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Detection and suppression of narrow band RFI for synthetic aperture radar imaging

Chinese Journal of Aeronautics 2015年第4期28卷 1189-1198页

作者： Yang Lin Zheng Huifang Feng Jin Li Ning Chen Jiaqi College of Electronic Science and Engineering National University of Defense Technology Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences College of Computer and Information Engineering Hohai University

Radio frequency interference（RFI） is becoming more and more frequently, which makes it an important issue in SAR *** presented in synthetic aperture radar either on purpose or inadvertent will distort the useful SAR echoes, thus degrade the SAR image *** resolve this issue, a long time study was carried out to study the characteristic of the RFI through the RFIaffected spaceborne and airborne SAR *** on the narrow band nature of RFI, this paper proposes a new process which contains both RFI detection and RFI suppression.A useful subband spectral kurtosis detector is first used to detect RFI, and then its results are used for RFI *** proposed process has two advantages： one is the economization on the compute time for unnecessary interference suppression when no RFI existed; the other is improving the performance of the suppression method with knowing the exact position where RFI ***, the previous RFI suppression method––subband spectral cancelation（SSC） is supplemented and *** subband division step is also elaborated detail in this *** experiment results show that the subband spectral kurtosis detector exhibits good performance in recognizing both weak and narrow-band *** addition, the validity of the SSC method with subband spectral kurtosis detector is also validated on the real SAR echoes.

关键词： Kurtosis detector Radio frequency interference Subband spectralcancelation Synthetic aperture radar

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On-road vehicle detection using support vector machine and decision tree classifications 20

On-road vehicle detection using support vector machine and d...

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6th International Conference on Engineering and MIS, ICEMIS 2020

作者： Ali, Asma Mustafa Eltarhouni, Wafa I. Bozed, Kenz A. Computer Science Department Faculty of Information Technology Benghazi University Jalo Libya Computer Science Department Faculty of Information Technology Benghazi University Benghazi Libya Computer System Design Department Faculty of Information Technology Benghazi University Benghazi Libya

ISBN: (纸本)9781450377362

On-road vehicle detection is a major part of various applications, such as driver assistance systems and auto-driving, and these systems need to detect vehicles robustly and accurately. This paper proposes a robust vehicle detection system to detect the front vehicles by using a single camera mounted on the car. The proposed system consists of two main steps, which are, hypotheses generation (HG) and hypotheses verification (HV). The first step is to find the candidate regions to the vehicles in the image. The guide to these regions is the shadow underneath the vehicle because it is always darker than the road surface. The system fits the generated regions with the width of the vehicle and reduces the number of hypotheses by calculating the entropy in two different ways. The second step is to verify whether the generated hypotheses contain a vehicle or not, and this done by Histogram of Oriented Gradients (HOG) to extract the features. In designing the proposed system, the Support Vector Machine (SVM) and Decision Tree (DT) classifiers are used for classification. Experiments were conducted using the challenging GTI DATA database to ascertain the usefulness of the approaches. The methodology was evaluated against the state-of the-art and it was found that the proposed approaches produce outstanding results. © 2020 ACM.

关键词： Entropy

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H∞$$ {H}_{\infty } $$PID Control for Singularly Perturbed systems With Randomly Switching Nonlinearities Under Dynamic Event-Triggered Mechanism

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International Journal of Robust and Nonlinear Control 2025年

作者： Zhenglong Jiao Zidong Wang Yuqiang Luo Fan Wang Shanghai Key Lab of Modern Optical System Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai China Department of Computer Science Brunel University London Uxbridge Middlesex UK School of Automation Nanjing University of Information Science and Technology Nanjing China

In this article, the observer-based H ∞ $$ {H}_{\infty } $$ proportional-integral-derivative (PID) control problem is studied for a class of singular perturbed systems with randomly switching nonlinearities. During the modeling process, a set of binary random sequences is introduced to describe the random behaviors of the nonlinear switching encountered in engineering practice. Considering the singular perturbation parameter (SPP), an observer-based H ∞ $$ {H}_{\infty } $$ PID controller is constructed for the concerned singularly perturbed systems (SPSs), where the state information is estimated. To improve communication efficiency and reduce resource waste, a dynamic event-triggered mechanism is employed during the measurement transmission. Meanwhile, an SPP-dependent Lyapunov-Krasovskii function is utilized to derive conditions under which the closed-loop system is guaranteed to be stochastically stable with the desired H ∞ $$ {H}_{\infty } $$ performance under a specified upper bound on the SPP. All the desired observer and controller gains are determined by solving a set of matrix inequalities. Finally, the effectiveness and superiority of the suggested PID control method are verified through a simulation example.

关键词： H∞$$ {H}_{\infty } $$ performance index dynamic event-triggered mechanism proportional-integral-derivative control randomly switching nonlinearities singularly perturbed systems

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Modeling and Analyzing Web Application with Aspect-Oriented Hierarchical Coloured Petri Nets

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China Communications 2016年第5期13卷 89-102页

作者： Xiaoxing Sun Huiqun Yu Honghao Liang Nianhua Yang Department of Computer Science and Engineering East China University of Science and Technology Department of System Technology East China University of Science and Technology Library Shanghai No NDA Smart Technology Co Ltd. School of Business Information Shanghai University of International Business and Economics

Aspect-oriented modeling can uncover potential design faults, yet most existing work fails to achieve both separation and composition in a natural and succinct way. This study presents an aspect-oriented modeling and analysis approach with hierarchical Coloured Petri Nets(HCPN). HCPN has sub-models and well-defined semantics combining a set of submodels. These two characteristics of HCPN are nicely integrated into aspect oriented modeling. Submodels are used to model aspects while the combination mechanism contributes to aspects weaving. Furthermore, the woven aspect oriented HCPN model can be simulated and analyzed by the CPN Tools. A systematic web application case study is conducted. The results show the system original properties are satisfied after weaving aspects and all design flaws are revealed. As such, the approach can support web application design and analysis in an aspect-oriented fashion concisely and effectively.

关键词： aspect-oriented modeling hierarchical coloured Petri net CPN Tools ASKCTL

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Smart Dynamic Resource Allocation Model for Patient-Driven Mobile Medical information system Using C4.5 Algorithm

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Journal of Electronic Science and technology 2019年第3期17卷 231-241页

作者： Ching-Kan Lo Hsing-Chung Chen Pei-Yuan Lee Ming-Chou Ku Lidia Ogiela Cheng-Hung Chuang the Department of Orthopedics and the Department of Information Show-Chwan Health Care SystemChanghua 50008 the Department of Computer Science and Information Engineering and the Department of Bioinformatics and Medical Engineering Asia UniversityTaichung 41354 the Department of Orthopedics Show-Chwan Health Care SystemChanghua 50008 the Department of Applied Informatics Akademia Gorniczo-Hutnicza University of Science and TechnologyKrakow 30-059

A mobile medical information system (MMIS) is an integrated application (app) of traditional hospital information systems (HIS) which comprise a picture archiving and communications system (PACS), laboratory information system (LIS), pharmaceutical management information system (PMIS), radiology information system (RIS), and nursing information system (NIS). A dynamic resource allocation table is critical for optimizing the performance to the mobile system, including the doctors, nurses, or other relevant health workers. We have designed a smart dynamic resource allocation model by using the C4.5 algorithm and cumulative distribution for optimizing the weight of resource allocated for the five major attributes in a cooperation communications system. Weka is used in this study. The class of concept is the performance of the app, optimal or suboptimal. Three generations of optimization of the weight in accordance with the optimizing rate are shown.

关键词： Dynamic resource allocation electronic health record hospital information system mobile medical information system

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Determinants of Lecturer Performance to Enhance Accreditation in Higher Education 8

Determinants of Lecturer Performance to Enhance Accreditatio...

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8th International Conference on Cyber and IT Service Management, CITSM 2020

作者： Rahardja, Untung Lutfiani, Ninda Setiani Rafika, Ageng Purnama Harahap, Eka Universitas Raharja Master of Information Technology Department Indonesia Universitas Raharja Computer System Department Indonesia

ISBN: (纸本)9781728177014

The current phenomenon is there are still many institutions that do not have institutional accreditation. Therefore, this study aims to be a solution in maximizing higher education achievement according to the standards set by BAN-PT. Lecturers as the spearhead of the teaching and learning process movement are expected to perform well. This research was conducted with the final aim to determine the factors that affect the performance of college lecturers with accreditation 'B' in LLDIKTI Region IV Banten Province. This research approach is descriptive and precise. The method used is observation and distribution of online questionnaires. Based on the characteristics of the program to be studied, the analysis of the factors that influence the process uses the SEM (Structural Equation Modeling) method using the Lisrel (Linear Structural Relationship) version 8.80 program. Based on research results, leadership, competence, and organizational climate partially and simultaneously have a positive and significant effect on work motivation with a contribution of 51%. Leadership is the most dominant variable affecting work motivation. Meanwhile, leadership, competence, organizational climate, and work motivation either partially or simultaneously have a positive and significant effect on lecturer performance with a contribution of 75%. Organizational climate is partly the most dominant variable affecting lecturer performance. The novelty of this research to improve the performance of lecturers in private tertiary institutions accredited 'B' in the IV environment of Banten Province, it is necessary to improve the organizational climate which is reflected in the dimension of responsibility while improving the work motivation performance it is necessary to increase the leadership. © 2020 IEEE.

关键词： Motivation

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Web service composition survey: State of the art review

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Recent Patents on computer Science 2010年第2期3卷 91-107页

作者： Al-Shargabi, Bassam Sheikh, Asim El Sabri, Alia Faculty of Information Technology Department of Computer Information System Middle East University for Graduate Studies P.O. Box 42 Amman 11610 Jordan Faculty of Information Systems and Technology Arab Academy for Banking and Financial Sciences Amman Jordan

The Web service composition has received much attention recently to support business-to-business enterprise applications integration. Many composition approaches, languages, platforms, and patents have been proposed to overcome this issue. In this paper we present some of Web service composition languages, platforms, patents, and composition strategies, we compared them to some of the features that must be held by composition process, features such as the dynamic adaptation, execution monitoring, and quality of service (QoS) constraints to satisfy client's requirements to execute Web process. And we give an outlook to essential future research work. Recent patent and research advances aim to find methods for dynamic learning and redesigning along with monitoring and optimization of Web service composition process to satisfy user constraints. © 2010 Bentham Science Publishers Ltd.

关键词： Quality of service

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An Ensemble Detection Method for Shilling Attacks Based on Features of Automatic Extraction

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China Communications 2019年第8期16卷 130-146页

作者： Yaojun Hao Fuzhi Zhang Jinbo Chao School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China Department of Computer Science and Technology Xinzhou Teachers UniversityXinzhou 034000China

Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA(marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA(marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.

关键词： shilling attack ensemble detection features of automatic extraction marginalized linear denoising autoencoder

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