Papers by Abdulfattah Noorwali
IEEE Access
The SAS-CBRS framework is being tested to share the federally held spectrum with licensed users a... more The SAS-CBRS framework is being tested to share the federally held spectrum with licensed users and opportunistic users to maximize the underutilized spectrum's utility and overcome spectrum scarcity. In the SAS-CBRS framework, radio resources are assigned to the incumbent access (IA), primary access licensees (PAL), and general authorized access (GAA) users according to the given priority. The SAS-CBRS three-tier framework is different from the conventional cognitive radio networks (CRN) as it involves a central entity that acts as a server called a spectrum access system (SAS). The methods to assign the resources using the SAS are still in the research phase. Yet, no standard method is defined by the FCC for resource allocation. The current CRN methods cannot be directly applied because of the addition of the third tier and a central server. Moreover, strict rules are defined for using the 3.5 GHz spectrum band for communication. In this paper, a novel DDRQ-SAS algorithm integrated with the double auction (DA) algorithm is proposed that uses deep recurrent double Q-learning. The DDRQ-SAS is used by the SAS to hold a spectrum auction and create a spectrum pool to get information on PAL channels. PAL operators use the DA algorithm to generate the asking prices intelligently for their available idle channels and the GAA users will use the DA algorithm to intelligently bid for their preferred channels. The DDRQ-SAS-DA algorithm allows the GAA users to get the guaranteed QoS offered by the PAL operators in an auction. GAA users maintain the preference list of the PAL reserved idle channels and bid intelligently based on the available QoS. SAS completes the transaction by allocating the channels to the winning GAAs. The defined problem is also modeled using the double auction multi-winner multi-channel technique and the TDSA-PS algorithm. Numerical results show that the proposed DDRQ-SAS-DA algorithm provides up to 20% better QoS at higher loads for GAA users, generates 24% more revenue for PAL operators, and is 1.6 times more efficient in assigning 500 GAA users. INDEX TERMS SAS-CBRS, double auction algorithm, deep learning, Q-learning, channel allocation. The associate editor coordinating the review of this manuscript and approving it for publication was Tiago Cruz .
Mathematics
This paper presents two new fault models for networked systems. These fault models are more reali... more This paper presents two new fault models for networked systems. These fault models are more realistic and generalized for networked systems in the sense that they can represent the effects of fault at the node and network levels. At the network layer, the uncertain effects of the network lines are modeled using a Markov chain with complex transition probabilities simultaneously with the stochastic behavior of the network using a Bernoulli process. A new output feedback-based controller, which is two-mode dependent and considers network uncertainties and output measurements for gain calculation, is presented. Using the tools of robust control and stochastic stability, linear matrix inequality-based sufficient conditions are derived. The proposed controller successfully maintains the system’s performance by tolerating the effects of simultaneous sensor and actuator faults, ensuring the stability of networked loops. Simulation results verify the applicability of the presented fault-tol...
Engineering Applications of Artificial Intelligence, Aug 1, 2023
Soft Computing, Jun 29, 2023
Channel Allocation to GAA Users Using Double Deep Recurrent Q-Learning Based on Double Auction Method, 2023
The SAS-CBRS framework is being tested to share the federally held spectrum with licensed users a... more The SAS-CBRS framework is being tested to share the federally held spectrum with licensed users and opportunistic users to maximize the underutilized spectrum's utility and overcome spectrum scarcity. In the SAS-CBRS framework, radio resources are assigned to the incumbent access (IA), primary access licensees (PAL), and general authorized access (GAA) users according to the given priority. The SAS-CBRS three-tier framework is different from the conventional cognitive radio networks (CRN) as it involves a central entity that acts as a server called a spectrum access system (SAS). The methods to assign the resources using the SAS are still in the research phase. Yet, no standard method is defined by the FCC for resource allocation. The current CRN methods cannot be directly applied because of the addition of the third tier and a central server. Moreover, strict rules are defined for using the 3.5 GHz spectrum band for communication. In this paper, a novel DDRQ-SAS algorithm integrated with the double auction (DA) algorithm is proposed that uses deep recurrent double Q-learning. The DDRQ-SAS is used by the SAS to hold a spectrum auction and create a spectrum pool to get information on PAL channels. PAL operators use the DA algorithm to generate the asking prices intelligently for their available idle channels and the GAA users will use the DA algorithm to intelligently bid for their preferred channels. The DDRQ-SAS-DA algorithm allows the GAA users to get the guaranteed QoS offered by the PAL operators in an auction. GAA users maintain the preference list of the PAL reserved idle channels and bid intelligently based on the available QoS. SAS completes the transaction by allocating the channels to the winning GAAs. The defined problem is also modeled using the double auction multi-winner multi-channel technique and the TDSA-PS algorithm. Numerical results show that the proposed DDRQ-SAS-DA algorithm provides up to 20% better QoS at higher loads for GAA users, generates 24% more revenue for PAL operators, and is 1.6 times more efficient in assigning 500 GAA users. INDEX TERMS SAS-CBRS, double auction algorithm, deep learning, Q-learning, channel allocation. The associate editor coordinating the review of this manuscript and approving it for publication was Tiago Cruz .
IEEE ACCESS, 2023
5G is a key enabler of Industrial Internet of Things (IIoT) that provides seamless connectivity b... more 5G is a key enabler of Industrial Internet of Things (IIoT) that provides seamless connectivity between machines, sensors and computing servers. Security and privacy are major concerns for 5G enabled IIoT. Physical Layer Security (PLS) is a promising technique that can enhance the security of 5G enabled IIoT. In this paper, we present an IRS-assisted PLS scheme for 5G enabled IIoT that improves the weighted secrecy sum-rate (WSSR) of the industrial wireless network, where eavesdroppers are present around the facility. The key idea is to jointly optimize active and passive beamforming vectors to increase the secrecy rate at the user. To maximize WSSR, we use the stable matching algorithm that optimally assigns IRSs for secure data sharing between industrial units. Simulation results show that the proposed scheme enhances the WSSR performance by 40% and minimum secrecy rate by 25% as compared to the random and maximum weight matching schemes, respectively.
Remote-sensing, 2023
Abstract: Micro-Doppler signatures obtained from the Doppler radar are generally used for human
a... more Abstract: Micro-Doppler signatures obtained from the Doppler radar are generally used for human
activity classification. However, if the angle between the direction of motion and radar antenna
broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human
body reduce significantly, thereby degrading the performance of the classification algorithm. For
the accurate classification of different human activities irrespective of trajectory, we propose a new
algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric
micro-motion signatures, using an interferometric radar. First, the motion of different parts of
the human body is simulated using motion capture (MOCAP) data, which is further utilized for
radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms
obtained from time-frequency analysis of a single Doppler receiver and interferometric output data,
respectively, are fed as input to the deep convolutional neural network (DCNN) for feature extraction
and the training/testing process. The performance of the proposed algorithm is analyzed and
compared with a micro-Doppler signatures-based classifier. Results show that a dual micro-motionbased
DCNN classifier using an interferometric radar is capable of classifying different human
activities with an accuracy level of 98%, where Doppler signatures diminish considerably, providing
insufficient information for classification. Verification of the proposed classification algorithm based
on dual micro-motion signatures is also performed using a real radar test dataset of different human
walking patterns, and a classification accuracy level of approximately 90% is achieved.
MDPI- Mathematics, 2023
(1) Objectives: Reliability is one of the major aspects for enhancing the operability, reusabilit... more (1) Objectives: Reliability is one of the major aspects for enhancing the operability, reusability, maintainability, and quality of a system. A software component is an independent entity that deploys to form a functional system (CBSS). The component becomes unreliable mainly because of errors introduced during its design and development; it is essential to estimate the reliability of a software component in advance. This research work proposes a novel Mamdani Fuzzy-Inference (M-FIS) model to estimate the components’ reliability and provides an intuitive solution for industry personnel; (2) Scope: The technology moves forward from traditional monolithic software development to scalable, integrated, business-driving software applications. Henceforth, the proposed paradigm can give a preliminary estimate of the reliability of software components, and it helps developers and vendors to produce it at high-quality; (3) Methods: In the component development and realization phase, failure data is unavailable; hence, designing metrics, inspections, statistical methods, soft-computing techniques are used to predict early reliability. The present work applies soft computing techniques to validate metrics. Moreover, estimating premature reliability reduces follow-up effort and component-development cost and time; (4) Finding: The proposed model aids the project manager in better estimating and predicting a components’ reliability. Adopting both an expert-based fuzzy inference system and an unsupervised, or self-learning, algorithm provides the basis for cross checking, and concludes with a better decision in an ambivalence state.
International Review of Electrical Engineering-iree, Feb 28, 2022
Remote Sensing
Micro-Doppler signatures obtained from the Doppler radar are generally used for human activity cl... more Micro-Doppler signatures obtained from the Doppler radar are generally used for human activity classification. However, if the angle between the direction of motion and radar antenna broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human body reduce significantly, thereby degrading the performance of the classification algorithm. For the accurate classification of different human activities irrespective of trajectory, we propose a new algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric micro-motion signatures, using an interferometric radar. First, the motion of different parts of the human body is simulated using motion capture (MOCAP) data, which is further utilized for radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms obtained from time-frequency analysis of a single Doppler receiver and interferometric output data, respectively, are fed as input to th...
Complex & Intelligent Systems, 2021
In this paper, chaos-guided artificial neural learning-based session key coordination for industr... more In this paper, chaos-guided artificial neural learning-based session key coordination for industrial internet-of-things (IIoT) to enhance the security of critical energy infrastructures (CEI) is proposed. An intruder might pose several security problems since the data are transferred across a public network. Although there have been substantial efforts to solve security problems in the IIoT, the majority of them have relied on traditional methods. A wide range of privacy issues (secrecy, authenticity, and access control) must be addressed to protect IIoT systems against attack. Owing to the unique characteristics of IIoT nodes, existing solutions do not properly address the entire security range of IIoT networks. To deal with this, a chaos-based triple layer vector-valued neural network (TLVVNN) is proposed in this paper. A chaos-based exchange of common seed value for the generation of the identical input vector at both transmitter and receiver is also proposed. This technique has ...
Intelligent Automation & Soft Computing, 2022
Early fetal cardiac diseases and heart abnormalities can be detected and appropriately treated by... more Early fetal cardiac diseases and heart abnormalities can be detected and appropriately treated by monitoring fetal health during pregnancy. Advancements in computer sciences and the technology of sensors show that is possible to monitor fetal electrocardiogram (fECG). Both signal processing and experimental aspects are needed to be investigated to monitor fECG. In this study, we aim to design and realize a non invasive fECG monitoring system. In the first part of this work, a remote study process of the electrical activity of the heart is achieved. In fact, our proposed design considers transmitting the detected signals in real time using a WiFi module and then analyzing the results on Raspberry Pi 3. As the signal acquired from the mother's abdomen is contaminated by several noises, in the second part, we propose a method to extract the fetal electrocardiogram FECG by using Independent Component Analysis (ICA) and Wavelet Transform (WT). The proposed method was tested on real data recordings from the publicly available Physionet database. In this paper, we proposed an efficient hardware design to well monitor the heart activity. Then, we presented our adopted method for fECG extraction. The obtained results with the mentioned method show the effectiveness of our proposed algorithm and it is suggested to be used in the portable designed fECG monitoring system.
PeerJ Computer Science, 2021
Breast cancer is one of the leading causes of death in the current age. It often results in subpa... more Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: ...
Engineering Applications of Artificial Intelligence, 2021
IEEE Access, 2021
This paper presents an in-depth analysis and investigation on the performance of static photovolt... more This paper presents an in-depth analysis and investigation on the performance of static photovoltaic (PV) array configurations subjected to various partial shading conditions (PSCs). Under PSCs, the electrical characteristics of the PV modules are critically monitored and reasons for their behavioral changes are highlighted. By doing so, this study aims to improve the efficiency of PV systems by minimizing mismatch losses and determining the optimum array configuration which is characterized by the highest maximum power and lowest relative losses under PSCs. Besides, this study complements and carries forward the previous studies through the detailed analysis of each configuration subjected to various practically probable PSCs. Three different PV array sizes (5 × 4, 5 × 5, and 3 × 10) are used to analyze the results and performance under considered shading scenarios. MATLAB/Simulink platform is used to model and simulate the PV array using the single diode (5-parameters) model. In-depth analysis of current flow across cross-ties and bypass diodes activation shows that the diagonal shading pattern leads to lower power loss (PL). Besides, the Total Cross-Tied (TCT) configuration demonstrates superior performance under most of the PSCs compared to other configurations. These results provide valuable information about the performance of PV array which may lead to better estimation and prediction of global maximum power (GMP) generation of a PV system. INDEX TERMS Single diode solar cell model, photovoltaic array, PV reconfiguration schemes, total cross tied configuration, performance of static photovoltaic array, partial shading conditions, energy loss.
IEEE Access, 2021
As Blockchain innovation picks up popularity in many areas, it is frequently hailed as a sound in... more As Blockchain innovation picks up popularity in many areas, it is frequently hailed as a sound innovation. Because of the decentralization and encryption, many imagine that data put away in a Blockchain is and will consistently be protected. Among various abstraction layers of Blockchain architecture, the consensus layer is the core component behind the performance and security measures of the Blockchain network. Consensus mechanisms are a critical component of a Blockchain system's long-term stability. Consensus forms the core of blockchain technology. Therefore, a range of consensus protocols has been introduced to maximize Blockchain systems' efficiency and meet application domains' individual needs. This research paper describes the layered architecture of Blockchain. A comprehensive review of mainstream consensus protocols mainly Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Activity (PoA) is presented in the paper. These mainstream consensus protocols have been explained and detailed performance analysis of these consensus protocols has been done. We have proposed a performance matrix of these consensus protocols based on different parameters like Degree of decentralization, Latency, Fault Tolerance Rate, Scalability, etc. Consensus protocols being the core of a strong fault-tolerant secured blockchain system, the proposed work intends to help inappropriate protocol selection and further research on strengthening trust and ownership in the technology. Depending upon different parameters like decentralization which is low in POA compared to other protocols, whereas POW is non-scalable, so depending on the priority of a particular performance parameter, the paper will help in the selection of a specific protocol.
IEEE Access, 2020
Pedestrian head detection plays an important role in identifying and localizing individuals in re... more Pedestrian head detection plays an important role in identifying and localizing individuals in real world visual data. Head detection is a nontrivial problem due to considerable variance in camera viewpoints, scales, human poses, and appearances in the scene. Thanks to the translation invariance property of convolutional neural networks (CNNs) which enables large capacity CNNs to handle the problem of appearance and pose variations in the scene. However, the problem of scale invariance is still an open issue. To address this problem, this paper presents a two-stage head detection framework that utilizes fully convolutional network (FCN) to generate scale-aware proposals followed by CNN that classifies each proposal into two classes, i.e. head and background. Experiments results show that using scale-aware proposals obtained by FCN, the object recall rate and mean average precision (mAP) are improved. Additionaly, we demonstrate that our framework achieved state-of-the-art results on four challenging benchmark datasets, i.e. HollywoodHeads, Casablanca, SHOCK, and WIDERFACE.
2016 Saudi Arabia Smart Grid (SASG), 2016
Several interconnected layers of network form the smart grid. The Home Area Network (HAN) is the ... more Several interconnected layers of network form the smart grid. The Home Area Network (HAN) is the lowest layer of this grid, where reports about the various parameters associated with electrical devices are generated. For example, parameters could include information about the operation, power consumption, energy losses, power factor, etc. associated with the electrical devices. The reports generated are often classified as either periodic or critical. The critical reports are required to be communicated to a control station with minimal delay so that corrective measures could be taken quickly at the site of the electrical device. This paper presents the modelling and delay of wireless HANs. The communication between electrical devices and their associated access point can be achieved using several architectures, such as Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA). For each of these multiple access techniques upper and lower bounds on delay are derived. These bounds are a function of Signal-to-Noise Ratio (SNR), channel interference range, and the number of electrical devices and channels. It is noted that transmission of critical reports from electrical devices to their access point under the CDMA scheme achieves the least delay among the three multiple access techniques considered in the paper.
2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2016
In smart grids, the critical packets generated by an Electrical Device (ED) at the customer site ... more In smart grids, the critical packets generated by an Electrical Device (ED) at the customer site must be communicated to a control station with minimal delay so that corrective measures, if required, can be initiated quickly at the site. This paper presents the modelling of Wireless Mesh Backbone Network (WMBN) to which customer sites are connected in a smart grid. The WMBN is comprised of Mesh Routers (MRs) that are connected wirelessly to provide an end-to-end path within the network. An analytical model is presented that takes into account Voronoi tessellation for selecting the shortest path for transmissions in this network. Each MR is equipped with a Straight-Line Path Routing (SLPR) algorithm that provides MR traversing packets with a sequence of cells to reach the identified gateway within WMBN. The long link distances between routers are subject to attenuation and communication challenges. An optimum solution to transmit critical packets with minimal delay to the identified gateway is also given. Upper and lower bounds on achievable minimal delay are derived for a Distributed Coordination Function (DCF) mode in terms of: i) signal-to-noise ratio, ii) number of channels, iii) number of hops, iv) distance, v) channel interference, and vi) interference range. Results show that in the worst-case critical packets can be delivered to their destinations in less than 4 ms, which meets the standard requirement of a smart grid.
2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2016
Smart grids are comprised of several interconnected layers of networks. A set of Electrical Devic... more Smart grids are comprised of several interconnected layers of networks. A set of Electrical Devices (EDs) in the lowest layer, the Home Area Network (HAN), communicates with the Mesh Client (MC). The HAN, in this paper, is modelled as a wireless network using Frequency Division Multiple Access (FDMA) and Time Division Multiple Access (TDMA) to facilitate communication between the EDs and their respective MCs. For these models, channel capacities over the Rayleigh and Nakagami fading channels are determined and delay analysis is presented. Upper and lower bound expressions for achievable delay, in closed-form, are derived as a function of Signal-to-Interference-plus-Noise Ratio (SINR), received power, number of EDs, number of channels, and Inter-channel Interference Range (ICR). Numerical simulations have been used to validate the analytical results. It is shown that transmission of packets using TDMA results in a shorter delay than by using FDMA.
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Papers by Abdulfattah Noorwali
activity classification. However, if the angle between the direction of motion and radar antenna
broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human
body reduce significantly, thereby degrading the performance of the classification algorithm. For
the accurate classification of different human activities irrespective of trajectory, we propose a new
algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric
micro-motion signatures, using an interferometric radar. First, the motion of different parts of
the human body is simulated using motion capture (MOCAP) data, which is further utilized for
radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms
obtained from time-frequency analysis of a single Doppler receiver and interferometric output data,
respectively, are fed as input to the deep convolutional neural network (DCNN) for feature extraction
and the training/testing process. The performance of the proposed algorithm is analyzed and
compared with a micro-Doppler signatures-based classifier. Results show that a dual micro-motionbased
DCNN classifier using an interferometric radar is capable of classifying different human
activities with an accuracy level of 98%, where Doppler signatures diminish considerably, providing
insufficient information for classification. Verification of the proposed classification algorithm based
on dual micro-motion signatures is also performed using a real radar test dataset of different human
walking patterns, and a classification accuracy level of approximately 90% is achieved.
activity classification. However, if the angle between the direction of motion and radar antenna
broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human
body reduce significantly, thereby degrading the performance of the classification algorithm. For
the accurate classification of different human activities irrespective of trajectory, we propose a new
algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric
micro-motion signatures, using an interferometric radar. First, the motion of different parts of
the human body is simulated using motion capture (MOCAP) data, which is further utilized for
radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms
obtained from time-frequency analysis of a single Doppler receiver and interferometric output data,
respectively, are fed as input to the deep convolutional neural network (DCNN) for feature extraction
and the training/testing process. The performance of the proposed algorithm is analyzed and
compared with a micro-Doppler signatures-based classifier. Results show that a dual micro-motionbased
DCNN classifier using an interferometric radar is capable of classifying different human
activities with an accuracy level of 98%, where Doppler signatures diminish considerably, providing
insufficient information for classification. Verification of the proposed classification algorithm based
on dual micro-motion signatures is also performed using a real radar test dataset of different human
walking patterns, and a classification accuracy level of approximately 90% is achieved.