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

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

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

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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

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MDEV Model:A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

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Computer Systems Science & Engineering 2023年第7期46卷 287-302页

作者： Mehwish Shaikh Isma Farah Siddiqui qasim arain Jahwan Koo Mukhtiar ali Unar Nawab Muhammad Faseeh Qureshi Department of Software Engineering Mehran University of Engineering and TechnologyJamshoroPakistan College of Software Sungkyunkwan UniversitySuwonKorea Department of Computer Systems Mehran University of Engineering and TechnologyJamshoroPakistan Department of Computer Education Sungkyunkwan UniversitySeoulKorea

Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful;thus,catching it early is crucial.Medical physicians’time is limited in outdoor situations due to many patients;therefore,automated systems can be a rescue.The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’experience.Therefore,radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays.In medical classifications,deep convolution neural networks are commonly used.This research aims to use deep pretrained transfer learning models to accurately categorize CXR images into binary classes,i.e.,Normal and Pneumonia.The MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models:Mobile-Net,DenseNet-201,EfficientNet-B0,and VGG-16,which have been finetuned and trained on 5,856 CXR images.The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision,accuracy,recall,AUC-roc,and f1-score.The model effectively decreases training loss while increasing accuracy.The findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%,an accuracy of 92.15%,a recall of 90.90%,an auc-roc score of 90.9%,and f-score of 91.49%with minimal data pre-processing,data augmentation,finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.

关键词： Deep transfer learning convolution neural network image processing computer vision ensemble learning pneumonia classification MDEV model

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