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An In-Depth Analysis on Image Mosaicing Methodologies

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An In-Depth Analysis on Image Mosaicing Methodologies

作者： mohamed ABDUL-RAHIM masoud ISSA 杭州电子科技大学

学位级别：硕士

Image mosaicing is one of the most important subjects of research in computer vision at *** mocaicing requires the integration of direct techniques and feature based *** techniques are found to be very useful for mosaicing large overlapping regions,small translations and rotations while feature based techniques are usenil for small overlapping *** based image mosaicing is a combination of comer detection,comer matching,motion parameters estimation and image *** addition to the above,image mosicing in real time applications continues to be a challenging field for experts in this *** research area has a wide application in the field of 3-dimention image reconstruction,video conferencing,numerous medical and computer vision areas as well as satellite *** are four commonly known steps involved in feature-based techniques,all identified as primary steps which include feature detection,feature matching,transformation model estimation,image resampling as well as *** Invariant Feature Transformation（SIFT）-based feature detector and Speeded Up Robust Feature（SURF）-based feature detector are both algorithms which are based on feature detection in order to accomplish image mosaicing,however both the two algorithms have strengths and limitations of their own in terms on their *** this thesis the author analyses and proposes techniques which are employed by both the above mentioned image mosaicing methods in order to generate an output image that is a result of the limitations in reference to the image *** this course of this thesis,the author classifies the past and current mosaicing techniques based on image registration as well as image *** each of these classifications,the author provides a comprehensive review of the major categories of the image mosaicing *** basics of these categories are first ***,for each of these basic categories,the evolving path

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Large deflection behavior effect in reinforced concrete columns exposed to extreme dynamic loads

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Frontiers of Structural and Civil Engineering 2020年第2期14卷 532-553页

作者： masoud ABEDINI Azrul AMUTALIB Chunwei ZHANG Javad MEHRMASHHADI Sudharshan Naidu RAMAN Roozbeh ALIPOUR Tohid MOM ENI mohamed HMUSSA School of Civil Engineering Qingdao University of Technology.Qingdao 266033China Department of Civil and Structural Engineering Universiti Kebangsaan MalaysiaUKM BangiSelangor 43600Malaysia Department of Mechanical and Materials Engineering University of Nebraska-LincolnLincolnNE 68588United States Department of Architecture and Built Environment Universiti Kebangsaan MalaysiaUKM BangiSelangor 43600 Malaysia Department of Mechanical Engineering Mahshahr BranchIslamic Azad UniversitMahshahrIran

Reinforced concretes(RC)have been widely used in *** construction,one of the eritical elements carrying a high percentage of the weight is columns which were not used 1o design to absorb large dynamic load like surface *** study focuses on investigating blast load parameters to design of RC columns to withstand blast *** numerical model is based on finite element amalysis using *** results are validated against blast feld tests available in the *** of simulations are performed with changing blast parameters *** efects of various scaled distances on the nonlinear bchavior of RC *** to simulation results,the scaled distance has a substantial infuence on the blast response of RC *** lower scaled distance,higher peak pressure and larger pressure impulse are applied on the RC ***,keeping the scaled disance unchanged,increasing the charge weight or shorter standoff distance cause more damage to the RC *** studies are carried out to investigate the efects of scaled distance and charge weight on the damage degree and residual axial load carrying capacity of RC columns with various column width,longitudinal reinforcement ratio and concrete *** of this research will be used to asessmsent the efet of an explosion on the dynamic behavior of RC columns.

关键词： RC column scaled disance blast load LS-DYNA

Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

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Computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

Can miRNA712＿3p be a promising biomarker for early diagnosis of toxoplasmosis?

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Asian Pacific Journal of Tropical Medicine 2018年第12期11卷 688-692页

作者： Nermine Mogahed Fawzy Hussein Mogahed Safaa Ibrahim Khedr Rasha Abdelmawla Ghazala Inas mohamed masoud Medical Parasitology Department Faculty of Medicine Alexandria University Medical Biochemistry Department Faculty of Medicine Alexandria University Pharmaceutical Chemistry Faculty of Pharmacy and Drug Manufacturing Pharos University in Alexandria

Objective: To assess the role of miRNA712_3p as a specific biomarker in early detection of toxoplasmosis in plasma of mice acutely infected with Toxoplasma gondii. Methods: Real-time PCR was used to measure the level of miRNA712_3p in plasma of infected mice. Immunecompetent and immune-suppressed mice were examined, three and five days post-infection. Results: Results revealed significant up-regulation of plasma miRNA712_3p in both immunecompetent and immune-compromised groups in comparison to the control non-infected group. Additionally, an increase in the level of miRNA712_3p was noticed correspondently in the parasite density detected in liver impression smears. Conclusions: miRNA712_3p can be used as a novel biomarker for the detection of Toxoplasma gondii infection in both immunecompetent and immune-compromised host.

关键词： Toxoplasma gondii microRNAs miRNA7123p Biomarker Real-time PCR

IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

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Computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： Saeed masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.Alzahrani Mohammed Maray Sana Alazwari mohamed A.Shamseldin Mesfer Al Duhayyim Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)*** presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RSO algorithm is exploited for effective selection of subset of ***,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of ***,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

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Computers, Materials & Continua 2022年第9期72卷 5499-5513页

作者： masoud Alajmi mohamed K.Nour Siwar Ben Haj Hassine Mimouna Abdullah Alkhonaini Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical *** greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of *** security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion *** EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion ***,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)*** extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying *** simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

关键词： Intrusion detection system metaheuristics stacked autoencoder deep learning cyber physical energy systems clustering wsn

利用BES进行废水处理的重金属与能量的回收

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利用BES进行废水处理的重金属与能量的回收

作者： Bashir masoud mohamed 东北林业大学

学位级别：硕士

基于微生物电化学理论知识,本研究构建了用于糖蜜废水处理和电力生产的自驱动式MFC,以及用于制氢和中药废水处理的动力化MEC。MFC和MEC都包含阳极室和阴极室。在MFC中,阳极室分别装有糖蜜废水和中药废水作为阳极基质以及电活性微生物,... 详细信息

基于微生物电化学理论知识,本研究构建了用于糖蜜废水处理和电力生产的自驱动式MFC,以及用于制氢和中药废水处理的动力化MEC。MFC和MEC都包含阳极室和阴极室。在MFC中,阳极室分别装有糖蜜废水和中药废水作为阳极基质以及电活性微生物,而阴极室则装有含有金属离子（Ag+）作为阴极电子受体的电镀废水。阳极基于经过MEC稍加修饰的MFC,阳极包含微生物,这些微生物充当生物催化剂并氧化有机废物并产生电子,氢质子（H+）和二氧化碳（CO2）。来自阳极的电子流过外部电路并产生电能,可以通过电压表进行测量。电子到达阴极并将Ag+还原为Ag。相反,施加电能并流向MEC的阴极,并还原来自微生物产生的阳极室的氢质子,从而产生氢气（H2）。这项研究表明,使用有机废水作为阳极的连续流MFC的使用,废水中含有不同浓度的Ag+作为阴极的电子受体,可以稳定地发电。就COD去除而言,以Ag+浓度为500 mg·L-1、1000 mg·L-1和2000 mg·L-1的MFC为阴极电子受体,阳极糖蜜废水的COD去除率为68.16%,分别为79.63%和81.2%。其中,最高的Ag+去除率达到71.6%。相对于500 mg·L-1、1000 mg·L-1 和 2000 mg·L-1 Ag+浓度,最大电压为 331.7 mV,447.2 mV,514.5 mV。最大功率密度分别为 62.82 mW·m-2、42.68 mW·m-2和 23.93 mW·m-2。结果表明,库仑效率不高,电子回收效果也不理想。这两种MEC系统通过使用固定化的活性污泥和非固定化的活性污泥,以中药废水作为底物来证明制氢。研究表明,当有机负荷为24 g COD L-1·d-1时,最佳的MEC系统达到的最大产氢率为8.39（±0.10）和10.0（±0.17）。固定化系统MEC的COD去除率稳定在43.6%-50.1%之间,高于非固定化反应器的COD去除率,非固定化COD去除率稳定在25.8%-38.9%之间。实验数据表明,固定化污泥系统具有较好的稳定性和较高的生产能力。

关键词：生化系统微生物燃料电池微生物电解池废水处理能量恢复

An Enquiry Into the Responsiveness of Corporate Profitability to Corporate Governance Mechanism＂ Evidences From Malaysian Government-Linked Companies （GLCs）

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Journal of Modern Accounting and Auditing 2014年第6期10卷 683-693页

作者： Mohammad Talha Abdullah Sallehhuddin Md Shukor masoud Musthafa mohamed King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Multimedia University Melaka Malaysia

Realizing the importance of corporate governance, many governments have embarked upon various initiatives. In Malaysia, the recently introduced Green Book program aims to strengthen the corporate governance among the government-linked companies （GLCs）, in view of their significant contributions to the national socio-economic development. The Green Book initiative, launched in 2005, contains, among other things, a provision to create a high-performing board. Performance of GLCs, therefore, is likely to scale better heights with the modification of the constitution of the boards. The present paper aims to examine the association between the selected corporate governance attributes and performance of GLCs in 2010, approximately five years since the implementation of the transformation program. The regression analysis reveals that none of the selected corporate governance indicators has significantly impacted the performance of GLCs. The findings serve as a wake-up call to the authorities to appraise the effectiveness of the transformation program in enhancing the performance of GLCs.

关键词： corporate governance government investment agencies government-linked companies (GLCs) Malaysia

Nutrient Fluxes and Sediments Composition in El Mex Bay and Surround Drains, Alexandria, Egypt

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American Journal of Analytical Chemistry 2015年第6期6卷 513-527页

作者： mohamed Attia Shreadah Mamdouh Saad masoud Abdel-Rhman Mosaad Khattab Gehan mohamed El Zokm National Institute of Oceanography and Fisheries Alexandria Egypt Faculty of Science Alexandria University Alexandria Egypt Faculty of Science Al-Azhar University Cairo Egypt

The regeneration of pore water (PW) nutrients was investigated and the contribution of benthic nutrient fluxes to the overlying bottom water (BW) was examined. Dissolved inorganic nutrients (NO-2, NO-3, PO3-4 and SIO4-4) were measured in PW and BW in El Mex Bay and surround drains during spring 2010. Nutrient concentrations gradiance in PW with overlying seawater were evaluated according to Fick’s Law. Average inorganic nutrient fluxes were estimated using the pore water gradient concentration across the sediment-water interface. Calculated nutrient fluxes had averages of ﹣7.24, ﹣1.36, ﹣7.86 and ﹣1.33 in El Mex Bay. Additionally, the fluxes in the drains were ﹣34.39, ﹣32.28, ﹣53.20 and ﹣117.6 mg·m﹣2·day﹣1 for NO-3, NO-2, PO3-4 and SIO4-4, respectively. Mineralogical studies of sediment samples by using IR, X-ray analysis were carried out to identify the chemical structure of sediments. The results revealed that calcite, aragonite and quartz are the dominant minerals. On the other hand, differential thermal analysis (DTA) was used to evaluate and discuss different kinetic parameters such as Ea#, ΔG#, ΔH#, ΔS#, Z and Tm support the view of the extra stability of these sediments.

关键词： Nutrient Fluxes IR-X-Ray-DTA Sediments El Mex Bay Alexandria Egypt