检索结果-南通市图书馆

Frontiers of Structural and Civil Engineering 2021年第3期15卷 643-651页

作者： Farnaz BATOOL muhammad saad khan Vivek BINDIGANAVILE Department of Civil Engineering NED University of Engineering&TechnologyKarachi75270Pakistan Department of Civil and Environmental Engineering University of AlbertaEdmontonT6G2R3Canada

This study presents the results of the 3D microstructure,thermal conductivity,and heat flow in cement-based foams and examines their changes with a range of densities.Images were captured using X-ray micro computed tomography(micro-CT)imaging technique on cement-based foam samples prepared with densities of 400,600,and 800 kg/m^(3).These images were later simulated and quantified using 3D data visualization and analysis software.Based on the analysis,the pore volume of 11000µm^(3)was determined across the three densities,leading to optimal results.However,distinct pore diameters of 15µm for 800 kg/m^(3),and 20µm for 600 and 400 kg/m^(3)were found to be optimum.Most of the pores were spherical,with only 10%appearing elongated or fractured.In addition,a difference of 15%was observed between the 2D and 3D porosity results.Moreover,a difference of 5%was noticed between the experimentally measured thermal conductivity and the numerically predicted value and this variation was constant across the three cast densities.The 3D model showed that heat flows through the cement paste solids and with an increase in porosity this flow reduces.

关键词： 3D pore volume distribution X-ray tomography 3D shape factor heat flow

Recognition of motor imagery tasks for BCI using CSP and chaotic PSO twin SVM

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

The Journal of China Universities of Posts and Telecommunications 2017年第3期24卷 83-90页

作者： Li Duan Zhang Hongxin muhammad saad khan Mi Fang School of Electronic Engineering Beijing University of Posts and Telecommunications School of Physics and Electronic Information Engineering Henan Polytechnic University

Accurate modeling and recognition of the brain activity patterns for reliable communication and interaction are still a challenging task for the motor imagery （MI） brain-computer interface （BCI） system. In this paper, we propose a common spatial pattern （CSP） and chaotic particle swarm optimization （CPSO） twin support vector machine （TWSVM） scheme for classification of MI electroencephalography （EEG）. The self-adaptive artifact removal and CSP were used to obtain the most distinguishable features. To improve the recognition results, CPSO was employed to tune the hyper-parameters of the TWSVM classifier. The usefulness of the proposed method was evaluated using the BCI competition IV-IIa dataset. The experimental results showed that the mean recognition accuracy of our proposed method was increased by 5.35%, 4.33%, 0.78%, 1.45%, and 9.26% compared with the CPSO support vector machine （SVM）, particle swarm optimization （PSO） TWSVM, linear discriminant analysis （LDA）, back propagation （BP） and probabilistic neural network （PNN）, respectively. Furthermore, it achieved a faster or comparable central processing unit （CPU） running time over the traditional SVM methods.

关键词： brain-computer interface motor imagery twin support vector machine chaotic particle swarm optimization

A PHEMT Based Wideband LNA for Wireless Applications

在线全文

学校读者我要写书评

暂无评论

China Communications 2015年第10期12卷 108-116页

作者： muhammad saad khan ZHANG Hongxin HE Pengfei Sulman Shahzad Rahat Ullah School of Electronic Engineering Beijing University of Posts and Telecommunications(BUPT) Electrical Engineering Department University College of Engineering & TechnologyBahauddin Zakariya University Institute of Science and Technology for Opto-electronic Information Yantai University State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications

This work is about the development of a super low noise amplifier with minimum power consumption and high gain for several wireless applications.The amplifier operates at frequency bands of 0.9-2.4 GHz and can be used in many applications like Wireless local area network(WLAN),WiFi,Bluetooth,ZigBee and Global System for mobile communications(GSM).This new design can be employed for the IEEE 802.15.4 standard in industrial,scientific and medical(ISM) Band.The enhancement mode pseudomorphic high electron mobility transistor PHEMT is used here due to its high linearity,better performance and less noisy operation.The common source inductive degeneration method is employed here to enhance the gain of amplifier.The amplifier produces a gain of more than 17 dB and noise figure of about 0.5 dB.The lower values of S11 and S22 reflect the accuracy of impedance matching network placed at the input and output sides of amplifier.Agilent Advance Design System(ADS) is used for the design and simulation purpose.Further the layout of design is developed on the FR4 substrate.

关键词：宽带低噪声放大器 PHEMT 无线应用赝配高电子迁移率晶体管全球移动通信系统设计系统 ZigBee 阻抗匹配网络

An Improved Empirical Mode Decomposition for Power Analysis Attack

在线全文

学校读者我要写书评

暂无评论

China Communications 2017年第9期14卷 94-99页

作者： Han Gan Hongxin Zhang muhammad saad khan Xueli Wang Fan Zhang Pengfei He School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and Telecommunications School of Science Beijing University of Posts and Telecommunications College of Information Science and Electrical Engineering Zhejiang University Institute of Science and Technology for Opto-electronic Information Yantai University

Correlation power analysis(CPA) has become a successful attack method about crypto-graphic hardware to recover the secret keys. However, the noise influence caused by the random process interrupts(RPIs) becomes an important factor of the power analysis attack efficiency, which will cost more traces or attack time. To address the issue, an improved method about empirical mode decomposition(EMD) was proposed. Instead of restructuring the decomposed signals of intrinsic mode functions(IMFs), we extract a certain intrinsic mode function(IMF) as new feature signal for CPA attack. Meantime, a new attack assessment is proposed to compare the attack effectiveness of different methods. The experiment shows that our method has more excellent performance on CPA than others. The first and the second IMF can be chosen as two optimal feature signals in CPA. In the new method, the signals of the first IMF increase peak visibility by 64% than those of the tradition EMD method in the situation of non-noise. On the condition of different noise interference, the orders of attack efficiencies are also same. With external noise interference, the attack effect of the first IMF based on noise with 15dB is the best.

关键词： power analysis attack, EMD,IMF, correlation power analysis, RPIs

Privacy Protection with Dynamic Pseudonym-Based Multiple Mix-Zones Over Road Networks

在线全文

学校读者我要写书评

暂无评论

China Communications 2017年第4期14卷 89-100页

作者： Qasim Ali Arain Zhongliang Deng Imran memon Asma Zubedi Jichao Jiao Aisha Ashraf muhammad saad khan Beijing University of posts and Telecommunication Beijing China College of Computer Science Zhejiang University Hangzhou 310027 China Department of Software Engineering Mehran UET Jamshoro

In this research we proposed a strategy for location privacy protection which addresses the issues related with existing location privacy protection techniques. Mix-Zones and pseudonyms are considered as the basic building blocks for location privacy; however, continuously changing pseudonyms process at multiple locations can enhance user privacy. It has been revealed that changing pseudonym at improper time and location may threat to user's privacy. Moreover, certain methods related to pseudonym change have been proposed to attain desirable location privacy and most of these solutions are based upon velocity, GPS position and direction of angle. We analyzed existing methods related to location privacy with mix zones, such as RPCLP, EPCS and MODP, where it has been observed that these methods are not adequate to attain desired level of location privacy and suffered from large number of pseudonym changes. By analyzing limitations of existing methods, we proposed Dynamic Pseudonym based multiple mix zone(DPMM) technique, which ensures highest level of accuracy and privacy. We simulate our data by using SUMO application and analysis results has revealed that DPMM outperformed existing pseudonym change techniques and achieved better results in terms of acquiring high privacy with small number of pseudonym change.

关键词： road network multiple mix-zones location privacy

X-Band Power Amplifier for Next Generation Networks Based on MESFET

在线全文

学校读者我要写书评

暂无评论

China Communications 2017年第4期14卷 11-19页

作者： muhammad saad khan Hongxin Zhang Fan Zhang Sulman Shahzad Rahat Ullah Sajid Ali Qasim Ali Arain Manzoor Ahmed School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing 100876 China Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and Telecommunications Beijing 100876 china College of Information Science and Electrical Engineering Zhejiang University Hangzhou Zhejiang 310027china Electrical Engineering Department University College of Engineering & Technology Bahauddin Zakariya University Multan Pakistan State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing 100876 China Department of Coxnputer Science D.G. Khan Campus. University of Education Lahore Pakistan Future lnternet & Communication Lab (FIB) Department of Electronic Engineering Tsinghua University Beijing 100084 China

Advanced wireless standards of communication like 3GPP and LTE are becoming more and more efficient and with this evolution of communication systems mobile equipment is also become smaller and smaller. Power amplifier designing has become a very crucial task in this era where efficiency and size are the main concern of any designer. In this paper we have design and analyzed X-band Class E Metal-semiconductor field effect transistor(MESFET) based Power Amplifier. This device targets the devices which use OFDM technique to improve their spectral efficiency for the next generation communication systems. Microstrip lines are used to achieve small size for our design instead of lumped components. Load Pull measurements are used to get MESFET input and output impedances optimum values. For linear and non linear operation small signal mathematical model of the design is used. To reduce thermal losses FR4 substrate is used to increase PA efficiency. Our designs shows small values of input and output return loss of about-22.3d B and-23.716 d B achieving a high gain of about25.6 d B respectively, with PAE of about 30 % having stability factor greater than 1 and 21.894 d Bm of output power.

关键词： MESFET X-band power amplifier advance design system PAE LTE

A Framework for Enhancing Privacy and Anonymity in Blockchain-Enabled IoT Devices

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第3期78卷 4263-4282页

作者： muhammad saad muhammad Raheel Bhutta Jongik Kim Tae-Sun Chung Department of Artificial Intelligence Ajou UniversitySuwon443-749Korea Department of Electrical and Computer Engineering University of Utah Asia CampusIncheon21985Korea Department of Artificial Intelligence Chungnam National UniversityDaejeon34134Korea

With the increase in IoT(Internet of Things)devices comes an inherent challenge of security.In the world today,privacy is the prime concern of every individual.Preserving one’s privacy and keeping anonymity throughout the system is a desired functionality that does not come without inevitable trade-offs like scalability and increased complexity and is always exceedingly difficult to manage.The challenge is keeping confidentiality and continuing to make the person innominate throughout the system.To address this,we present our proposed architecture where we manage IoT devices using blockchain technology.Our proposed architecture works on and off blockchain integrated with the closed-circuit television(CCTV)security camera fixed at the rental property.In this framework,the CCTV security camera feed is redirected towards the owner and renter based on the smart contract conditions.One entity(owner or renter)can see the CCTV security camera feed at one time.There is no third-party dependence except for the CCTV security camera deployment phase.Our contributions include the proposition of framework architecture,a novel smart contract algorithm,and the modification to the ring signatures leveraging an existing cryptographic technique.Analyses are made based on different systems’security and key management areas.In an empirical study,our proposed algorithm performed better in key generation,proof generation,and verification times.By comparing similar existing schemes,we have shown the proposed architectures’advantages.Until now,we have developed this system for a specific area in the real world.However,this system is scalable and applicable to other areas like healthcare monitoring systems,which is part of our future work.

关键词： Privacy anonymity blockchain IoT smart contracts

Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： muhammad Armghan Latif Zohaib Mushtaq saad Arif Sara Rehman muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.Sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq saad Arif Zohaib Mushtaq muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2024年第2期48卷 363-394页

作者： Nasser Alshammari Shumaila Shahzadi saad Awadh Alanazi Shahid Naseem muhammad Anwar Madallah Alruwaili muhammad Rizwan Abid Omar Alruwaili Ahmed Alsayat Fahad Ahmad Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72341Saudi Arabia Department of Computer Sciences Kinnaird College forWomenLahorePunjab54700Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72341Saudi Arabia Computer Science Florida Polytechnic UniversityLakelandFlorida33805USA Delta3T LahorePunjab54700Pakistan Department of Basic Sciences Common First YearJouf UniversitySakaka72341Saudi Arabia

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

关键词： Software defined network network function virtualization network function virtualization management and orchestration virtual infrastructure manager virtual network function Kubernetes Kubectl artificial intelligence machine learning