Recent Submissions

  • Floating solar photovoltaic plants in India – A rapid transition to a green energy market and sustainable future

    Charles, Kumar; Majid, M A; Department Collaboration; Energy Lab; Electrical and Computer Engineering; Kumar, Charles (Sage, 2023-03-16)
    The 18,000 square kilometers of water reservoirs in India can generate 280 GW of solar power through floating solar photovoltaic plants. The cumulative installed capacity of FSPV is 0.0027 GW, and the country plans to add 10 GW of FSPV to the 227 GW renewable energy target of 2022. The FSPV addition is small related to the entire market for solar energy, but each contribution is appreciated in the renewable energy market. FSPV could be a viable alternative for speeding up solar power deployment in the country and meeting its NDC targets. So far, the country has achieved the world's lowest investment cost for a floating solar installation. Despite the lower costs, generalizations are still premature because FSPV is still in its initial stages of market entry. Continuous innovation and timely adoption of innovative ideas and technology will support India in meeting its solar energy goals and progressing toward a more sustainable future. Governments must establish clear and enforceable policies to assist developers in reducing risks and increasing investor confidence in the sector. Economic and financial feasibility are examined, and various difficulties in technology, design, finances, environment, maintenance, and occupational health that impact the FSPV deployment are discussed. Based on the research, effective and comprehensive FSPV policy suggestions are included to support establishing an appropriate market, fostering competition and innovation, and attracting large-scale investment. This paper aims to stimulate interest among various policy developers, energy suppliers, industrial designers, ergonomists, project developers, manufacturers, health and safety professionals, executing agencies, training entities, and investment institutions of the FSPV plant to implement effective governance planning and help them to participate in their ways to assure sustainable growth.
  • Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images

    Salem, Nema; Naveed, Khuram; Akram, Awais; Afaq, Amir; Madni, Hussain; Khan, Mohammad; din, Mui; Raza, Mohsin; External Collaboration; Electrical and Computer Engineering; et al. (PLOS ONE, 2021-12-31)
    In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.
  • Thermodynamics of the Bardeen Regular Black Hole

    Salem, Nema; Hussien, Sahar; Akbar, Muhammed; College collaboration; Electrical and Computer Engineering; Akbar, Muhammed (IOP Science, 2012-03-15)
    We deal with the thermodynamic properties of the Bardeen regular black hole with reference to their respective horizons. It is argued here that the expression of the heat capacity at horizons is positive in one parameter region and negative in the other, and between them the heat capacity diverges where the black hole undergoes the second-order phase transition.
  • Response of one-dimensional ionised layer to oscillatory electric fields

    Kabbaj, Narjisse; Im, Hong G.; External Collaboration; Energy Lab; Electrical and Computer Engineering; Kabbaj, Narjisse (Taylor & Francis, 2023-01-17)
    To provide fundamental insights into the response of laminar flames to alternating current (AC) electric fields, a simplified one-dimensional model using an ionised layer model is formulated with the conservation equations for the ion species with ionisation, recombination, and transport due to molecular diffusion and electric mobility. A parametric study is conducted to investigate the response of the ion layer at different voltages and oscillation frequencies, and the results are examined mainly in terms of the net current–voltage (I–V) characteristics. As the oscillation frequency is increased, a nonmonotonic response in the I–V curve is seen such that the current may exceed the saturation condition corresponding to the steady DC condition. In general the current reaches a peak as the unsteady time scale becomes comparable to the ion transport time scale, which is dictated by the mobility, and eventually becomes attenuated at higher frequencies to behave like a low-pass filter. The extent of the peak current rise and the cut-off frequency are found to depend on the characteristic time scales of the ion chemistry and mobility-induced transport. The simplified model serves as a framework to characterise the behaviour of complex flames in terms of the dominant ionisation and transport processes. The current overshoot behaviour may also imply that the overall effect of the electric field may be further magnified under the AC conditions, motivating further studies of multi-dimensional flames for the ionic wind effects.
  • High-performance, energy-efficient, and memory-efficient FIR filter architecture utilizing 8x8 approximate multipliers for wireless sensor network in the Internet of Things

    J Charles, Rajesh Kumar; Majid, M A; Vinod, Kumar; External Collaboration; Energy Lab; Electrical and Computer Engineering (2022-12-01)
    IoT uses wireless sensor networks (WSN) to deploy many sensors to track environmental and physical parameters. The WSN measurements are frequently contaminated and altered by noise. The noise in the signal increases the sensor node’s computation and energy utilization, resulting in less longevity of the sensor node. The Finite Impulse Response (FIR) filter is commonly employed in WSN to pre-process sensed signals to remove noise from the sensed signals using delay elements, multipliers, and adders. Traditional multiplier-based FIR filter designs result in hardware-intensive multipliers that consume a lot of energy, and area and have low computation speed. These drawbacks make them unsuitable for IoT-based WSN systems with stringent power efficiency necessities. Approximate computing enhances the energy efficiency of an FIR filter. Arithmetic circuits utilizing approximate computing improve the hardware performance, with some loss of accuracy to save energy utilization and boost speed. A novel approximate multiplier architecture employing a fast and straightforward approximation adder is proposed in this study. Approximate multiplier M1 using OR gate and approximate multiplier M2 using proposed approximate adders are compared. The proposed approximate adder is suited for building an adder tree to accumulate partial product (PP) because it is less complicated than traditional adders. Compared to a one-bit-full adder, the critical path delay (CPD) is reduced significantly in the proposed methods. The accuracy comparison of M1. M2 and Wallace tree using the normalized mean error distance (NMED), the mean relative error distance (MRED), the maximum error (ME), and the error rate (ER) with the number of bits utilized for reducing error. For the area (delay) optimized circuit, when the bit used is 4, the delay is 0.4 ns for M1, 0.43 ns for M2, and 1.08 ns for the Wallace tree multiplier. For the delay (area) optimized circuit, when the bit used is 4, the delay is 0.16 ns for M1, 0.16 ns for M2, and 0.40 ns for the Wallace tree multiplier. To more accurately evaluate performance at the circuit level, the PDP and ADP are computed. The NMED, MRED, ME, and ER versus PDP and ADP are computed. The proposed multipliers M1 and M2 are compared with existing approximate multipliers. When an equivalent MRED, NMED, or ER is taken into account, M1 has the smallest ADP and PDP among other multipliers. The very low likelihood of a significant ED occurring is indicated by the small values of NMED and MRED in M1 and M2. The proposed solutions effectively reduce delay, area, and power while maintaining increased accuracy and performance.
  • Advances and development of wind–solar hybrid renewable energy technologies for energy transition and sustainable future in India

    J Charles, Rajesh Kumar; Majid, M A; Department Collaboration; Electrical and Computer Engineering (SAGE Publications, 2023-01-29)
    While solar power projects are built on a continuous ground, wind power projects require scattered land, raising transmission costs and increasing the risk of land-related complications. Wind–solar hybrid (WSH) projects have been proposed to address these issues and accelerate installation. WSH power projects will create a well-defined area with sufficient infrastructure, including evacuation facilities, where the project’s risks can be reduced. The extensive coastline of India is endowed with high wind flow speed and plentiful solar power resources, creating an ideal environment for WSH projects to prosper while simultaneously improving grid stability and reliability. WSH plants guarantee higher transmission efficiency and cost-effectiveness than their stand-alone counterparts. As of 30.11.2021, 3.75 GW of WSH projects have been granted, with 0.148 GW of operational capacity and 1.7 GW of WSH projects in various bidding phases. In this paper, we discussed state-wise WSH potential, the key players in the WSH project, the National WSH, and the State WSH policy and amendments. Also, the WSH project’s physical progress and commercial details are covered. A feasibility study of the WSH plant is performed, and the primary design strategy for deploying WSH power facilities in India is discussed. It covers every step of this process, from design technique to choosing and evaluating potential locations for such hybrid projects, optimally placing wind turbines and solar panels, overall capacity mix for hybrid plants, and ultimately power evacuation optimization. Additionally, a brief study of the savings from these hybrid plants and the environmental, social, and governance standards which are necessary to implement these projects are provided. The potential challenges connected with WSH technologies are examined in depth, and potential solutions and mitigations for the challenges are provided. Designing a WSH for small-scale irrigation is provided along with the size and choice of wind and solar systems. Degradation of PV systems and carbon savings are included, along with some policy measures to boost the proportion of WSH in the entire power mix. In India, the development of large-scale WSH projects is still in its early stages, and more research is required to explore technical, commercial, and policy elements that influence project design. The policy suggestions for improvement of the WSH project are provided. The WSH project developers, potential investors, stakeholders, innovators, policymakers, manufacturers, designers, and researchers will benefit from the recommendations based on the review’s findings.
  • A Composite Exponential Reaching Law Based SMC with Rotating Sliding Surface Selection Mechanism for Two Level Three Phase VSI in Vehicle to Load Applications

    Haroon, Faheem; Aamir, Muhammad; Waqar, Asad; Mian Qaisar, Saeed; Syed, Umaid Ali; Turki Almaktoom, Abdulaziz; University Collaboration; Electrical and Computer Engineering (MDPI, 2022-12-28)
    first_page settings Order Article Reprints Open AccessArticle A Composite Exponential Reaching Law Based SMC with Rotating Sliding Surface Selection Mechanism for Two Level Three Phase VSI in Vehicle to Load Applications by Faheem Haroon 1, Muhammad Aamir 2, Assad Waqar 1,* [ORCID] , Saeed Mian Qaisar 3,4,* [ORCID] , Syed Umaid Ali 1 [ORCID] and Abdulaziz Turki Almaktoom 5,* [ORCID] 1 Department of Electrical Engineering, Bahria School of Engineering and Applied Sciences Islamabad Campus (BSEAS-IC), Islamabad 44000, Pakistan 2 Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur 22620, Pakistan 3 Electrical and Computer Engineering Department, Effat University, Jeddah 22332, Saudi Arabia 4 Communication and Signal Processing Lab, Energy and Technology Research Center, Effat University, Jeddah 22332, Saudi Arabia 5 Supply Chain Management Department, Effat University, Jeddah 22332, Saudi Arabia * Authors to whom correspondence should be addressed. Energies 2023, 16(1), 346; Received: 14 November 2022 / Revised: 16 December 2022 / Accepted: 20 December 2022 / Published: 28 December 2022 (This article belongs to the Special Issue Electrochemical Energy Storage Technology and Management Systems for Vehicular Applications) Download Browse Figures Versions Notes Abstract Voltage source inverters (VSIs) are an integral part of electrical vehicles (EVs) to enhance the reliability of the supply power to critical loads in vehicle to load (V2L) applications. The inherent properties of sliding mode control (SMC) makes it one of the best available options to achieve the desired voltage quality under variable load conditions. The intrinsic characteristic of robustness associated with SMC is generally achieved at the cost of unwanted chattering along the sliding surface. To manage this compromise better, optimal selection of sliding surface coefficient is applied with the proposed composite exponential reaching law (C-ERL). The novelty of the proposed C-ERL is associated with the intelligent mix of the exponential, power, and difference functions blended with the rotating sliding surface selection (RSS) technique for three phase two level VSI. Moreover, the proposed reaching law along with the power rate exponential reaching law (PRERL), enhanced exponential reaching law (EERL), and repetitive reaching law (RRL) were implemented on two level three phase VSI under variable load conditions. A comparative analysis strongly advocates the authenticity and effectiveness of the proposed reaching law in achieving a well-regulated output voltage with a high level of robustness, reduced chattering, and low %THD.
  • Network load prediction and anomaly detection using ensemble learning in 5G cellular networks

    Haider, Usman; Waqas, Muhammad; Hanif, Muhammad; Alasmary, Hisham; Mian Qaisar, Saeed; External Collaboration; Electrical and Computer Engineering (Elsevier, 2023-01)
    Network data analytics significantly improved the 5G cellular networks. Data analytics allows network administrators and operators to use the machine and deep learning to analyse the network data efficiently. The standard protocols defined by the 3rd Generation Partnership Project (3GPP) for the network data analytics function are discussed to incorporate into the dataset. The dataset is based on cells in the network considering anomalies and fields of 3GPP, i.e., data rates and information related to the network area. Moreover, machine and deep learning techniques can be used to classify the anomalies. In this regard, we employed Decision trees (DT), Random Forest (RF), Support Vector Machines (SVM) and ensemble learning (EL) to enhance the network prediction performance. For this purpose, we used machine and deep learning techniques, i.e., one-dimensional Convolutional Neural Networks (1D CNN), Multi-Layer Perceptron (MLP), and k-Nearest Neighbours (kNN), respectively. We also used bagging-based three regressors, i.e., 1D CNN, MLP, and kNN, to predict the network load. In addition, we addressed both anomaly detection and load prediction because the presence of anomalies results in high load. The accurate detection of anomalies will result in less network load. Thus, anomalies like a sudden increase in network traffic from a certain cell are also added based on the network traffic pattern to make the dataset more realistic. The simulation results showed that the bagging-based EL outperformed the existing techniques in predicting network load. Moreover, the voting technique outperforms in the case of anomaly detection.
  • AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions

    Melarkode, Navneet; Srinivasan, Kathiravan; Mian Qaisar, Saeed; Plawiak, Pawel; External Collaboration; Electrical and Computer Engineering (MDPI, 2023-02)
    Skin cancer continues to remain one of the major healthcare issues across the globe. If diagnosed early, skin cancer can be treated successfully. While early diagnosis is paramount for an effective cure for cancer, the current process requires the involvement of skin cancer specialists, which makes it an expensive procedure and not easily available and affordable in developing countries. This dearth of skin cancer specialists has given rise to the need to develop automated diagnosis systems. In this context, Artificial Intelligence (AI)-based methods have been proposed. These systems can assist in the early detection of skin cancer and can consequently lower its morbidity, and, in turn, alleviate the mortality rate associated with it. Machine learning and deep learning are branches of AI that deal with statistical modeling and inference, which progressively learn from data fed into them to predict desired objectives and characteristics. This survey focuses on Machine Learning and Deep Learning techniques deployed in the field of skin cancer diagnosis, while maintaining a balance between both techniques. A comparison is made to widely used datasets and prevalent review papers, discussing automated skin cancer diagnosis. The study also discusses the insights and lessons yielded by the prior works. The survey culminates with future direction and scope, which will subsequently help in addressing the challenges faced within automated skin cancer diagnosis.
  • Measures of acutance and shape for classification of breast tumors

    Salem, Nema; Alim, Onsy; Desautels, J. E. Leo; Salem, Nema; External Collaboration; Electrical and Computer Engineering; Rangayyan, Rangaraj (IEEE, 1997-12)
    Most benign breast tumors possess well-defined, sharp boundaries that delineate them from surrounding tissues, as opposed to malignant tumors. Computer techniques proposed to date for tumor analysis have concentrated on shape factors of tumor regions and texture measures. While shape measures based on contours of tumor regions can indicate differences in shape complexities between circumscribed and spiculated tumors, they are not designed to characterize the density variations across the boundary of a tumor. Here, the authors propose a region-based measure of image edge profile acutance which characterizes the transition in density of a region of interest (ROI) along normals to the ROI at every boundary pixel. The authors investigate the potential of acutance in quantifying the sharpness of the boundaries of tumors, and propose its application to discriminate between benign and malignant mammographic tumors. In addition, they study the complementary use of various shape factors based upon the shape of the ROI, such as compactness. Fourier descriptors, moments, and chord-length statistics to distinguish between circumscribed and spiculated tumors. Thirty-nine images from the Mammographic Image Analysis Society (MIAS) database and an additional set of 15 local cases were selected for this study. The cases included 16 circumscribed benign, 7 circumscribed malignant, 12 spiculated benign, and 19 spiculated malignant lesions. All diagnoses were proven by pathologic examinations of resected tissue. The contours of the lesions were first marked by an expert radiologist using X-Paint and X-Windows on a SUN-SPARCstation 2 Workstation. For computation of acutance, the ROI boundaries were iteratively approximated using a split/merge and end-point adjustment technique to obtain the best-fitting polygonal approximation. The jackknife method using the Mahalanobis distance measure in the BMDP (Biomedical Programs) package was used for classification of the lesions using acutance and the shape factors as features in various combinations. Acutance alone resulted in a benign/malignant classification accuracy of 95% the MIAS cases. Compactness alone gave a circumscribed/spiculated classification rate of 92.3% with the MIAS cases. Acutance in combination with a moment-based shape measure and a Fourier descriptor-based measure gave four-group classification rate of 95% with the MIAS cases. The results indicate the importance of including lesion edge definition with shape information for classification of tumors, and that the proposed measure of acutance fills this need.
  • Artificial Intelligence-Based Detection of Human Embryo Components for Assisted Reproduction by in Vitro Fertilization

    Salem, Nema; External Collaboration; Electrical and Computer Engineering; Mushtaq, Abeer (MDPI, 2022)
    Assisted reproductive technology is helping humans by addressing infertility using different medical procedures that help in a successful pregnancy. In vitro fertilization (IVF) is one of those assisted reproduction methods in which the sperm and eggs are combined outside the human body in a specialized environment and kept for growth. Assisted reproductive technology is helping humans by addressing infertility using different medical procedures that help in a successful pregnancy. The morphology of the embryological components is highly related to the success of the assisted reproduction procedure. In approximately 3–5 days, the embryo transforms into the blastocyst. To prevent the multiple-birth risk and to increase the chance of pregnancy the embryologist manually analyzes the blastocyst components and selects valuable embryos to transfer to the women’s uterus. The manual microscopic analysis of blastocyst components, such as trophectoderm, zona pellucida, blastocoel, and inner cell mass, is time-consuming and requires keen expertise to select a viable embryo. Artificial intelligence is easing medical procedures by the successful implementation of deep learning algorithms that mimic the medical doctor’s knowledge to provide a better diagnostic procedure that helps in reducing the diagnostic burden. The deep learning-based automatic detection of these blastocyst components can help to analyze the morphological properties to select viable embryos. This research presents a deep learning-based embryo component segmentation network (ECS-Net) that accurately detects trophectoderm, zona pellucida, blastocoel, and inner cell mass for embryological analysis. The proposed method (ECS-Net) is based on a shallow deep segmentation network that uses two separate streams produced by a base convolutional block and a depth-wise separable convolutional block. Both streams are densely concatenated in combination with two dense skip paths to produce powerful features before and after upsampling. The proposed ECS-Net is evaluated on a publicly available microscopic blastocyst image dataset, the experimental segmentation results confirm the efficacy of the proposed method. The proposed ECS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% for embryological analysis.
  • Fusion of Multivariate EEG Signals for Schizophrenia Detection using CNN and Machine Learning Techniques

    Hassan, Fatima; Hussain, Syed Fawad; Qaisar, Saeed; External Collaboration; Electrical and Computer Engineering; Hassan, Fatima (Elsevier, 2023-04)
    Schizophrenia is a severe mental disorder that has adverse effects on the behavior of an individual such as disorganized speech and delusions. Electroencephalography (EEG) signals are widely used for its identification as they are non-invasive and have high temporal resolution. EEG signals may be captured using wearable devices but transmission of complete data from all channels is both battery and data consuming. Several studies on Schizophrenia have either used all channels or relied on sophisticated feature extraction algorithms to find the most relevant EEG channels for further processing. That too, however, needs data from all channels beforehand to identify the most relevant features. In this study, a publicly available multi-channel EEG signals dataset from the institute of Psychiatry and Neurology in Warsaw, Poland is studied for an automated identification of Schizophrenia using only a subset of data from selected channels. To achieve this, we device a channel selection mechanism based on a rigorous performance analysis of the Convolutional Neural Network (CNN) while considering the individual EEG channels at different brain regions. The selected channels are combined, and we use a fusion of CNN and different machine learning (ML) classifiers to train the classification model. Our experiments show that a combination of three channels namely, T4, T3, and Cz achieves 90% and 98% accuracies on subject-based and non-subject based testing, respectively, using a hybridization of CNN and logistic regression (LR).
  • Numerical Modeling and Analysis of Harvesting Atmospheric Water Using Porous Materials

    El-Amin, Mohamed F.; Al Kanay, Sedam; Brahimi, Tayeb; College Collaboration; Energy Lab; Electrical and Computer Engineering; Al Kanay, Sedam (MDPI, 10 Novembe)
    Nowadays, harvesting water from the atmosphere is becoming a new alternative for generating fresh water. To the author’s best knowledge, no mathematical model has been established to describe the process of harvesting water from the atmosphere using porous materials. This research seeks to develop a new mathematical model for water moisture absorption in porous materials to simulate and assess harvesting atmospheric water. The mathematical model consists of a set of governing partial differential equations, including mass conservation equation, momentum equation, associated parameterizations, and initial/boundary conditions. Moreover, the model represents a two-phase fluid flow that contains phase-change gas–liquid physics. A dataset has been collected from the literature containing five porous materials that have been experimentally used in water generation from the air. The five porous materials include copper chloride, copper sulfate, magnesium sulfate, manganese oxides, and crystallites of lithium bromide. A group of empirical models to relate the relative humidity and water content have been suggested and combined with the governing to close the mathematical system. The mathematical model has been solved numerically for different times, thicknesses, and other critical parameters. A comparison with experimental findings was made to demonstrate the validity of the simulation model. The results show that the proposed mathematical model precisely predicts the water content during the absorption process. In addition, the simulation results show that; during the absorption process, when the depth is smaller, the water content reaches a higher saturation point quickly and at a lower time, i.e., quick process. Finally, the highest average error of the harvesting atmospheric water model is around 1.9% compared to experimental data observed in manganese oxides.
  • Comparison between Two-Port Converter and Three-Port Converter at Different Weather Conditions

    Hussein, Aziza; Marwa M. Ahmed; Amani S. Alzahrani; Mohamed A. Enany; External Collaboration; Electrical and Computer Engineering
    Worldwide energy demand is growing fast because of the population explosion. Technological advancements paved the way toward utilizing renewable energy sources instead of fossil fuels as they cause harmful effects on the environment. To increase the efficiency of a solar PV system, Maximum Power Point Tracking (MPPT) techniques are applied to a power converter that is connected to the PV array. Thus, maximum power is extracted from the PV array. Since sunlight is not always available, a battery is used as a storage device. Traditionally, two two-port converters are used with such systems. Recently, three-port converter that connect the PV, the battery, and the load together using a single converter is introduced in the literature. This paper compares between these two types of converters based on the PV power output when the solar irradiance changes. The simulation is done using MATLAB/SIMULINK software. It is found out that two-port converter responds more efficiently to irradiance changes than the three-port converter. However, three- port converter has more steady attitude when the temperature is constant for a certain amount of time. The significance of this paper reiles on comparing between these two types of converters to determine the best choice for a certain application.
  • Applications of Capacity Enlargement and Traffic Management Control in Hybrid Satellite-Terrestrial 5G Networks

    Hussein, Aziza; M. Mourad Mabrook; External Collaboration; Electrical and Computer Engineering
    The significant volume of data traffic, ultimate data rates, reduced latency, more transmissions with efficient energy, spectrum consumption, and novel technological use cases must all be considered using the fifth generation (5G) mobile network. Future 5G networks will connect billions of objects, known as the Internet of Things (IoT) or massive Machine-T Communications (mMTC), in addition to standard Mobile Broadband (MBB) services. Non-Terrestrial Networks (NTNs) may serve connection requests anywhere and anytime by offering wide-area coverage and preserving service continuity, availability, and scalability. The satellite platform is a crucial NTN for integrating with the 5G network and delivering the necessary services. Soon, 5G network development will depend on satellite access, as stated in the 3Gpp Release 16 specification. This paper provides options for incorporating satellite systems into a 5G network design to increase capacity and extend coverage in 5G mobile networks. In addition, 5G networks must address a network model to resolve outage, congestion, and cell edge context issues. The last phase is the simulation of the problem formulation for the investigation of capacity and traffic in hybrid satellite-terrestrial 5G mobile backhauling networks.
  • Hybrid PAPR reduction schemes for different OFDM-based VLC systems

    Hussein, Aziza; Mohamed Y. El-Ganiny; Hesham F. A. Hamed; Ashraf A. M. Khalaf; External Collaboration; Electrical and Computer Engineering
    Orthogonal frequency division multiplexing has been widely used in many radio frequency wireless communication standards as a preferable multicarrier modulation scheme. The modulated signals of a conventional orthogonal frequency division multiplexing system are complex and bipolar. In intensity-modulated direct detection optical wireless communications, transmitted signals should be real and unipolar due to non-coherent emissions of an optical light emitting diode. In this paper, different hybrid optical systems have been proposed to satisfy real and unipolar signals. Peak-to-average power ratio is one of the biggest challenges for orthogonal frequency division multiplexing-based visible light communications. They are based on a combination of non-linear companding techniques with spreading or precoding techniques. Simulation evaluation is performed under direct current-biased optical orthogonal frequency division multiplexing, asymmetrically clipped optical orthogonal frequency division multiplexing, and Flip-orthogonal frequency division multiplexing systems in terms of peakto-average power ratio, bit error rate, and spectral efficiency. The proposed schemes are investigated to determine a scheme with a low peak-to-average power ratio and an acceptable bit error rate. MATLABTM software has been successfully used to show the validity of the proposed schemes.
  • Automated Design Error Debugging of Digital VLSI Circuits

    Hussein, Aziza; Hanafy, M Ali; Mohammed, Moness; Lamya, Gaber; External Collaboration; Electrical and Computer Engineering
    As the complexity and scope of VLSI designs continue to grow, fault detection processes in the pre-silicon stage have become crucial to guaranteeing reliability in IC design. Most fault detection algorithms can be solved by transforming them into a satisfiability (SAT) problem decipherable by SAT solvers. However, SAT solvers consume significant computational time, as a result of the search space explosion problem. This ever- increasing amount of data can be handled via machine learning techniques known as deep learning algorithms. In this paper, we propose a new approach utilizing deep learning for fault detection (FD) of combinational and sequential circuits in a type of stuck-at-faults. The goal of the proposed semi-supervised FD model is to avoid the search space explosion problem by taking advantage of unsupervised and supervised learning processes. First, the unsupervised learning process attempts to extract underlying concepts of data using Deep sparse autoencoder. Then, the supervised process tends to describe rules of classification that are applied to the reduced features for detecting different stuck-at faults within circuits. The FD model proposes good performance in terms of running time about 187 × compared to other FD algorithm based on SAT solvers. In addition, it is compared to common classical machine learning models such as Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB) classifiers, in terms of validation accuracy. The results show a maximum validation accuracy of the feature extraction process at 99.93%, using Deep sparse autoencoder for combinational circuits. For sequential circuits, stacked sparse autoencoder presents 99.95% as average validation accuracy. The fault detection process delivers around 99.6% maximum validation accuracy for combinational circuits from ISCAS’85 and 99.8% for sequential circuits from ISCAS’89 benchmarks. Moreover, the proposed FD model has achieved a running time of about 1.7x, compared to DT classifier and around 1.6x, compared to RF classifier and GB machine learning classifiers, in terms of validation accuracy in detecting faults occurred in eight different digital circuits. Furthermore, the proposed model outperforms other FD models, based on Radial Basis Function Network (RBFN), achieving 97.8% maximum validation accuracy.
  • Comparison of two-lifetime models of solid-state lighting based on sup-entropy

    Kittaneh, Omar; Majid, M.A.; College Collaboration; Electrical and Computer Engineering (Elsevier B.V., 2019)
    On the basis of the efficiency function introduced by Kittaneh and Beltagy [18], we compare the performance of censored samples from lognormal and Weibull distributions as two possible fitting models of solid-state lighting (SSL) luminaire lifetime. The validity of the efficiency function is demonstrated through several correlations with the accuracy in estimating the mean lifetime to fail.
  • Artificial intelligence and machine learning research: towards digital transformation at a global scale

    Sarirete, Akila; Balfagih, Zain; Brahimi, Tayeb; Lytras, Miltiadis; Visvizi, Anna; Computer Science (Springer Nature, 2021)
  • Artificial Intelligence: Towards Digital Transformation of Life, Work, and Education

    Sarirete, Akila; Balfagih, Zain; Brahimi, Tayeb; Mohamed El Amin Mousa; Lytras, Miltiadis; Anna Visvizi; Computer Science (Elsevier B.V., 2021)

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