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Articles 1 - 30 of 41
Full-Text Articles in Engineering
A Secure Cross-Domain Authentication Scheme Based On Threshold Signature For Mec, Lei Chen, Chong Guo, Bei Gong, Muhammad Waqas, Lihua Deng, Haowen Qin
A Secure Cross-Domain Authentication Scheme Based On Threshold Signature For Mec, Lei Chen, Chong Guo, Bei Gong, Muhammad Waqas, Lihua Deng, Haowen Qin
Research outputs 2022 to 2026
The widespread adoption of fifth-generation mobile networks has spurred the rapid advancement of mobile edge computing (MEC). By decentralizing computing and storage resources to the network edge, MEC significantly enhances real-time data access services and enables efficient processing of large-scale dynamic data on resource-limited devices. However, MEC faces considerable security challenges, particularly in cross-domain service environments, where every device poses a potential security threat. To address this issue, this paper proposes a secure cross-domain authentication scheme based on a threshold signature tailored to MEC’s multi-subdomain nature. The proposed scheme employs a (t,n) threshold mechanism to bolster system resilience and security, …
Prediction Of Mechanical And Electrical Properties Of Carbon Fibre-Reinforced Self-Sensing Cementitious Composites, Zehao Kang, Farhad Aslani, Baoguo Han
Prediction Of Mechanical And Electrical Properties Of Carbon Fibre-Reinforced Self-Sensing Cementitious Composites, Zehao Kang, Farhad Aslani, Baoguo Han
Research outputs 2022 to 2026
The transmission of signal values in self-sensing concrete allows us to precisely locate damaged structures and prevent disasters. Currently, there are over ten functional materials used in self-sensing concrete applications. Carbon fibre (CF) is a well-known functional material that has been extensively studied for its reproducibility and accuracy in self-sensing concrete experiments. In contrast, this study is based on finite element modelling to rapidly predict the impact of the functional filler material, CF, on concrete performance. This paper simulates the mechanical and piezoresistive properties of concrete with unsized and desized short-cut CFs at lengths of 3, 6, and 12 mm. …
Enhancing Water Safety: Exploring Recent Technological Approaches For Drowning Detection, Salman Jalalifar, Andrew Belford, Eila Erfani, Amir Razmjou, Rouzbeh Abbassi, Masoud Mohseni-Dargah, Mohsen Asadnia
Enhancing Water Safety: Exploring Recent Technological Approaches For Drowning Detection, Salman Jalalifar, Andrew Belford, Eila Erfani, Amir Razmjou, Rouzbeh Abbassi, Masoud Mohseni-Dargah, Mohsen Asadnia
Research outputs 2022 to 2026
Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection …
An Improved Genetic Algorithm Based Fractional Open Circuit Voltage Mppt For Solar Pv Systems, Aakash Hassan, Octavian Bass, Mohammad A. S. Masoum
An Improved Genetic Algorithm Based Fractional Open Circuit Voltage Mppt For Solar Pv Systems, Aakash Hassan, Octavian Bass, Mohammad A. S. Masoum
Research outputs 2022 to 2026
To extract the maximum power from solar PV, maximum power point tracking (MPPT) controllers are needed to operate the PV arrays at their maximum power point under varying environmental conditions. Fractional Open Circuit Voltage (FOCV) is a simple, cost-effective, and easy to implement MPPT technique. However, it suffers from the discontinuous power supply and low tracking efficiency. To overcome these drawbacks, a new hybrid MPPT technique based on the Genetic Algorithm (GA) and FOCV is proposed. The proposed technique is based on a single decision variable, reducing the complexity and convergence time of the algorithm. MATLAB/Simulink is used to test …
Numerical Investigation Of Lead Free Cs2tibr6 Based Perovskite Solar Cell With Optimal Selection Of Electron And Hole Transport Layer Through Scaps-1d Simulation, Hironmoy Karmaker, Ayesha Siddique, Barun K. Das
Numerical Investigation Of Lead Free Cs2tibr6 Based Perovskite Solar Cell With Optimal Selection Of Electron And Hole Transport Layer Through Scaps-1d Simulation, Hironmoy Karmaker, Ayesha Siddique, Barun K. Das
Research outputs 2022 to 2026
Halide-based perovskite has several advantages, including high efficiency, ease of manufacture, and low cost. In general, hazardous lead (Pb) is used in perovskite solar cells as an absorber layer, while polymer, which is unstable in nature used as the electron/hole transport layer. Despite its appealing characteristics, the device uses of lead and degradable components must be addressed. Lead-free titanium-based inorganic perovskite solar cells (PSCs) have drawn strong scientific interest in recent years in order to reduce the possibly detrimental effects of lead on the environment. Titanium is non-toxic, robust, inexpensive, and widely available when compared to other elements. The performance …
Time Reduction For Slm Ofdm Papr Based On Adaptive Genetic Algorithm In 5g Iot Networks, Esam A. A. Hagras, Sameh F. Desouky, Saad Aldosary, Haitham Khaled, Tarek M. Hassan
Time Reduction For Slm Ofdm Papr Based On Adaptive Genetic Algorithm In 5g Iot Networks, Esam A. A. Hagras, Sameh F. Desouky, Saad Aldosary, Haitham Khaled, Tarek M. Hassan
Research outputs 2022 to 2026
In this paper, a new peak average power and time reduction (PAPTR) based on the adaptive genetic algorithm (AGA) strategy is used in order to improve both the time reduction and PAPR value reduction for the SLM OFDM and the conventional genetic algorithm (GA) SLM-OFDM. The simulation results demonstrate that the recommended AGA technique reduces PAPR by about 3.87 dB in comparison to SLM-OFDM. Comparing the suggested AGA SLM-OFDM to the traditional GA SLM-OFDM using the same settings, a significant learning time reduction of roughly 95.56% is achieved. The PAPR of the proposed AGA SLM-OFDM is enhanced by around 3.87 …
On The Effect Of Emotion Identification From Limited Translated Text Samples Using Computational Intelligence, Madiha Tahir, Zahid Halim, Muhmmad Waqas, Shanshan Tu
On The Effect Of Emotion Identification From Limited Translated Text Samples Using Computational Intelligence, Madiha Tahir, Zahid Halim, Muhmmad Waqas, Shanshan Tu
Research outputs 2022 to 2026
Emotion identification from text data has recently gained focus of the research community. This has multiple utilities in an assortment of domains. Many times, the original text is written in a different language and the end-user translates it to her native language using online utilities. Therefore, this paper presents a framework to detect emotions on translated text data in four different languages. The source language is English, whereas the four target languages include Chinese, French, German, and Spanish. Computational intelligence (CI) techniques are applied to extract features, dimensionality reduction, and classification of data into five basic classes of emotions. Results …
Cyberattacks And Security Of Cloud Computing: A Complete Guideline, Muhammad Dawood, Shanshan Tu, Chuangbai Xiao, Hisham Alasmary, Muhammad Waqas, Sadaqat Ur Rehman
Cyberattacks And Security Of Cloud Computing: A Complete Guideline, Muhammad Dawood, Shanshan Tu, Chuangbai Xiao, Hisham Alasmary, Muhammad Waqas, Sadaqat Ur Rehman
Research outputs 2022 to 2026
Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-to-service frameworks. However, just like any other technology, cloud computing opens up many forms of security threats and problems. In this work, we focus on discussing different cloud models and cloud services, respectively. Next, we discuss the security trends in the cloud models. Taking these security trends into account, we move to security problems, including data breaches, data confidentiality, data access controllability, authentication, inadequate diligence, phishing, …
Low-Voltage Ride-Through Capability Improvement Of Type-3 Wind Turbine Through Active Disturbance Rejection Feedback Control-Based Dynamic Voltage Restorer, El M. Boulaoutaq, Asma Aziz, Abdelmounime El Magri, Ahmed Abbou, Mohamed Ajaamoum, Azeddine Rachdy
Low-Voltage Ride-Through Capability Improvement Of Type-3 Wind Turbine Through Active Disturbance Rejection Feedback Control-Based Dynamic Voltage Restorer, El M. Boulaoutaq, Asma Aziz, Abdelmounime El Magri, Ahmed Abbou, Mohamed Ajaamoum, Azeddine Rachdy
Research outputs 2022 to 2026
Disconnections due to voltage drops in the grid cannot be permitted if wind turbines (WTs) contribute significantly to electricity production, as this increases the risk of production loss and destabilizes the grid. To mitigate the negative effects of these occurrences, WTs must be able to ride through the low-voltage conditions and inject reactive current to provide dynamic voltage support. This paper investigates the low-voltage ride-Through (LVRT) capability enhancement of a Type-3 WT utilizing a dynamic voltage restorer (DVR). During the grid voltage drop, the DVR quickly injects a compensating voltage to keep the stator voltage constant. This paper proposes an …
Unlocking Market Secrets: Revealing Wholesale Electricity Market Price Dynamics With A Novel Application Of Spectrum Analysis, Martin J. Maticka, Thair S. Mahmoud
Unlocking Market Secrets: Revealing Wholesale Electricity Market Price Dynamics With A Novel Application Of Spectrum Analysis, Martin J. Maticka, Thair S. Mahmoud
Research outputs 2022 to 2026
Understanding market participants' competitive behaviour is essential for optimising financial performance in liberalised electricity markets. However, this is challenging due to complex market structures, generation dependent on different primary energy sources and lack of transparency. This paper introduces a novel approach using power spectrum analysis applied to wholesale electricity markets to uncover hidden patterns. Applying this novel method to the Western Australian Wholesale Electricity Market (WEM) revealed periodic cycles in different fuel types and technologies that offered insights into competitor behaviour not immediately evident in the dataset. Surprisingly, the approach uncovered that in a power system with high penetration of …
Optimal Allocation Of Battery Energy Storage Systems To Enhance System Performance And Reliability In Unbalanced Distribution Networks, Dong Zhang, G. M. Shafiullah, Choton K. Das, Kok W. Wong
Optimal Allocation Of Battery Energy Storage Systems To Enhance System Performance And Reliability In Unbalanced Distribution Networks, Dong Zhang, G. M. Shafiullah, Choton K. Das, Kok W. Wong
Research outputs 2022 to 2026
The continuously increasing renewable distributed generation (DG) penetration rate significantly reduces environmental pollution and power generation cost and satisfies society’s rapid growth in electricity demand. Nevertheless, high penetration of renewable DGs, such as wind power and photovoltaics (PV), might deteriorate the system’s efficiency and reliability due to its intermittent and stochastic natures. Introducing battery energy storage systems (BESSs) to the distribution system provides a practical method to compensate for the above deficiency since it can deliver and absorb power when needed. Hence, it is important to determine the optimal allocation of BESS to achieve maximum assistance in the grid. This …
Harnessing The Power Of Neural Networks For The Investigation Of Solar-Driven Membrane Distillation Systems Under The Dynamic Operation Mode, Pooria Behnam, Masoumeh Zargar, Abdellah Shafieian, Amir Razmjou, Mehdi Khiadani
Harnessing The Power Of Neural Networks For The Investigation Of Solar-Driven Membrane Distillation Systems Under The Dynamic Operation Mode, Pooria Behnam, Masoumeh Zargar, Abdellah Shafieian, Amir Razmjou, Mehdi Khiadani
Research outputs 2022 to 2026
Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system …
Pymaivar: An Open-Source Python Suit For Audio-Image Representation In Human Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar
Pymaivar: An Open-Source Python Suit For Audio-Image Representation In Human Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar
Research outputs 2022 to 2026
We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.
System Strength Shortfall Challenges For Renewable Energy-Based Power Systems: A Review, Md O. Qays, Iftekhar Ahmad, Daryoush Habibi, Asma Aziz, Thair Mahmoud
System Strength Shortfall Challenges For Renewable Energy-Based Power Systems: A Review, Md O. Qays, Iftekhar Ahmad, Daryoush Habibi, Asma Aziz, Thair Mahmoud
Research outputs 2022 to 2026
Renewable energy sources such as wind farms and solar power plants are replacing conventional coal-based synchronous generators (SGs) to achieve net-zero carbon emissions worldwide. SGs play an important role in enhancing system strength in a power system to make it more stable during voltage/frequency disruptions. However, traditional coal-fired SGs are being decommissioned in many parts of the world, owing to stringent environmental regulations and low levelized cost of energy of renewables. Consequently, maintaining system strength in a renewable energy-dominated power system has become a major challenge, and without adequate mitigation techniques, low system strength can potentially cause widespread power outages. …
Peak To Average Power Ratio Reduction In Spectrally Efficient Fdm Using Repeated Clipping And Filtering, Buthaina Mosa Omran, Yamaan Esmiel Majeed, Iftekhar Ahmad
Peak To Average Power Ratio Reduction In Spectrally Efficient Fdm Using Repeated Clipping And Filtering, Buthaina Mosa Omran, Yamaan Esmiel Majeed, Iftekhar Ahmad
Research outputs 2022 to 2026
Multi-carrier transmission may be considered one of the important developments in wireless communications. Spectrally efficient frequency division multiplexing (SEFDM) is a promising multi-carrier modulation which can significantly improve utilization of spectral. The SEFDM has high peak to average power ratio (PAPR) like any multicarrier system. High PAPR reduces the random forest (RF) transmitter power amplifier efficiency, which minimize the use of this technique in limited power supply transmitters. In this work, a repeated clipping and filtering method is introduced to reduce the PAPR in SEFDM with minimum or no out of band radiation. The results of the simulated approach show …
Design And Concept Of Renewable Energy Driven Auto-Detectable Railway Level Crossing Systems In Bangladesh, Iftekharuzzaman Iftekharuzzaman, Susmita Ghosh, Mohammad K. Basher, Mohammad A. Islam, Narottam Das, Mohammad Nur-E-Alam
Design And Concept Of Renewable Energy Driven Auto-Detectable Railway Level Crossing Systems In Bangladesh, Iftekharuzzaman Iftekharuzzaman, Susmita Ghosh, Mohammad K. Basher, Mohammad A. Islam, Narottam Das, Mohammad Nur-E-Alam
Research outputs 2022 to 2026
Bangladesh’s railway system mostly uses typical manual railway crossing techniques or boom gates through its 2955.53 km rail route all over the country. Accidents frequently happen at railway crossings due to the lack of quickly operating gate systems, and to fewer safety measures at the railway crossing as well. Currently, there are very few automatic railway crossing systems available (without obstacle detectors). Additionally, all of them are dependent on the national power grid, without a backup plan for any emergency cases. Bangladesh is still running a bit behind in generating enough power for its consumption; hence, it is not possible …
A Computational Offloading Optimization Scheme Based On Deep Reinforcement Learning In Perceptual Network, Yongli Xing, Tao Ye, Sami Ullah, Muhammad Waqas, Hisham Alasmary, Zihui Liu
A Computational Offloading Optimization Scheme Based On Deep Reinforcement Learning In Perceptual Network, Yongli Xing, Tao Ye, Sami Ullah, Muhammad Waqas, Hisham Alasmary, Zihui Liu
Research outputs 2022 to 2026
Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces …
Digital Twin Applications In 3d Concrete Printing, Yuxin Wang, Farhad Aslani, Arcady Dyskin, Elena Pasternak
Digital Twin Applications In 3d Concrete Printing, Yuxin Wang, Farhad Aslani, Arcady Dyskin, Elena Pasternak
Research outputs 2022 to 2026
The benefits of 3D concrete printing (3DCP) include reducing construction time and costs, providing design freedom, and being environmentally friendly. This technology is expected to be effective in addressing the global house shortage. This review highlights the main 3DCP applications and four critical challenges. It is proposed to combine 3D concrete printing with Digital Twin (DT) technology to meet the challenges the 3DCP faces and improve quality and sustainability. This paper provides a critical review of research into the application of DT technology in 3DCP, categorize the applications and directions proposed according to different lifecycles, and explore the possibility of …
Physical Layer Authenticated Image Encryption For Iot Network Based On Biometric Chaotic Signature For Mpfrft Ofdm System, Esam A. A. Hagras, Saad Aldosary, Haitham Khaled, Tarek Hassan
Physical Layer Authenticated Image Encryption For Iot Network Based On Biometric Chaotic Signature For Mpfrft Ofdm System, Esam A. A. Hagras, Saad Aldosary, Haitham Khaled, Tarek Hassan
Research outputs 2022 to 2026
In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform–Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which …
Effect Of 3d Printing Parameters On The Fatigue Properties Of Parts Manufactured By Fused Filament Fabrication: A Review, Hamed Bakhtiari, Muhammad Aamir, Majid Tolouei-Rad
Effect Of 3d Printing Parameters On The Fatigue Properties Of Parts Manufactured By Fused Filament Fabrication: A Review, Hamed Bakhtiari, Muhammad Aamir, Majid Tolouei-Rad
Research outputs 2022 to 2026
The advancement in 3D printing techniques has raised the hope to use additively manufactured parts as final products in various industries. However, due to the layer-by-layer nature of AM parts, they are highly susceptible to failure when they are subjected to fatigue loading. This review provides a detailed account of the influence of 3D printing parameters on the fatigue properties of parts manufactured by fused filament fabrication (FFF). Existing standards for fatigue testing of polymers and their limitation for 3D-printed parts are discussed. In addition, the cyclic behaviour of polymers is reviewed, and the impact of 3D printing parameters on …
Improved Rate Of Secret Key Generation Using Passive Re-Configurable Intelligent Surfaces For Vehicular Networks, Hina Ayaz, Muhammad Waqas, Ghulam Abbas, Ziaul Haq Abbas, Muhammad Bilal, Kyung-Sup Kwak
Improved Rate Of Secret Key Generation Using Passive Re-Configurable Intelligent Surfaces For Vehicular Networks, Hina Ayaz, Muhammad Waqas, Ghulam Abbas, Ziaul Haq Abbas, Muhammad Bilal, Kyung-Sup Kwak
Research outputs 2022 to 2026
The reconfigurable intelligent surfaces (RIS) is a new technology that can be utilized to provide security to vehicle-to-vehicle (V2V) communications at the physical layer. In this paper, we achieve a higher key generation rate for V2V communications at lower cost and computational complexity. We investigate the use of a passive RIS as a relay, to introduce channel diversity and increase the key generation rate (KGR), accordingly. In this regard, we consider the subsets of consecutive reflecting elements instead of the RIS as a whole in a time slot, i.e., instead of a single reflector, the subsets of reflectors are utilized …
A Provable Secure And Efficient Authentication Framework For Smart Manufacturing Industry, Muhammad Hammad, Akhtar Badshah, Ghulam Abbas, Hisham Alasmary, Muhammad Waqas, Wasim A. Khan
A Provable Secure And Efficient Authentication Framework For Smart Manufacturing Industry, Muhammad Hammad, Akhtar Badshah, Ghulam Abbas, Hisham Alasmary, Muhammad Waqas, Wasim A. Khan
Research outputs 2022 to 2026
Smart manufacturing is transforming the manufacturing industry by enhancing productivity and quality, driving growth in the global economy. The Internet of Things (IoT) has played a crucial role in realizing Industry 4.0, where machines can communicate and interact in real-time. Despite these advancements, security remains a major challenge in developing and deploying smart manufacturing. As cyber-attacks become more prevalent, researchers are making security a top priority. Although IoT and Industrial IoT (IIoT) are used to establish smart industries, these systems remain vulnerable to various types of attacks. To address these security issues, numerous authentication methods have been proposed. However, many …
A Survey On Artificial Intelligence-Based Acoustic Source Identification, Ruba Zaheer, Iftekhar Ahmad, Daryoush Habibi, Kazi Y. Islam, Quoc Viet Phung
A Survey On Artificial Intelligence-Based Acoustic Source Identification, Ruba Zaheer, Iftekhar Ahmad, Daryoush Habibi, Kazi Y. Islam, Quoc Viet Phung
Research outputs 2022 to 2026
The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we …
Memory-Based Adaptive Sliding Mode Load Frequency Control In Interconnected Power Systems With Energy Storage, Farhad Farivar, Octavian Bass, Daryoush Habibi
Memory-Based Adaptive Sliding Mode Load Frequency Control In Interconnected Power Systems With Energy Storage, Farhad Farivar, Octavian Bass, Daryoush Habibi
Research outputs 2022 to 2026
This paper presents a memory-based adaptive sliding mode load frequency control (LFC) strategy aimed at minimizing the impacts of exogenous power disturbances and parameter uncertainties on frequency deviations in interconnected power systems with energy storage. First, the dynamic model of the system is constructed by considering the participation of the energy storage system (ESS) in the conventional decentralized LFC model of a multiarea power system. A disturbance observer (DOB) is proposed to generate an online approximation of the lumped disturbance. In order to enhance the transient performance of the system and effectively mitigate the adverse effects of power fluctuations on …
Optimizing Technical And Economic Aspects Of Off-Grid Hybrid Renewable Systems: A Case Study Of Manoka Island, Cameroon, Reagan J. J. Molu, Serge R. D. Naoussi, Patrice Wira, Wulfran F. Mbasso, Saatong T. Kenfack, Barun K. Das, Enas Ali, Muhannad J. Alshareef, Sherif S. M. Ghoneim
Optimizing Technical And Economic Aspects Of Off-Grid Hybrid Renewable Systems: A Case Study Of Manoka Island, Cameroon, Reagan J. J. Molu, Serge R. D. Naoussi, Patrice Wira, Wulfran F. Mbasso, Saatong T. Kenfack, Barun K. Das, Enas Ali, Muhannad J. Alshareef, Sherif S. M. Ghoneim
Research outputs 2022 to 2026
The lack of accessible and reliable electrical energy in Cameroon has become a pervasive obstacle to the nation's progress, with energy availability, quality, and cost identified as key hindrances to development over the past 15 years. Conventional solutions that rely on combustion engines and electrochemical storage systems have proven to be cost-prohibitive, limited in power output, and constrained in capacity. The dependence on traditional diesel generators has perpetuated maintenance challenges and a continuous demand for fuel supply, while the accompanying noise and pollution have restricted their use in residential areas. Recognizing the imperative of reducing dependence on fossil fuels and …
Instantaneous Frequency Estimation Of Fm Signals Under Gaussian And Symmetric Alpha-Stable Noise: Deep Learning Versus Time-Frequency Analysis, Huda Saleem Razzaq, Zahir M. Hussain
Instantaneous Frequency Estimation Of Fm Signals Under Gaussian And Symmetric Alpha-Stable Noise: Deep Learning Versus Time-Frequency Analysis, Huda Saleem Razzaq, Zahir M. Hussain
Research outputs 2022 to 2026
Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation …
Establishment And Mapping Of Heterogeneous Anomalies In Network Intrusion Datasets, Liam Riddell, Mohiuddin Ahmed, Paul Haskell-Dowland
Establishment And Mapping Of Heterogeneous Anomalies In Network Intrusion Datasets, Liam Riddell, Mohiuddin Ahmed, Paul Haskell-Dowland
Research outputs 2022 to 2026
Anomaly detection in the scope of network security aims to identify network instances for the unexpected and unique, with various security operations employing such techniques to facilitate effective threat detection. However, many systems have been designed based on the absolute mapping of attacks to one of three anomaly types (i.e. point, collective, or contextual), a strategy not supported by the recent findings of hybrid anomaly classifications. Given the growing usage of network anomaly detection and the implications of hybrid anomalies, we propose several heterogeneous anomaly types and provide an unsupervised approach for the automated mapping of network threats. Initial findings …
Passive Cooling Analysis Of An Electronic Chipset Using Nanoparticles And Metal-Foam Composite Pcm: An Experimental Study, Faisal Hassan, Abid Hussain, Furqan Jamil, Adeel Arshad, Hafiz Muhammad Ali
Passive Cooling Analysis Of An Electronic Chipset Using Nanoparticles And Metal-Foam Composite Pcm: An Experimental Study, Faisal Hassan, Abid Hussain, Furqan Jamil, Adeel Arshad, Hafiz Muhammad Ali
Research outputs 2022 to 2026
Thermal management of electronic components is critical for long-term reliability and continuous operation, as the over-heating of electronic equipment leads to decrement in performance. The novelty of the current experimental study is to investigate the passive cooling of electronic equipment, by using nano-enriched phase change material (NEPCM) with copper foam having porosity of 97 %. The phase change material of PT-58 was used with graphene nanoplatelets (GNPs) and magnesium oxide (MgO) nanoparticles (NPs), having concentrations of 0.01 wt. % and 0.02 wt. %. Three power levels of 8 W, 16 W, and 24 W, with corresponding heating inputs of 0.77 …
Decentralized Disturbance Observer-Based Sliding Mode Load Frequency Control In Multiarea Interconnected Power Systems, Farhad Farivar, Octavian Bass, Daryoush Habibi
Decentralized Disturbance Observer-Based Sliding Mode Load Frequency Control In Multiarea Interconnected Power Systems, Farhad Farivar, Octavian Bass, Daryoush Habibi
Research outputs 2022 to 2026
The load frequency control (LFC) problem in interconnected multiarea power systems is facing more challenges due to increasing uncertainties caused by the penetration of intermittent renewable energy resources, random changes in load patterns, uncertainties in system parameters and unmodeled system dynamics, leading to a compromised reliability of power systems and increasing the risk of power outages. In responding to this problem, this paper proposes a decentralized disturbance observer-based sliding mode LFC scheme for multiarea interlinked power systems with external disturbances. First, a reduced power system order is constructed by lumping disturbances from tie-line power deviations, load variations and the output …
Feddp: A Privacy-Protecting Theft Detection Scheme In Smart Grids Using Federated Learning, Muhammad Mansoor Ashraf, Muhammad Waqas, Ghulam Abbas, Thar Baker, Ziaul Haq Abbas, Hisham Alasmary
Feddp: A Privacy-Protecting Theft Detection Scheme In Smart Grids Using Federated Learning, Muhammad Mansoor Ashraf, Muhammad Waqas, Ghulam Abbas, Thar Baker, Ziaul Haq Abbas, Hisham Alasmary
Research outputs 2022 to 2026
In smart grids (SGs), the systematic utilization of consumer energy data while maintaining its privacy is of paramount importance. This research addresses this problem by energy theft detection while preserving the privacy of client data. In particular, this research identifies centralized models as more accurate in predicting energy theft in SGs but with no or significantly less data protection. Current research proposes a novel federated learning (FL) framework, namely FedDP, to tackle this issue. The proposed framework enables various clients to benefit from on-device prediction with very little communication overhead and to learn from the experience of other clients with …