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AMC Integrated Multilayer Wearable Antenna for Multiband WBAN Applications

Computers, Materials & Continua 2022年第5期71卷 3227-3241页

作者： Iqra Aitbar Nosherwan Shoaib akram alomainy Abdul Quddious Symeon Nikolaou Muhammad Ali Imran Qammer H.Abbasi Research Institute for Microwave and Millimeter-Wave Studies(RIMMS)National University of Sciences and Technology(NUST) H-1244000IslamabadPakistan School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK KIOS Research and Innovation Center of Excellence University of Cyprus2109NicosiaCyprus Frederick Research Center(FRC)and Department of Electrical Engineering Frederick University1036NicosiaCyprus James Watt School of Engineering University of GlasgowGlasgowG128QQU.K

In this paper,a compact,efficient and easy to fabricate wearable antenna integrated with Artificial Magnetic Conductor(AMC)is *** of slots and bevels/cuts in the rectangular monopole patch antenna yield a wide bandwidth along with band *** proposed antenna is backed with an AMC metasurface that changes the bidirectional radiation pattern to a unidirectional,thus,considerably reducing the Specific Absorption Ratio(SAR).The demonstrated antenna has a good coverage radiating away from the body and presents reduced radiation towards the body with a front-to-back ratio of 13 dB and maximum gain of 3.54 *** proposed design operates over a wide frequency band of 2.9 to 12 GHz(exceeding the designated 3.1−10.6 GHz Ultra-Wideband(UWB)band).The band notches were created using slots on the radiating patch in the sub-bands from 5.50 to 5.67 GHz and 7.16 to 7.74 *** overall dimensions of the structure are 33×33×6.75 *** antenna’s radiation performance increased considerably with the addition of the AMC *** SAR values for the antenna are reduced by 85.3%when the AMC is used and are 0.083 W/kg which is well below the FCC SAR *** simple design,miniaturized profile,low SAR and wide operating bands with multiple band notches make the presented antenna an appealing choice for several UWB wearable body area network(WBAN)applications.

关键词： Ultra-wideband(UWB) monopole antenna wearable antenna specific absorption ratio(SAR)

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Machine learning enabled identification and real-time prediction of living plants’ stress using terahertz waves

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Defence Technology（防务技术） 2022年第8期18卷 1330-1339页

作者： Adnan Zahid Kia Dashtipour Hasan T.Abbas Ismail Ben Mabrouk Muath Al-Hasan Aifeng Ren Muhammad A.Imran akram alomainy Qammer H.Abbasi School of Engineering and Physical Science Heriot-Watt UniversityEdinburghEH144ASUK James Watt School of Engineering University of GlasgowGlasgowG128QQUK College of Engineering Al-Ain UniversityAbu DhabiUnited Arab Emirates School of Electronic Engineering Xidian UniversityXi'anShaanxiChina School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonUK

Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality.

关键词： Terahertz sensing Plants health Machine learning

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On the calibration of a shear stress criterion for rock joints to represent the full stress-strain profile

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第2期16卷 379-392页

作者： akram Deiminiat Jonathan D.Aubertin Yannic Ethier Department of Construction Engineering École de Technologie Supérieure(ÉTS)MontréalCanada

Conventional numerical solutions developed to describe the geomechanical behavior of rock interfaces subjected to differential load emphasize peak and residual shear *** detailed analysis of preand post-peak shear stress-displacement behavior is central to various time-dependent and dynamic rock mechanic problems such as rockbursts and structural instabilities in highly stressed *** complete stress-displacement surface(CSDS)model was developed to describe analytically the pre-and post-peak behavior of rock interfaces under differential *** formulations of the CSDS model required extensive curve-fitting iterations which limited its practical applicability and transparent integration into engineering *** present work proposes modifications to the CSDS model aimed at developing a comprehensive and modern calibration protocol to describe the complete shear stressdisplacement behavior of rock interfaces under differential *** proposed update to the CSDS model incorporates the concept of mobilized shear strength to enhance the post-peak ***’s concepts of joint roughness coefficient(JRC)and joint compressive strength(JCS)are incorporated to facilitate empirical estimations for peak shear stress and normal closure ***/uniaxial compression test and direct shear test results are used to validate the updated model and exemplify the proposed calibration *** results illustrate that the revised model successfully predicts the post-peak and complete axial stressestrain and shear stressedisplacement curves for rock joints.

关键词： Full shear profile Post-peak shear behavior Rock joint Joint roughness coefficient(JRC) Axial stress-strain curve

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Two-dimensional investigation of characteristic parameters and their gradients for the self-generated electric and magnetic fields of laser-induced zirconium plasma

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Plasma Science and Technology 2024年第5期26卷 138-155页

作者： Tayyaba SAJID Shazia BASHIR Mahreen akram Maira RAZZAQ Khaliq MAHMOOD Centre for Advanced Studies in Physics Institute of PhysicsGovernment College University LahoreLahore 54000Pakistan Department of Physics Institute of PhysicsGovernment College University LahoreLahore 54000Pakistan

Two-dimensional diagnosis of laser-induced zirconium(Zr)plasma has been experimentally performed using the time-of-flight method by employing Faraday cups in addition to electric and magnetic *** characteristic parameters of laser-induced Zr plasma have been evaluated as a function of different laser irradiances ranging from 4.5 to 11.7 GW cm-2 at different axial positions of 1–4 cm with a fixed radial distance of 2 cm.A well-supporting correlation between the plume parameters and the laser-plasma-produced spontaneous electric and magnetic(E and B)fields was *** measurements of the characteristic parameters and spontaneously induced fields were observed to have an increasing trend with the increasing laser ***,when increasing the spatial distance in both the axial and radial directions,the plasma parameters(electron/ion number density,temperature and kinetic energy)did not show either continuously increasing or decreasing trends due to various kinetic and dynamic processes during the spatial evolution of the ***,the E and B fields were observed to be always diffusing away from the *** radial component of electron number densities remained higher than the axial number density component,whereas the axial ion number density at all laser irradiances and axial distances remained higher than the radial ion number *** higher axial self-generated electric field(SGEF)values than radial SGEF values are correlated with the effective charge-separation mechanism of electrons and *** generation of a self-generated magnetic field is observed dominantly in the radial direction at increasing laser irradiance as compared to the axial one due to the deflection of fast-moving electrons and the persistence of two-electron temperature on the radial axis.

关键词： Faraday cup axial and radial expansion space-charge effect laser-induced zirconium plasma two-electron temperature distribution self-generated electric and magnetic fields

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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Weber Law Based Approach for Multi-Class Image Forgery Detection

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Computers, Materials & Continua 2024年第1期78卷 145-166页

作者： Arslan akram Javed Rashid Arfan Jaffar Fahima Hajjej Waseem Iqbal Nadeem Sarwar Department of Computer Science Superior UniversityLahore54000Pakistan Information Technology Services University of OkaraOkara56300Pakistan Departmet of Computer Science MLC LabOkara56300Pakistan Department of CS&SE International Islamic UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science Bahria UniversityLahore CampusLahore54600Pakistan

Today’s forensic science introduces a new research area for digital image analysis formultimedia ***,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered *** forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing ***,an image can contain one or more types of *** study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial ***,images are divided into equal blocks to get scale-invariant *** law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing *** proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and ***(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and *** average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.

关键词： Copy-Move and splicing non-overlapping block division texture features weber law spatial domain xgboost

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Enhancing ChatGPT’s Querying Capability with Voice-Based Interaction and CNN-Based Impair Vision Detection Model

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Computers, Materials & Continua 2024年第3期78卷 3129-3150页

作者： Awais Ahmad Sohail Jabbar Sheeraz akram Anand Paul Umar Raza Nuha Mohammed Alshuqayran College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudia Arabia 2 School of Computer Science and EngineeringKyungpook National UniversityDaegu41566South Korea 3 Department of EngineeringManchester Metropolitan UniversityManchesterM156BHUK

This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-based impaired vision detection *** proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice ***,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate *** research tackles head-on the challenges of user experience and inclusivity in artificial intelligence(AI).It underscores our commitment to overcoming these obstacles,making ChatGPT more accessible and valuable for a broader *** integration of voice-based interaction and impaired vision detection represents a novel approach to conversational ***,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual *** modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these ***,the solution places the user at its *** responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.

关键词： Accessibility in conversational AI CNN-based impair vision detection ChatGPT voice-based interaction recommender system

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Test particles and quasiperiodic oscillations around Kiselev black hole with cloud of strings

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Chinese Physics C 2024年第5期48卷 293-308页

作者： Javlon Rayimbaev Uktambek Eshimbetov Bushra Majeed Ahmadjon Abdujabbarov Alisher Abduvokhidov Bakhrom Abdulazizov akram Xalmirzayev School of Mathematics and Natural Sciences New Uzbekistan UniversityMovarounnahr Str.1Tashkent 100000Uzbekistan School of Engineering Central Asian UniversityTashkent 111221Uzbekistan University of Tashkent for Applied Sciences Str.Gavhar 1Tashkent 100149Uzbekistan Institute of Fundamental and Applied Research National Research University TIIAMEKori Niyoziy 39Tashkent 100000Uzbekistan National University of Uzbekistan Tashkent 100174Uzbekistan Ulugh Beg Astronomical Institute Astronomy Str.33Tashkent 100052Uzbekistan Tashkent State Technical University Tashkent 100095Uzbekistan College of Electrical and Mechanical Engineering(CEME) National University of Sciences and TechnologyRawalpindiPakistan Andijan State University Universitet Str.129Andijan 170100Uzbekistan Namangan State University Boburshoh Str.161Namangan 160107Uzbekistan

In the present study,we investigate the dynamics of test particles around a Schwarzschild black hole surrounded by quintessence and immersed in a scalar string cloud *** start our study by defining the possible values of the quintessence and cloud of string parameters corresponding to the existence of the black hole horizon for fixed values of the parameters of the equation of state for dark *** also study the effects of the effective potential on the circular motion,energy,and angular momentum of the test particles together with the innermost stable circular orbits(ISCOs).We investigate the fundamental frequencies in the particle oscillations along stable circular *** relate the stability of the orbits to the Lyapunov exponent,and the chaotic behavior is studied ***,we apply the fundamental frequencies to describe quasiperiodic oscillations(QPOs)and find that,in the presence of both fields,low-frequency twin-peak QPOs are not *** addition,we obtain the constraint values for the string cloud parameter and mass of the black hole candidates located in the center of the microquasars GRO J1655-40 and GRS 1915+105 as well as the Milky Way galaxy.

关键词： quintessential field black holes string cloud quasiperiodic oscillations

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