This document provides a summary of the education and experience of Assoc. Prof. Dr. Eng. Ahmed Serwa. It details his positions, including his current role as Associate Professor of Geomatics and Geoinformatics at Helwan University in Cairo, Egypt. It lists his various degrees, including a PhD in Civil Engineering, as well as his areas of research and over 20 published papers. It also outlines his teaching interests and samples of educational software he has developed.
Basem G. Elbarashy is an electrical engineer and computer vision specialist with experience developing products for emotion recognition, person re-identification, and action recognition. He has worked as a computer vision engineer for Cleo and as a machine learning engineer for Upwork, where he achieved Top Rated status. Elbarashy's areas of focus include computer vision, machine learning, and deep learning, and he has completed projects involving emotion recognition, traffic control, vehicle detection, and power signature location analysis.
3d Modelling of Structures using terrestrial laser scanning techniqueIJAEMSJORNAL
In recent times, interest in the study of engineering structures has been on the rise as a result of improvement in the tools used for operations such as, As-built mapping, deformation studies to modeling for navigation etc. There is a need to be able to model structure in such way that accurate needed information about positions of structures, features, points and dimensions can be easily extracted without having to pay physical visits to site to obtain measurement of the various components of structures. In this project, the data acquisition system used is the terrestrial laser scanner, High Definition Surveying (HDS) equipment; the methodology employed is similar to Close Range Photogrammetry (CRP). CRP is a budding technique or field used for data acquisition in Geomatics. It is a subset of the general photogrammetry; it is often loosely tagged terrestrial photogrammetry. The terrestrial laser scanning technology is a data acquisition system similar to CRP in terms of deigning the positioning of instrument and targets, calibration, ground control point, speed of data acquisition, data processing (interior, relative and absolute orientation) and the accuracy obtainable. The aim of this project was to generate the three-dimensional model of structures in the Faculty of Engineering, University of Lagos using High Definition Surveying, the Leica Scan Station 2 HDS equipment was used along with Cyclone software for data acquisition and processing. The result was a 3D view (of point clouds) of the structure that was studied, from which features were measured from the model generated and compared with physical measurement on site. The technology of the laser scanner proved to be quite useful and reliable in generating three dimensional models without compromising accuracy and precision. The generation of the 3D models is the replica of reality of the structures with accurate dimensions and location.
Critical Infrastructure Monitoring Using UAV Imageryaditess
The use of two rapidly evolving approaches, the Unmanned Aerial Vehicles (UAVs) and Dense Image Matching (DIM) techniques is an attractive solution to extract high quality photogrammetric products like 3D point clouds and orthoimages.
Aritra Sarkar is a PhD candidate at Delft University of Technology researching quantum machine learning algorithms for bioinformatics. He received his Master's in computer engineering from Delft University focusing on quantum computing architecture. His PhD thesis involves developing quantum-accelerated algorithms for feature learning in DNA sequences. He has worked as a scientist at ISRO Satellite Centre developing software for satellites and as a research intern on projects involving spiking neural networks, brain-inspired robotics, and multi-agent systems.
ARTIFICIAL intelligence technique for space exploration TECHNICAL SEMINAR.pptxPoorvikaNPoorvi
This document summarizes a technical seminar presentation on techniques of artificial intelligence for space applications. The presentation discusses how AI enables autonomous operations of spacecraft through machine learning algorithms and predictive maintenance. It also examines how AI facilitates data analysis of information collected from space missions using neural networks and natural language processing. Computer vision plays a key role in autonomous navigation and object recognition for space exploration. The advantages of AI include enabling autonomous decision-making to reduce human intervention and optimizing resource management for space missions.
ARTIFICIAL intelligence technique for space exploration TECHNICAL SEMINAR.pptxPoorvikaNPoorvi
This document summarizes a technical seminar presentation on techniques of artificial intelligence for space applications. The presentation discusses how AI enables autonomous operations of spacecraft through machine learning algorithms and predictive maintenance. It also examines how AI facilitates data analysis of information collected from space missions using neural networks and natural language processing. Computer vision plays a key role in autonomous navigation and object recognition for space exploration. The advantages of AI include enabling autonomous decision-making to reduce human intervention and optimizing resource management for space missions.
The document discusses machine learning methods and their applications in space engineering. It provides an overview of recent advances in machine learning techniques such as deep learning, genetic programming, and smart search methods. It also summarizes areas explored by the European Space Agency's Advanced Concepts Team (ACT), including using neurocontrollers, swarm intelligence, biomimetics, and evolution/search methods for applications like spacecraft control, formation flying, computer vision, and trajectory optimization. The document envisions that machine learning could enable more intelligent spacecraft by 2040 if the gap in onboard computing is filled.
Grant Anderson has over 30 years of experience in mechanical engineering, simulation software, control systems, and avionics. He currently works as a senior systems engineer at Boeing Defence UK, where he is responsible for terrain generation and distributed simulation integration. Previously he held engineering roles at MBDA Missile Systems, DERA, Matra Marconi Space, Rolls-Royce, and British Aerospace working on flight simulation, spacecraft control systems, engine modeling, and aircraft autopilots. He has extensive experience in simulation software including C/C++, OpenGL, and HLA/DIS protocols.
Basem G. Elbarashy is an electrical engineer and computer vision specialist with experience developing products for emotion recognition, person re-identification, and action recognition. He has worked as a computer vision engineer for Cleo and as a machine learning engineer for Upwork, where he achieved Top Rated status. Elbarashy's areas of focus include computer vision, machine learning, and deep learning, and he has completed projects involving emotion recognition, traffic control, vehicle detection, and power signature location analysis.
3d Modelling of Structures using terrestrial laser scanning techniqueIJAEMSJORNAL
In recent times, interest in the study of engineering structures has been on the rise as a result of improvement in the tools used for operations such as, As-built mapping, deformation studies to modeling for navigation etc. There is a need to be able to model structure in such way that accurate needed information about positions of structures, features, points and dimensions can be easily extracted without having to pay physical visits to site to obtain measurement of the various components of structures. In this project, the data acquisition system used is the terrestrial laser scanner, High Definition Surveying (HDS) equipment; the methodology employed is similar to Close Range Photogrammetry (CRP). CRP is a budding technique or field used for data acquisition in Geomatics. It is a subset of the general photogrammetry; it is often loosely tagged terrestrial photogrammetry. The terrestrial laser scanning technology is a data acquisition system similar to CRP in terms of deigning the positioning of instrument and targets, calibration, ground control point, speed of data acquisition, data processing (interior, relative and absolute orientation) and the accuracy obtainable. The aim of this project was to generate the three-dimensional model of structures in the Faculty of Engineering, University of Lagos using High Definition Surveying, the Leica Scan Station 2 HDS equipment was used along with Cyclone software for data acquisition and processing. The result was a 3D view (of point clouds) of the structure that was studied, from which features were measured from the model generated and compared with physical measurement on site. The technology of the laser scanner proved to be quite useful and reliable in generating three dimensional models without compromising accuracy and precision. The generation of the 3D models is the replica of reality of the structures with accurate dimensions and location.
Critical Infrastructure Monitoring Using UAV Imageryaditess
The use of two rapidly evolving approaches, the Unmanned Aerial Vehicles (UAVs) and Dense Image Matching (DIM) techniques is an attractive solution to extract high quality photogrammetric products like 3D point clouds and orthoimages.
Aritra Sarkar is a PhD candidate at Delft University of Technology researching quantum machine learning algorithms for bioinformatics. He received his Master's in computer engineering from Delft University focusing on quantum computing architecture. His PhD thesis involves developing quantum-accelerated algorithms for feature learning in DNA sequences. He has worked as a scientist at ISRO Satellite Centre developing software for satellites and as a research intern on projects involving spiking neural networks, brain-inspired robotics, and multi-agent systems.
ARTIFICIAL intelligence technique for space exploration TECHNICAL SEMINAR.pptxPoorvikaNPoorvi
This document summarizes a technical seminar presentation on techniques of artificial intelligence for space applications. The presentation discusses how AI enables autonomous operations of spacecraft through machine learning algorithms and predictive maintenance. It also examines how AI facilitates data analysis of information collected from space missions using neural networks and natural language processing. Computer vision plays a key role in autonomous navigation and object recognition for space exploration. The advantages of AI include enabling autonomous decision-making to reduce human intervention and optimizing resource management for space missions.
ARTIFICIAL intelligence technique for space exploration TECHNICAL SEMINAR.pptxPoorvikaNPoorvi
This document summarizes a technical seminar presentation on techniques of artificial intelligence for space applications. The presentation discusses how AI enables autonomous operations of spacecraft through machine learning algorithms and predictive maintenance. It also examines how AI facilitates data analysis of information collected from space missions using neural networks and natural language processing. Computer vision plays a key role in autonomous navigation and object recognition for space exploration. The advantages of AI include enabling autonomous decision-making to reduce human intervention and optimizing resource management for space missions.
The document discusses machine learning methods and their applications in space engineering. It provides an overview of recent advances in machine learning techniques such as deep learning, genetic programming, and smart search methods. It also summarizes areas explored by the European Space Agency's Advanced Concepts Team (ACT), including using neurocontrollers, swarm intelligence, biomimetics, and evolution/search methods for applications like spacecraft control, formation flying, computer vision, and trajectory optimization. The document envisions that machine learning could enable more intelligent spacecraft by 2040 if the gap in onboard computing is filled.
Grant Anderson has over 30 years of experience in mechanical engineering, simulation software, control systems, and avionics. He currently works as a senior systems engineer at Boeing Defence UK, where he is responsible for terrain generation and distributed simulation integration. Previously he held engineering roles at MBDA Missile Systems, DERA, Matra Marconi Space, Rolls-Royce, and British Aerospace working on flight simulation, spacecraft control systems, engine modeling, and aircraft autopilots. He has extensive experience in simulation software including C/C++, OpenGL, and HLA/DIS protocols.
May_2024 Top 10 Read Articles in Computer Networks & Communications.pdfIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Lane and Object Detection for Autonomous Vehicle using Advanced Computer VisionYogeshIJTSRD
The vision of this project is to develop lane and object detection in Autonomous Vehicle system to run efficiently in normal road condition and to eliminate the use of high cost Light based LiDAR system to implement high resolution cameras with advanced computer vision and deep learning technology to provide an Advanced Driver Assistance System ADAS . Detecting lane lines could be a crucial task for any self driving autonomous vehicle. Hence, this project was focused to identify lane lines on the road using OpenCV. The OpenCV tools such as colour selection, the region of interest selection, grey scaling, canny edge detection and perspective transformation are being employed. This project is modelled as an integration of two systems to solve the real time implementation problem in autonomous vehicles. The first part of the system is lane detection by advanced computer vision techniques to detect the lane lines to command the vehicle to stay inside the lane marking. The second part of the system is object detection and tracking is to detect and track the vehicle and pedestrians on the road to get a clear understanding of the environment to plan and generate a trajectory to navigate the autonomous vehicle safely to its destination without any crashes, this is done by a special deep learning method called transfer learning with Single Shot multibox Detection SSD algorithm and Mobile Net architecture. G. Monika | S. Bhavani | L. Azim Jahan Siana | N. Meenakshi "Lane and Object Detection for Autonomous Vehicle using Advanced Computer Vision" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39952.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39952/lane-and-object-detection-for-autonomous-vehicle-using-advanced-computer-vision/g-monika
Sandeep Chakraborty has over 11 years of experience in aviation, including 5.5 years as a scientist for the Defence Research and Development Organization of India. He currently works as a senior systems engineer for Honeywell, where he leads integration activities for the Boeing 777X program. He has expertise in aircraft systems including avionics, sensors, flight management, and displays. He holds patents and publications in areas like enhanced vision systems, traffic prioritization, and decluttering aircraft displays.
International Journal of Computational Science, Information Technology and Co...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
This curriculum vitae summarizes the educational and professional experience of Loay Edwar George. It lists his academic degrees including a Ph.D. in Digital Image Processing from Baghdad University. It details his research interests and extensive experience in areas such as image and video compression, information hiding, biometrics, and computer vision. It also lists his leadership roles in education and research organizations as well as professional experience in software development companies and research centers. Finally, it provides an overview of his teaching experience, publications, and computer skills.
3D BUILDING Modeling with MULTI-SOURCE DATA: A STUDY OF HIGH-DENSITY URBAN ...DeWolf Xue
Chen, K., Xue, F., and Lu, W. (2017). “Development of 3D building models using multi-source data: A study of high-density urban area in Hong Kong.” In: LC3 2017: Volume I – Proceedings of the Joint Conference on Computing in Construction (JC3), July 4-7, 2017, Heraklion, Greece, pp. 611-618. DOI: https://doi.org/10.24928/JC3-2017/0252.
This document discusses using convolutional neural networks (CNNs) to classify and segment satellite imagery. It presents a novel approach using a CNN to perform per-pixel classification of multispectral satellite imagery and a digital surface model into five categories (vegetation, ground, roads, buildings, water). The CNN is first pre-trained with unsupervised clustering then fine-tuned for classification and segmentation. Results show the CNN approach outperforms existing methods, achieving 94.49% classification accuracy and improving segmentation by reducing salt-and-pepper effects from per-pixel classification alone.
International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication
Artificial intelligence (AI) is experiencing steadily growing interest over the recent years. For good reason, since these innovative algorithms and methods, such as machine learning and deep neural networks, in which knowledge is acquired and applied based on data, enable the automation of a wide range of processes and quickly deliver precise results. AI is also getting more and more popular in the space sector. The Institute of Space Technology & Space Applications (ISTA) at the Universität der Bundeswehr in Munich is conducting research around AI for space operations, science, and technology. An overview of activities and current developments towards fault management, autonomous collision avoidance, autonomous landing, as well as radio science at ISTA will be presented.
January 2021: Top Ten Cited Article in Computer Science, Engineering IJCSEA Journal
International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.
Person Detection in Maritime Search And Rescue OperationsIRJET Journal
This document discusses recent research on using computer vision and machine learning techniques for person detection in maritime search and rescue operations from images and video captured by drones. Specifically, it summarizes 12 research papers on this topic, covering approaches such as training convolutional neural networks on bird's eye view datasets to detect people from aerial images, using multiple detection methods like sliding windows and precise localization, combining data from multiple drones and sensors to optimize search efforts, and evaluating models on both RGB and thermal image datasets. The goal of this research is to automate part of the search process to make maritime rescue operations more efficient and effective.
Person Detection in Maritime Search And Rescue OperationsIRJET Journal
1) The document discusses using machine learning and computer vision techniques for person detection in maritime search and rescue operations using drones/UAVs. It aims to automatically detect people in images/videos captured by drones to help with search efforts.
2) A key challenge is that people appear small in drone footage and are often obscured by vegetation or terrain. The models need to be trained on similar bird's eye view data to achieve high accuracy. The document reviews different person detection models and their use in search and rescue.
3) It discusses recent work involving using efficient neural networks like MobileNet for object detection from drones. Other work involves using depth sensors and pose estimation for person tracking, as well as using distributed deep learning
This document presents a system for managing spatial databases on mobile devices. It proposes an architecture with three tiers: a data tier providing open standards interfaces, a middleware tier enabling access to spatial data services, and a mobile tier with GIS software to access, display, and edit spatial data locally. It describes a prototype implementation on Android phones using OpenStreetMap data stored locally in a spatial database to allow spatial queries without internet. The prototype demonstrates line-of-sight and field-of-view queries to filter objects not visible to the user and reduce information overload.
Network Based Kernel Density Estimation for Cycling Facilities Optimal Locati...Beniamino Murgante
Network Based Kernel Density Estimation for Cycling Facilities Optimal Location Applied to Ljubljana
Nicolas Lachance-Bernard, Timothée Produit - Ecole polytechnique fédérale de Lausanne
Biba Tominc, Matej Niksic, Barbara Golicnik Marusic - Urban Planning Institute of the Republic of Slovenia
Network Based Kernel Density Estimation for Cycling Facilities Optimal Locati...Nicolas Lachance-Bernard
This document discusses using network-based kernel density estimation (NetKDE) to determine optimal locations for cycling facilities in Ljubljana, Slovenia. It provides background on cycling and urban planning, and challenges in using GPS data. The methodology section describes using GPS tracking devices to collect cycling data in Ljubljana, creating an OpenStreetMap network and grids, and calculating low-resolution KDE and high-resolution NetKDE densities. Results show NetKDE identifying flux corridors at different bandwidths better than KDE. Further work aims to optimize NetKDE calculations and integrate it with other spatial analysis tools.
This document describes two new user interface techniques for directly manipulating isosurfaces and cutting planes in virtual reality environments for scientific visualization. It proposes a widget for interactive isosurface generation that uses a time-critical algorithm to locally generate the isosurface around a probe in real-time as it is manipulated. It also proposes a widget for manipulating isosurfaces that allows dragging a handle perpendicular to the surface to change its value without significantly changing the center of interest.
Geospatial machine learning for urban developmentMLconf
This document discusses using machine learning and computer vision techniques with satellite imagery to generate addressing systems for areas that currently lack adequate street addressing. It describes a generative addressing scheme that uses hierarchical and linear descriptors, such as region names indicating orientation and distance from downtown and road names indicating distance from the center and orientation. The document outlines a pipeline that involves predicting road networks from satellite imagery, partitioning regions based on road connectivity, and assigning addressing cells along roads with distance and block offsets. Results show this approach can improve street address coverage to 80% for unmapped developing areas by discovering road networks in non-urban settings and defining regions according to natural boundaries.
Ara V. Nefian is seeking a challenging research position involving computer vision, machine learning, robotics, and multimedia processing. She has a PhD in electrical engineering from Georgia Tech and over 10 years of research experience. Her skills include computer vision, Bayesian networks, image and video processing. She has published 40 papers and holds 10 patents related to these fields. Her most recent role is as a Senior System Scientist at Carnegie Mellon University where she leads projects in 3D terrain reconstruction from planetary images and autonomous robotics.
Ara V. Nefian is seeking a challenging research position involving computer vision, machine learning, robotics, and multimedia processing. She has over 10 years of research experience and 40 publications. Her background includes a PhD in electrical engineering focused on face recognition using HMMs. She has led numerous projects at companies like CMU, Intel, and Nokia involving 3D reconstruction, computer vision, Bayesian networks, and multimedia processing. She has also filed 20 patents related to these areas.
Visualising large spatial databases and Building bespoke geodemographicsDr Muhammad Adnan
This presentation outlines my work at the Local Futures and the PhD research. I have been working on a combined project between Local Futures and UCL and the presentation starts by giving an introduction of the project. My PhD investigated the creation of Real-time bespoke geodemographics, and this presentation presents the work i did during the PhD journey.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
May_2024 Top 10 Read Articles in Computer Networks & Communications.pdfIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Lane and Object Detection for Autonomous Vehicle using Advanced Computer VisionYogeshIJTSRD
The vision of this project is to develop lane and object detection in Autonomous Vehicle system to run efficiently in normal road condition and to eliminate the use of high cost Light based LiDAR system to implement high resolution cameras with advanced computer vision and deep learning technology to provide an Advanced Driver Assistance System ADAS . Detecting lane lines could be a crucial task for any self driving autonomous vehicle. Hence, this project was focused to identify lane lines on the road using OpenCV. The OpenCV tools such as colour selection, the region of interest selection, grey scaling, canny edge detection and perspective transformation are being employed. This project is modelled as an integration of two systems to solve the real time implementation problem in autonomous vehicles. The first part of the system is lane detection by advanced computer vision techniques to detect the lane lines to command the vehicle to stay inside the lane marking. The second part of the system is object detection and tracking is to detect and track the vehicle and pedestrians on the road to get a clear understanding of the environment to plan and generate a trajectory to navigate the autonomous vehicle safely to its destination without any crashes, this is done by a special deep learning method called transfer learning with Single Shot multibox Detection SSD algorithm and Mobile Net architecture. G. Monika | S. Bhavani | L. Azim Jahan Siana | N. Meenakshi "Lane and Object Detection for Autonomous Vehicle using Advanced Computer Vision" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39952.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39952/lane-and-object-detection-for-autonomous-vehicle-using-advanced-computer-vision/g-monika
Sandeep Chakraborty has over 11 years of experience in aviation, including 5.5 years as a scientist for the Defence Research and Development Organization of India. He currently works as a senior systems engineer for Honeywell, where he leads integration activities for the Boeing 777X program. He has expertise in aircraft systems including avionics, sensors, flight management, and displays. He holds patents and publications in areas like enhanced vision systems, traffic prioritization, and decluttering aircraft displays.
International Journal of Computational Science, Information Technology and Co...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
This curriculum vitae summarizes the educational and professional experience of Loay Edwar George. It lists his academic degrees including a Ph.D. in Digital Image Processing from Baghdad University. It details his research interests and extensive experience in areas such as image and video compression, information hiding, biometrics, and computer vision. It also lists his leadership roles in education and research organizations as well as professional experience in software development companies and research centers. Finally, it provides an overview of his teaching experience, publications, and computer skills.
3D BUILDING Modeling with MULTI-SOURCE DATA: A STUDY OF HIGH-DENSITY URBAN ...DeWolf Xue
Chen, K., Xue, F., and Lu, W. (2017). “Development of 3D building models using multi-source data: A study of high-density urban area in Hong Kong.” In: LC3 2017: Volume I – Proceedings of the Joint Conference on Computing in Construction (JC3), July 4-7, 2017, Heraklion, Greece, pp. 611-618. DOI: https://doi.org/10.24928/JC3-2017/0252.
This document discusses using convolutional neural networks (CNNs) to classify and segment satellite imagery. It presents a novel approach using a CNN to perform per-pixel classification of multispectral satellite imagery and a digital surface model into five categories (vegetation, ground, roads, buildings, water). The CNN is first pre-trained with unsupervised clustering then fine-tuned for classification and segmentation. Results show the CNN approach outperforms existing methods, achieving 94.49% classification accuracy and improving segmentation by reducing salt-and-pepper effects from per-pixel classification alone.
International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication
Artificial intelligence (AI) is experiencing steadily growing interest over the recent years. For good reason, since these innovative algorithms and methods, such as machine learning and deep neural networks, in which knowledge is acquired and applied based on data, enable the automation of a wide range of processes and quickly deliver precise results. AI is also getting more and more popular in the space sector. The Institute of Space Technology & Space Applications (ISTA) at the Universität der Bundeswehr in Munich is conducting research around AI for space operations, science, and technology. An overview of activities and current developments towards fault management, autonomous collision avoidance, autonomous landing, as well as radio science at ISTA will be presented.
January 2021: Top Ten Cited Article in Computer Science, Engineering IJCSEA Journal
International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.
Person Detection in Maritime Search And Rescue OperationsIRJET Journal
This document discusses recent research on using computer vision and machine learning techniques for person detection in maritime search and rescue operations from images and video captured by drones. Specifically, it summarizes 12 research papers on this topic, covering approaches such as training convolutional neural networks on bird's eye view datasets to detect people from aerial images, using multiple detection methods like sliding windows and precise localization, combining data from multiple drones and sensors to optimize search efforts, and evaluating models on both RGB and thermal image datasets. The goal of this research is to automate part of the search process to make maritime rescue operations more efficient and effective.
Person Detection in Maritime Search And Rescue OperationsIRJET Journal
1) The document discusses using machine learning and computer vision techniques for person detection in maritime search and rescue operations using drones/UAVs. It aims to automatically detect people in images/videos captured by drones to help with search efforts.
2) A key challenge is that people appear small in drone footage and are often obscured by vegetation or terrain. The models need to be trained on similar bird's eye view data to achieve high accuracy. The document reviews different person detection models and their use in search and rescue.
3) It discusses recent work involving using efficient neural networks like MobileNet for object detection from drones. Other work involves using depth sensors and pose estimation for person tracking, as well as using distributed deep learning
This document presents a system for managing spatial databases on mobile devices. It proposes an architecture with three tiers: a data tier providing open standards interfaces, a middleware tier enabling access to spatial data services, and a mobile tier with GIS software to access, display, and edit spatial data locally. It describes a prototype implementation on Android phones using OpenStreetMap data stored locally in a spatial database to allow spatial queries without internet. The prototype demonstrates line-of-sight and field-of-view queries to filter objects not visible to the user and reduce information overload.
Network Based Kernel Density Estimation for Cycling Facilities Optimal Locati...Beniamino Murgante
Network Based Kernel Density Estimation for Cycling Facilities Optimal Location Applied to Ljubljana
Nicolas Lachance-Bernard, Timothée Produit - Ecole polytechnique fédérale de Lausanne
Biba Tominc, Matej Niksic, Barbara Golicnik Marusic - Urban Planning Institute of the Republic of Slovenia
Network Based Kernel Density Estimation for Cycling Facilities Optimal Locati...Nicolas Lachance-Bernard
This document discusses using network-based kernel density estimation (NetKDE) to determine optimal locations for cycling facilities in Ljubljana, Slovenia. It provides background on cycling and urban planning, and challenges in using GPS data. The methodology section describes using GPS tracking devices to collect cycling data in Ljubljana, creating an OpenStreetMap network and grids, and calculating low-resolution KDE and high-resolution NetKDE densities. Results show NetKDE identifying flux corridors at different bandwidths better than KDE. Further work aims to optimize NetKDE calculations and integrate it with other spatial analysis tools.
This document describes two new user interface techniques for directly manipulating isosurfaces and cutting planes in virtual reality environments for scientific visualization. It proposes a widget for interactive isosurface generation that uses a time-critical algorithm to locally generate the isosurface around a probe in real-time as it is manipulated. It also proposes a widget for manipulating isosurfaces that allows dragging a handle perpendicular to the surface to change its value without significantly changing the center of interest.
Geospatial machine learning for urban developmentMLconf
This document discusses using machine learning and computer vision techniques with satellite imagery to generate addressing systems for areas that currently lack adequate street addressing. It describes a generative addressing scheme that uses hierarchical and linear descriptors, such as region names indicating orientation and distance from downtown and road names indicating distance from the center and orientation. The document outlines a pipeline that involves predicting road networks from satellite imagery, partitioning regions based on road connectivity, and assigning addressing cells along roads with distance and block offsets. Results show this approach can improve street address coverage to 80% for unmapped developing areas by discovering road networks in non-urban settings and defining regions according to natural boundaries.
Ara V. Nefian is seeking a challenging research position involving computer vision, machine learning, robotics, and multimedia processing. She has a PhD in electrical engineering from Georgia Tech and over 10 years of research experience. Her skills include computer vision, Bayesian networks, image and video processing. She has published 40 papers and holds 10 patents related to these fields. Her most recent role is as a Senior System Scientist at Carnegie Mellon University where she leads projects in 3D terrain reconstruction from planetary images and autonomous robotics.
Ara V. Nefian is seeking a challenging research position involving computer vision, machine learning, robotics, and multimedia processing. She has over 10 years of research experience and 40 publications. Her background includes a PhD in electrical engineering focused on face recognition using HMMs. She has led numerous projects at companies like CMU, Intel, and Nokia involving 3D reconstruction, computer vision, Bayesian networks, and multimedia processing. She has also filed 20 patents related to these areas.
Visualising large spatial databases and Building bespoke geodemographicsDr Muhammad Adnan
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john krisinger-the science and history of the alcoholic beverage.pptx
CV HR2022.pdf
1. Assoc. Prof. Dr. Eng. Ahmed Serwa
Assoc. Prof. of Geomatics and Geoinformatics
Faculty of Engineering in Mararia- Helwan University
Cairo- Egypt
BSc, MSc, PhD, MCP, MCSD, MCT
ORCID: 0000-0002-5121-5242 - Research ID: P-9953-2015
E-mail: Dr.A.Serwa@m-eng.helwan.edu.eg
ahmed_serwa@yahoo.com
https://www.linkedin.com/pub/drahmedserwa
Mob WA: +201003808123
2. Personal Info.:
Name: Ahmed Serwa Abd Elhamid
Title: Assoc. Prof. Dr. Eng.
Address: 93 Elahram St. Giza- Egypt.
Basic Info:
Current Job: Assoc. Prof. of Geomatics and Geoinformatics in Faculty of Engineering
of El Mataria-Helwan University-Cairo. (Chief of scientific group of Geomatics and
geoinformatics).
Nationality: Egyptian but looking.
Birth date:5/12/1975.
Gender: Male.
Marital Status: Married.
Military Status: Completed.
Education:
PhD: Civil Engineering (Remote Sensing).
Automatic Extraction of Topographic Features from Digital Satellite Images. Civil
Engineering Department, Faculty of Engineering Azhar University, Sept 2009.
MSc: Civil Engineering (Digital Photogrammetry)
Potential of Using High Resolution Scanners and Different Camera Lenses in Digital
Photogrammetric Applications. Civil Engineering Department, Faculty of Engineering,
Assiut, March 2003.
BSc: Civil Engineering (Geodesy Project)
Geomatics project including Surveying, Geodesy, Photogrammetry, Cadastral and
Topographic Mapping. Civil Engineering Department, Faculty of Engineering. Assiut
University. Rank: Excellent.
Languages:
Arabic: Excellent
English: Very Good
French: Good
Career Objective:
Academic Engineering Career (Research or Teaching).
History:
Demonstrator:1998 to 2003 at Faculty of Engineering- Assiut Univ. Egypt.
Assistant Lecturer andLecturer:2003 to 2012 in Faculty of Engineering in Qena, Azhar
Univ. Egypt.
AssistantProf.:2012 to 2017 in Faculty of Engineering of Mataria.
AssociateProf.:2017 until now in Faculty of Engineering of Mataria (Chief of scientific
group of geomatics and geoinformatics).
Technical Summary:
Experiences:
Teaching: 25 Years.
3. Field Engineer (Part time): 23 Years.
Consultant Engineer: 8 Years.
SW Developer: 25 Years.
Research: 25 Years.
Certificates:
MCP 2002 (Microsoft).
MCSD 2002 (Microsoft).
MCT 2004 (Microsoft)
Special Courses:
Computer Graphics, Computer Skills (VB, C, C++, C#, Pascal, Free Pascal, Delphi, SQL
Server, Access, Data Base, Oracle UML, Analysis of IS... etc.) and AI (Artificial
Intelligence).
Soft Skills:
Programming with excellent knowledge of digital image processing, direct X, open GL,
Artificial Intelligence, VB6, VB.net, C#, DB and SQL server, Oracle…etc. CAD
Systems, GIS (ArcGIS, Envy, PCI, Erdas Imagine…etc.).
Hard Skills:
GNSS (Leica Geo-Systems, TOPCON, and GARMIN series). Total Station and levels
Laser Scanners
Engineering Skills:
-Surveying and mapping
-Structural Engineering (RC Design).
-Quantity Surveying.
-GIS.
-GNSS.
Research Topics:
Geomatics (Adjustment of Surveying observations using AI, Detection of construction
defects using Laser scanning data).
Geoinformatics (Development of soft computational simulators for Ground features.
Automatic information extraction, Building and administration of GeoDB).
Remote Sensing (Feature extraction, Image classification, Change detection, Satellite
image processing –MSS-RADAR-LiDAR).
Photogrammetry (Automatic DEM generation using PCL and stereo models, SFM, 3D
object recognition, Building 3d reality models).
Geodesy (Estimation of best ellipsoid using RADAR data, Adjustment of Geodetic
networks using AI).
Artificial Intelligence (Developments of new techniques by fusion of existing
algorithms, development of new techniques of Deep learning).
Software (development of new programming language -VIP, Development of Code
generators).
Statement of Research:
1- Development of soft computational simulator for optimized deep artificial neural networks for
geomatics applications: remote sensing classification as an application, A Serwa, Geodesy and
Cartography 48 (4), 224–232-224–232. (2022).
4. 2- Geomorphological hazard analysis using dem along the Eastern coast between Marsa Alam and
Ras Gharib, Egypt. Mohamed Mahmoud Shaaban Mabrouk, Ehab Mohamed Wafaie Abdel-Salam, Ahmed
Serwa, Engineering Research Journal 174, 1-21.( 2022).
3- New semi-automatic 3D registration method for terrestrial laser scanning data of bridge
structures based on artificial neural networks, A Serwa, M Saleh, The Egyptian Journal of Remote Sensing
and Space Science 24 (3), 787-798. (2021).
4- Enhancement of classification accuracy of multi-spectral satellites’ images using Laplacian
pyramids, A Serwa, S Elbialy, The Egyptian Journal of Remote Sensing and Space Science 24 (2), 283-291.
(2021).
5- Semi-Automatic Approach for Forming and Processing Laser Sensing Data of Urban Truss
MS Sedek, A Serwa, SVU-International Journal of Engineering Sciences and Applications 2 (1), 1-8. (2021).
6- Studying the potentiality of using digital gaussian pyramids in multi-spectral satellites images
classification, A Serwa, Journal of the Indian Society of Remote Sensing 48 (12), 1651-1660, (2020).
7- Studying the potentiality of using low cost system based on image analysis technique to survey
the gravel’s sizein asphalt mixes, A Nabil, A Serwa, AE Mostafa, Engineering Research Journal 167, 257-
274, (2020).
8- Semi-automatic general approach to achieve the practical number of clusters for classification
of remote sensing MS satellite images, A Serwa, HH El-Semary, Spatial Information Research 28 (2), 203-
213. (2020).
9- A Study of the Use of Deep Artificial Neural Network in the Optimization of the Production of
Antifungal Exochitinase Compared with the Response Surface Methodology, SA Ismail, A Serwa, A
Abood, B Fayed, SA Ismail, AM Hashem, Jordan Journal of Biological Sciences 12 (5), (2019).
10- Optimizing Activation Function in Deep Artificial Neural Networks Approach for Landcover
Fuzzy Pixel-Based Classification, A Serwa, International Journal of Remote Sensing Applications 7 (1), 1-
10, (2017).
11- Development of new system for detection of bridges construction defects using terrestrial laser
remote sensing technology, M Sedek, A Serwa, The Egyptian Journal of Remote Sensing and Space Science
19 (2), 273-283, (2016).
12-Development of soft computational simulator for aerial imagery project planning, A Serwa,
Surveying and Land Information Science 75 (2), 65-75, (2016).
13- Integration of soft computational simulator and strapdown inertial navigation system for aerial
surveying project planning, A Serwa, HH El-Semary, Spatial Information Research 24 (3), 279-290, (2016).
14- Development of soft computational simulator for traversing, A Serwa, HH El-Semary, Surveying and
Land Information Science 75 (1), 7-16, (2016).
15- Development of Application for Automatic Aerial Photogrammetric Project Planning, A Serwa
11PthP International Conference on Civil and Architecture Engineering, Military Technical College, (2016).
16- Development of Software Application for Digital Photogrammetric Systems (ADPS): Basic
Level, A Serwa, 11PthP International Conference on Civil and Architecture Engineering, Military Technical
College, (2016).
17- Integration Of Traverse Computations And Cad By Developing Of Travcad Sw Package., A
Serwa, HH El-Semary, Scientific Herald of the Voronezh State University of Architecture & Civil Engineering,
(2016).
18- New Method for Feature Reduction of Mss Satellite Bands to Produce Single equivalent Band,
A Serwa, AEIC 2012 7 (1), 519-526, (2012).
19- Optimizing the Egyptian coal flotation using an artificial neural network, M Farghaly, A Serwa, M
Ahmed, Journal of Mining World Express Oct 1 (2), 27-33, (2012).
20- Potential of fusion of fuzzy based and neural network classifiers for unsupervised classification,
A Serwa, MENO Ali, MAM Dief-Allah, Al-Azhar University Engineering Journal 5 (1), 713-726, (2010)
21- New method to determine the optimum bands of MSS satellite images for unsupervised
classification, A Serwa, MENO Ali, MAM Dief-Allah, AEIC, 2010, Al-Azhar Engineering Eleventh International
Conference, (2010).
22- Automatic extraction of topographic features from digital images, A Serwa, PhD Thesis, Azhar
University, Cairo Egypt, (2009).
5. Conferences:
1-ICCES1 "1st International Conference of Civil Engineering Science" –Assiut
University. Author.
2-AL-AZHARENGINEERINGELEVENTHINTERNATIONAL
CONFERENCE, December 21 -23, 2010, Author.
3-International Joint Conference on Advances in Signal Processing and Information
Technology –SPIT 2011, Author.
4-AL-AZHARENGINEERINGELEVENTHINTERNATIONAL
CONFERENCE, December 21 -23, 2012, Author.
5-2nd International Conference on Bridge Testing, Monitoring& Assessment, Cairo,
Egypt-DEC 27-29, 2015, Author.
Teaching Interests:
Plane Surveying-Topographic Surveying-Geodetic Surveying-Photogrammetry-Remote
Sensing-GIS-GPS and GNSS-Computer Applications in Civil Engineering-Civil
Engineering Drawing-Transportations Engineering-Computer Graphics-Computer
Object Oriented Programming. 13-System Analysis and Design-AutoCAD-Revit-Project
Management-Artificial Intelligence-Digital Image Processing.
New Teaching Strategy:
Besides traditional methods such as white board, smart board, and data show, I am using
my programming skill to develop many learning and teaching tools such as simulators
and automated problem-solving applications to ease teaching engineering problems.
Samples of Self-Developed SW:
1- DANNDO: Novel application for Deep Artificial Neural Networks Designer and
Optimizer (published in Geodesy and Cartography Journal).
https://jau.vgtu.lt/index.php/GAC/article/view/15642
6. 2- 3A3P: Development of Application for Automatic Aerial Photogrammetric Project
Planning. (Published in American Association for Geodetic Surveying AAGS).
https://www.ingentaconnect.com/content/aags/salis/2016/00000075/00000002/art00003
7. 3- TravCAD: Traversing application.
(Published in American Association for Geodetic Surveying AAGS).
https://www.ingentaconnect.com/content/aags/salis/2016/00000075/00000001/art00003
8. 4- ADPS: Development of Educational SW package for Photogrammetry.
(Under publish).
9. 5- GCAD: Ground simulator for earth works application.
(Under publish)
10.
11. 6- LSAR: Laser scanning data processing application.
(Under publish).
19. Sample of scientific social activities:
1- International Transportation Project with JICA from Japan teamwork 2010.
20. 2- International workshop in GUC with Prof. Dr. Paulo Jamba (Italy) from IEEE
Geoscience 2016.
3- International workshop in Helwan University with SuperMap GIS(China)team 2019.
21. 4- On air scientific shows in Egyptian TV 2019.
23. Current References:
1- Prof. Dr. Mohamed El Nashar (former Minister of Higher Education and Scientific
Research of Egypt).
2- Prof. Dr. Ayman Habib (Purdue university).
3- Prof. Dr. Mahmoud El Nokrashy (Professor of Geomatics in Faculty of Engineering
Az.) my supervisor in PhD.