphones. The precision of sound localization has been demonstrated to increase with the use of more microphones. In this research, however, we minimized the use of microphones to reduce the complexity of the algorithm and the computation time as well. The method results in novelty in the field of sound source localization by using less resources and providing results that are at par with the more complex methods requiring more microphones and additional tools to locate the sound source. The average accuracy of the system is found to be 96.77% with an error factor of 3.8%.

ACS Style

Muhammad Liaquat; Hafiz Munawar; Amna Rahman; Zakria Qadir; Abbas Kouzani; M. Mahmud. Sound Localization for Ad-Hoc Microphone Arrays. Energies 2021, 14, 3446 .

AMA Style

Muhammad Liaquat, Hafiz Munawar, Amna Rahman, Zakria Qadir, Abbas Kouzani, M. Mahmud. Sound Localization for Ad-Hoc Microphone Arrays. Energies. 2021; 14 (12):3446.

Chicago/Turabian Style

Muhammad Liaquat; Hafiz Munawar; Amna Rahman; Zakria Qadir; Abbas Kouzani; M. Mahmud. 2021. "Sound Localization for Ad-Hoc Microphone Arrays." Energies 14, no. 12: 3446.

Case report
Published: 24 May 2021 in Applied Sciences
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The study was conducted to assess the post 2010 flood risk management and resilience-building practices in District Layyah, Pakistan. Exploratory research was applied to gain knowledge of flood risk management to embed the disaster risk reduction, mitigation, and adaptation strategies at the local government and community level. Around 200 questionnaires were collected from the four devastated areas/union councils. Primary data from the field uncovered flood risk management practices by organizations, local government, and the community. It highlights resilience-building practices undertaken by the community through rehabilitation, community participation, and local indigenous practices. The role of the District Layyah’s local government and organizations to mitigate the 2010 flood and their contribution towards flood resilience in affected communities was investigated, as no comparable studies were carried out in the riverine belt of District Layyah previously. Moreover, the tangible and non-tangible measures to lessen the vulnerability to floods and improve flood risk governance at a local level were identified. This study makes a valuable contribution in strengthening the resilience building of vulnerable communities by recommending few changes in existing practices concerning flood risk at a local level.

ACS Style

Hafiz Munawar; Sara Khan; Numera Anum; Zakria Qadir; Abbas Kouzani; M. Parvez Mahmud. Post-Flood Risk Management and Resilience Building Practices: A Case Study. Applied Sciences 2021, 11, 4823 .

AMA Style

Hafiz Munawar, Sara Khan, Numera Anum, Zakria Qadir, Abbas Kouzani, M. Parvez Mahmud. Post-Flood Risk Management and Resilience Building Practices: A Case Study. Applied Sciences. 2021; 11 (11):4823.

Chicago/Turabian Style

Hafiz Munawar; Sara Khan; Numera Anum; Zakria Qadir; Abbas Kouzani; M. Parvez Mahmud. 2021. "Post-Flood Risk Management and Resilience Building Practices: A Case Study." Applied Sciences 11, no. 11: 4823.

Journal article
Published: 02 May 2021 in Sensors
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Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.

ACS Style

Muhammad Shaukat; Haafizah Shaukat; Zakria Qadir; Hafiz Munawar; Abbas Kouzani; M. Mahmud. Cluster Analysis and Model Comparison Using Smart Meter Data. Sensors 2021, 21, 3157 .

AMA Style

Muhammad Shaukat, Haafizah Shaukat, Zakria Qadir, Hafiz Munawar, Abbas Kouzani, M. Mahmud. Cluster Analysis and Model Comparison Using Smart Meter Data. Sensors. 2021; 21 (9):3157.

Chicago/Turabian Style

Muhammad Shaukat; Haafizah Shaukat; Zakria Qadir; Hafiz Munawar; Abbas Kouzani; M. Mahmud. 2021. "Cluster Analysis and Model Comparison Using Smart Meter Data." Sensors 21, no. 9: 3157.

Perspective
Published: 26 January 2021 in Sustainability
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The Coronavirus Disease 2019 (COVID-19) is a major virus outbreak of the 21st century. The Australian government and local authorities introduced some drastic strategies and policies to control the outspread of this virus. The policies related to lockdown, quarantine, social distancing, shut down of educational institute, work from home, and international and interstate travel bans significantly affect the lifestyle of citizens and, thus, influence their activity patterns. The transport system is, thus, severely affected due to the COVID-19 related restrictions. This paper analyses how the transport system is impacted because of the policies adopted by the Australian government for the containment of the COVID-19. Three main components of the transport sector are studied. These are air travel, public transport, and freight transport. Various official sources of data such as the official website of the Australian government, Google mobility trends, Apple Mobility trends, and Moovit were consulted along with recently published research articles on COVID-19 and its impacts. The secondary sources of data include databases, web articles, and interviews that were conducted with the stakeholders of transport sectors in Australia to analyse the relationship between COVID-19 prevention measures and the transport system. The results of this study showed reduced demand for transport with the adoption of COVID-19 prevention measures. Declines in revenues in the air, freight, and public transport sectors of the transport industry are also reported. The survey shows that transport sector in Australia is facing a serious financial downfall as the use of public transport has dropped by 80%, a 31.5% drop in revenues earned by International airlines in Australia has been predicted, and a 9.5% reduction in the freight transport by water is expected. The recovery of the transport sector to the pre-pandemic state is only possible with the relaxation of COVID-19 containment policies and financial support by the government.

ACS Style

Hafiz Munawar; Sara Khan; Zakria Qadir; Abbas Kouzani; Mortoza Mahmud. Insight into the Impact of COVID-19 on Australian Transportation Sector: An Economic and Community-Based Perspective. Sustainability 2021, 13, 1276 .

AMA Style

Hafiz Munawar, Sara Khan, Zakria Qadir, Abbas Kouzani, Mortoza Mahmud. Insight into the Impact of COVID-19 on Australian Transportation Sector: An Economic and Community-Based Perspective. Sustainability. 2021; 13 (3):1276.

Chicago/Turabian Style

Hafiz Munawar; Sara Khan; Zakria Qadir; Abbas Kouzani; Mortoza Mahmud. 2021. "Insight into the Impact of COVID-19 on Australian Transportation Sector: An Economic and Community-Based Perspective." Sustainability 13, no. 3: 1276.

Journal article
Published: 21 January 2021 in Energy Reports
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In the current technological era, predicting the power and energy output based on the changing weather factors play an important role in the economic growth of the renewable energy sector. Unlike traditional fossil fuel-based resources, renewable energy sources potentially play a pivotal role in sustaining a country’s economy and improving the quality of life. As our planet is nowadays facing serious challenges due to climate change and global warming, this research could be effective to achieve good prediction accuracy in smart grids using different weather conditions. In the current study, different machine learning models are compared to estimate power and energy of hybrid photovoltaic (PV)-wind renewable energy systems using seven weather factors that have a significant impact on the output of the PV–wind renewable energy system. This study classified the machine learning model which could be potentially useful and efficient to predict energy and power. The historic hourly data is processed with and without data manipulation. While data manipulations are carried out using recursive feature elimination using cross-validation (RFECV). The data is trained using artificial neural network (ANN) regressors and correlations between different features within the dataset are identified. The main aim is to find meaningful patterns that could help statistical learning models train themselves based on these usage patterns. The results suggest that opting feature selection technique using linear regression model outperforms all the other models in all evaluation metrics having to mean squared error (MSE) of 0.000000104, mean absolute error (MAE) of 0.00083, R2 of 99.6%, and computation time of 0.02 s The results investigated depict that the sustainable computational scheme introduced has vast potential to enhance smart grids efficiency by predicting the energy produced by renewable energy systems.

ACS Style

Zakria Qadir; Sara Imran Khan; Erfan Khalaji; Hafiz Suliman Munawar; Fadi Al-Turjman; M.A. Parvez Mahmud; Abbas Z. Kouzani; Khoa Le. Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids. Energy Reports 2021, 1 .

AMA Style

Zakria Qadir, Sara Imran Khan, Erfan Khalaji, Hafiz Suliman Munawar, Fadi Al-Turjman, M.A. Parvez Mahmud, Abbas Z. Kouzani, Khoa Le. Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids. Energy Reports. 2021; ():1.

Chicago/Turabian Style

Zakria Qadir; Sara Imran Khan; Erfan Khalaji; Hafiz Suliman Munawar; Fadi Al-Turjman; M.A. Parvez Mahmud; Abbas Z. Kouzani; Khoa Le. 2021. "Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids." Energy Reports , no. : 1.

Review
Published: 19 January 2021 in Computer Communications
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UAVs are increasingly incorporated in a wide range of domains such as disaster management and rescue missions. UAV path planning deals with finding the most optimal or shortest path for UAVs such that minimum energy and resources are utilized. This paper examines the path planning algorithms for UAVs through a literature survey conducted on 139 systematically retrieved articles published in the last decade that are narrowed down to 36 highly relevant articles. As retrieved from the shortlisted articles, the path planning algorithms include RRT, Artificial Potential, Voronoi, D-Star, A-Star, Dijkstra, MILP, Neural Network, Ant Colony Optimization, and Particle Swarm Optimization that are classified into four main types: Model-based, Conventional, Learning-based, and Cell-based. Most of the disaster-related articles are focused on the post-disaster phase only and use conventional and learning-based algorithms with applications to localize victims and optimize paths. Regarding the UAV communication network (UAVCN), the key challenges are communication issues, resource allocation, UAV deployment, defining UAV trajectory, and content security. UAV path planning’s key barriers are path optimization, path completeness, optimality, efficiency, and achieving robustness. Accordingly, a holistic IoT-powered UAV-based smart city management system has been recommended in the current study where all the smart city key components are integrated to address disasters like floods, earthquakes, and bush fire. The proposed holistic system can help prepare for disasters and mitigate them as soon as these arise and help enhance the smart city governance.

ACS Style

Zakria Qadir; Fahim Ullah; Hafiz Suliman Munawar; Fadi Al-Turjman. Addressing disasters in smart cities through UAVs path planning and 5G communications: A systematic review. Computer Communications 2021, 168, 114 -135.

AMA Style

Zakria Qadir, Fahim Ullah, Hafiz Suliman Munawar, Fadi Al-Turjman. Addressing disasters in smart cities through UAVs path planning and 5G communications: A systematic review. Computer Communications. 2021; 168 ():114-135.

Chicago/Turabian Style

Zakria Qadir; Fahim Ullah; Hafiz Suliman Munawar; Fadi Al-Turjman. 2021. "Addressing disasters in smart cities through UAVs path planning and 5G communications: A systematic review." Computer Communications 168, no. : 114-135.

Journal article
Published: 08 April 2020 in International Journal of Wireless and Microwave Technologies
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ACS Style

Hafiz Suliman Munawar. An Overview of Reconfigurable Antennas for Wireless Body Area Networks and Possible Future Prospects. International Journal of Wireless and Microwave Technologies 2020, 10, 1 -8.

AMA Style

Hafiz Suliman Munawar. An Overview of Reconfigurable Antennas for Wireless Body Area Networks and Possible Future Prospects. International Journal of Wireless and Microwave Technologies. 2020; 10 (2):1-8.

Chicago/Turabian Style

Hafiz Suliman Munawar. 2020. "An Overview of Reconfigurable Antennas for Wireless Body Area Networks and Possible Future Prospects." International Journal of Wireless and Microwave Technologies 10, no. 2: 1-8.

Review
Published: 26 March 2020 in Big Data and Cognitive Computing
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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

ACS Style

Hafiz Suliman Munawar; Siddra Qayyum; Fahim Ullah; Samad Sepasgozar. Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis. Big Data and Cognitive Computing 2020, 4, 4 .

AMA Style

Hafiz Suliman Munawar, Siddra Qayyum, Fahim Ullah, Samad Sepasgozar. Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis. Big Data and Cognitive Computing. 2020; 4 (2):4.

Chicago/Turabian Style

Hafiz Suliman Munawar; Siddra Qayyum; Fahim Ullah; Samad Sepasgozar. 2020. "Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis." Big Data and Cognitive Computing 4, no. 2: 4.